health and fitness research paper

• Research article
• Open access
• Published: 16 November 2020

Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews

• Pawel Posadzki 1 , 2 ,
• Dawid Pieper   ORCID: orcid.org/0000-0002-0715-5182 3 ,
• Ram Bajpai 4 ,
• Hubert Makaruk 5 ,
• Nadja Könsgen 3 ,
• Annika Lena Neuhaus 3 &
• Monika Semwal 6  

BMC Public Health volume  20 , Article number:  1724 ( 2020 ) Cite this article

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Sedentary lifestyle is a major risk factor for noncommunicable diseases such as cardiovascular diseases, cancer and diabetes. It has been estimated that approximately 3.2 million deaths each year are attributable to insufficient levels of physical activity. We evaluated the available evidence from Cochrane systematic reviews (CSRs) on the effectiveness of exercise/physical activity for various health outcomes.

Overview and meta-analysis. The Cochrane Library was searched from 01.01.2000 to issue 1, 2019. No language restrictions were imposed. Only CSRs of randomised controlled trials (RCTs) were included. Both healthy individuals, those at risk of a disease, and medically compromised patients of any age and gender were eligible. We evaluated any type of exercise or physical activity interventions; against any types of controls; and measuring any type of health-related outcome measures. The AMSTAR-2 tool for assessing the methodological quality of the included studies was utilised.

Hundred and fifty CSRs met the inclusion criteria. There were 54 different conditions. Majority of CSRs were of high methodological quality. Hundred and thirty CSRs employed meta-analytic techniques and 20 did not. Limitations for studies were the most common reasons for downgrading the quality of the evidence. Based on 10 CSRs and 187 RCTs with 27,671 participants, there was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% confidence intervals (CI) 0.78 to 0.96]; I 2  = 26.6%, [prediction interval (PI) 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]. Data from 15 CSRs and 408 RCTs with 32,984 participants showed a small improvement in quality of life (QOL) standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]. Subgroup analyses by the type of condition showed that the magnitude of effect size was the largest among patients with mental health conditions.

There is a plethora of CSRs evaluating the effectiveness of physical activity/exercise. The evidence suggests that physical activity/exercise reduces mortality rates and improves QOL with minimal or no safety concerns.

Trial registration

Registered in PROSPERO ( CRD42019120295 ) on 10th January 2019.

Peer Review reports

The World Health Organization (WHO) defines physical activity “as any bodily movement produced by skeletal muscles that requires energy expenditure” [ 1 ]. Therefore, physical activity is not only limited to sports but also includes walking, running, swimming, gymnastics, dance, ball games, and martial arts, for example. In the last years, several organizations have published or updated their guidelines on physical activity. For example, the Physical Activity Guidelines for Americans, 2nd edition, provides information and guidance on the types and amounts of physical activity that provide substantial health benefits [ 2 ]. The evidence about the health benefits of regular physical activity is well established and so are the risks of sedentary behaviour [ 2 ]. Exercise is dose dependent, meaning that people who achieve cumulative levels several times higher than the current recommended minimum level have a significant reduction in the risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events [ 3 ]. Benefits of physical activity have been reported for numerous outcomes such as mortality [ 4 , 5 ], cognitive and physical decline [ 5 , 6 , 7 ], glycaemic control [ 8 , 9 ], pain and disability [ 10 , 11 ], muscle and bone strength [ 12 ], depressive symptoms [ 13 ], and functional mobility and well-being [ 14 , 15 ]. Overall benefits of exercise apply to all bodily systems including immunological [ 16 ], musculoskeletal [ 17 ], respiratory [ 18 ], and hormonal [ 19 ]. Specifically for the cardiovascular system, exercise increases fatty acid oxidation, cardiac output, vascular smooth muscle relaxation, endothelial nitric oxide synthase expression and nitric oxide availability, improves plasma lipid profiles [ 15 ] while at the same time reducing resting heart rate and blood pressure, aortic valve calcification, and vascular resistance [ 20 ].

However, the degree of all the above-highlighted benefits vary considerably depending on individual fitness levels, types of populations, age groups and the intensity of different physical activities/exercises [ 21 ]. The majority of guidelines in different countries recommend a goal of 150 min/week of moderate-intensity aerobic physical activity (or equivalent of 75 min of vigorous-intensity) [ 22 ] with differences for cardiovascular disease [ 23 ] or obesity prevention [ 24 ] or age groups [ 25 ].

There is a plethora of systematic reviews published by the Cochrane Library critically evaluating the effectiveness of physical activity/exercise for various health outcomes. Cochrane systematic reviews (CSRs) are known to be a source of high-quality evidence. Thus, it is not only timely but relevant to evaluate the current knowledge, and determine the quality of the evidence-base, and the magnitude of the effect sizes given the negative lifestyle changes and rising physical inactivity-related burden of diseases. This overview will identify the breadth and scope to which CSRs have appraised the evidence for exercise on health outcomes; and this will help in directing future guidelines and identifying current gaps in the literature.

The objectives of this research were to a. answer the following research questions: in children, adolescents and adults (both healthy and medically compromised) what are the effects (and adverse effects) of exercise/physical activity in improving various health outcomes (e.g., pain, function, quality of life) reported in CSRs; b. estimate the magnitude of the effects by pooling the results quantitatively; c. evaluate the strength and quality of the existing evidence; and d. create recommendations for future researchers, patients, and clinicians.

Our overview was registered with PROSPERO (CRD42019120295) on 10th January 2019. The Cochrane Handbook for Systematic Reviews of interventions and Preferred Reporting Items for Overviews of Reviews were adhered to while writing and reporting this overview [ 26 , 27 ].

Search strategy and selection criteria

We followed the practical guidance for conducting overviews of reviews of health care interventions [ 28 ] and searched the Cochrane Database of Systematic Reviews (CDSR), 2019, Issue 1, on the Cochrane Library for relevant papers using the search strategy: (health) and (exercise or activity or physical). The decision to seek CSRs only was based on three main aspects. First, high quality (CSRs are considered to be the ‘gold methodological standard’) [ 29 , 30 , 31 ]. Second, data saturation (enough high-quality evidence to reach meaningful conclusions based on CSRs only). Third, including non-CSRs would have heavily increased the issue of overlapping reviews (also affecting data robustness and credibility of conclusions). One reviewer carried out the searches. The study screening and selection process were performed independently by two reviewers. We imported all identified references into reference manager software EndNote (X8). Any disagreements were resolved by discussion between the authors with third overview author acting as an arbiter, if necessary.

We included CSRs of randomised controlled trials (RCTs) involving both healthy individuals and medically compromised patients of any age and gender. Only CSRs assessing exercise or physical activity as a stand-alone intervention were included. This included interventions that could initially be taught by a professional or involve ongoing supervision (the WHO definition). Complex interventions e.g., assessing both exercise/physical activity and behavioural changes were excluded if the health effects of the interventions could not have been attributed to exercise distinctly.

Any types of controls were admissible. Reviews evaluating any type of health-related outcome measures were deemed eligible. However, we excluded protocols or/and CSRs that have been withdrawn from the Cochrane Library as well as reviews with no included studies.

Data analysis

Three authors (HM, ALN, NK) independently extracted relevant information from all the included studies using a custom-made data collection form. The methodological quality of SRs included was independently evaluated by same reviewers using the AMSTAR-2 tool [ 32 ]. Any disagreements on data extraction or CSR quality were resolved by discussion. The entire dataset was validated by three authors (PP, MS, DP) and any discrepant opinions were settled through discussions.

The results of CSRs are presented in a narrative fashion using descriptive tables. Where feasible, we presented outcome measures across CSRs. Data from the subset of homogeneous outcomes were pooled quantitatively using the approach previously described by Bellou et al. and Posadzki et al. [ 33 , 34 ]. For mortality and quality of life (QOL) outcomes, the number of participants and RCTs involved in the meta-analysis, summary effect sizes [with 95% confidence intervals (CI)] using random-effects model were calculated. For binary outcomes, we considered relative risks (RRs) as surrogate measures of the corresponding odds ratio (OR) or risk ratio/hazard ratio (HR). To stabilise the variance and normalise the distributions, we transformed RRs into their natural logarithms before pooling the data (a variation was allowed, however, it did not change interpretation of results) [ 35 ]. The standard error (SE) of the natural logarithm of RR was derived from the corresponding CIs, which was either provided in the study or calculated with standard formulas [ 36 ]. Binary outcomes reported as risk difference (RD) were also meta-analysed if two more estimates were available. For continuous outcomes, we only meta-analysed estimates that were available as standardised mean difference (SMD), and estimates reported with mean differences (MD) for QOL were presented separately in a supplementary Table  9 . To estimate the overall effect size, each study was weighted by the reciprocal of its variance. Random-effects meta-analysis, using DerSimonian and Laird method [ 37 ] was applied to individual CSR estimates to obtain a pooled summary estimate for RR or SMD. The 95% prediction interval (PI) was also calculated (where ≥3 studies were available), which further accounts for between-study heterogeneity and estimates the uncertainty around the effect that would be anticipated in a new study evaluating that same association. I -squared statistic was used to measure between study heterogeneity; and its various thresholds (small, substantial and considerable) were interpreted considering the size and direction of effects and the p -value from Cochran’s Q test ( p  < 0.1 considered as significance) [ 38 ]. Wherever possible, we calculated the median effect size (with interquartile range [IQR]) of each CSR to interpret the direction and magnitude of the effect size. Sub-group analyses are planned for type and intensity of the intervention; age group; gender; type and/or severity of the condition, risk of bias in RCTs, and the overall quality of the evidence (Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria). To assess overlap we calculated the corrected covered area (CCA) [ 39 ]. All statistical analyses were conducted on Stata statistical software version 15.2 (StataCorp LLC, College Station, Texas, USA).

The searches generated 280 potentially relevant CRSs. After removing of duplicates and screening, a total of 150 CSRs met our eligibility criteria [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 ] (Fig.  1 ). Reviews were published between September 2002 and December 2018. A total of 130 CSRs employed meta-analytic techniques and 20 did not. The total number of RCTs in the CSRs amounted to 2888; with 485,110 participants (mean = 3234, SD = 13,272). The age ranged from 3 to 87 and gender distribution was inestimable. The main characteristics of included reviews are summarised in supplementary Table  1 . Supplementary Table  2 summarises the effects of physical activity/exercise on health outcomes. Conclusions from CSRs are listed in supplementary Table  3 . Adverse effects are listed in supplementary Table  4 . Supplementary Table  5 presents summary of withdrawals/non-adherence. The methodological quality of CSRs is presented in supplementary Table  6 . Supplementary Table  7 summarises studies assessed at low risk of bias (by the authors of CSRs). GRADE-ings of the review’s main comparison are listed in supplementary Table  8 .

Study selection process

There were 54 separate populations/conditions, considerable range of interventions and comparators, co-interventions, and outcome measures. For detailed description of interventions, please refer to the supplementary tables . Most commonly measured outcomes were - function 112 (75%), QOL 83 (55%), AEs 70 (47%), pain 41 (27%), mortality 28 (19%), strength 30 (20%), costs 47 (31%), disability 14 (9%), and mental health in 35 (23%) CSRs.

There was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% CI 0.78 to 0.96]; I 2  = 26.6%, [PI 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]; 10 CSRs, 187 RCTs, 27,671 participants) following exercise when compared with various controls (Table 1 ). This reduction was smaller in ‘other groups’ of patients when compared to cardiovascular diseases (CVD) patients - RR 0.97 [95% CI 0.65, 1.45] versus 0.85 [0.76, 0.96] respectively. The effects of exercise were not intensity or frequency dependent. Sessions more than 3 times per week exerted a smaller reduction in mortality as compared with sessions of less than 3 times per week RR 0.87 [95% CI 0.78, 0.98] versus 0.63 [0.39, 1.00]. Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low ROB exerted smaller reductions in mortality when compared to RCTs at an unclear or high ROB, RR 0.90 [95% CI 0.78, 1.02] versus 0.72 [0.42, 1.22] versus 0.86 [0.69, 1.06] respectively. CSRs with moderate quality of evidence (GRADE), showed slightly smaller reductions in mortality when compared with CSRs that relied on very low to low quality evidence RR 0.88 [95% CI 0.79, 0.98] versus 0.70 [0.47, 1.04].

Exercise also showed an improvement in QOL, standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]; 15 CSRs, 408 RCTs, 32,984 participants) when compared with various controls (Table 2 ). These improvements were greater observed for health related QOL when compared to overall QOL SMD 0.30 [95% CI 0.21, 0.39] vs 0.06 [− 0.08, 0.20] respectively. Again, the effects of exercise were duration and frequency dependent. For instance, sessions of more than 90 mins exerted a greater improvement in QOL as compared with sessions up to 90 min SMD 0.24 [95% CI 0.11, 0.37] versus 0.22 [− 0.30, 0.74]. Subgroup analyses by the type of condition showed that the magnitude of effect was the largest among patients with mental health conditions, followed by CVD and cancer. Physical activity exerted negative effects on QOL in patients with respiratory conditions (2 CSRs, 20 RCTs with 601 patients; SMD -0.97 [95% CI -1.43, 0.57]; I 2  = 87.8%; MES = -0.46 [IQR-0.97, 0.05]). Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low or unclear ROB exerted greater improvements in QOL when compared to RCTs at a high ROB SMD 0.21 [95% CI 0.10, 0.31] versus 0.17 [0.03, 0.31]. Analogically, CSRs with moderate to high quality of evidence showed slightly greater improvements in QOL when compared with CSRs that relied on very low to low quality evidence SMD 0.19 [95% CI 0.05, 0.33] versus 0.15 [− 0.02, 0.32]. Please also see supplementary Table  9 more studies reporting QOL outcomes as mean difference (not quantitatively synthesised herein).

Adverse events (AEs) were reported in 100 (66.6%) CSRs; and not reported in 50 (33.3%). The number of AEs ranged from 0 to 84 in the CSRs. The number was inestimable in 83 (55.3%) CSRs. Ten (6.6%) reported no occurrence of AEs. Mild AEs were reported in 28 (18.6%) CSRs, moderate in 9 (6%) and serious/severe in 20 (13.3%). There were 10 deaths and in majority of instances, the causality was not attributed to exercise. For this outcome, we were unable to pool the data as effect sizes were too heterogeneous (Table 3 ).

In 38 CSRs, the total number of trials reporting withdrawals/non-adherence was inestimable. There were different ways of reporting it such as adherence or attrition (high in 23.3% of CSRs) as well as various effect estimates including %, range, total numbers, MD, RD, RR, OR, mean and SD. The overall pooled estimates are reported in Table 3 .

Of all 16 domains of the AMSTAR-2 tool, 1876 (78.1%) scored ‘yes’, 76 (3.1%) ‘partial yes’; 375 (15.6%) ‘no’, and ‘not applicable’ in 25 (1%) CSRs. Ninety-six CSRs (64%) were scored as ‘no’ on reporting sources of funding for the studies followed by 88 (58.6%) failing to explain the selection of study designs for inclusion. One CSR (0.6%) each were judged as ‘no’ for reporting any potential sources of conflict of interest, including any funding for conducting the review as well for performing study selection in duplicate.

In 102 (68%) CSRs, there was predominantly a high risk of bias in RCTs. In 9 (6%) studies, this was reported as a range, e.g., low or unclear or low to high. Two CSRs used different terminology i.e., moderate methodological quality; and the risk of bias was inestimable in one CSR. Sixteen (10.6%) CSRs did not identify any studies (RCTs) at low risk of random sequence generation, 28 (18.6%) allocation concealment, 28 (18.6%) performance bias, 84 (54%) detection bias, 35 (23.3%) attrition bias, 18 (12%) reporting bias, and 29 (19.3%) other bias.

In 114 (76%) CSRs, limitation of studies was the main reason for downgrading the quality of the evidence followed by imprecision in 98 (65.3%) and inconsistency in 68 (45.3%). Publication bias was the least frequent reason for downgrading in 26 (17.3%) CSRs. Ninety-one (60.7%) CSRs reached equivocal conclusions, 49 (32.7%) reviews reached positive conclusions and 10 (6.7%) reached negative conclusions (as judged by the authors of CSRs).

In this systematic review of CSRs, we found a large body of evidence on the beneficial effects of physical activity/exercise on health outcomes in a wide range of heterogeneous populations. Our data shows a 13% reduction in mortality rates among 27,671 participants, and a small improvement in QOL and health-related QOL following various modes of physical activity/exercises. This means that both healthy individuals and medically compromised patients can significantly improve function, physical and mental health; or reduce pain and disability by exercising more [ 190 ]. In line with previous findings [ 191 , 192 , 193 , 194 ], where a dose-specific reduction in mortality has been found, our data shows a greater reduction in mortality in studies with longer follow-up (> 12 months) as compared to those with shorter follow-up (< 12 months). Interestingly, we found a consistent pattern in the findings, the higher the quality of evidence and the lower the risk of bias in primary studies, the smaller reductions in mortality. This pattern is observational in nature and cannot be over-generalised; however this might mean less certainty in the estimates measured. Furthermore, we found that the magnitude of the effect size was the largest among patients with mental health conditions. A possible mechanism of action may involve elevated levels of brain-derived neurotrophic factor or beta-endorphins [ 195 ].

We found the issue of poor reporting or underreporting of adherence/withdrawals in over a quarter of CSRs (25.3%). This is crucial both for improving the accuracy of the estimates at the RCT level as well as maintaining high levels of physical activity and associated health benefits at the population level.

Even the most promising interventions are not entirely risk-free; and some minor AEs such as post-exercise pain and soreness or discomfort related to physical activity/exercise have been reported. These were typically transient; resolved within a few days; and comparable between exercise and various control groups. However worryingly, the issue of poor reporting or underreporting of AEs has been observed in one third of the CSRs. Transparent reporting of AEs is crucial for identifying patients at risk and mitigating any potential negative or unintended consequences of the interventions.

High risk of bias of the RCTs evaluated was evident in more than two thirds of the CSRs. For example, more than half of reviews identified high risk of detection bias as a major source of bias suggesting that lack of blinding is still an issue in trials of behavioural interventions. Other shortcomings included insufficiently described randomisation and allocation concealment methods and often poor outcome reporting. This highlights the methodological challenges in RCTs of exercise and the need to counterbalance those with the underlying aim of strengthening internal and external validity of these trials.

Overall, high risk of bias in the primary trials was the main reason for downgrading the quality of the evidence using the GRADE criteria. Imprecision was frequently an issue, meaning the effective sample size was often small; studies were underpowered to detect the between-group differences. Pooling too heterogeneous results often resulted in inconsistent findings and inability to draw any meaningful conclusions. Indirectness and publication bias were lesser common reasons for downgrading. However, with regards to the latter, the generally accepted minimum number of 10 studies needed for quantitatively estimate the funnel plot asymmetry was not present in 69 (46%) CSRs.

Strengths of this research are the inclusion of large number of ‘gold standard’ systematic reviews, robust screening, data extractions and critical methodological appraisal. Nevertheless, some weaknesses need to be highlighted when interpreting findings of this overview. For instance, some of these CSRs analysed the same primary studies (RCTs) but, arrived at slightly different conclusions. Using, the Pieper et al. [ 39 ] formula, the amount of overlap ranged from 0.01% for AEs to 0.2% for adherence, which indicates slight overlap. All CSRs are vulnerable to publication bias [ 196 ] - hence the conclusions generated by them may be false-positive. Also, exercise was sometimes part of a complex intervention; and the effects of physical activity could not be distinguished from co-interventions. Often there were confounding effects of diet, educational, behavioural or lifestyle interventions; selection, and measurement bias were inevitably inherited in this overview too. Also, including CSRs only might lead to selection bias; and excluding reviews published before 2000 might limit the overall completeness and applicability of the evidence. A future update should consider these limitations, and in particular also including non-CSRs.

Conclusions

Trialists must improve the quality of primary studies. At the same time, strict compliance with the reporting standards should be enforced. Authors of CSRs should better explain eligibility criteria and report sources of funding for the primary studies. There are still insufficient physical activity trends worldwide amongst all age groups; and scalable interventions aimed at increasing physical activity levels should be prioritized [ 197 ]. Hence, policymakers and practitioners need to design and implement comprehensive and coordinated strategies aimed at targeting physical activity programs/interventions, health promotion and disease prevention campaigns at local, regional, national, and international levels [ 198 ].

Availability of data and materials

Data sharing is not applicable to this article as no raw data were analysed during the current study. All information in this article is based on published systematic reviews.

Abbreviations

Adverse events

Cardiovascular diseases

Cochrane Database of Systematic Reviews

Cochrane systematic reviews

Confidence interval

Grading of Recommendations Assessment, Development and Evaluation

Hazard ratio

Interquartile range

Mean difference

Prediction interval

Quality of life

Randomised controlled trials

Relative risk

Risk difference

Risk of bias

Standard error

Standardised mean difference

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PP wrote the protocol, ran the searches, validated, analysed and synthesised data, wrote and revised the drafts. HM, NK and ALN screened and extracted data. MS and DP validated and analysed the data. RB ran statistical analyses. All authors contributed to writing and reviewing the manuscript. PP is the guarantor. The authors read and approved the final manuscript.

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Supplementary Table 1. Main characteristics of included Cochrane systematic reviews evaluating the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 2. Additional information from Cochrane systematic reviews of the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 3. Conclusions from Cochrane systematic reviews “quote”. Supplementary Table 4 . AEs reported in Cochrane systematic reviews. Supplementary Table 5. Summary of withdrawals/non-adherence. Supplementary Table 6. Methodological quality assessment of the included Cochrane reviews with AMSTAR-2. Supplementary Table 7. Number of studies assessed as low risk of bias per domain. Supplementary Table 8. GRADE for the review’s main comparison. Supplementary Table 9. Studies reporting quality of life outcomes as mean difference.

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Posadzki, P., Pieper, D., Bajpai, R. et al. Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews. BMC Public Health 20 , 1724 (2020). https://doi.org/10.1186/s12889-020-09855-3

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A systematic review of intention to use fitness apps (2020–2023)

• Salvador Angosto   ORCID: orcid.org/0000-0001-7281-794X 1 , 2 ,
• Jerónimo García-Fernández   ORCID: orcid.org/0000-0001-6574-9758 2   na1 &
• Moisés Grimaldi-Puyana   ORCID: orcid.org/0000-0003-4722-1532 2   na1  

Humanities and Social Sciences Communications volume  10 , Article number:  512 ( 2023 ) Cite this article

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Technology advances and digital transformation are constantly growing, resulting in an increase in the number of sports-related technologies and apps on the market, particularly during the COVID-19 pandemic. The aim of this study is to update a comprehensive evaluation of the literature published since 2020 on the desire to use and embrace fitness and physical activity-related apps. Using the PERSiST adapted from the PRISMA 2020 statement, a total of 29 articles that provide assessment models of sports consumers’ desires to utilise fitness applications were discovered. Several major conclusions emerge from the findings: (1) the use of alternative models to the Technology Acceptance Model has increased in recent years with new theories not derived from that model now being associated with it; (2) studies in Europe are increasing as well as a specifical interest in fitness apps; (3) the UTAUT and UTAUT2 model are more widely used within the sport sector and new models appear connected with behaviour intentions; and (4) the number of exogenous and endogenous variables that are linked to the main technology acceptance variables and their behavioral intentions is diverse within the academic literature. These findings could help technology managers to increase user communication, physical activity levels and participation in their fitness centres, as well as to modify the policies and services of sports organisations.

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Introduction.

In recent years, the number of smartphone users has steadily increased throughout the world, with nearly half of the population now owning a device (Newzoo, 2021 ). As a result, the smartphone is quickly becoming a vital instrument in the lives of the general public (Byun et al., 2018 ). This digital change can also be found in the sports and fitness industry, where the digital explosion in the usage of smartphones and wearables has allowed fitness apps to become one of the market’s most important categories (Jones et al., 2020 ).

Fitness apps are swamping the mobile app market (Beldad and Hegner, 2018 ), with almost one in every five users downloading this type of app on their device (Fox and Duggan, 2021 ). Due to the lockdown placed on people and the requirement to stay at home, the demand for fitness apps has grown significantly since the onset of the COVID-19 pandemic (Clement, 2020 ; Ting et al., 2020 ). A fitness app is a third-party programme for smartphones or wearables that may help consumers in recording physical activity data, guiding sports learning and leading a healthy lifestyle (Eshet and Bouwman, 2015 ). A recent study conducted a social comparison of fitness-related posts on social media platforms by fitness app users. Specifically, Kim ( 2022 ) found that when fitness comparison decreased there was a decrease in user self-efficacy towards physical activity, whereas if fitness comparison increased, self-efficacy towards physical activity increased. Consequently, Kim ( 2022 ) highlighted that self-efficacy is a key element for fitness app users’ motivation and participation in physical activity, and they should be compared to high-performing individuals. In addition, gamification is another important element concerning fitness apps for user satisfaction, and a specific design adapted to the type of user is necessary given the number of existing elements in gamification, highlighting feedback and rewards (Yin et al., 2022 ).

The popularity of fitness apps has grown over the years, coinciding with a greater understanding of the value and advantages of physical activity and a healthy lifestyle (Lim and Noh, 2017 ). Fitness apps have become a trend in the worldwide fitness sector, resulting in new patterns of training behaviour (Hu et al., 2023 ; Kercher et al., 2022 ; Thompson, 2022 ). These new behaviour patterns are connected to physical activity monitoring, a shift in health-care perceptions, and changes in lifestyle habits (Lin et al., 2019 ). Middelweerd et al. ( 2014 ), for their part, emphasise that fitness apps employ many behaviour modification approaches such as goal planning, self-control, feedback, the use of contingent incentives and social support.

In the fitness context, it is also important to address the importance that apps can have in the management of sports centres as a two-way communication tool between the organisation (managers or trainers) and users. In this way, Ferreira-Barbosa et al. ( 2021 ) consider that the use of notifications and communications through the fitness app costs less and produces a greater and better interaction with the client. Thus, the use of applications in fitness centres can enable more direct and dynamic communication with users, providing a better and more personalised service.

Despite this, while studies have begun to find the factors that lead to the desire of using technologies such as apps in numerous fields (Gao et al., 2012 ), a deeper knowledge of the intention to use using certain apps is required (Cho et al., 2020 ). As a result, there are several theoretical frameworks in the scientific literature that explain the acceptance of new technology by sports customers. This ‘acceptance of technology’ refers to an individual’s readiness to adopt technology (Dillon, 2001 ).

The technology adoption model (TAM) developed by Davis ( 1989 ) and Davis et al. ( 1989 ) is the principal model utilised in most research to quantify consumer acceptance of new technologies. The TAM assumes an extension of Ajzen and Fishbein’s ( 1980 ) Theory of Reasoned Action, in which the behavioural intention is decided by the attitude towards this conduct (Davis, 1989 ). According to this author, attitudes are developed around two beliefs: perceived usefulness (PU) and perceived ease of use (PEOU). PU is described as the individual’s belief about the worth of a system, such as its performance or efficiency, in order to gain an advantage, while PEOU is defined as the degree to which the individual believes that the system requires no physical or mental effort and is easily accessible (Davis, 1989 ; Davis et al., 1989 ). PU and PEOU provide for the prediction of user intentions in relation to the adoption of both devices and mobile apps (Kim et al., 2016 ; Koenig-Lewis et al., 2015 ). The TAM has been employed in a variety of areas, including finance, tourism, gaming, health and sports (Rivera et al., 2015 ).

A number of TAM-based theories have been established, including the technology readiness and acceptance model (TRAM), which is derived from the TAM and the "Technology Readiness" (TR) model. Parasuraman ( 2000 ) created the TR with the goal of reflecting consumers’ views and dispositions to implement new technologies, linking their usage with the fulfilment of personal or work objectives. The TRAM has been used in a variety of apps, including social innovation (Rahman et al., 2017 ), branding (Jin, 2020 ) and sports technology (Kim and Chiu, 2019 ). Venkatesh and Davis ( 2000 ) introduced the TAM2 model, which integrates social influence and cognitive belief processes. Other models developed from the TAM are those proposed by Venkatesh et al. ( 2003 ), who suggested the Unified Theory of Acceptance and Use of Technology (UTAUT), its extension called UTAUT2 proposed by Venkatesh et al. ( 2012 ) and UTAUT3 proposed by Farooq et al. ( 2017 ). These theories are concerned with both customers and users (Ferreira et al., 2021 ). According to Venkatesh et al. ( 2003 ), the UTAUT model identifies four elements that influence ‘intention to use’: (i) performance expectancy (PE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (ii) effort expectancy (EE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (iii) social influence (SI), defined as the degree to which individuals believe that their social referents believe that they should use the system; and (iv) facilitating conditions (FC), identified as the degree to which the individual believes in the existence of a technical and organisational benefit.

In addition to the four factors derived from the UTAUT model, the UTAUT2 approach integrates three additional variables (Venkatesh et al., 2012 ): (i) hedonic motivation (HM), which reflects the individual’s intrinsic motivations for accepting new technology; (ii) price value (PV) considered as acceptance of the cost involved in using new technology; and (iii) habit (HA) or the degree to which the individual tends to use the new technology automatically after a learning process. Regarding the UTAUT3 model, Farooq et al. ( 2017 ) introduce a new variable, Personal Innovativeness (PI). Dutta et al. ( 2015 ) indicate that personality traits, such as PI, play an essential role in Information Technology (IT) adoption. As a trait, PI is stable and situation-specific and has a high tendency to influence IT adoption and acceptance (Farooq et al., 2017 ; Thatcher and Perrewé, 2002 ). Thus, PI can be defined as the perceived predisposition or personal attitude of individuals that reflect their tendency to independently experience and adopt new developments in IT (Schillewaert et al., 2005 ). This means that PI can be conceptualised as the willingness to adopt the latest technological gadgets or be linked to trying out new IT features and developments (Farooq et al., 2017 ).

Figure 1 shows the conceptual model of the different theories discussed (TAM, UAUT, UAUT2, UTAUT3). The UTAUT and the UTAUT2 models were performed to investigate consumer acceptance and usage of new technologies (Beh et al., 2021 ), and have been used in a variety of research in the sports, fitness and wearable sectors (Beh et al., 2021 ; Dhiman et al., 2020 ; Yuan et al., 2015 ). However, the UTAUT3 model has not yet been used in the sport context, but it has been employed in other contexts such as tourism (Pinto et al., 2022 ), virtual communication (Gupta et al., 2022 ) and education (Gunasinghe et al., 2020 ).

TAM (Davis, 1989 ), UAUT (Venkatesh et al., 2003 ), UAUT2 (Venkatesh et al., 2012 ), UTAUT3 (Farooq et al., 2017 ). Source: Own elaboration.

In conclusion, despite the recent systematic review conducted by Angosto et al. ( 2020 ) on research that examined the intentions to use and implement apps in the fitness and health sector, or a recent meta-analysis of the Intention to use wearable devices in health and fitness (Gopinath et al., 2022 ), more research is needed. Regarding the need for a new review update, this is necessary for three reasons: (a) the previous review developed by Angosto et al. ( 2020 ) has some shortcomings that will be addressed in the discussion; (b) to analyse the evolution of TAM-derived models such as UTAUT, UTAUT2 or UTAUT3; and (c) the previous review was conducted just before the COVID-19 pandemic, a period in which digitalisation underwent a major evolution to respond to the needs of society. The pandemic has impacted the need to adopt modern technology to monitor, record and control physical activity for both people and sports groups (Núñez Sánchez et al., 2022 ; Ruth et al., 2022 ). As a result, the study’s aim is to perform a comprehensive systematic review that updates the number of studies that have investigated the intention to use or adopt fitness apps from 2020 to May 2023.

Review design and protocol

The Prisma in Exercise, Rehabilitation, Sports Medicine and SporTs science (PERSiST) guidelines (Ardern et al., 2022 ) based on the sports science adaptation of the Prisma 2020 statements (Page et al., 2021 ) were followed for this systematic review. The systematic review was not registered on the PROSPERO platform because, not being in the field of health, it did not meet the requirements for registering the systematic review protocol. Therefore, a prior search protocol was not established and all aspects were marked directly in the methodology of this study.

Inclusion and exclusion criteria

This systematic review includes empirical research published in peer-reviewed journals. However, grey literature was excluded, as were assessment reports, periodic reports, dissertations, abstracts and other forms of publishing. The following criteria were used to include studies in the search: (i) peer-reviewed journal articles; (ii) usage of any form of sports and fitness app; (iii) assessment of the intentions using the app through a survey and (iv) publications in English and Spanish. The following items were excluded: (i) books, book chapters, congress proceedings, or other forms of publications; (ii) qualitative approaches, theoretical research, or reviews; (iii) studies written in a language other than English or Spanish; (iv) no mobile apps were utilised in the sports environment; and (v) duplicate articles.

Search strategy

Table 1 shows the categories of terms that were utilised in the search across multiple databases. Six databases were chosen in an attempt to cover a wide variety of topics linked to this multidisciplinary study, such as sports science, health, psychology and marketing. The databases employed were Pubmed, Web of Science, PsycINFO, Scopus, ABI/Inform and SPORTDiscus. The search lasted from December 27, 2021, through May 26, 2023. The search included all years and there were no restrictions on document type or language from 2020 to the present, considering the previous work by Angosto et al. ( 2020 ).

Figure 2 illustrates the flow chart of all the points proposed by the PRISMA 2020 methodology for conducting systematic reviews (Page et al., 2021 ). The first database search found 8647 results, which were reduced to 3471 once duplicates were removed. A thorough scan of titles and abstracts was carried out by one reviewer, in addition to a full-text review of the selected studies after applying the inclusion and exclusion criteria. A second reviewer evaluated the abstracts of the publications that remained at the abstract level ( n  = 12) to check their eligibility, and there were no disagreements with the first reviewer.

This conceptual diagram shows the protocol of the systematic review process (Page et al., 2021 ).

Assessment of methodological quality

The methodological quality analysis was tested using a rating scale measure of 20 items developed by Angosto et al. ( 2020 ) in the sport consumer research type framework where there were no intervention methods on the themes of the CONSORT checklist (Schulz et al., 2010 ). Two reviewers independently assessed each study by examining the multiple elements that make up an investigation. Each element scored one point if the study met the criterion satisfactorily or zero if the research did not meet the criterion or if the element was not applicable to this study. When disagreement emerged, the reviewers resolved this by re-examining the study until an agreement was reached. Supplementary Table S2 (see the section “Data availability”) indicated the methodological quality evaluation results for each research.

Data extraction

For data extraction, an Excel form was created that includes the following characteristics: (a) publishing year; (b) country of study , country of the institution of the first author of the study; (c) number of participants , total of the sample used in the study; (d) gender , percentage of males and females in the sample; (e) age of participants , average age or age ranges of the study sample; (f) type of Application evaluated , fitness or sport apps and their combination with other types of apps such as health or diet apps.; (g) theory used , evaluation model used in the study; (h) analyses performed , types of analysis used in the results; and (i) variables included , assessed variables included in the model proposed in the study. Supplementary Table S3 (see the “Data availability” section) showed the individual data of each study.

Analysis of the assessment of methodological quality

To assess methodological quality, the analysis of the 29 research papers reviewed in the study (Supplementary Table S2 ) found that 16 studies had the best rating of 15 points or more out of a possible 20. There have been 12 studies with an average score between 10 and 15 points, and one research had a score of <10 points (Jeong and Chung, 2022 ). It should be noted that none of the studies reviewed estimated the sample needed for the generalisability of the results, which could be attributed to the fact that all the studies selected their samples by convenience within a certain group. Furthermore, none of the research defined inclusion criteria for the sample selection. Three studies revealed which author performed each phase of the study (García-Fernández et al., 2020 ; Vinnikova et al., 2020 ; Yu et al., 2021 ), and nine studies indicated whether or not they received funding.

Summary of reported intervention outcomes

Supplementary Table S3 shows the descriptive data taken from each research. According to the findings, this issue of assessing the intention to use applications in the sports marketing industry has garnered considerable attention in recent years. A total of 29 research works were chosen, based on the studies published following the systematic review conducted by Angosto et al. ( 2020 ) that focused on the quantitative evaluation of the intention to use sports applications, using either paper-based or online surveys. The results showed that 2022 was the year with the highest number of publications ( n  = 12), while nine articles were published in 2021, there were five articles published in 2020 and three articles in 2023. The location of the research revealed that 64% of the total articles published were from Asia ( n  = 18), ~32% were from Europe ( n  = 9) and 4% were from America ( n  = 1). Among the countries with the highest number of publications, the following should be highlighted China which had the most papers, with six, followed by Spain with four articles, and Hong Kong, Taiwan, and Germany, each with three articles.

A total of 22,942 respondents were examined in the sample of studies, with a range of total size between 200 and 8840 participants, and an average of 791.1 participants per research work. With respect to the type of the sample, the vast majority considered fitness users or community members, with ten and nine articles respectively. To a limited extent, the authors used students ( n  = 6) or the general population ( n  = 2). The sociodemographic data of the sample revealed that the majority of the studies had a greater proportion of females than males ( n  = 18), with an average of 46.1% males and 53.1% females. Seven articles indicated the average age of the participants, with an average age for all 30 years old. A total of 19 articles indicated age by range, with 10 articles having a higher proportion of young people under 30 years, eight articles having a higher population between 30 and 50 years, and one article with a majority of participants over 50 years. Two articles did not indicate age in any of the above ways. Regarding the type of apps used within the sports context, they were fitness apps used in sports centres ( n  = 18), followed by sports apps ( n  = 6), four used apps that also had a health aspect and one included diet-related aspects.

Analysing the theoretical background on which the authors have based their studies, the use of the TAM model still stands out ( n  = 12), and there was an increase in the number of articles that used the UTAUT or its derivatives (UAUT = 4; UTAUT2 = 6). In addition, three studies were based on another TAM-derived model, TRAM, while one article relied on the expectation-confirmation model (ECM), or the theory of normative social behavior (TNSB), and another study encompassed several models such as the theory of consumption values (TCV) and the theory of perceived risk (TPR). When examining the link between the various constructs studied, 25 studies used structural equation analysis (SEM), while one used regression analysis and another used correlation analysis. The SEM analysis was carried out using the PLS and AMOS statistical tools.

One issue to take into account in the variables used is that intention to use (ITU) is a common variable as it is a criterion for inclusion. Although the intention to use is referred to in many different ways, the concept is the same. The results show that more than 40 variables have been directly or indirectly associated with UTI in the different articles published. The most analysed variables are those that form the basis of the TAM. PU or PE was another of the most important factors analysed together with UTI, appearing in 26 articles, followed by PEOU or EE, which was evaluated in a total of 23 articles. Among the most frequently used variables associated with the different models were Perceived Enjoyment (PEN) in eight articles, Satisfaction (SA) in five articles, Innovativeness (INN) in four studies, and Health Consciousness (HC), Optimism (OP) and Subjective Norms (SN) with three articles each.

The constructs associated with the UTAUT or UTAUT2 models have also been studied in almost all the articles that have considered these models. Among them, the use of SI stands out in eight articles, while other factors such as HA, HM, or FC have been analysed in five studies and PV in four studies. Other variables associated with the UTAUT or UTATU2 models include Self-efficacy (SE) in four articles, and PI, perceived playfulness, goal setting, attractiveness, privacy protection and barriers in one article. Other factors linked with other models that have been studied once were Insecurity, Discomfort, Need for interaction, Personal attachment, Word-of-mouth, Commitment and Quality aspects or Motivations. Appendix B shows all the variables analysed in each individual study.

Finally, considering the main results, it has been shown that, although the TAM factors (PU and PEOU) are widely studied and evidence has been found of the influence of both on UTI and PEOU on PU, there are many factors that also both directly and indirectly influence, using these two constructs as mediators of UTI. For example, PEN is a variable that eight studies have found to influence UTIs. SI and HA were other factors that also significantly influence UTI ( n  = 5 for each one). Other elements from the UTAUT/UTAUT2 models that have also been shown to influence UTI, to a lesser extent across studies, have been PV ( n  = 3), FC ( n  = 2), and HM ( n  = 3). Other aspects external to the TAM-based models that directly and significantly influence ITU were Innovativeness, Subjective Knowledge, Trust, Commitment, Perceived Playfulness, Health Consciousness, Personal Innovativeness, Autonomous Motivation, Self-efficacy, Attractiveness, Perceived Privacy Protection, Subjective Norms, Goal Setting, Risk Perception, Physical Appearance, Affiliation, Condition, Privacy Risk and Security Risk.

As for the indirect effects of the external variables considering PEOU/EE, PU/PE, or PEN as mediating variables, the influence of factors common to these three variables such as Innovativeness, Insecurity, Optimism, Perceived Attractiveness, Information Quality,and System Quality has been evidenced. Other external factors that significantly influenced both PEOU/EE and PU/PE were Subjective Knowledge, Task-Technology Fit, Accuracy, SE, PEN and Subjective Norms. While certain factors only influenced some of the variables considered, especially PU/PE, which was influenced by a greater number of external variables (Discomfort, Confirmation of Expectations, Trustworthiness, Perceived Benefits, Risk Perception, Perceived Threats), PEN only influenced Discomfort and PEOU/EE e-Lifestyles. Therefore, it was observed that there is no consensus in the scientific literature when it comes to addressing common external variables for further research in several contexts.

The aim of this systematic review was to update research that has analysed the intention to use or adopt fitness apps from 2020 to May 2023, following the study conducted by Angosto et al. ( 2020 ). It is relevant to highlight the differences between this review and the previous one by Angosto et al. ( 2020 ). For this purpose, it is important to consider the review of studies that used UTAUT or UTAUT2 developed by Venkatesh et al. ( 2016 ) as a model. In this review, the author argues the need to expand existing reference models with new exogenous, endogenous, moderating, or outcome mechanisms, as well as theorising influences at different levels. As a clear example in this line, the author himself increased the number of endogenous variables of the UAUT model including HM, PV and HA resulting in the UTAUT2 model or, in the case of Farooq et al. ( 2017 ), incorporating PI to obtain the UTAUT3 model. In addition, Davis ( 1989 ) proposed the initial TAM model by inducing external or exogenous variables in order to be able to analyse in different contexts.

Based on these aspects, the review previously carried out by Angosto et al. ( 2020 ) presents a clear limitation as it only focuses on analysing the influence of TAM or TAM2 factors, omitting the possible influences of exogenous, endogenous, or moderating variables. In this way, it should be noted that these authors do not carry out an in-depth analysis of user behaviour and its effects (both direct and indirect) that influence the ITU fitness app. On the other hand, another error is observed because the authors discriminated the variables of the UTAUT or UTAUT2 models, only focusing in the end on the studies based on TAM, TAM2, or TRAM. Therefore, when they conducted their analysis on the influence of variables, they omitted data from these studies as well. It should be noted that the UTAUT and UTAUT2 models are based on TAM, thus PE is the equivalent of PU, while EE is the equivalent of PEOU.

In view of the previous reasons, together with the period experienced by the world population as a result of the COVID-19 pandemic, it is necessary to update the previous review carried out by Angosto et al. ( 2020 ). It should be remembered that during the pandemic the population was forced to be confined to their homes. This has represented a milestone in the digitalisation of society and sports and fitness services. In fact, it can be observed that while in the review by Angosto et al. ( 2020 ), the authors identified 19 articles, from the beginning of the pandemic to the present day this review has found a total of 29 articles that met the inclusion/exclusion criteria. In short, the number of publications has more than doubled in the last three years. It is true that five research works overlapped with the prior review, which might explain why these studies were published in the press, and by assigning them a journal number, they seem published at a later date. This review emphasises the significance of this topic’s rising popularity in the fitness sector from several domains such as sociology, psychology and management (Cai et al., 2022 ).

To summarise, the results of this review and the previous review by Angosto et al. ( 2020 ) will be compared. In general, regarding the location of the studies, an increase in the number of studies conducted in Europe was observed compared to the previous review (Acikgoz et al., 2022 ; Baubonytė et al., 2021 ; Damberg, 2021 ; Ferreira et al., 2021 ; García-Fernández et al., 2020 ; Gómez-Ruiz et al., 2022 ; Pérez-Aranda et al., 2021 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ), and a decrease in the number of studies in the Americas (Won et al., 2023 ). Concerning countries, there is an exponential increase in the number of studies conducted by authors in Chinese universities and, when compared to the previous review, there is a majority of studies from South Korea.

In relation to gender, both reviews obtained similar results in which the proportion of female participants was higher than male participants in most of the studies. Although the gender of the customers or users studied was primarily female, Baubonyte et al. ( 2021 ) believe this to be rather immaterial in research that compared the intention to use new technologies based on gender. When the mean age was analysed, this review showed that the mean age of the participants was around 30 years old, while in the review by Angosto et al. ( 2020 ), this was 24 years old. Also, it should be noted that the age groups with the highest representation and the highest proportion of users were either very young (<23 years) or adult (30–50 years), while in this review most studies have a higher proportion of the population under 30 years versus adults. The reason for these results may be due to the fact that females tend to prioritise collective practice over individual practice (Vogler et al., 2008 ), and therefore there is a higher proportion of users of fitness centres or communities, while young people present fewer digital barriers when it comes to using apps than, perhaps, the adult population (Schreurs et al., 2017 ).

Depending on the type of app analysed in the different studies, variations have also been observed with respect to the previous review. The previous review emphasised that most studies considered fitness and diet apps while fitness or sports apps were the least considered. This review reports completely inverse results where the large majority of apps analysed were fitness apps followed by sport, while diet-fitness apps have been the least evaluated, with only one study. This change in trend may be clearly influenced by the context of the COVID-19 pandemic where the population forced to stay at home due to confinement felt the need to do physical exercise to be active and use leisure time in a more entertaining way. A significant proportion of the scientific literature highlights the features and functions and results of using fitness and sports apps (Kim et al., 2017 ), despite the fact that some studies have evaluated other health-related apps alongside this type of app (Aboelmaged et al., 2022 ; Chiu et al., 2021 ; Chiu and Cho, 2021 ; Zhu et al., 2023 ), or that of diet (Chiu et al., 2021 ). It is vital to highlight that the link between physical activity, fitness and health is extremely close, as is eating to live a healthy lifestyle.

Most research that has analysed technology adoption or intention to use has used the TAM model, which offers an understanding of why people embrace these technologies based on their PU and PEOU views (Márquez et al., 2020 ). However, this study found that recent research increasingly employs theories developed from the TAM, such as the TRAM model (Aboelmaged et al., 2022 ; Chiu and Cho, 2021 ), the UTAUT (Guo, 2022 ; Pérez-Aranda et al., 2021 ; Vinnikova et al., 2020 ; Wei et al. 2021 ), or the UTAUT2 model (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira-Barbosa et al., 2021 ; Kim and Lee, 2022 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ). In addition, other theories also appear in different articles such as the ECM (Chiu et al., 2021 ; Zhang and Xu 2020 ), the TNSB (Yeoh et al. 2022 ) or the TCV/TPR (Zhu et al., 2023 ). An interesting aspect to note is that, although no study based on the UTAUT3 model suggested by Farooq et al. ( 2017 ) has been found, Dhiman et al. ( 2020 ) proposed the UAUT2 model, but incorporated the PI variable which is included as a new endogenous variable within the UTAUT3.

In general, previous research on the acceptance of new technologies in the sports industry has found that PEOU (Mohammadi and Isanejad, 2018 ), or PU are the primary influences on the ‘intention to use’ (Kim et al., 2017 ). According to Venkatesh ( 2000 ), when a customer or user sees a technology to be simple to use, he or she would also regard it to be valuable. According to Cho and Kim ( 2015 ), PEOU typically has a benefit for users since it helps them to carry out activities with a more comfortable and simple method while driving the desire to continue using the app. In this regard, Liu et al. ( 2017 ) revealed that PEOU was the most important belief since the majority of fitness users thought apps were easy and simple to use when they met their expectations. Based on one research work, if the user must make an effort to learn how to use the app, this will favourably affect the consumer’s propensity to use the app (Lin et al., 2020 ). When a customer has a strong desire to use the app, the person is more likely to promote it to others (Cheng et al., 2021 ). As a result, the usage of fitness apps will be related to an increase in physical activity levels and, consequently, in health (Kim, 2022 ; Litman et al., 2015 ).

However, in spite of this more than contrasted evidence in the scientific literature, it is important to address the extent to which other variables (exogenous, endogenous, or moderating) can influence the ITU fitness app. To begin with the influence of exogenous variables, the TR model has been shown in different studies to have an external influence on TAM factors (Aboelmaged et al., 2022 ; Chen and Lin, 2018 ; Chiu and Cho, 2021 ). For example, PEOU is moderately influenced by Innovativeness and slightly influenced by Optimism and Insecurity, while PU is moderately influenced by Optimism and slightly influenced by Innovativeness, Discomfort and Insecurity (Aboelmaged et al., 2022 ; Chang et al., 2023 ; Chiu and Cho, 2021 ). Furthermore, Chiu and Cho ( 2021 ) found that both positive (Innovativeness and Optimism) and negative (Discomfort and Insecurity) factors of TR significantly influenced PEN. In another context, Raman and Aashish ( 2022 ), evaluating wearables, revealed that positive aspects of the TR positively influenced PEOU and PU, while negative aspects of TR negatively influenced these variables.

In contrast, Acikgoz et al. ( 2022 ) found a moderate influence of Innovativeness on PU and Subjective Knowledge on both PEOU and PU. Chang et al. ( 2023 ) reported a slight influence of the variable Task-Technology Fit on PEOU and PU. Other influential variables on PEOU have also been shown to be Self-efficacy (Dhiman et al. 2020 ), e-Lifestyles (García-Fernández et al., 2020 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ; Jeong and Chung, 2022 ), Accuracy (Jeong and Chung, 2022 ), Information Quality and System Quality (Won et al., 2023 ) and Subjective Norms (Yu et al., 2021 ). As for external influential variables also in PU/PE, there are Confirmation of Expectations (Chiu et al., 2021 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ), Accuracy and Trustworthiness (Jeong and Cheung, 2022 ), Self-efficacy, Perceived Barriers, Perceived Benefits, Risk Perception, and Perceived Threats (Wei et al., 2021 ), Information Quality and System Quality (Won et al. 2023 ) and Subjective Norms (Yu et al., 2021 ). Won et al. ( 2023 ) also found the influence of Information Quality and System Quality on PEN.

Some studies have also assessed the effects of exogenous or endogenous variables on attitudes as a moderator with ITU. Some variables that had a significant influence were PU/PE (García-Fernández et al., 2020 , Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEOU/EE (Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEN, Gamification and Satisfaction (Pérez-Aranda et al., 2021 ). Cai et al. ( 2022 ) found that Satisfaction acted as a moderating variable for PEOU, PU and Trust with ITU. Regarding the influence of endogenous variables that influenced ITU in addition to PEOU, PU, or PEN we found Subjective Knowledge (Acikgoz et al., 2022 ), Commitment (Chiu et al., 2021 ; Cho et al., 2020 ), PV (Damberg, 2021 ; Dhiman et al., 2020 ; Yang and Koenigstorfer, 2021 ), HA (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira et al. 2021 ; Schomakers et al. 2022 ; Yang and Koenigstorfer, 2021 ), Health Consciousness (Damberg, 2021 ), Perceived Playfulness (Damberg, 2021 ), SI (Dhiman et al., 2020 ; Ferreira et al., 2021 ; Guo, 2022 ; Vinnikova et al., 2020 ), PI (Dhiman et al., 2020 ), HM (Ferreira et al., 2021 ; Schomakers et al., 2022 ); FC (Ferreira et al., 2021 ; Yang and Koenigstorfer, 2021 ), Perceived Trust (Gómez-Ruiz et al., 2022 ), Autonomous Motivation (Guo, 2022 ), SE (Huang and Ren, 2020 ; Vinnikova et al., 2020 ), Privacy Perceived Protection (Kim and Lee, 2022), Subjective Norms (Pérez-Aranda et al., 2021 ) and Goal-setting (Vinnikova et al., 2020 ).

Particularly interesting are the studies that did not rely on TAM models or derivatives that found different variables that significantly influenced ITU. For example, Zhu et al. ( 2023 ) showed that the variables of General Health, Affiliation, Physical appearance, Condition, Perceived Risk and Security Risk influenced UTI. Yeoh et al. ( 2022 ) indicated that Outcome Expectation, Descriptive Norms and Perceived Behavioural Control influence UTI. Pérez-Aranda et al. ( 2023 ) found that attitudinal, cognitive and behavioural antecedents increase the intention to continue using a sports app. Finally, according to the influence on outcome variables, Cheng et al. ( 2021 ) observed that the ITU significantly influenced the Word-of-Mouth outcome variable. On the other hand, Ferreira et al. ( 2021 ) found that ITU influenced current use and Satisfaction, and Guo ( 2022 ) that ITU and Controlled Motivation also influenced current use. At the same time, SI, SE and Goal-setting also influenced current use (Vinnokova et al., 2020 ).

Lastly, we will discuss some evidence reported by other studies focused on the sport context, but which did not take into account fitness apps. For example, Wang et al. ( 2022 ) noted in a fitness software that SI, PE and EE significantly affected the ITU of university students. In an e-Sport game during a pandemic, Ong et al. ( 2023 ) showed that HA was the most significant factor in UTI, followed by usability, FC, SI and HM. In a similar vein, Yang et al. ( 2022 ) found that HA was the only predictor for the use of metaverse technology for basketball learning in college students. Ahn and Park ( 2023 ) showed that hedonic, user burden, pragmatic and social values were key predictors of fitness app user satisfaction. Gu et al. ( 2022 ) observed that attitudes toward exercise and the use of sports apps have a significant impact on physical activity intentions. Finally, Ferreira et al. ( 2023 ) demonstrated that the relationship between UTIs and members’ overall satisfaction with the gym is positively mediated by e-Lifestyles.

Limitations and future research

There are obvious limitations to this systematic review. The first point to mention is maybe the shorter time restriction compared to the prior review by Angosto et al. ( 2020 ). However, this is required since the COVID-19 pandemic is still active and national governments are implementing preventative measures based on the pandemic’s progress (Ferrer, 2021 ; Official State Bulletin, 2021 ). Many nations are enacting new temporary confinements, which may encourage the usage of exercise or health applications. Other potential constraints include publication bias, which occurs when journals publish research with favourable and significant results while rejecting papers with irrelevant outcomes. Another source of bias might have been the language, since there may have been publications in languages other than those specified in the inclusion criteria (English, Spanish and Portuguese). Another constraint might be the choice of search databases, because missing specific databases may result in prospective articles not being detected for inclusion in the review. A third issue is inclusion bias, which occurs when the inclusion or exclusion criteria itself prejudices against a research work. The last limitation is that the great diversity of variables analysed by the authors does not allow the generation of an adequate database that would enable a more in-depth analysis of the results through a meta-analysis beyond the TAM variables such as PEOU and PU.

Future research should try to assess sports consumers or users in other European or American contexts, with the possibility of analysing the results according to socio-demographic characteristics such as gender, age, sport, or digital experience. Age is an interesting aspect to investigate since, depending on the generation to which the person belongs, he or she will identify with new technologies to different degrees. In addition, there are variables such as those in the UTAUT model and derivatives or TR that have been more common than others, but there is still a need to increase the number of studies that use them. Other studies could take a longitudinal approach, assessing the consumer’s desire to use and actual use of the application, as well as whether or not this affects their behaviour towards a more active or healthy life.

Future lines of research relating to the evaluation of the intention to use fitness apps, or any other form of app or wearable, should examine the differences between the models in the same population using the TAM model and some of the other derived models such as the UTAUT or UTATU2. Furthermore, the proposed theoretical models should be assessed by linking them to other factors related to smartphones or other technical devices, such as attachment to the gadget, social influence for its usage, or actual use of the item, among others. Theoretical models such as the TAM, TAM2, UTAUT, UTAUT2 or UTAUT3 should be examined in various sports settings such as the usage of apps for managerial duties, sports training, or marketing/sports products.

Another key issue that has not been studied is the variation in intention to use across the different age groups of the population, since the elderly population may have a different aim than the younger population. Along similar lines, additional elements such as educational level or socioeconomic position may impact the inclination to use the fitness app or any other gadget or technology. Finally, longitudinal research might be utilised to determine how well the intention to use fitness apps matches the actual use of them.

Conclusions

This systematic review update highlights that research on the usage intention and adoption of fitness apps is a topic of interest within the digital sports marketing industry. In recent years there has been a significant increase in the number of publications, with an increasing number of European studies focusing on fitness or sports apps themselves and not associated with health or diet. In addition, the models used beyond the TAM itself are becoming more diversified, as well as the number of exogenous, endogenous and moderating variables in the different studies. Although there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis, there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis.

Finally, a practical aspect of sports organisation management is the desire that this sort of study may assist in learning the opinions of users or customers while adopting or establishing new policies with a digital transformation. This is especially important because it allows for improving the organisation’s communication in a bidirectional way. In short, the implementation of the use of apps in sports centres implies more direct and closer communication with users. In addition, physical activity and management might be monitored without eliminating travel and human interaction. For example, sports organisations make extensive use of sports digital marketing, through the use of social tools, to make the organisation more visible and to offer a more direct image and contact with current or future consumers (Angosto et al., 2022 ). However, not all users have the same social media, therefore the use of push notifications and in-app communication in a venue allows for better notification of relevant news and at a lower cost.

Furthermore, the theoretical models reviewed above identify factors that influence the ITU of technology, such as PU, PEOU, SI and FC. Sport managers can therefore use these models to identify and assess which factors are relevant in their particular context. This will help them to understand the needs and preferences of their users and to adapt their strategies accordingly.

Also, PU is a critical factor in the intention to use technology. Therefore, sports managers should assess how their users perceive the usefulness of technology in their sport context. Among the actions to be taken, they can conduct surveys, interviews or focus groups to collect data on how users feel technology can enhance their sport experience. This will allow sports managers to identify areas for improvement or additional features that can add value to the user experience. Similarly, PEOU is also an important factor in the acceptance and use of technology. In this regard, sports managers must ensure that the technology they use is easy to use and accessible to their users. This involves providing clear instructions, intuitive interfaces and adequate training to ensure that users feel comfortable using the technology.

Another variable that has been shown to influence ITU is SI. In this regard, sports managers could leverage these positive SI to promote the adoption of technology in their sports community. For example, they can collaborate with influential athletes or well-known coaches to support and promote the use of technology. They could also encourage social interaction among technology users by creating online communities or support groups. Finally, FC and perceived barriers have also been shown to influence the intention to use. Sports managers should identify and address any potential barriers that may hinder the adoption and use of technology in their sport environment. This may include a lack of technology resources, resistance to change, or privacy and security concerns. By proactively addressing these barriers, sports managers could encourage greater acceptance and use of technology.

Data availability

The datasets generated during and/or analysed during the current study are available in the Figshare repository, https://figshare.com/s/d0a13d89538847f00b67 .

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This research was funded by the Junta de Andalucía, Regional Ministry of Economic Transformation, Industry, Knowledge and Universities (grant number AT 21_00031). SA is funded by the European Union—NextGenerationEU through a postdoctoral contract with Margarita Salas.

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Angosto, S., García-Fernández, J. & Grimaldi-Puyana, M. A systematic review of intention to use fitness apps (2020–2023). Humanit Soc Sci Commun 10 , 512 (2023). https://doi.org/10.1057/s41599-023-02011-3

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ORIGINAL RESEARCH article

Thirty years of research on physical activity, mental health, and wellbeing: a scientometric analysis of hotspots and trends.

Parts of this article's content have been modified or rectified in:

Erratum: Thirty years of research on physical activity, mental health, and wellbeing: A scientometric analysis of hotspots and trends

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• 1 Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Thonex, Switzerland
• 2 College of Computing and Informatics, Drexel University, Philadelphia, PA, United States
• 3 Scientific Research Department, GGz Centraal, Amersfoort, Netherlands
• 4 School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
• 5 Katholieke Universiteit Leuven Department of Rehabilitation Sciences, Leuven, Belgium
• 6 University Psychiatric Center Katholieke Universiteit Leuven, Leuven, Belgium
• 7 Division of Psychology and Mental Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
• 8 Greater Manchester Mental Health National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
• 9 Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, United Kingdom
• 10 Physiotherapy Department, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
• 11 Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
• 12 Discipline of Psychiatry and Mental Health, Medicine and Health, University of New South Wales, Kensington, NSW, Australia
• 13 School of Health Sciences, Medicine and Health, University of New South Wales, Kensington, NSW, Australia
• 14 Department of Sports Methods and Techniques, Federal University of Santa Maria, Santa Maria, Brazil
• 15 Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
• 16 Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
• 17 Ottawa Hospital Research Institute, Clinical Epidemiology Program, University of Ottawa, Ottawa, ON, Canada
• 18 Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany

The sheer volume of research publications on physical activity, mental health, and wellbeing is overwhelming. The aim of this study was to perform a broad-ranging scientometric analysis to evaluate key themes and trends over the past decades, informing future lines of research. We searched the Web of Science Core Collection from inception until December 7, 2021, using the appropriate search terms such as “physical activity” or “mental health,” with no limitation of language or time. Eligible studies were articles, reviews, editorial material, and proceeding papers. We retrieved 55,353 documents published between 1905 and 2021. The annual scientific production is exponential with a mean annual growth rate of 6.8% since 1989. The 1988–2021 co-cited reference network identified 50 distinct clusters that presented significant modularity and silhouette scores indicating highly credible clusters ( Q = 0.848, S = 0.939). This network identified 6 major research trends on physical activity, namely cardiovascular diseases, somatic disorders, cognitive decline/dementia, mental illness, athletes' performance, related health issues, and eating disorders, and the COVID-19 pandemic. A focus on the latest research trends found that greenness/urbanicity (2014), concussion/chronic traumatic encephalopathy (2015), and COVID-19 (2019) were the most active clusters of research. The USA research network was the most central, and the Chinese research network, although important in size, was relatively isolated. Our results strengthen and expand the central role of physical activity in public health, calling for the systematic involvement of physical activity professionals as stakeholders in public health decision-making process.

Introduction

Physical activity can be considered as medicine and has been used in both the treatment and prevention of a variety of chronic conditions ( 1 ). Longitudinal cohort studies demonstrate that a low cardiorespiratory fitness constitutes the largest attributable fraction for all-cause mortality ( 2 ). There is also overwhelming evidence that low physical activity (i.e., not meeting physical activity recommendations) is considered as an important risk factor for chronic conditions including some cancers, cardiovascular disease, diabetes, dementia, and in particular for a patient with mental illness (schizophrenia, bipolar disorder, or major depressive disorder) ( 3 – 5 ). Patients with mental illness have poor physical health compared with the general population, with reduced life expectancy and a higher risk of premature death beyond suicide, from natural causes ( 6 ). At least partially, among other factors, their poor physical health is due to higher sedentary behavior and lower physical activity compared with the general population ( 7 , 8 ). Physical activity, and its structured form of exercise, seem to affect the brain and mind, beyond physical health, both as a factor associated with poor mental health and quality of life and as a treatment for mental disorders ( 9 ). Indeed, exercise has shown to be efficacious in a number of mental disorders, according to a previous umbrella review pooling 27 systematic reviews ( 10 , 11 ). Exercise is also now seen as a potential preventive or disease-modifying treatment of dementia and brain aging ( 12 ) or as a possible treatment for negative symptoms in schizophrenia ( 13 ).

Importantly, systematic reviews, meta-analysis, and umbrella reviews have offered a deep synthesis of specific research questions addressed within the exponential volume of physical activity literature related to mental health and wellbeing. However, such systematic methods may not be appropriate to encompass hundreds or thousands of new publications per year. In fact, systematic reviews have to be narrow in their inclusion criteria and offer a comprehensive view on a specific and restricted research or clinical question. For instance, a meta-analysis can inform if an intervention is efficacious for a given population on an outcome of interest ( 14 , 15 ) or an umbrella review can assess the credibility of an association between a risk factor and an incident condition ( 16 – 19 ). Nevertheless, none of the two offers an insight on the temporal trend of research, the complex network of topics, authors, publications, networks, institutions, and their bibliometric performance. Gaining such overarching views of how an entire field of research on a particular topic is important and useful, in order to gauge how the academic literature is developing and inform the next steps for the science to pursue.

The integration of developments in data visualization, text mining, and network analysis has permitted the emergence of a new framework and a new generation of research synthesis of both evidence and influence, named research weaving ( 20 ). This framework combines visual analytics and scientometrics to visualize and delineate the development of a field, its underlying intellectual structure and the dynamics of scholarly communication over time ( 21 ). A comprehensive delineation of how scientometrics and bibliometrics overlap and distinct can be found in Hood and Wilson 2001 paper ( 22 ).

To the best of our knowledge, no broad-ranging scientometric study of research trends and influence networks of physical activity, mental health and wellbeing has yet been conducted. Thus, in this article, we present one to bridge the gap.

Materials and methods

Search strategy and data collection.

We searched the Web of Science Core Collection (WOSCC) on December 7, 2021, using a combination of keywords and Medical Subject Headings such as “physical activity,” “mental health,” and “mental illness * .” WOSCC provides full references and complete citations of articles published in major journals since 1900 and is one of the largest comprehensive sources for bibliometric studies ( 23 ). The full protocol with the search key is available on osf.io. This current study protocol is based on a first large-scale scientometric analysis ( 24 ). The database source was limited to the Web of Science Citation Index Expanded. The document types are limited to “article,” “review,” “editorial material,” and “proceeding papers,” without restrictions on language or time. The dataset was extracted from the WOSCC in tag-delimited plain text files.

In order to assess the quality of the reference filtering process and the homogeneity of the dataset, we independently inspected each of the most cited references (604 articles in total), and a randomly selected sample of 10% of included articles to allow a margin of error (i.e., inclusion of non-relevant papers) of 5% with a 95% confidence interval ( Supplementary Table 1 ; Figure 1 ).

Figure 1 . Co-citation reference network with cluster visualization (1988–2021). The unit of measure are articles and constitutes nodes. Nodes are organized according to year of publication. The size of a node (article) is proportional to the number of times the node has been co-cited. Colored shades indicate the passage of the time, from past (purplish) to the present time (yellowish).

The primary outcome was to visualize research trends on physical activity related to mental health and wellbeing and to characterize the evolution of research trends using networks of co-cited references and networks of co-occurring keywords assigned to relevant publications.

The secondary outcome was to provide clinicians, researchers, and policymakers with a specific unit of measure of the research network (countries, institutions, authors, and journals) and to identify emerging trends and limitations.

Data analysis

Two different software tools for constructing bibliometric networks were used: Bibliometrix R package (3.1.4) ( 25 ) and CiteSpace (version 5.8.R4) ( 21 ). Bibliometric outcomes included citation counts, co-citations, and co-occurrences. A co-citation count is defined as the frequency with which two published articles are cited together by subsequently published articles ( 26 ). Co-occurrence networks are based on how frequently two entities, such as keywords, appear in the same articles.

The Bibliometrix R package was used for the analysis of publication outputs and the trend of growth. CiteSpace was used for the study of several types of networks, namely, networks of co-cited references, networks of co-cited authors, and co-occurrence networks of authors, keywords, institutions, and countries. For instance, the co-cited (authors') institutions network accounts for the cooperation between two or more institutions, which reflects the cooperation between authors and the influence networks.

CiteSpace produces a variety of metrics of significance, with temporal metrics such as citation burstness, structural metrics such as betweenness centrality, modularity, and silhouette score as well as a combination of both, namely, the sigma metric. The betweenness centrality of a node measures the fraction of shortest paths in an underlying network passing through the node ( 27 ). The burstness of the frequency of an entity over time indicates a specific duration of a surge of the frequency ( 28 ). The sigma indicator combines structural and temporal properties of a node, namely, its betweenness centrality and citation burst ( 29 ). Modularity (the Q score) measures the quality of dividing a network into clusters, and the silhouette score (the S score) of a cluster measures the quality of a clustering configuration ( 30 ). The Q score ranges from 0 to +1. The cluster structure is considered significant with a Q score >0.3, and higher values indicate a well-structured network. The S score ranges from −1 to +1. If the S score is >0.3, 0.5, or 0.7, the network is considered homogenous, reasonable, or highly credible, respectively. In addition, we conducted a structural variation analysis that focuses on novel boundary-spanning connections to detect transformative papers ranked on their divergence modularity ( 31 ). These transformative papers can potentially change to the existing structure of knowledge.

We extracted cluster labels from keywords associated with articles that are responsible for the formation of a cluster selected by the likelihood ratio test ( p < 0.001). Each cluster was closely inspected, and eventually cluster labels were improved based on the authors' judgment.

The second level of the data filtering process was applied during the generation of networks within each dataset (e.g., most cited reference) in order to detect duplicates, references without authors, or any non-relevant unit of measure that was excluded (e.g., DSM reference; CIM-10) or merged (e.g., author Motl RW and Motl W Robert).

The g-index was used for all calculations. This index permits to give credit to lowly cited or non-cited papers while giving credit for highly cited papers, thus partially alleviating bias from highly cited papers as seen with the h-index ( 32 ). CiteSpace general parameters are reported in Supplementary Information 1 .

Analysis of publication outputs, major journals, and growth trend prediction

We report a flowchart with detail of the 56,442 retrieved documents from the WOS Science citation index expanded and the different steps of our scientometric study: identification and screening of studies, software analyses, and expert review's interpretation (Supplementary Figure 1 ).

Among the retrieved documents, 1,089 documents were excluded, and 55,353 documents encompassing 1,306,828 references were retained (47,105 articles; 6,671 reviews; 564 editorial material; 1,013 proceeding papers). The data filtering process consisted of the inspection of each 604 highly cited papers, editorial material, and proceeding papers and the inspection of 10% randomly selected titles of the retrieved documents. Only 4% ( n = 224 articles) were not relevant ( Supplementary Figure 1 ).

The retained 55,353 articles were published between 1905 and May 2022 in 24 different languages (95.1% of articles in English). The annual scientific production is still in 2022 exponential with a mean annual growth rate of 6.8% since 1989 ( n = 17) and 2022 ( n = 5,604) ( Supplementary Figures 2, 3 ).

The first article identified was a Franz SI and Hamilton GV article on “the effects of exercise upon the retardation in conditions of depression” published in the American Journal of Insanity ( 33 ).

Analysis of co-citation reference: Clusters of research and most cited papers

Clusters of research.

We constructed a synthesized network of co-cited references based on articles published during the 1988–2021 time period as suggested by CiteSpace after the removal of empty time intervals to optimize time slicing ( Figure 1 ). In this network, each node represents a highly co-cited article. We further explored the latest research trends with the extraction of co-citation networks for the 2016-(May) 2022 time period, and the monthly time sliced networks for the year of 2021 ( Supplementary Figure 4 ). All three networks presented significant modularity and silhouette scores indicating highly credible clusters ( Q = 0.8481, S = 0.9394; Q = 0.7712, S = 0.9445; and Q = 0.4854, S = 0.8376, respectively).

The 1988–2021 network identified 50 different clusters, with a single constellation of 26 clusters that reveals six distinct major trends of research on physical activity, namely cardiovascular disease, somatic disorders, cognitive decline/dementia, mental illness, athletes' performance, related health issues and eating disorders and COVID-19 pandemic.

The earliest research trend identified concerns physical activity and cardiovascular diseases consisting of four distinct clusters during the years 1991 to 1997 as follows, with clusters number (clusters' size decreased from cluster number #0), label, silhouette score, size, pooled mean year of publication, the most representative reference; #14, “exercise electrocardiography” ( S = 0.987; 65; 1987) ( 34 ), #7 “silent ischemia” ( S = 0.964; 145; 1989) ( 35 ), #17 “catecholamine” ( S = 0.987; 46; 1989) ( 36 ), and #8 “coronary artery disease” ( S = 0.962; 141; 1994) ( 37 ). This research trend then vanished until it recently reappeared in the 2016–2021 network with cluster #14 “cardio-metabolic health markers” ( S = 0.991; 6; 2014) ( 38 ), #31 “cardiometabolic risk” ( S = 0.999; 5; 2014) ( 39 ), and continues to evolve, as shown in the 2021 network with cluster #9 “cardiovascular disease” ( S = 0.998; 4; 2016) ( 40 ).

The second major trend of research emerged in 1995 on “somatic disorders/public health,” cluster #5 ( S = 0.953; 238; 1995) ( 41 ) that directly evolved into cluster #2 “diabetes” ( S = 0.918; 289; 2001) ( 42 ) and further develop into a relatively isolated cluster #10 “fibromyalgia/copd” ( S = 0.981; 97; 2005) ( 43 ), compared to a succession of other clusters on somatic disorders #16 “cancer” ( S = 0.993; 52; 2009) ( 44 ), #15 “NAFLD” ( S = 0.998; 55; 2011) ( 45 ), #48 “pre-diabetes” ( S = 0.994; 4; 2012) ( 46 ) and #24 “multiple sclerosis” ( S = 0.999; 9; 2014) ( 47 ).

The third major trend concerned cognitive decline and dementia and started in 1997 with a small cluster #20 “Alzheimer's disease” ( S = 0.993; 16; 1997) ( 48 ), then evolved in a much larger cluster #3 “dementia” ( S = 0.916; 269; 2014) ( 49 ), and the largest cluster of the network, cluster #0 “cognitive decline” ( S = 0.923; 324; 2006) ( 50 ). This cluster continues as the most prominent cluster of the 2016–2021 network #0 “evidence-synthesis/cognitive decline” ( S = 0.938; 221; 2015) ( 51 ) and also extended to a cluster on frailty, #9 “frailty” ( S = 0.991; 15; 2014) ( 52 ).

The fourth major trend on research concerned mental illness. This trend started in 2007 with a small cluster #18 “severe mental illness” ( S = 0.985; 46; 2007) ( 53 ), and rapidly evolved in two major clusters, #1 “depression” ( S = 0.823; 292; 2009) ( 42 ), and #4 “schizophrenia” ( S = 0.912; 267; 2015) ( 54 ). The 2016-2021 network confirmed the importance of this major trend with #2 “evidence-synthesis/depression” ( S = 0.819; 142; 2016) ( 55 ). This trend now mainly focus on evidence-synthesis and became #12 “children/adolescents/evidence-synthesis” ( S = 0.963; 75; 2016) ( 56 ).

The fifth trend concerns physical activity, athlete's performance, related health issues, and eating disorders with a succession of small and isolated clusters: #19 “self-confidence” ( S = 0.995; 38; 1998) ( 57 ), #13 “female athletes/eating disorders” ( S = 0.967; 74; 2000) ( 58 ), #50 “motivation” ( S = 0.997; 4; 2005) ( 59 ), and #11 “concussion/chronic traumatic encephalopathy” ( S 0 = 0.996; 11; 2014) ( 60 ). A focus on the 2021 network reveals the latest cluster of the trend, #7 “elite athletes” ( S = 0.986; 75; 2017) ( 61 ).

The sixth and last trend concerned COVID-19 pandemic and starts with cluster #6 “COVID-19' ( S = 0.968; 26; 2019) ( 62 ), that continues to evolve in the 2016–2021 network with #1 “COVID-19” ( S = 0.987; 172; 2019) ( 63 ), #20 “post-COVID-19/long COVID” ( S = 1; 4; 2019) ( 64 ) and became in 2021 the most important cluster with #0 “COVID” ( S = 0.818; 147; 2019) ( 63 ), and #4 “COVID/children” ( S = 0.837; 59; 2019) ( 65 ).

Finally, two recent isolated clusters that we cannot relate to a specific trend have also emerged: cluster #9 “greenness/urbanicity” ( S = 0.998; 2015) ( 66 ), and #40 “behavior change” ( S = 0.996; 7; 2013) ( 67 ).

The link walkthrough over time between clusters based on burstness dynamics for the 1988–2021 network is available as a video on osf.io.

Most cited papers

We report the top 10 most co-cited references for the 1988–2021 time period in Table 1 . The top three most co-cited articles in our network were the Schuch et al.'s meta-analysis on exercise as a treatment of depression ( 55 ), followed by the Erickson et al.'s randomized-controlled trial (RCT) on exercise increasing the size of the anterior hippocampus in older adults ( 50 ), and the Ngandu et al.'s RCT on the multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring vs. control to prevent cognitive decline in at-risk elderly people ( 54 ).

Table 1 . The top 10 most cited journals and reference.

Moreover, we produced the analysis of burstness for the top references of the 1988–2021, 2016–2021, and 2021 time periods ( Supplementary Tables 2P–R ). The analysis of burstness revealed that the top three references with the latest and strongest beginning of citation burst were the Warburton and Bredin paper on health benefits of physical activity ( 72 ), the Brooks et al. paper on the psychological impact of quarantine ( 63 ), and the Stubbs et al. EPA guidance on physical activity as a treatment for severe mental illness ( 11 ).

Another important aspect of scientometric studies is the detection of potentially transformative papers, by conducting a structural variation analysis for the 2016–2021 and the 2021–2021 time period ( Supplementary Table 3 ). For the 2016–2021 time period, the top three identified articles based on the strongest centrality divergence were the Stubbs et al. study on factors influencing physical activity among 204,186 people across 46 low-and middle-income countries ( 73 ), Vancampfort et al.'s meta-analysis on sedentary behavior and physical activity levels in people with severe mental illness ( 7 ), and Vancampfort et al.'s review on physical activity and metabolic disease among people with severe mental illness ( 74 ). For the time period January 2021 to May 2021, the top three studies were the Aguilar et al.'s study on the association between leisure-time exercise and depressive symptoms ( 75 ), the Schuch et al.'s study on the ELSA-Brasil cohort concerning the association between leisure-time, transport, depression and anxiety symptoms ( 76 ), and the van Sluijs et al.'s review on physical activity behaviors during adolescence ( 77 ).

Analysis of co-occurrence of keywords

The use of author keywords can help identify the latest trends of research and choose search keywords for future reviews. The co-occurrence author keywords network for 1988–2021 is shown in Supplementary Figure 5 , and the 2016–2021 time period is shown in Figure 2 . In this network, each node is a highly co-occurring keyword. Both networks presented significant modularity and silhouette scores indicating credible clusters ( Q = 0.3327, S = 0.6823 and Q = 0.3971, S = 0.6614 respectively).

Figure 2 . Co-occurrence authors' keyword network (2016–2021). In this co-occurrence author's keywords analysis, the size of the cross is proportional to the frequency of keyword occurrence.

The 1988–2021 network presented six different clusters: #0 “mental health”; #1 “hippocampus”; #2 “quality of life”; #3 “coronary artery disease”; #4 “obesity,” and #5 “dementia,” and the 2016–2021 network presented seven different clusters: #0 “adolescent”; #1 “copd”; #2 “dementia”; #3 “bdnf”; #4 “concussion”; #5 “non-alcoholic fatty liver disease”; #6 “green space” and #7 “depression”.

The burstness analysis extracted the top 30 co-cited keywords; the latest and strongest beginning of citation bursts for the 1988–2021 network were “quality of life,” “major depression,” “controlled trial,” “meta-analysis,” and “sedentary behavior,” and for the 2016–2021 network were “psychological impact,” “acute respiratory syndrome,” “rat model,” “epidemic,” and “deficiency” ( Supplementary Tables 3S–V ).

Analysis of influence and co-operation network

Co-cited countries and co-cited institutions network.

We produced the co-cited countries and co-cited institutions network ( Figures 3A,B ). Units of measures were authors' countries and authors' institutions. A significant modularity and silhouette score were found ( Q = 0.5321; S = 0.785).

Figure 3 . Co-cited author's countries (A) (1988–2021) and co-cited author's institutions network with corresponding clusters (B) (2016–2021). Both the co-cited author's countries and co-cited author's institutions permits to reveal the collaborative country network. Betweenness centrality organize the network, with the countries presenting the most important centrality being at the center of the network. Nodes are according to each network, countries or institutions. The outermost purple ring denotes the centrality level, and highly central nodes are considered pivotal points in the research field. We limited the nodes to the 80 first countries.

Overall, 176 different countries were identified. In the 1988–2021 network, the country with the most important number of author's citation were the United States of America (USA) ( n = 17,988), followed by the United Kingdom ( n = 5,720) Australia ( n = 4,431), Canada ( n = 3,773), and People's Republic of China ( n = 3,160). Similarly, in the 2016–2021 network, the most cited top countries were identical; however, China was now in fourth place ( Supplementary Figure 6 ; Supplementary Table 4 ). The analysis of burstness reveals confirmed that China was from far the country with the most important strength of burst these last 2 years (231.72), whereas the USA latest important burst date to the 1998–2003 period (83.54) ( Supplementary Tables 2A,B ). The co-cited author's institutions network reveals what institutions are the most cited. We produced the last five-year network (2016–2021) and identified 757 different organizations ( Figure 3B , Supplementary Figure 7 ).

The most central network was the USA research network #0, with the greatest betweenness centrality to other clusters, such as the Central Europe research network #1, or the United Kingdom and Australian research network #2. The Chinese research network #3, although important in size, was relatively isolated, sharing few links with the Japanese research network #10, whereas the Spanish #8 and the South Korean #9 network were more isolated ( Supplementary Table 4 ). The burstness analysis revealed that the five institutions with the latest and strongest strength of citation burst were as follows: Central South University (China), University of Extremadura (Spain), Federal University of Santa Maria (Brazil), University of Paris (France), and University of Lisbon (Portugal) ( Supplementary Tables 2C,D ). The sigma score revealed that the institutions with the greatest scores were Charité (#1; 2016), Medical University of Vienna (#1; 2017), and Peking University (#3; 2016).

Co-authorship, co-cited and co-cited journals network

Our dataset includes 1,306,827 citations with an average of 31.85 citations per document. About 175,508 different authors were found, with an average of 3.17 authors and 5.76 co-authors per document in 4,193 different sources (e.g., books and journals) ( Supplementary Figure 1 ).

We produced the co-authorship networks, which are the social networks encompassing researchers that reflect collaboration among them, each node representing a different highly cited co-author ( Supplementary Figure 8 , Supplementary Table 4 ). The network revealed that French researchers are closely collaborating within France and on physical exercise and aging/depression (#9; 2018). The burstness analysis revealed that the co-authors that were the most participating in articles these last years were Stubbs B, Smith L, De Hert M, Vancampfort D and Probst M ( Supplementary Tables 2G,H ). We further produce the co-cited author network that permits to visualize “who cites who” for the last 5 years (2016–2021 network) was also conducted ( Supplementary Figure 9 ). The burstness analysis revealed that the most co-cited first authors according to our datasets were Brooks SK, Wang CY, Ogden CL, Holmes EA, and Kandola SA. Furthermore, the latest top cited authors (as first authors) with the most important strength of burst were Brooks SK, Schuch FB, Wang CY, Firth J, and Stubbs B ( Supplementary Tables 3I,J ).

The top five journals with the most documents were as follows: the International Journal of Environmental Research and Public Health ( n = 1,164) in first place with a massive raise of documents these last 3 years; PLOS ONE ( n = 1,017); BMC Public Health ( n = 625); BMJ OPEN ( n = 513) and the Journal of Affective Disorders ( n = 453) ( Supplementary Figure 10 ). We conducted the co-cited journal network that retained 2,879 journals and showed the highly cited journals with high betweenness centrality ( Supplementary Figure 11 ).

The top five highly cited journals were Archives of General Psychiatry (JAMA), The Lancet, PLOS ONE, Medicine and Science in Sports and Exercise , and the New England Journal of Medicine ( Table 2 ). The burstness analysis further reveals that five journals with the latest beginning of burst were Frontiers in Psychology, The Lancet Psychiatry, International Journal of Environmental Research and Public Health, Nutrients , and Frontiers in Psychiatry ( Supplementary Tables 2E,F ).

Table 2 . Journals with most articles and citations.

Summary of the main findings

To the best of our knowledge, this is the first broad scientometric that proposes a comprehensive overview of the development of research on physical activity, mental health, and wellbeing.

We retained 55,353 documents revealing an exponential growth of scientific production since the 90s. The USA holds for decades the leading position in research; however, China is very active since 2020 with an important burst of citations, mainly due to publication on COVID-19. The King's College London and Harvard University were the most influential institutions in terms of citation count. In supplement to actual reviews, this scientometric study reveals the influence and collaboration network, which could help researchers to identify major scholarly communities and establish potential research collaboration.

Identification of research trends

The six distinct major trends of research identified expose the history and the latest development of research on physical activity, mental health, and wellbeing. The first major trend of research concerns physical activity and cardiovascular disease, reminding the past and present intertwine. First research focused on cardiovascular disease ( 35 ). The large body of research on evidence synthesis of the last decades that mainly focused on the prevention to treatment role of physical activity for cardiovascular disease started with guidelines for exercise testing ( 37 , 78 ), and that continues to date with consideration of cardiometabolic risk factors ( 39 ).

The extension of prevention and treatment of physical activity to other somatic disorders constituted the second major trend, making levels of physical activity a public health priority ( 41 ), that continues to date ( 79 ). Another trend, which emerged after 2000, is the potential of physical activity for the prevention and treatment of dementia with increased importance of evidence-synthesis studies ( 51 , 80 , 81 ).

Physical activity has also been explored as a potential intervention for the prevention and treatment of dementia. As regards to prevention, it has been demonstrated that physical activity is a protective factor against Alzheimer's disease and other types of dementia ( 82 , 83 ). As a treatment, recently an umbrella review has pooled evidence from as many as 27 systematic reviews, including 18 with meta-analyses, overall reporting on 28,205 participants with mild cognitive impairment or dementia ( 84 ). The authors showed that mind-body intervention and mixed physical activity interventions had a small effect on global cognition, whereas resistance training had a large effect on global cognition in those with mild cognitive impairment. In people affected by dementia, a small effect of physical activity/exercise emerged in improving global cognition in Alzheimer's disease and all types of dementia. Importantly, physical activity/exercise also improved other outcomes not strictly related to cognition, including the risk of falls, and neuropsychiatric symptoms.

Adjacently, a massive body of evidence has organized an important trend of research on the benefits of physical activity for both prevention and treatment of severe mental disorders, in particular depression ( 4 , 85 , 86 ) and schizophrenia ( 71 , 87 ). More recently, the evidence has focused on evidence-synthesis ( 10 , 74 ) and mental health/wellbeing ( 9 ).

Other lesser, although highly relevant trends were also uncovered, such as the importance of physical activity for athlete's performance ( 88 , 89 ). While most of the research efforts in that area have focused on how to optimize performance in the context of professional athletics ( 90 ), perfectionism, and excessive physical activity can also be a symptom of mental disorders, and eating disorders in particular ( 58 ). This research trends now focus on concussion and its consequence (chronic traumatic encephalopathy) ( 60 ).

Finally, a large body of research has focused on physical activity and COVID-19. Physical activity is a protective factor for COVID-19 complications ( 91 ). During COVID-19 research has also focused on restrictions and physical activity ( 63 ). Finally, physical activity's relevance has also been shown to extend beyond the clinical sciences and start to dialogue with greenness and urban planning ( 66 , 92 , 93 ).

Although various trends of research have developed these last decades, we can identify two important gaps, the one of the roles of physical activity in the prevention or treatment of substance-use disorders, and the one regarding the socioeconomic inequalities in access to physical exercise ( 94 ). Meta-review covering this subject ( 10 ) concluded that exercise can improve multiple mental health outcomes in those with alcohol-use disorders and substance-use disorders; however, further research is needed in these conditions, notably with the use of mind-body practices ( 95 , 96 ).

Strengths and limitations

This work has strengths and weaknesses. Strengths are its novel evidence-synthesis approach, complete systematic reviews, and meta-analysis, by providing information on the evolution of research trends over time, the visualization of networks of authors, countries, and institutions, and that go beyond common measures of academic bibliometric performance (i.e., impact factor, H-Index, number of papers or citations). This novel research framework permits repeatable, reproducible, and comparable analysis with less bias than conventional time-consuming reviews that are vulnerable to biased coverage/selection.

Limitations are that, despite the quality check procedures outlined in the methods, this is not a systematic review. Furthermore, gathered data were only obtained from WOSCC, which can limit retrieved publication ( 94 , 97 ). Also, the centrality and number of citations are not necessarily indicative of the quality of a work, as faulty publications can be highly cited because they are frequently criticized as well ( 98 ). Finally, no reporting guidance is available for scientometric studies yet, given their recent introduction in the literature.

In conclusion, researchers have consistently focused on the role of physical activity on cardiovascular disease, other somatic disorders, dementia, mental disorders, athlete's performance, and eating disorders and more recently on COVID-19 pandemic, which clearly shows the role of physical activity as medicine across physical and mental disorders. More recently, the literature has focused on green space, urban planning, and behavior change, further expanding the multidisciplinary reach of physical activity. Taken together our results strengthen and expand the specific and central role of physical activity in public health, calling for the systematic involvement of physical activity professionals as stakeholders in the public health decision-making process.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

MSa and MSo: conceptualization and writing—original draft preparation. MSa, CC, and MSo: methodology, formal analysis, and investigation. MSa, CC, OS, JD, DV, JF, LS, BS, SR, FS, and MSo: writing—review and editing. CC and MSo: supervision. All authors contributed to the article and approved the submitted version.

Open access funding was provided by the University of Geneva.

Conflict of interest

Author OS has received advisory board honoraria from Otsuka, Lilly, Lundbeck, Sandoz, and Janssen in an institutional account for research and teaching. Author JF has received consultancy fees from Parachute BH for a separate project. Author BS is on the Editorial Board of Ageing Research Reviews, Mental Health and Physical Activity, the Journal of Evidence Based Medicine and the Brazilian Journal of Psychiatry. Author BS has received honorarium from a co-edited a book on exercise and mental illness, advisory work from ASICS & ParachuteBH for unrelated work. Author MSo has received honoraria/has been a consultant for Angelini, Lundbeck and Otsuka.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2022.943435/full#supplementary-material

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Keywords: physical exercise, mental illness, evidence synthesis, scientometrics, CiteSpace

Citation: Sabe M, Chen C, Sentissi O, Deenik J, Vancampfort D, Firth J, Smith L, Stubbs B, Rosenbaum S, Schuch FB and Solmi M (2022) Thirty years of research on physical activity, mental health, and wellbeing: A scientometric analysis of hotspots and trends. Front. Public Health 10:943435. doi: 10.3389/fpubh.2022.943435

Received: 17 May 2022; Accepted: 15 July 2022; Published: 09 August 2022.

Reviewed by:

Copyright © 2022 Sabe, Chen, Sentissi, Deenik, Vancampfort, Firth, Smith, Stubbs, Rosenbaum, Schuch and Solmi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Michel Sabe, michel.sabe@hcuge.ch

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Research on the Influence of Physical Exercise Health on the Mental Health of University Students

Affiliations.

• 1 Yeungnam University, Physical Education Major/Department of Kinesiology, Gyeongsan 38541, Republic of Korea.
• 2 School of Physical Education, Fuyang Normal University, Fuyang, Anhui 236037, China.
• PMID: 36089977
• PMCID: PMC9458412
• DOI: 10.1155/2022/6259631

Retraction in

• Retracted: Research on the Influence of Physical Exercise Health on the Mental Health of University Students. Environmental And Public Health JO. Environmental And Public Health JO. J Environ Public Health. 2023 Sep 27;2023:9813752. doi: 10.1155/2023/9813752. eCollection 2023. J Environ Public Health. 2023. PMID: 37811425 Free PMC article.

Exercise has become one of the essential life skills of university students and is also an integral part of physical exercise and mental health. As a positive influence on their physical and mental health, sports can help people to adjust and regulate their emotions and behavior. At the same time, the mental health of university students has been on the decline in recent years. At the university level, students tend to develop anxiety, depression and other emotions and problems, and temptations of all kinds provide a direction for psychological problems to arise. For university educators, the way to improve the mental health of university students is to study the current psychological situation of university students, identify the symptoms, treat them, and adopt scientific and effective interventions while sports are popular among young people as a positive and fun activity. In order to investigate the relationship between sports health and mental health of university students, the author chose thousands of students in a school as the research subjects using experimental methods and mathematical and statistical methods to conduct physical and psychological tests on the research subjects through different comparative experimental content and data results to explore the impact of sports health on the mental health of university students and using professional data analysis software IBM. The data from the physical health assessment and the psychological health assessment obtained from the school assessment center were analyzed by using the independent sample t -test and correlation analysis method in SPSS Statistics 19.0, so as to investigate the influence of physical health on psychological health.

Copyright © 2022 Jinjin Wang and Chengbao Li.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

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Physical Fitness Research Paper Topics

Looking for captivating physical fitness research paper topics ? You’ve come to the right place! This page is your ultimate resource, providing an extensive list of research paper topics that delve into the fascinating world of physical fitness. With ten categories, each containing ten unique physical fitness research paper topics, you’ll discover a wide range of subjects to explore, analyze, and present in your research. From exercise physiology to nutrition, psychology to biomechanics, this comprehensive list covers various dimensions of physical fitness. So, whether you’re passionate about understanding the effects of exercise on cardiovascular health or exploring the role of nutrition in athletic performance, these topics will ignite your curiosity and help you embark on a rewarding research journey in the realm of physical fitness.

100 Physical Fitness Research Paper Topics

The field of physical fitness offers a rich landscape for research, providing numerous opportunities for students to explore various aspects of human health, exercise, and performance. This comprehensive list of physical fitness research paper topics is designed to inspire and guide health science students in their quest for compelling research ideas. The list is divided into ten categories, each containing ten unique topics, offering a diverse range of subjects to delve into. Whether you are interested in the physiological, psychological, or social aspects of physical fitness, there is something for everyone in this extensive compilation.

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Exercise Physiology

• The impact of different exercise modalities on cardiovascular health.
• Effects of resistance training on muscle strength and hypertrophy.
• The role of aerobic exercise in improving cognitive function.
• Exploring the physiological mechanisms behind exercise-induced fatigue.
• Exercise and its impact on metabolic disorders such as diabetes.
• The influence of exercise on bone health and prevention of osteoporosis.
• Effects of high-intensity interval training (HIIT) on athletic performance.
• The relationship between exercise and immune function.
• Exploring the effects of exercise on sleep quality and duration.
• The impact of exercise on mental health and well-being.

Nutrition and Physical Fitness

• The role of macronutrients in optimizing athletic performance.
• Exploring the effects of different diets on body composition and fitness.
• The influence of nutritional supplements on exercise recovery and performance.
• The impact of hydration status on exercise performance and physiological responses.
• Nutritional strategies for optimizing muscle glycogen storage and utilization.
• Exploring the relationship between nutrition, gut microbiota, and physical fitness.
• The effects of fasting and intermittent fasting on exercise adaptations.
• Nutritional considerations for vegan and vegetarian athletes.
• The role of antioxidants in mitigating exercise-induced oxidative stress.
• Investigating the effects of pre- and post-exercise nutrition timing on performance and recovery.

Psychology of Exercise

• The psychological benefits of physical activity on stress reduction.
• Exploring the motivational factors influencing exercise adherence.
• The role of exercise in improving body image and self-esteem.
• Examining the relationship between exercise and cognitive function in older adults.
• The impact of exercise on mood disorders such as depression and anxiety.
• Investigating the psychological effects of team sports participation.
• Exploring the psychological strategies for enhancing exercise performance.
• The influence of exercise on cognitive and academic performance in students.
• The role of exercise in promoting healthy aging and cognitive longevity.
• Psychological factors influencing exercise behavior among different populations.

Biomechanics and Kinetics

• Investigating the biomechanics of human gait and its implications for injury prevention.
• The role of biomechanical analysis in optimizing sports performance.
• Understanding the mechanics of resistance training exercises for muscle activation.
• Examining the biomechanical factors influencing running economy and performance.
• Biomechanical analysis of joint loading during different types of exercise.
• The influence of footwear on biomechanics and injury risk in athletes.
• Exploring the mechanics of human balance and stability during exercise.
• The role of motion capture technology in studying human movement patterns.
• Biomechanical analysis of sports-specific movements and injury prevention.
• Investigating the mechanics of plyometric training and its impact on power development.

Sports Medicine and Injury Prevention

• Exploring the most common sports-related injuries and their prevention strategies.
• The role of rehabilitation and physical therapy in sports injury recovery.
• Investigating the effects of preventive measures on reducing concussion risk in contact sports.
• Understanding the biomechanics of common overuse injuries in athletes.
• Exploring the psychological factors influencing injury rehabilitation and return to sport.
• The impact of sports specialization on injury risk and long-term athlete development.
• Investigating the efficacy of different injury prevention programs in specific sports.
• The role of bracing and protective equipment in injury prevention.
• Exploring the influence of environmental factors on injury risk in outdoor sports.
• The effects of fatigue on injury occurrence and prevention in sports.

Physical Fitness Assessment and Testing

• Evaluating the validity and reliability of different fitness assessment methods.
• The role of body composition analysis in assessing physical fitness and health.
• Investigating the accuracy of wearable fitness trackers in monitoring exercise intensity.
• Examining the utility of field-based fitness tests in predicting athletic performance.
• Comparing the effectiveness of laboratory-based and field-based fitness assessments.
• Exploring novel approaches to assessing muscular strength and power.
• The role of cardiovascular fitness testing in predicting health outcomes.
• Investigating the assessment of flexibility and its relationship with injury risk.
• Examining the utility of functional movement screening in assessing physical fitness.
• Evaluating the psychometric properties of self-report physical activity questionnaires.

Exercise Prescription and Training Programs

• The effects of different exercise intensity and duration on fitness outcomes.
• Investigating the impact of periodization models on long-term athletic development.
• Optimizing resistance training program design for muscle hypertrophy.
• The role of concurrent training in maximizing strength and endurance gains.
• Exploring the benefits of high-intensity interval training (HIIT) in various populations.
• Examining the effects of different exercise modalities on body composition changes.
• Investigating the efficacy of exercise programs for older adults in improving functional capacity.
• The impact of exercise programming on cardiovascular health and disease prevention.
• Exploring the effects of exercise on insulin sensitivity and metabolic health.
• The role of exercise prescription in promoting weight loss and weight management.

Exercise and Special Populations

• Investigating the effects of exercise on pregnancy outcomes and maternal health.
• Exercise interventions for individuals with chronic diseases such as diabetes and cardiovascular disorders.
• The impact of exercise on bone health in postmenopausal women.
• Exploring exercise programs for individuals with physical disabilities.
• The role of exercise in managing symptoms and improving quality of life in cancer patients.
• Exercise interventions for individuals with mental health conditions such as depression and anxiety.
• Investigating the effects of exercise on cognitive function in children and adolescents.
• The impact of exercise on sleep quality and patterns in different populations.
• Exercise programs for older adults to enhance mobility, balance, and functional independence.
• The role of exercise in promoting well-being and reducing stress in the workplace.

Exercise and Public Health

• Investigating the impact of physical activity interventions on population health outcomes.
• The role of exercise in preventing and managing non-communicable diseases.
• Examining the socioeconomic factors influencing physical activity levels in different populations.
• Exploring the effectiveness of community-based exercise programs in promoting health.
• The impact of built environment and neighborhood design on physical activity levels.
• Investigating the relationship between physical fitness and academic performance in school settings.
• Exploring strategies to promote physical activity in sedentary populations.
• The role of exercise in reducing healthcare costs and burden on the healthcare system.
• Investigating the effects of policy and environmental changes on physical activity promotion.
• The role of exercise in promoting healthy aging and preventing age-related chronic diseases.

Emerging Trends and Innovations in Physical Fitness

• Investigating the effects of wearable technology on exercise motivation and behavior change.
• Exploring the potential of virtual reality in enhancing exercise experiences.
• The impact of exergaming on physical activity levels and health outcomes.
• Investigating the use of artificial intelligence in personalized exercise prescription.
• Exploring the effects of biofeedback techniques on performance and exercise adherence.
• The role of genomics in understanding individual responses to exercise.
• Investigating the effects of mind-body exercise modalities on physical and mental well-being.
• Exploring the potential of outdoor adventure and nature-based activities in promoting physical fitness.
• The impact of social media and online platforms on exercise motivation and support.
• Investigating the effects of environmental factors on exercise performance and adherence.

This comprehensive list of physical fitness research paper topics offers a vast array of possibilities for students to explore in their research endeavors. From exercise physiology to sports medicine, psychology to emerging trends, there are numerous avenues to delve into the fascinating field of physical fitness. Whether you have a specific interest in a particular category or wish to explore cross-disciplinary topics, this list provides a solid foundation for selecting a compelling research topic. So, let your curiosity guide you, and embark on a journey of discovery and knowledge in the realm of physical fitness research.

Physical Fitness: Exploring the Range of Research Paper Topics

Physical fitness is a multidimensional concept that encompasses various aspects of health, performance, and well-being. As a student of health sciences, delving into the realm of physical fitness research can provide you with a rich opportunity to explore a wide range of captivating topics. From understanding the physiological adaptations to exercise to investigating the psychological aspects of physical activity, the field of physical fitness offers an expansive landscape for research. In this article, we will explore the diverse range of physical fitness research paper topics, providing you with a comprehensive understanding of the exciting possibilities that lie ahead.

Exercise Physiology: Unraveling the Mysteries of Human Performance

Exercise physiology is a fundamental area of study within physical fitness research. It focuses on understanding how the body responds and adapts to exercise. One fascinating research area within exercise physiology is the investigation of physiological adaptations to different types of exercise. You can explore the effects of various exercise modalities, such as aerobic training, resistance training, or high-intensity interval training, on cardiovascular health, muscular strength, endurance, and body composition. Additionally, examining the impact of exercise on metabolic disorders, bone health, immune function, and fatigue can provide valuable insights into the physiological mechanisms underlying human performance.

Psychology of Physical Activity: Understanding the Mind-Body Connection

Understanding the psychological aspects of physical activity is crucial for promoting and maintaining engagement in exercise. The psychology of physical activity encompasses a broad range of physical fitness research paper topics that explore the factors influencing exercise motivation, adherence, and the interplay between physical activity and mental health. You can investigate the role of motivation in initiating and sustaining exercise behavior, exploring strategies to enhance exercise adherence and overcome barriers to physical activity participation. Furthermore, exploring the relationship between exercise and mental health outcomes, such as depression, anxiety, stress management, and cognitive function, can shed light on the potential psychological benefits of physical fitness.

Sports Nutrition: Fueling the Body for Optimal Performance

Nutrition plays a critical role in supporting physical fitness and performance. Researching the impact of nutrition on exercise performance and recovery is a dynamic field within the realm of physical fitness. You can explore topics such as the influence of macronutrient composition on endurance or strength performance, the effects of hydration on exercise capacity, the role of dietary supplements in enhancing athletic performance, and the timing and composition of pre- and post-exercise meals. Investigating the nutritional requirements of specific populations, such as athletes, older adults, or individuals with chronic diseases, can provide valuable insights into optimizing nutrition strategies for diverse populations.

Injury Prevention and Rehabilitation: Ensuring Safe and Effective Exercise

Injury prevention and rehabilitation are essential components of physical fitness research. Exploring topics related to injury prevention and rehabilitation can encompass a wide range of areas, including the identification of risk factors for exercise-related injuries, the development of effective training programs to reduce injury rates, the investigation of rehabilitation techniques to facilitate recovery and return to physical activity, and the evaluation of preventive strategies in specific populations. Understanding the mechanisms underlying injuries and developing strategies to mitigate their occurrence can contribute to safer and more effective exercise practices.

Exercise Prescription and Programming: Tailoring Fitness Interventions

Exercise prescription and programming focus on the design and implementation of exercise interventions tailored to individual needs and goals. This research area encompasses topics such as the development of personalized exercise programs for different populations, the optimization of training variables (intensity, duration, frequency) for specific outcomes, the evaluation of novel training methods and technologies, and the use of wearable devices and digital technologies in exercise prescription. Investigating exercise prescription and programming can provide valuable insights into the most effective strategies for achieving desired fitness outcomes, improving overall health and well-being, and promoting behavior change.

Biomechanics and Movement Analysis: Exploring Human Motion

Biomechanics and movement analysis involve the study of human motion and the forces that act upon the body during physical activities. This research area explores topics such as the mechanics of joint movement, muscle function, gait analysis, balance and coordination, and the effects of external factors on movement performance. Investigating biomechanics and movement analysis can contribute to a deeper understanding of optimal movement patterns, injury mechanisms, ergonomics, and the development of assistive devices or rehabilitation strategies.

Environmental and Occupational Health: Exploring the Impact of Work and Environment on Health

Environmental and occupational health focuses on the effects of work and environmental factors on human health and well-being. This research area encompasses topics such as the impact of physical activity in occupational settings, the effects of environmental pollutants on health outcomes, the role of physical fitness in occupational performance, and the development of strategies to promote a healthy work environment. Investigating environmental and occupational health can provide insights into the relationship between physical fitness, work-related factors, and overall health and safety.

Public Health and Health Promotion: Advancing Population Health

Public health and health promotion research aim to improve the health and well-being of populations through disease prevention, health education, and promotion of healthy behaviors. This research area explores topics such as the impact of physical fitness on chronic disease prevention, the effectiveness of health promotion interventions in promoting physical activity, strategies for increasing physical activity in underserved populations, and the development of policies to support physical fitness initiatives. Investigating public health and health promotion can contribute to the development of evidence-based interventions and policies to enhance population health.

Geriatric Exercise Science: Enhancing Health in Aging Populations

Geriatric exercise science focuses on promoting health and functional independence in older adults through exercise and physical activity. This research area explores topics such as the effects of exercise on age-related declines in muscle strength, balance, and mobility, the role of physical activity in preventing age-related chronic diseases, and the development of exercise programs for older adults with specific health conditions. Investigating geriatric exercise science can provide valuable insights into maintaining health and well-being in aging populations and improving the quality of life for older adults.

The field of physical fitness research offers a vast array of topics to explore, ranging from exercise physiology and psychology of physical activity to sports nutrition, injury prevention and rehabilitation, exercise prescription and programming, biomechanics and movement analysis, environmental and occupational health, public health and health promotion, and geriatric exercise science. By choosing a research topic that aligns with your interests and career aspirations, you can contribute to the advancement of knowledge in the field while gaining a deeper understanding of the intricacies of physical fitness. Embrace the opportunities that physical fitness research presents and let your passion for health science drive your exploration of these captivating topics.

Choosing Physical Fitness Research Paper Topics

Selecting an engaging and relevant research topic is a crucial step in the process of writing a research paper on physical fitness. With a wide range of possibilities within the field, it can be challenging to narrow down your focus and identify a topic that aligns with your interests and academic goals. In this section, we will provide expert advice on how to choose physical fitness research paper topics that are compelling, meaningful, and contribute to the existing knowledge in the field.

• Identify Your Interests : Start by reflecting on your personal interests within the realm of physical fitness. Consider the aspects of exercise, health, performance, or well-being that fascinate you the most. Are you passionate about exercise physiology, psychology of physical activity, sports nutrition, injury prevention and rehabilitation, exercise prescription and programming, biomechanics and movement analysis, environmental and occupational health, public health and health promotion, or geriatric exercise science? By identifying your interests, you can focus on areas that resonate with you and spark your curiosity.
• Stay Informed : Keep up-to-date with the latest research and advancements in the field of physical fitness. Subscribe to academic journals, attend conferences, and follow reputable websites and research institutes dedicated to exercise science. By staying informed, you will gain insights into current trends, emerging topics, and gaps in knowledge that may inspire your research interests.
• Conduct a Literature Review : Before finalizing your research topic, conduct a comprehensive literature review to explore existing studies, theories, and gaps in knowledge. Identify areas where further research is needed or where conflicting findings exist. A literature review will help you refine your research question and ensure that your topic contributes to the existing body of knowledge.
• Consult with Faculty or Experts : Reach out to your faculty members or experts in the field for guidance and advice. They can provide valuable insights, suggest potential research directions, and help you refine your research topic. Utilize their expertise to gain a deeper understanding of the field and identify relevant research questions.
• Consider Practical Applications : Think about the practical applications and implications of your research topic. How can your findings contribute to real-world situations, enhance practice, or inform policy decisions? Identifying the practical significance of your research can add value and relevance to your study.
• Balance Specificity and Feasibility : Strive for a research topic that is specific enough to provide depth and focus but also feasible within the constraints of your research project. Consider the available resources, time, and access to data or participants when determining the scope of your research topic. Finding the right balance will ensure that your research is manageable and achievable within the given timeframe.
• Collaborate and Network : Collaborate with peers, researchers, or professionals in the field to broaden your perspective and generate new ideas. Engaging in discussions and exchanging thoughts with others can spark creativity and open doors to potential research collaborations.
• Think Outside the Box : Don’t be afraid to think outside the box and explore innovative or unconventional research topics within physical fitness. Consider interdisciplinary approaches or emerging areas of research that intersect with exercise science, such as technology, digital health, or social determinants of health. Embracing innovative ideas can lead to exciting discoveries and contribute to the evolution of the field.
• Consider Ethical Considerations : When choosing a research topic, consider the ethical implications and potential risks associated with your study. Ensure that your research adheres to ethical guidelines and protects the rights and well-being of participants. Consulting with ethics committees or institutional review boards can help ensure that your research is conducted ethically and responsibly.
• Seek Feedback and Refine Your Topic : Once you have identified a potential research topic, seek feedback from mentors, peers, or academic advisors. They can provide constructive criticism, suggest modifications, or help you clarify your research objectives. Use their input to refine your research topic and ensure that it aligns with your academic goals and the requirements of your research paper.

Choosing a research topic in the field of physical fitness requires careful consideration and alignment with your interests, academic goals, and the existing knowledge in the field. By following these expert tips, you can select a compelling research topic that contributes to the advancement of knowledge, engages your passion, and offers opportunities for meaningful exploration. Embrace the journey of research and let your curiosity drive you to uncover new insights in the fascinating world of physical fitness.

How to Write a Physical Fitness Research Paper

Writing a research paper on physical fitness requires careful planning, organization, and adherence to academic conventions. In this section, we will provide you with expert advice on how to write a compelling and well-structured physical fitness research paper. By following these guidelines, you can effectively communicate your research findings, contribute to the existing body of knowledge, and showcase your understanding of the subject matter.

• Define Your Research Objective : Start by clearly defining the objective of your research paper. What is the specific question or problem that your study aims to address? Clearly articulate your research objective to guide your literature review, data collection, and analysis.
• Conduct a Comprehensive Literature Review : Before diving into the writing process, conduct a thorough literature review to familiarize yourself with existing research on the topic. Identify key theories, methodologies, and findings that will inform your study. Analyze and critically evaluate the literature to identify gaps in knowledge that your research can fill.
• Develop a Solid Research Methodology : Outline your research methodology, including the study design, sample size, data collection methods, and data analysis techniques. Clearly explain how you will collect and analyze data to answer your research question. Ensure that your methodology is rigorous, ethical, and aligned with the standards of your academic institution.
• Organize Your Paper : A well-organized research paper follows a logical structure. Start with an introduction that provides background information, states the research question, and outlines the significance of your study. Follow this with a literature review that synthesizes existing research and highlights the gaps in knowledge. Next, present your research methodology, including the sample characteristics, data collection procedures, and statistical analysis methods. Present your findings in a clear and concise manner, using tables, graphs, or charts as appropriate. Finally, conclude your paper by summarizing your findings, discussing their implications, and suggesting avenues for future research.
• Write Clearly and Concisely : Use clear and concise language to convey your ideas. Avoid jargon or technical terms that may be unfamiliar to your readers. Explain complex concepts in a way that is accessible to a broad audience. Ensure that your writing is well-structured, with paragraphs that flow logically and smoothly.
• Support Your Arguments with Evidence : Back up your arguments and claims with credible evidence. Use scholarly sources, peer-reviewed articles, and reputable databases to support your statements. Include proper citations and references to acknowledge the work of other researchers and avoid plagiarism.
• Analyze and Interpret Your Findings : Once you have collected and analyzed your data, interpret the results in the context of your research question. Discuss the implications of your findings and consider alternative explanations or limitations of your study. Engage in critical thinking and provide thoughtful insights based on your analysis.
• Address Limitations : Acknowledge the limitations of your study and discuss potential sources of bias or confounding factors. This demonstrates a critical understanding of the research process and adds credibility to your work. Suggest areas for future research that can overcome these limitations and contribute to further knowledge in the field.
• Follow Proper Formatting and Citation Style : Adhere to the formatting guidelines specified by your academic institution or the journal you intend to submit your research paper to. Use the appropriate citation style, such as APA, MLA, or Chicago, and ensure consistency throughout your paper. Pay attention to details, such as margins, font size, headings, and references.
• Revise and Edit : Before submitting your research paper, revise and edit it thoroughly. Check for grammar and spelling errors, sentence structure, and overall coherence. Read your paper aloud or ask a colleague to review it for clarity and flow. Make necessary revisions to improve the quality and readability of your paper.

Writing a physical fitness research paper requires careful planning, diligent research, and effective communication of your findings. By following these guidelines, you can craft a well-structured and informative paper that contributes to the field of physical fitness. Embrace the process of writing and view it as an opportunity to share your knowledge, insights, and passion for the subject matter. With dedication and attention to detail, your research paper can make a valuable contribution to the body of knowledge in physical fitness.

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• Custom Written Works : Every research paper we deliver is customized to meet your specific requirements. We take into account your instructions, guidelines, and desired outcomes to create a paper that aligns with your academic goals. Our writers follow a systematic approach, conducting thorough research, analyzing data, and crafting a coherent and well-structured paper tailored to your research question.
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Open Access

Peer-reviewed

Research Article

The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia

Roles Investigation, Methodology, Writing – review & editing

Affiliation Alliance for Research in Exercise, Nutrition and Activity, UniSA Allied Health and Human Performance, University of South Australia, Adelaide, Australia

Roles Writing – review & editing

Affiliation Deakin University, Geelong, Australia, Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences

Roles Data curation, Formal analysis, Software, Writing – review & editing

Affiliation Royal Melbourne Hospital, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

Affiliations NIHR Imperial Patient Safety Translational Research Centre, Imperial College of London, London, United Kingdom, Centre for Health Technology and Services Research, Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, Porto, Portugal

Affiliation Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia

Affiliations Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia, Department of Cardiology, Westmead Hospital, Sydney, Australia

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

¶ ‡ These authors are joint senior authors on this work.

Affiliations Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia, Western Sydney Primary Health Network, Sydney, Australia

Affiliations Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia, Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia

• Huong Ly Tong, 
• Carol Maher, 
• Kate Parker, 
• Tien Dung Pham, 
• Ana Luisa Neves, 
• Benjamin Riordan, 
• Clara K. Chow, 
• Liliana Laranjo, 
• Juan C. Quiroz

• Published: August 18, 2022
• https://doi.org/10.1371/journal.pdig.0000087
• Peer Review
• Reader Comments

To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups.

An online cross-sectional survey was conducted during June–September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher’s exact tests. Three open-ended questions were included to elicit participants’ views; thematic analysis was conducted.

Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18–44 (46.1%), more people aged 60+ (74.5%) and more people aged 45–60 (57.6%) used a COVID-19 related app ( P < .001). Qualitative data suggest people viewed technologies (especially social media) as a ‘double-edged sword’: helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19.

Conclusions

Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.

Author summary

Technologies such as mobile apps or fitness trackers may play a key role in supporting healthy behaviors and deliver public health interventions during the COVID-19 pandemic. We conducted an international survey that asked people about their health behaviors, and their use of technologies before and during the pandemic. Sixty percent reported using a mobile app for health purposes; 38% used a fitness tracker. People who used mobile apps and fitness trackers during the pandemic were more active than people who did not. Women were more likely to use health apps than men, and people aged 45+ were more likely to use COVID-19 apps than people under 45. Differences in app usage based on sex and age indicate that tailored technologies are needed to support different groups. Participants revealed that they had to adapt their use of mobile apps to fit their needs during the highly restricted circumstances caused by COVID-19. Altogether, our findings provide new insights into how mobile apps and devices can deliver health support remotely during a pandemic, and highlight the need for these technologies to adapt to support people’s changing needs.

Citation: Tong HL, Maher C, Parker K, Pham TD, Neves AL, Riordan B, et al. (2022) The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey. PLOS Digit Health 1(8): e0000087. https://doi.org/10.1371/journal.pdig.0000087

Editor: Laura M. König, University of Bayreuth: Universitat Bayreuth, GERMANY

Received: December 25, 2021; Accepted: July 14, 2022; Published: August 18, 2022

Copyright: © 2022 Tong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data that support the findings of this study are openly available at https://osf.io/wa5p8/?view_only=06a70c1321114dfc8f45bd4e1affca4b .

Funding: HLT was supported by the International Macquarie University Research Excellence Scholarship (iMQRES) (Macquarie University funded Scholarship – No. 2018148) and the Australian Government Research Training Program Scholarship. CM is supported by a Medical Research Future Fund Investigator Grant (APP1193862). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coronavirus disease 2019 (COVID-19) and subsequent public health measures have drastically impacted lifestyles worldwide and have had adverse effects on health behaviors [ 1 – 6 ]. Several cross-sectional surveys of adults in Australia, the US and UK have reported negative changes in health behaviors and mental health during the pandemic, including reduced physical activity [ 3 , 4 ], unhealthy eating habits and lower diet quality [ 3 , 4 ], increased alcohol consumption [ 1 ], and higher prevalence of anxiety and depression symptoms [ 1 , 2 , 6 ]. In addition to self-reported changes, studies using objective smartphone-based data also showed a decline in daily step count worldwide [ 5 , 7 ]. During the pandemic, the World Health Organization highlighted the importance of maintaining healthy behaviors in the fight against COVID-19 [ 8 ]. With restrictions on face-to-face clinical consultations and the strain on health care systems in delivering patient care, mobile devices were increasingly harnessed to remotely deliver health care support [ 9 , 10 ].

Mobile devices such as mobile apps and fitness trackers [ 11 ] can be leveraged to deliver behavior change interventions and might play a role in supporting healthy behaviors during the pandemic. Specifically, mobile apps and fitness trackers can incorporate behavior change techniques (i.e., the active component of an intervention designed to regulate behavior change [ 12 ]) that are known to be effective in changing behaviors. Systematic reviews have found that behavior change techniques such as goal setting and self-monitoring of behavior are effective at improving physical activity and diet outcomes [ 13 , 14 ]. Mobile apps or fitness trackers can deliver these behavior change techniques, such as by enabling users to set their own goals, or to self-monitor some behaviors, as demonstrated in prior reviews [ 15 , 16 ]. During the pandemic, mobile apps and fitness trackers can offer unique benefits, by allowing people to access health support remotely and engage in virtual activities (e.g., livestreamed exercise class), in replacement of disrupted in-person activities. Evidence from systematic reviews suggests that under pre-pandemic or ‘normal’ conditions, mobile apps and fitness trackers can improve physical activity [ 17 – 21 ], diet [ 17 , 22 ], sleep [ 23 ], reduce smoking and alcohol intake [ 22 , 24 , 25 ], and help manage mental health [ 17 , 26 ]. However, little is known about the use of these technologies for health behaviors during the COVID-19 pandemic, and the association between using mobile apps and fitness trackers, and healthy behaviors.

A few studies have examined the use of digital technologies for physical activity and mental health during the pandemic. Specifically, a study of Google Trends showed an increase in searches for physical activity and exercise in Australia, the US and the UK [ 27 ]. An analysis of App store data in the US showed an increase in downloads of mental health apps [ 28 ]. Cross-sectional surveys found that the use of digital platforms (e.g., streaming services, mobile apps) was associated with higher physical activity levels [ 29 – 31 ]. While this evidence is promising, the scope was limited to physical activity and mental health and did not explore other behaviors (e.g., diet, smoking, alcohol intake) that are important to maintain good health during the pandemic. Moreover, existing research has not examined the use of fitness trackers, which have been known to have a positive impact on health behaviors [ 18 , 20 , 21 ]. Thus, there remain gaps in understanding how a range of mobile devices were being used for physical and mental wellbeing during the pandemic, and the association between usage and health behaviors.

In addition to supporting healthy behaviors, mobile devices have also been leveraged to deliver public health interventions during the pandemic. Specifically, mobile apps have been developed for COVID-19 purposes, such as to support contact tracing [ 9 ], self-management of symptoms, or home monitoring [ 32 – 34 ]. Despite rapid growth in the number of COVID-19 mobile apps, little is known about their adoption, with preliminary evidence suggesting that specific subgroups (e.g., older people) are more likely to adopt such apps [ 35 ]. It is important to better understand how different subgroups might adopt COVID-19 apps, to inform public health strategies and policy makers in their response to the pandemic.

To address these gaps, we conducted a cross-sectional survey to examine use of mobile apps and fitness trackers to support health behaviors (i.e., self-reported physical activity, diet, sleep, smoking, alcohol consumption), mental wellbeing, and public health interventions (e.g., COVID-19 apps) during the pandemic.

The secondary aims of the study were to examine:

• Whether using mobile apps and/or fitness trackers was associated with healthy behaviors,
• What was the adoption of COVID-19 related apps (i.e., mobile apps designed specifically for COVID-19), and
• Whether specific subgroups showed a higher use of COVID-19 related apps and mobile apps and fitness trackers for health-related purposes.

Study design

This study is a cross-sectional survey that examined the use of mobile apps and fitness trackers for health behaviors and public health interventions during the COVID-19 pandemic. The reporting adheres to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guideline for cross-sectional studies [ 36 ] ( S1 Appendix ). Ethical approval was granted by Macquarie University’s Human Research Ethics Committee (Approval number: 52020674017063). All participants provided electronic written consent prior to participation ( S2 Appendix ).

Settings and participants

An anonymous online survey was hosted on the Qualtrics platform [ 37 ]. The study was advertised via various channels, including social media (Facebook, Twitter, LinkedIn, Instagram, Reddit), public posters (e.g., at parks, libraries, university campus), and research institute networks (e.g., email lists, university website). In our social media advertisements, we also asked people to share the study with their networks (e.g., re-tweet on Twitter), in order to expand the geographical scope of the study. Study recruitment was self-selected, i.e., interested individuals could click on the survey link, upon which they were provided with the study information and provided an electronic written consent prior to participation. Eligible study participants were adults aged over 18 years who were proficient in English. We followed published heuristics for sampling for behavioral research and aimed to recruit at least 500 participants into the study [ 38 ]. The survey was open from start of June to end of September 2020 to achieve the targeted sample size.

During the data collection period (June–September 2020), the World Health Organization assessed the global risk of COVID-19 to be very high [ 39 ]. The number of infected cases globally increased from over 10 million [ 40 ] to 32.7 million [ 41 ] during this period, with vastly different infection rates amongst countries. Public health policies across countries varied considerably with respect to lifestyle restrictions such as lockdown measures, travel restrictions, and mask mandates [ 42 , 43 ]. It is worth noting that during June–September 2020, a few countries had started to ease lifestyle restrictions (e.g., Australia, UK, Canada) [ 43 ].

Survey development and measures

Existing COVID-19 surveys [ 44 – 46 ] were reviewed to inform the wording and structure of the present survey. Subsequently, a draft survey was prepared and reviewed independently in three rounds to establish face validity. Specifically, in round one, a draft survey was prepared by the first author and reviewed by a clinician and a computer science expert, with revisions made accordingly. In round two, the survey was sent out to three experts in digital health and behavioral research for feedback, and revised accordingly. Finally, the revision made in round two was reviewed again by a clinician prior to being finalized. A copy of the Qualtrics survey can be found in S2 Appendix .

Demographic characteristics.

Participants reported their age (years), gender (female, male, other, prefer not to say), highest level of education completed (primary school, high school, vocational training, bachelor’s degree, postgraduate degree), country of residence, and whether they had medical conditions that required regular medical care or medication (yes, no).

Health behaviors.

Health behaviors including physical activity, diet, smoking and alcohol consumption during the pandemic were self-reported. Participants were asked how many minutes of moderate-to-vigorous physical activity they completed each week. Participants were considered to have adhered to the recommended levels of aerobic physical activity if they self-reported at least 150 minutes of moderate-to-vigorous physical activity in a week, based on the World Health Organization’s guidelines [ 47 ].

Participants self-reported daily servings of vegetables and fruits. Participants were considered to have adhered the recommended intake of vegetables and fruits if they self-reported consuming at least five servings of vegetables and fruits in a day, based on the World Health Organization’s recommendation [ 48 ]. Participants also reported the number of standard drinks they typically have in a week, their smoking status (yes, no) and number of cigarettes smoked in a day. Examples of moderate-to-vigorous physical activity, fruit and vegetable servings, and standard alcoholic drink servings were provided.

The use of mobile apps and fitness trackers for health behaviors.

The survey contained 20 questions about participants’ usage of mobile apps (including health apps, general apps, and social media apps) and fitness trackers to support health-related purposes before and during the COVID-19 pandemic. In the survey, health-related purposes were defined as staying active, eating healthily, sleeping better, reducing/stopping smoking and alcohol drinking, and managing mental wellbeing, and it was specified that the focus was not on chronic disease management (e.g., monitor blood glucose, medication reminders). Usage status during the pandemic was classified into three groups: current users, past users and never-users, based on existing literature [ 30 , 31 , 49 ]. The definition of usage status is provided in Box 1 . Additionally, participants were asked to indicate the extent to which they agreed with the usefulness of technologies in supporting different health behaviors. These items were measured using a five-point Likert scale, ranging from strongly disagree to strongly agree. The survey also contained three optional, open-ended questions to collect qualitative data on how participants used mobile apps, fitness trackers, and other technologies to support health behaviors and mental wellbeing during the COVID-19 pandemic.

Box 1: Classification based on technology usage during the pandemic*

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https://doi.org/10.1371/journal.pdig.0000087.t001

COVID-19 related apps

The survey included two questions about whether people used COVID-19 related apps (i.e., mobile apps created specifically for use during the COVID-19 pandemic), and for what purposes (e.g., for contact tracing, symptom checking).

Data analysis

Quantitative data were analyzed using R version 4.0.4 [ 50 – 52 ]. Descriptive statistics, including frequencies and percentages, were generated for categorical variables; means and standard deviations (SD) were generated for continuous variables. Two logistic regression models were used to examine the association between 1) the use of mobile apps and fitness trackers and adherence to aerobic physical activity guidelines, and 2) the use of mobile apps and adherence to fruit and vegetable consumption guidelines. Specifically, one logistic regression model included adherence to aerobic physical activity guidelines as the outcome variable, and the independent variables were current use of mobile apps or fitness trackers, whether participants used an app or tracker before COVID-19 (as a proxy for interest in technology before COVID-19), and whether participants started using a new app or tracker since COVID-19. Another model included adherence to fruit and vegetable consumption guidelines as the outcome variable, and the independent variables were current use of mobile apps, whether participants used a mobile app before COVID-19, and whether participants started using a new app since COVID-19. Both models were adjusted for factors selected a priori, including age, gender, education, and the existence of current medical conditions. Odds ratios (OR) and 95% confidence intervals (CI) were reported. Post-hoc sensitivity analyses were conducted to include only Australia-based participants, given the large proportion of this group in the sample.

Subgroup analyses were conducted to explore to explore whether age and gender subgroups were more likely to use mobile apps for health-related purposes or COVID-19 related apps. These subgroups were chosen based on the literature, as previous cross-sectional surveys have found that app usage might differ by age and gender [ 30 , 35 ]. Specifically, Thomas et al found that COVID-19 app downloads appeared to increase with age, with the 65+ age group having the highest proportion of downloads [ 35 ]. Additionally, Parker et al also found that more women than men used digital platforms for their physical activity during the pandemic [ 30 ]. Chi-square tests were used for categorical data. When the assumption of chi-square test was violated, Fisher’s exact test was used instead. The significance level for all statistical tests was set at P < .05, two-tailed.

Qualitative data (from free-text responses) were analyzed using thematic analysis [ 53 ] in NVivo 12 [ 54 ] to explore the different ways people used technologies to maintain health and wellbeing during the pandemic. Integration of results was conducted after quantitative and qualitative analyses were completed, through embedding of the data. Integration is presented throughout the Discussion section.

Sample description

While 554 people consented to participation, two were under 18, and thus, were not eligible. In total, 552 participants (mean age 38±13.6 years, 76.6% women) were included in data analysis. Responses were recorded from 32 countries, with most participants (382/549, 69.6%) living in Australia. The majority (359/552, 65%) had completed a postgraduate degree, and 71.1% (385/541) reported having no current medical condition requiring regular care or medication. The self-reported average weekly time spent in moderate-to-vigorous physical activity was 164 (SD 152) minutes. The average vegetable and fruit consumption reported by participants were 2.7 and 1.7 daily servings, respectively. Most of the sample (525/541, 97%) were non-smokers. The average alcohol consumption was reported as 3 drinks per week. The sociodemographic and health characteristics of the study sample are presented in Table 1 .

https://doi.org/10.1371/journal.pdig.0000087.t002

Technology use for health behaviors and mental wellbeing during COVID-19

Mobile apps..

Regarding participants’ app usage habits, 59.9% (302/504) were currently using apps for health purposes during the pandemic (i.e., current users) ( Table 2 ). Amongst the current app users, 77.8% (235/302) consistently used mobile apps for their health before COVID-19. A greater proportion of women were current app users than men (64.0% vs 46.8%, P = .004, S4 Appendix provides more details on subgroup analyses). The most popular apps used for health purposes during the pandemic were general and social media apps (e.g., Zoom, Facebook, Youtube), which were not purposedly built to promote health behavior change ( Table 2 ).

https://doi.org/10.1371/journal.pdig.0000087.t003

Compared to pre-pandemic times, nearly half (192/401, 47.8%) used mobile apps more frequently for health purposes during the COVID-19 pandemic ( Table 2 ). Forty percent (164/401, 40.9%) started using a new mobile app for health-related purposes since the outbreak of COVID-19.

During the COVID-19 pandemic, the most reported health purpose of app usage was to stay active (248/298, 83%) ( Table 2 ). Amongst those who used apps for physical activity, the majority used them to track activity levels (196/246, 79.7%), or to follow an exercise video (148/246, 60.1%) ( Table 2 ). Over two-third of participants (203/298, 68.1%) used mobile apps for more than one health purpose during the COVID-19 pandemic. Compared to men, a greater proportion of women used mobile apps to stay active (48% vs 36.7%, P = .02) and to connect with other people (22.7% vs 9.2%, P = .004, S4 Appendix ).

Regarding the perceived usefulness of mobile apps for health, 59.4% (232/390) of participants agreed that mobile apps helped them incorporate more activity in their days; 43.5% (167/384) agreed that mobile apps helped them manage their mental wellbeing. Compared to men, a greater proportion of women found mobile apps helpful for managing their mental wellbeing (80.6% vs 63.2%, P = .04, S4 Appendix ).

Fitness trackers.

Over a third of participants (188/492, 38.2%) were current users of fitness trackers, 19.3% (95/492) were past users, and 42.7% (210/492) had never used fitness trackers for their health. The median length of usage for current and past users was 2 years (range 1 month—10 years). Forty-eight percent of responders (237/492, 48.1%) mentioned that they had used fitness trackers before the pandemic. Amongst those who used trackers before the pandemic, the most popular trackers used pre-COVID were Fitbit, and Apple Watch. Since the COVID-19 outbreak, 5.1% of respondents (25/492) started using a new fitness tracker.

During the pandemic, the most common reasons for using fitness trackers were to track different measurements (e.g., distance run or walked, heart rate), and to receive reminders to move. Over half (147/274, 53.6%) agreed that fitness trackers helped them incorporate more activity in their daily lives.

The association between technology usage and healthy behaviors.

People who currently used a mobile app or fitness tracker during the pandemic had almost two times the odds of meeting aerobic physical activity guidelines (OR = 1.91, 95% CI 1.07 to 3.46) compared to non-users ( Table 3 ). Whether participants used mobile apps or fitness trackers before COVID-19, and whether participants started using a new app or tracker since COVID-19 were also statistically associated with meeting aerobic physical activity guidelines. Specifically, people who started using a new app or tracker since COVID-19 had 1.7 times the odds of meeting aerobic physical activity guidelines than people who did not (OR = 1.66, 95% CI 1.06 to 2.61) ( Table 3 ). People who had used mobile apps or trackers before COVID-19 had more than 2 times the odds of meeting aerobic physical activity guidelines than non-users (OR = 2.32, 95% CI 1.36 to 4.02). Mobile app usage was not associated with meeting fruit and vegetables consumption guidelines (OR = 0.97, 95% CI 0.53 to 1.76) ( Table 3 ).

https://doi.org/10.1371/journal.pdig.0000087.t004

Given the large proportion of Australia-based participants in our sample, we conducted a sensitivity analysis with this subgroup ( S5 Appendix ). The sensitivity analysis showed that current app or tracker usage was no longer statistically associated with meeting aerobic physical activity guidelines (OR = 1.63, 95% CI 0.79 to 3.43). Age, whether participants used an app or tracker before COVID-19, and whether participants started using a new app or tracker since COVID-19 were statistically associated with meeting aerobic physical activity guidelines. Mobile app usage was also not associated with meeting fruit and vegetable consumption guidelines in this subgroup (OR = 1.08, 95% CI 0.52 to 2.27).

COVID-19 related apps.

Less than half of the participants (235/508, 46.3%) used a COVID-19 related app. Of those that used COVID-19 related apps, most used country-specific apps (e.g., COVIDSafe in Australia). The main purpose of using COVID-19 related apps was to support contact tracing. Twelve percent (59/508, 11.6%) used COVID-19 related apps for more than one purpose, most often to support contact tracing and get COVID-19 information.

Use of COVID-19 related apps differed by age and whether they were currently using mobile apps for their health. Compared to people aged 18–44, a larger proportion of people aged 60+ (74.5% versus 46.1%) and a larger proportion of people aged 45–60 (57.6% versus 46.1%) used a COVID-19 related app ( P < .001, S4 Appendix ). Compared to never-users, a greater proportion of current users (50.3% vs 35.3%) and past users (47.6% vs 35.3%) of mobile apps for health used COVID-19 related apps ( P = .034, S4 Appendix ).

Qualitative results.

The most common and central themes from the responses to open-ended questions are described below and comprised: maintaining a sense of normalcy and social connections; technologies as a double-edged sword; desired features of technology. S6 Appendix includes demographic details of the subset of participants who answered each of the open-ended questions.

Maintaining a sense of normalcy and social connections.

Participants mentioned that during the pandemic, mobile devices has allowed them to maintain a routine despite the disruption caused by COVID-19, and maintain a sense of normalcy, which in turn gave them motivation to exercise ( Table 4 , quotes 1–2). Additionally, most participants mentioned that technologies helped them stay socially connected with their family and friends, which alleviated some emotional stress and allowed them to share their fitness progress ( Table 4 , quote 3–4).

https://doi.org/10.1371/journal.pdig.0000087.t005

Technologies as a double-edged sword.

Participants cited both positive and negative effects from the use of technologies, especially social media, during the COVID-19 pandemic. On one hand, social media allowed people to stay updated with COVID-19 news ( Table 4 , quote 5). On the other hand, participants also mentioned that the high volume of COVID-19 news could cause information overload and emotional stress ( Table 4 , quote 6). Similarly, when talking about fitness trackers, some participants indicated negative emotions associated with self-monitoring, as their physical activity had declined due to COVID-19 circumstances ( Table 4 , quote 7).

Desired features of technology.

There were two subthemes within the area of desired features of technology: adaptability and gamification. Participants mentioned that while technologies had been helpful, one key thing missing was the adaptability of technologies to the unprecedented circumstances caused by COVID-19 ( Table 4 , quote 8). Consequently, several mentioned that they took the initiative to repurpose existing health apps to serve their needs during COVID-19 pandemic ( Table 4 , quotes 9–10). Many participants across different ages also valued gamification features of technologies (e.g., competition, exercise challenges, exercise role-playing games), which helped them to incorporate fitness into their life with an element of fun and enjoyment ( Table 4 , quotes 11–12).

Principal results

Our study found that 60% of participants used mobile apps and 38% used fitness trackers for health behaviors during June–September 2020. People who used mobile apps or fitness trackers during the pandemic were more likely to self-report meeting recommended levels of aerobic physical activity than non-users. A greater proportion of women used apps for their health during the pandemic than men. Additionally, 46% of respondents self-reported using COVID-19 apps. Specific subgroups such as people aged 45+ and current or past users of mobile apps for health purposes were more likely to use COVID-19 related apps. We note that these subgroup analyses based on age and gender are exploratory in nature and should be confirmed in future research. The generalizability of our quantitative findings is limited, given our sample of highly educated individuals who might have been more health-conscious, and had better access and more inclined to use technologies. Qualitative findings complemented quantitative findings by showing while mobile devices helped maintain a sense of normalcy, there were potential negative effects of using technologies (e.g., stress and information overload from seeing COVID-19 information on social media, guilt when seeing low activity levels), which might have impacted users’ motivation and continued use of mobile devices. Our participants highlighted the need for technologies to adapt to changing circumstances.

Impact of mobile devices on health behaviors

Our results are consistent with existing literature showing that users of mobile apps and other digital technologies seem to be more active than non-users during the pandemic [ 29 – 31 , 55 ]. Uniquely, by adjusting our model to variables related to ‘previous use of mobile devices before COVID’ and ‘adoption of new apps or trackers during the outbreak’, we found these were associated with adherence to physical activity guidelines. It is possible that the physical activity benefits observed in our study are influenced by an overrepresentation in our sample of health-conscious and tech-adopting people. Future research is needed to understand how mobile devices can extend its reach and benefit other groups beyond the typical highly motivated and ‘worried-well’ adopters [ 56 ]. A sensitivity analysis including only Australia-based participants found that current mobile app or tracker usage was not associated with adherence to physical activity guidelines. It is possible that the smaller sample size made it difficult to detect the difference. Given the inconsistency between the primary and sensitivity analyses, the potential physical activity benefits associated with mobile devices observed in our findings should be interpreted with caution, and future research is needed to ascertain the potential impact of mobile devices on health behaviors.

Our qualitative data highlight the need for mobile apps and fitness trackers to adapt quickly to the changing circumstances of human lives, especially in health crises like COVID-19. Given the disruption to normal routines and closure of exercise and health facilities, people might need additional, or different types of support to maintain healthy behaviors, which is difficult to accommodate by mobile apps and devices based on static algorithms. With recent development in artificial intelligence and machine learning, mobile apps and devices can collect information about its users (including users’ behaviors, context or preferences) to continuously adapt their content, timing and delivery, and personalize their support to suit the person’s needs [ 57 , 58 ].

Differences in app usage between genders

Findings suggested that a greater proportion of women used mobile apps during the pandemic than men. Specifically, women were more likely to use apps to support physical activity and to connect with others, and more likely to report apps as useful for mental health. It is worth noting that this gender difference is based on a subgroup analysis and is exploratory in nature. However, we also note that our finding is in line with previous research reporting higher use of digital platforms for physical activity amongst women [ 30 ]. There are several possible explanations for this observed gender difference. Research has shown that during the pandemic, women reported increased overeating [ 4 ] and less physical activity than men [ 59 ], and heightened stress from taking on more caring or home-schooling responsibilities [ 1 , 59 – 62 ]. Thus, women might have needed additional support and turned to mobile devices to support their wellbeing. Another possible explanation is linked to the type of health activities that can be accommodated in health apps. Research has suggested that women were more likely to engage in directed activities (e.g., exercise classes [ 63 , 64 ]), which could be delivered online more easily, compared to competitive sports usually done by men [ 63 ]. Future research is needed to explore how the adoption of mobile devices might differ by gender and how to design health interventions to reduce the existing gender differences in adoption.

Adoption and usage of COVID-19 related apps

Only 46.3% of our participants used a COVID-19 related app. Previous research has reported uptake ranging from 20% [ 65 , 66 ] to 40% [ 35 , 67 ] amongst European countries and Australia. Given that the most common purpose is contact tracing, this low uptake is concerning as digital tracing apps rely on a high adoption rate to work effectively [ 9 ]. Research has suggested that the reasons for low uptake are mainly privacy and functionality concerns (e.g., battery drain, apps not working as intended) [ 35 ]. This indicates the need to improve the functionality of digital tracing apps, as well as public health communication regarding the privacy protections of tracking technologies [ 68 ]. Our study found a greater proportion of people aged 60+, and people aged 45–60 used COVID-19 related apps compared to those less than 45 years. This is in line with previous research which suggests that the higher uptake in older adults might be related to concerns about their vulnerability to COVID-19 [ 35 ]. This trend highlights the need for public health communication to also target younger populations to ensure a high adoption rate in this subgroup. It is worth noting that since 2021, some countries (e.g., Australia) have made ‘signing-into’ venues mandatory, usually through a ‘check-in’ function in government apps to support contact tracing. Thus, since the completion of this study, it is likely that the use of these government apps for COVID-19 purposes have increased. Furthermore, given the exploratory nature of this subgroup analysis, future research is needed to confirm potential age differences in COVID-19 app uptake.

Strengths and limitations

A strength of our study is the mixed-methods design, including qualitative, open-ended questions, which allowed us to acquire a deeper exploration of users’ perspectives. However, the results must be interpreted considering some limitations. While face validity was established through multiple co-authors independently reviewing the survey draft, the survey questions were not formally assessed for criterion or content validity, and the survey was not pilot tested. Health behaviors were assessed through self-report. We assessed the impact of technologies on only aerobic physical activity and the intake of fruits and vegetables. To enable a more comprehensive analysis on the link between technologies and physical activity and diet, future research should collect data on other types of activity (e.g., muscle strengthening exercises) and food groups (e.g., salt or sugar intake). We were not able to examine the link between technologies usage and alcohol intake and smoking because only a small percentage of our sample used technologies for these purposes. While our sampling was worldwide, the majority of participants resided in Australia. As a large proportion of participants were women, and had high level of education, this might bias our findings and affect the generalizability to other population groups. Previous surveys have reported a similarly high participation rate from women and people with higher education levels [ 1 , 3 , 4 , 30 ]. The survey was conducted online and proficiency in English was required, which might have precluded participation from non-English speaking individuals and those lacking access to the Internet. Finally, our findings are also impacted by common limitations of survey research—self-reported answers and self-selection sampling method. This might have led to sampling bias, social desirability bias, or recall bias, which affect the generalizability of the findings and the reliability of the responses.

Implications

Mobile apps and fitness trackers seem promising in promoting physical activity during the COVID-19 outbreak. Potential improvements on these technologies from users’ perspectives should focus on personalization and adaptability, such as allowing for higher customization of content delivered and a better ability to support people’s changing needs. This is in line with previous research which suggests that personalization can increase user engagement with mobile devices [ 69 ]. By leveraging recent advances in big data and artificial intelligence [ 58 ], mobile devices may be able to provide more in-time, personalized support to users. Future research is needed to investigate whether the engagement with health apps and devices is sustained post-COVID, and robust clinical trials are needed to ascertain their objective benefits for preventative health, including physical activity and other health behaviors.

Our findings may be influenced by the large proportion of highly educated individuals who might be more health-conscious and have access to technologies more easily than other population groups. Previous research has described this phenomenon as the “digital divide” [ 70 , 71 ], which can widen existing social inequalities. The benefits of mobile apps and devices would be limited if they can only reach high socioeconomic status groups. Thus, efforts must be made to bridge this gap in technology adoption, such as through increasing access, promoting collaborative and inclusive design, and improving digital literacy [ 70 , 71 ].

Our study found a positive impact of mobile apps and fitness trackers on physical activity during the pandemic, in a sample of likely health-conscious and technology-inclined individuals. Qualitative data revealed the lack of flexibility of mobile apps and devices and highlighted the need for these technologies to adapt quickly to changes in life circumstances. Future research should assess the use of mobile apps and fitness trackers post-COVID, and whether these technologies provide objective benefits to health behaviors.

Supporting information

S1 appendix. strobe checklist..

https://doi.org/10.1371/journal.pdig.0000087.s001

S2 Appendix. Survey.

https://doi.org/10.1371/journal.pdig.0000087.s002

S3 Appendix. Country of residence breakdown by the number of responses and %.

https://doi.org/10.1371/journal.pdig.0000087.s003

S4 Appendix. Subgroup analyses.

https://doi.org/10.1371/journal.pdig.0000087.s004

S5 Appendix. Sensitivity analyses in the Australia sub-sample.

https://doi.org/10.1371/journal.pdig.0000087.s005

S6 Appendix. Demographic information of participants who responded to open-ended questions.

https://doi.org/10.1371/journal.pdig.0000087.s006

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Status of the research in fitness apps: A bibliometric analysis

a Ph.D. Student in Business Administration, Faculty of Economics, Complutense University of Madrid, Campus de Somosaguas. 28223, Pozuelo de Alarcón, Madrid, Spain

Maria Avello

b Department of Management and Marketing, Faculty of Economics, Complutense University of Madrid, Campus de Somosaguas, 28223, Pozuelo de Alarcón, Madrid, Spain

• • A bibliometric analysis of the fitness apps research field to gain insight into the state of the art.
• • Scopus and Web of Science were used to collect the data (481 records).
• • Statistical analysis and science mapping were used to analyze the data.
• • Provides basic data, research classifications and future research directions in the area.

Fitness applications have undergone considerable development in the last few years and becoming popular and significant in both academic and practical areas. However, contributions to the systematic mapping of this field continue to be lacking. This paper constitutes the first bibliometric study in this field to better understand the current state of research. We examined 481 records from databases Scopus and Web of Science (Core Collection) using several bibliometric analysis methods. All the records on this emerging topic were published between 2011 and 2019. We processed these records using statistical analysis and science mapping. The bibliometric analysis included the year of publication, journal name, citation, author, country, and particularly, research methodology. Additionally, we used the VOSViewer software to perform bibliometric mapping of co-authorship, co-citation of authors, and co-occurrence of keywords. This field of study, it was found, is currently in its precursor stage, contributing primarily to the fields of medicine, computer science, and health sciences. The United States appeared to have made the largest contribution to this field. However, author productivity, number of citations, and number of core journals all indicated a high degree of fragmentation of research in this filed. Remarkably, scientific research in this area is expected to progress tremendously over time. Overall, this study provides basic data and research classifications for the initial phase of research and research direction for future research in this area.

1. Introduction

With the global outbreak of the COVID-19 pandemic in 2020, almost every country is facing problems concerning the shortage of medical and healthcare resources, and people have become more aware of the importance of following a healthy lifestyle and incorporating physical exercise into their daily lives. As the most downloaded type of mobile health applications (mHealth apps), fitness apps can help people manage their nutritional intake, assist their participation in fitness and physical activities, and promote a healthy lifestyle. Therefore, these apps are gradually occupying the commercial mobile app market ( Beldad and Hegner, 2018 ).

Nowadays, fitness apps are rapidly developing in the commercial application market and are attracting the attention of academia ( Beldad and Hegner, 2018 ). Numerous studies have implemented empirical protocols to verify the results of using fitness apps for improving the level of physical activity and/or diet in users ( Schoeppe et al., 2017 ). However, from the academic side, it is still a novel and young area of research.

As a diverse field of research that is related to an emerging phenomenon, and with the integration of new technologies, the research available on fitness apps is still scarce. Both empirical research and theoretical orientation reviews, mostly focus on summarizing the functions and features of fitness apps and user perspectives. As a result, there appears to be a lack of more macro and objective quantitative research in this field. And the various types of literature are not as substantial or abundant compared to other mature areas of research. It is necessary to carry out a bibliometric study to know the main empirical and theoretical orientations in this case. The data obtained from the bibliometric analysis will be essential to assess the intensity and orientation of new lines of research ( Bartoli and Medvet, 2014 ). Moreover, it is essential to classify the existing research in the research field to track the research progress and research trends in the field ( Gaviria-Marin et al., 2019 ). Bibliometrics study can achieve this objective. It helps display past academic research activities and achievements visually.

To our knowledge, there is no bibliometric study in the field of fitness app research, even though this type of literature has been used widely in other fields in recent years ( Zanjirchi et al., 2019 ). Bibliometrics can supplement existing experiments and review studies, help researchers identify hidden research lines, hot issues, and research methods in the field, and reduce the problems of neglecting certain excellent articles due to the deviation of researchers' subjective judgments ( Zanjirchi et al., 2019 , Veloutsou and Mafe, 2020 ).

Therefore, this study offers a bibliometric study of the advancements in research on the mobile-fitness app. It is based on data from a bibliometric analysis. It seeks to assess the intensity and research topics dominant in the scientific community when it comes to this emerging phenomenon, focusing explicitly on the fitness segment of mHealth. This study also aims to provide relevant data and bibliometric indicators for the initial stage of fitness application research and provide primary data for advancing future research in this field. The data used in this study is obtained from two leading databases for scientific research: Scopus and Web of Science.

The research is organized as follows. First, a research background is provided. Second, the research methods and the sources of research data are outlined. Third, the results are presented and discussed. Finally, the main conclusions, limitations, and further opportunities for research are stated.

2. Background

2.1. mhealth apps and fitness apps.

Nowadays, mobile apps pertain to a wide range of topics and areas of users' personal and social lives and fulfill various purposes. The use of advanced medical information systems and telematics applications is one of them, which has resulted in the increased availability of medical services at lower overall costs ( Kao et al., 2018 ). Medical and sanitary institutions have begun to appreciate the potential of mHealth apps for communication with patients as well as for the utilization of mobile devices that are specifically designed to monitor specific biomedical data. mHealth is defined as the provision of medical care and health-related services through mobile communication devices that enable user-interaction capability ( Cummiskey, 2011 , Lupton, 2013 ). “Mobile Health (mHealth) has become an essential field for disease management, assessment of healthy behaviors, and for interventions on healthy behaviors” ( Mas et al., 2016, p. 32 ).

There are two main areas of implementation of mHealth apps: in professional medical practices (both on the side of doctors and patients; e.g., Skyscape, MySugr), and self-monitoring of healthy habits (e.g., MyFitnessPal). The first area has a field of an app exclusively in the healthcare field, involving the relationships between doctors and their patients. The second area represents fitness apps, which is the subject of this study, is concerned with the personal monitoring of the activities of individuals within the framework of adopting healthy lifestyles or disease prevention habits, and this category is often implemented through commercial apps that are developed without the supervision of medical administrations.

The term “fitness” has a wide semantic field: on the one hand, it refers to the practice of physical exercise to obtain or maintain good body shape and composition; on the other hand, more generally, it refers to a good state of vitality and physical well-being ( Corbin et al., 2000 ). Since the 1980s, academic as well as medical attention to Health-Related Physical Fitness (HRPF) has increased considerably. Fitness is understood within the HRPF framework, which is defined as a set of people's abilities to perform certain physical activities, their energy level to perform daily tasks, and their capacity to reduce the risk of diseases related to sedentarism ( Cheng and Chen, 2018 ).

2.2. Importance of fitness apps

The WHO warns of the development of non-communicable diseases, the pathologies of which are associated with unhealthy lifestyles and diets, as these diseases currently constitute a serious cause of death worldwide ( WHO, 2018 ). In particular, the WHO has established a set of minimum criteria for physical activity for different age groups as well as balanced dietary patterns to maintain optimal health conditions such that people can achieve a reduction in risk factors for non-communicable diseases, including cancer, cardiovascular ailments, and diabetes.

The high rate of obesity is one of the most worrying factors for health globally, particularly in developed countries, but also in emerging countries, with a drastic growth among children ( Anderson et al., 2019 ). For this reason, the WHO recommends avoiding a sedentary lifestyle and following balanced diets for all age groups. Interventions for population self-management, based on changes in lifestyle, are effective in reducing risk factors and the incidence of non-communicable diseases ( Burke et al., 2011 ).

The use of applications on mobile devices has become a key factor in helping and advising people on the adoption of healthy lifestyles in the 21st century. Although some clinicians lack confidence in the protocols and recommendations of fitness apps, these fitness apps have a great potential to be effective due to their ability to educate a large portion of the population on healthy habits at a low operating cost ( Blackman et al., 2013 ).

3. Methodology

The methodology used in this research work is depicted in Fig. 1 . It consists of four steps.

The general framework of methodology.

3.1. Step 1: Determining the field of study and database used

We identified “fitness app” as the field for this study with the aim of finding as many articles as possible on fitness-related apps closer to health behaviors than to a professional medical approach. However, in the compilation of the final set of articles, we also included those that, without being strictly articles on fitness apps, contained relevant keywords linked to the subject of study, even though they were papers dealing with other types of mHealth apps.

The data was obtained from two databases: Scopus and Web of Science Core Collection (WoS). These two databases are currently the leading sources for indexing scientific articles and allow for the collection of data from a large number of journals ( Adriaanse and Rensleigh, 2013 ).

Scopus owns high-quality and reliable coverage and complete data for each reference. It is the largest abstract and citation database for peer-review literature ( Zanjirchi et al., 2019 ). The WoS is also recognized by the scientific community as a digital bibliometric platform with high-quality literature, which can also provide metadata for bibliometric analysis and covers a wide range of disciplines ( Gaviria-Marin et al., 2019 , Hew, 2017 ).

The combination of more than one database for mining scientific data can provide more robust results for the bibliometric analysis ( de Oliveira et al., 2019 ) even though it makes it necessary to integrate the information from both databases with different structures and review the articles one by one.

3.2. Step 2: Mining of bibliometric data

Mining the data is the most basic and crucial step to obtain valuable and credible research results. The search for this study was conducted in April 2020 and included all relevant publications until the end of December 2019.

The study focused on scientific research related to personal care applications of fitness, using the keywords “ fitness app” and its plural form in English for searching through titles, abstracts, keywords, or topics. Our search criteria are detailed in Table 1 . These two keywords represent the technological concept (app) associated with the lifestyle (fitness), whose specific relationship makes the object of the present investigation. No more keywords related to the fitness industry were used (e.g., weight loss/running, dieting) since we wanted to examine which other specific categories were reviewed under the category of fitness apps in general. Our search does not have a low-time frame limit, and the aim is to learn about the starting time of research in this field ( Table 1 ).

Search criteria for the study field “fitness apps”.

*No low time frame limit was set, but articles published before 2010, while containing relevant keywords, were seen not to be relevant to the field.

After searching in the two databases separately, we performed a manual review of the titles and abstracts (also full text if necessary), excluding articles whose topics did not meet the criteria of the study, and subsequently removing duplicate literature. When the same article appeared in both databases, we opted to keep the references in Scopus because Scopus provides broader bibliographic information than WoS. The search returned 1095 records. We decided to keep the conference papers and meeting abstracts due to the youth and relative novelty of the field of study. After filtering out the irrelevant and incomplete records, we ended up with a total sample of 481 records ( Table 2 ).

Search results in academic databases.

3.3. Step 3: Analysis of bibliometric data

The records were then analyzed using bibliometric analysis. Bibliometrics is “the quantitative study of physical published units, or bibliographic units, or of the surrogates for either” ( Broadus, 1987, p. 376 ). The bibliometric analysis allows us to understand the intensity of the research available on a topic as well as the different research fields explored by the academic community.

The variables analyzed for the bibliometric study were the year of publication, author, country of institutional origin, language of publication, type of document, journal, number of citations, area of research, topics analyzed, and the research method used.

Additionally, bibliometric mapping was also conducted. The construction of bibliometric maps has always received attention in bibliometric studies ( Van Eck and Waltman, 2010 ). We used Vosviewer software to present the relation of co-citation, co-occurrence of keywords, etc.

3.4. Step 4: Grouping and analysis of trends

Finally, we summarized the current research hotspots and trends in this field, based on the content of these 481 articles and the information presented by the keywords of their authors, to inform and inspire further studies.

4.1. Publication frequency per year

The first article on fitness apps was published in 2011, and until 2014, the intensity of research was very low. 95.2% of the articles are published from 2014 onwards. In 2014, there was a significant increase in the number of publications, doubling the number of 2013 ( Table 3 ).

Frequency of publication of articles related to fitness apps per year.

These results represent a Price's Index of 89.4% until the end of 2019. Price's Index ( Price, 1970 ) refers to the percentage of references less than five-year-old. As the Price Index's value is relatively high, this area is considered to be novel and dynamic.

Price’s Law ( Price, 1963 ) proposes that the development of the scientific field follows an exponential growth, which doubles in size every 10–15 years. The development of the scientific field goes through four stages: the precursor stage, the exponential growth stage, the consolidation of the body knowledge stage, and the decrease in the production stage. As shown in Fig. 2 , publications in related fields underwent a growth process from 2011 to 2019. A linear mathematical adjustment of the measured values provided us with a correlation coefficient r = 0.964, which implies that 7.07% of variance failed to explain this fitting. In contrast, a mathematical adjustment to the exponential curve provides a coefficient r = 0.788, indicating an unexplained variance of 37.86%. This reveals that the data analyzed is more consistent with a linear fitting rather than an exponential one ( Fig. 2 ).

Growth of scientific production in fitness apps.

While the third stage of growth also showed a linear trend, the first contribution in this field was produced in 2011, and the exponential growth trend stage was not detected. So, research in this field is still in its precursor stage. Additionally, the number of publications in 2018–2019 was close to 50% of the total, exhibiting rapid growth. Although there was a small decline in 2019 compared to 2018, we expect the scientific production in this field to enter the exponential growth stage in the coming years.

4.2. Most productive and influential journals/conferences and type of documents

Articles on fitness apps are published in a wide range of journals, from medical and health-related ones to computer science-related ones. Out of the 481 records, 328 were published in academic journals, and 153 were published as conference proceedings. The publication source also indicates a great dispersion: there were 189 journals and 109 different conference proceedings in total Fig. 3 .

Type of documents.

Among all the relevant journals, eight journals have published three or more articles. However, only nine conference proceedings had more than one article. Compared to other fields of study, this number seems very small and indicates a low level of source concentration.

Table 4 presents the field's 18 most productive and influential journals, and Table 5 outlines the nine most productive conference proceedings.

The most productive journals in fitness app research.

The most productive conference proceedings.

JMIR mHealth and uHealth and Journal of Medical Internet Research have achieved a prominent position here, with a relatively high number of articles. Both are sister journals of JMIR Publications. It is worth noting that although only three articles were sourced from the International Journal of Behavioral Nutrition and Physical Activity, it ranked third overall in the number of citations.

Besides, 30% of the publications were from conference proceedings. The first and second positions by the number of publications came from the field of computer science. The high proportion may be explained by the fact that, although the importance of conference proceedings in areas such as the natural sciences is decreasing, they still play an important role in computer science, with nearly 20% of citations also distributed in the proceedings ( Michels and Fu, 2014 , Lisée et al., 2008 ). It also shows the importance of the development of fitness apps in the domain of computer applications.

Bradford’s Law ( Bradford, 1934 ) is a tool used in bibliometric studies to evaluate the concentration/dispersion factor of a set of publications. In essence, it allows the determination of the most productive nucleus in a particular subject. It postulates the existence of a small nucleus of journals that address the topic more broadly as well as a vast peripheral region that is divided into several zones with journals that have a decreasing representation in the subject studied ( Alvarado, 2016 ). The number of journals in the core and the number in the successive zones are in a ratio of 1: n: n 2 .

Therefore, journals included in the core have a comparatively high concentration of publication, while those involved in the surrounding areas are increasingly dispersed. Thus, we can see that there is an unequal distribution of articles in the journals. A large number of articles are found in a small number of journals. As shown in Fig. 4 and Table 6 , within the core of the ring, only 10 journals contained one-third of all published articles (109 records). Zone 1 comprises 70 journals, and zone 2 comprises 109 journals. Zona 2 contains a much smaller number of journals than the theoretical value (570). This result suggests the innovative and youthful nature of the field under study, which has not been considered in depth by many journals.

Dispersion in Bradford rings of scientific production related to fitness apps.

Publication dispersion zones under Bradford's Law.

4.3. Most cited articles

The number of citations is an important indicator of the influence and the attention presented by the scientific community. According to the results shown in Table 7 , a total of 28 articles received more than 60 citations—all from academic journals. This number is relatively low compared to other more mature fields of research.

Most Cited Articles.

The most cited article (598 citations) is a multidisciplinary review by Boulos M.N.K. et al., published in 2011, one of the first published articles in the field, followed by the research by Krebs P., Duncan D.T., published in 2015 with 316 citations.

4.4. Most productive and influential authors

A total of 1,776 authors have contributed to this field. The average number of authors per article was 3.69, which indicates the trend towards multi-author contributions in the field and a wide dispersion of research. Table 8 summarizes the first 30 authors in the list, with more than two contributions ( Table 8 ).

The most productive and influential authors in fitness app research.

The data source was Scopus.

In those cases where the information was not available at Scopus, we used the information provided by WoS.

The most productive authors in terms of the number of articles published are Oyibo K. and Vassileva J., both from the University of Saskatchewan (Canada), with 8 contributions. Third and fourth-ranked Gay V. and Leijdekkers P. are co-authors. In the scope of the subject of our study, they co-authored a total of six articles.

The work of the most productive authors does not attract the highest number of citations. The author, with the highest number of citations in the fitness apps field, is West J.H. His six articles have garnered a total of 655 citations. Three of them are ranked in the top ten most influential papers in Table 8 . They were all published in the journal with the most contributions in the field, Journal of Medical Internet Research .

The author with the highest h-index (78) is Salmon J., from Deakin University, whose research pertains to the fields of medicine, health professions, and nursing. However, the total number of citations for his three articles was only 35. No other author had an h-index above 20.

The high inconsistency in the number of citations, the number of author contributions, and the h-index show that no scholar or team of scholars has yet had a decisive influence on the field, which is also related to the fact that the field is still in the precursor stage of research.

Additionally, the authors in Table 8 are not widely dispersed in terms of institutional affiliation, with several authors (and close rankings) being from the same institution. This suggests that a high proportion of the top 30 productive authors are co-authors, as evidenced in Fig. 5 . It highlights that only four authors did not co-author papers with others. The remaining 26 authors make up the remaining nine clusters. Moreover, members in each group usually come from the same institutions or countries, with less cross-national/interregional cooperation.

Correlation in co-authorship (for top 30 authors with more than two contributions).

The authors' productivity data are much lower than the values suggested by Lotka’s Law ( Lotka, 1926 ). This law states that the number of authors making n contributions in a given period is approximately equal to the number of authors who make 1/n 2 contributions. Generally, the application of Lotka's Law gives the theoretical result that about 60% of authors make only one contribution in their field of study. In the field of research on fitness applications, the value of Lotka's Law is 92.62% ( Table 9 ). This confirms the huge dispersion of the field, which can be explained either by the novelty of the phenomenon or by a multidisciplinary approach.

Productivity of authors.

Additionally, the analysis of co-citation of authors shows the structure and connections of the co-cited authors, i.e., “which authors are cited together more frequently” ( Gaviria-Marin et al., 2019, p. 213 ). Fig. 6 shows the results of the analysis conducted using VOSviewer, and the number of citations for each author is indicated by the size of the colored dot ( Fig. 6 ).

Co-citation of authors.

Authors with more than 35 citations were clustered in five groups. Some of these authors did not contribute directly to our field. However, their articles are frequently cited by other authors in the fitness app research field.

Authors in Cluster 1 mainly tend to focus on research in the areas of social sciences, business, management and accounting, and mathematics. Sub-topics of interest to them include behavior change, physical activities, etc.

Authors in Cluster 2 primarily devote their research to the field of biochemistry, genetics and molecular biology, and health professions. Physical and health education is also one of the sub-topics they are interested in.

In Cluster 3, the main research interests include psychology, and besides, the authors have contributed to the areas of computer science, nursing, and decision making.

The main research interests of the authors of Cluster 4 lie in the arts and humanities, social sciences, computer science, and psychology. They have also undertaken certain interpretative explorations of technological acceptance.

Cluster 5 consisted of only two authors, Richard M Ryan and Edward L. Deci. They are also co-authors of articles with fairly high citations, and both of them have an h-index of no less than 150. Their main areas of research are psychology, in which self-determination theory and motivation are also a point of interest.

4.5. Most productive countries/regions

6 out of the 481 records did not specify the country/region of origin. Of the remaining 475 records, the countries that contributed the most were the United States (29.3%), the United Kingdom (11.2%), and Australia (10%). It should be noted that almost half of the studies were carried out in English-speaking countries. Among the Asian countries, China, India, and South Korea stood out. National/regional contributions are double counted when authors of the same article are affiliated with institutions from different countries ( Table 10 ) ( Fig. 7 ).

Most cited countries/regions.

Map of contributions by countries/regions.

4.6. Most productive fields of research

Our results show that the main research areas of study are medicine (23.95%), computer sciences (17.88%), behavioral sciences (6.7%), computer medicine (6.5%), and psychology (6.2%). Most articles contribute to more than one field ( Table 11 ).

Frequency of published articles by research field.

It seems that research in fitness apps has flourished through its study in the medical area, followed by its computational features. However, the study from the point of view of consumer behavior, integrated into the field of social sciences, seems not to have taken off yet. We predict significant growth in this domain as fitness apps become more popular, and communication through social networking sites goes viral, particularly among young people.

4.7. Most used research methods

The applied research methods allow the collection of empirical data to contribute to scientific knowledge. It is an important variable to understand the empirical orientations of research in this field of knowledge.

As shown in Table 12 , the most frequently used research method was the experiment. The experimental design was used in 24.5% of all research. Most of them were “in the wild” experiments, implemented on a small group of participants (n < 50) who were asked to use a fitness app, developed expressly for the research, for a short period. The second most used research method was the survey (18.5% of the articles), which allowed the evaluation of the user perspective and behavior with self-reported data.

Main research methods used.

*Out of the total 481 articles, 25 articles (5.2%) used multiple methods. Of these, 24 articles used two methods and one article used three methods.

The third-ranked research method was content analysis. The articles that used this method analyzed and evaluated the total or partial functionality of a range of fitness-related apps, their technical characteristics and the attributes that make them more valued by users, more effective in changing consumer behavior, etc. For example, Cowan et al. (2013) calculated a theoretical score for each of the 127 health and fitness applications to determine whether the applications included relevant aspects of the behavioral change theory.

The content analysis articles allow us to understand how fitness-related apps have evolved over the years and how researchers' focus has changed over that same period. By reviewing relevant articles, we found that behavior change techniques, gamification features, and consumer engagement strategies have been attracting attention, as shown in Fig. 8 . Fig. 8 summarizes articles on content analytics from 2012 to 2019 from West et al., 2012 , Cowan et al., 2013 , Direito et al., 2014 , Lister et al., 2014 , Edwards et al., 2016 , Rose et al., 2017 , Moral-Munoz et al., 2018 , Priesterroth et al., 2019 and Cotton and Patel (2019) .

Timeline of hot topics of content analysis articles.

5. Main topics analyzed and lines of research

5.1. keywords.

The analysis of the frequency of appearance of the keywords allows the reader to approach the main topics analyzed in the articles in this field. The analysis of the keywords selected by the authors allows the determination of which relationships are established between a field of research and others close to it ( Duran-Sanchez et al., 2016 ).

As shown in Table 13 , the terms “physical activity” and “mHealth” appear in 28.1% of all the contributions. Both keywords are the conceptual core of fitness app research. Physical activity is also related to the terms “exercise” (6.9%), “obesity” (1.7%), and “weight loss” (2.3%).

Frequency of occurrence of keywords (>6 times).

Portability is a concept associated with new devices for self-monitoring of activity: the terms “wearables” and “fitness tracker(s)” appeared in 3.1% and 4.8% of articles, respectively.

The principle of playful functions is reflected in the term “gamification,” with 3.33% of the articles, which is a factor that can increase user adherence to the programs.

Fig. 9 maps the correlation between the keywords. To make the map clearer, with more focus on the core of the field of study, we removed the keyword “app” and its various related forms from the mapping analysis.

Correlation map between keywords.

The most frequent keywords were located in five differentiated clusters.

Cluster 1, which we named “Digital mHealth” is mainly related to mHealth and eHealth (electronic health). They are platforms for fitness apps. Also included in this group are keywords such as privacy and security, which are all related to the technology and device issues of fitness applications.

Cluster 2, which we named “mHealth and fitness trackers,” is pretty similar to Cluster 1, with only an emphasis on fitness trackers and persuasive technology as well as health apps and wearable electronic devices.

Cluster 3, which we named “Physical activity, motivation, and social support,” comprises keywords such as physical activity, exercise, physical fitness, etc. Social support and motivation are also included in this group, which may be since these two are also important factors that support people to stick to physical activity ( Tang et al., 2015 ).

Cluster 4, which we named “Generalistic keywords,” is more macro in nature and contains a wide range of topics such as fitness, mobile, and public health.

Cluster 5, which we named “Behavior change and gamification,” includes keywords such as behavior change, gamification, wearables, and self-determination theory.

5.2. Main topics of research

Finally, based on all the information obtained as well as our thorough review of the contributions that are part of this bibliometric study, we now describe the main topics of research on the subject of fitness apps:

• 1) Descriptive studies of the possibilities of the applications and the quality of their functions. Most of the research is related exclusively to physical activity, alongside some studies on diet. For example, Li et al. (2019) analyzed the quality of nutritional recommendations of applications available in China for a healthy lifestyle, nutrition, and disease prevention.
• 2) Analysis of the quality and performance of the use of the apps concerning the objectives of the users. The performance is measured through an evaluation of different indicators, such as the level of physical activity or weight loss. In this criterion of research, the use of innovative features is particularly important. For example, Mata et al. (2018) tested the performance of the training planning function of the relevant apps and confirmed the high performance of these app-generated training and nutrition plans through expert validation.
• 3) Analysis of the benefit of the use of fitness apps for the chronically ill. Patients affected by severe chronic diseases can undergo improvement in their general condition through lifestyle improvements. For example, Bonato et al. (2019) analyzed the possibility of using an app for monitoring physical exercise routines for people affected by HIV. The apps are used to encourage patients to exercise to improve their general condition.
• 4) Examination of the use of fitness applications to encourage people with a specific need due to their socio-demographic profile to follow the minimum physical activity requirements established by the WHO. This includes the specific physical exercise needs that can be implemented through apps for the elderly ( Mas et al., 2016 ), children ( Tripicchio et al. 2017 ), or people with disabilities ( Pérez-Cruzado and Cuesta-Vargas, 2013 ).
• 5) Study of factors affecting user motivation to continue using Fitness Apps. Increasing user motivation is an integral part of a significant number of articles. Very high abandonment rates are observed in the use of these applications, and there is a lack of user engagement ( Bardus et al., 2016 ). Among the factors that may influence the use of the apps, some researchers are interested in the aesthetics of the user interface ( Bardus et al., 2016 ), social relations ( Lewis et al., 2019 ) and the personalization ( Zhou et al., 2018 ).
• 6) Exploration of the social problems associated with fitness apps. Some articles focus on the problems related to fitness apps and the adherence to hegemonic beauty canons. In this line of research, Honary et al. (2019) concluded that the use of these apps might increase social pressure to achieve unrealistic beauty ideals and could thus increase the incidence of eating problems, such as anorexia or excessive physical exercise. Another issue of concern relates to the privacy of and the large amount of personal data collected by these apps ( Adhikari et al., 2014 ).
• 7) Examination of fitness apps as complementary products to wearable devices. Wearable devices provide more accurate and convenient data for measuring people's daily activity levels. However, they are usually associated with relevant mobile apps for health data visualization and analysis. For example, Lee et al. (2019) concluded that children who use wearable devices with mobile app interventions increase their physical activity over time. The emergence of the Internet of Things (IoT) has provided more help to improve people's health behaviors. However, this then brings up the issue of information security and privacy. Thus, Bohé et al. (2019) offer complementary approaches for building a better IoT ecosystem.

6. Conclusions and limitations

This study aimed to present in detail the current state of research on fitness applications through an exhaustive bibliometric analysis and bibliometric mapping. The social function and health potential of fitness apps represent a recent and growing phenomenon, which justifies an increase in the intensity of scientific research in recent years. 89.4% of the contributions were published 2014 onwards when the usage of these apps had already been an important trend in the commercial market for several years. Several bibliometric indicators (e.g., distribution of years of publication, Price's index, author productivity, Bradford's Law, h-index, number of citations, source of publication, research areas, research methods, etc.) were analyzed to understand the main features and patterns of research on fitness apps. Moreover, the scientific mapping analysis of the co-occurring keywords, co-authors, and co-citing authors provided an additional analysis from a time-depth perspective.

In general, it is important to note the great dispersion of research, with a very high number of authors who have only made one contribution being a characteristic of a field of research that has not yet reached maturity. Research in this field is still in its precursor stage. Moreover, many of the studies have a relatively high number of co-authors. This situation is reflected in the indicator of author productivity, which is relatively low (Oyibo, K. and Vassileva, J. being the most active author with eight published articles). However, the most productive authors are not the most influential authors. West. J.H. has gained 655 citations for his four articles, ranking first for this field of study.

This dispersion of research is also reflected in the source of the publications. Although there is a specialized journal in mHealth (JMIR mHealth and uHealth), it can be found that submissions on fitness apps are distributed across a large number of academic journals and conference proceedings.

With this data and support from the analysis of scientific mapping, it can be concluded that authors or prestigious journals have not been integrated and the research references in this field are relatively fragmented, partly due to their novelty and multidisciplinary requirements but also due to the technical orientation of the developers to circumvent the basic health, social, and behavioral aspects of health, society, and behavior.

As in many other areas, the United States remains a prominent contributor in this area. China and India are the most productive in developing countries. These two countries are increasing their productivity and expanding their influence in various fields of scientific research at present.

The most common research method used in this field is the experimental procedure that measures behavioral changes or changes in health indicators after a period of use. The second most used method is the survey, followed by the analysis of content.

A considerable amount of literature is related to medicine, computer science, and healthcare. Many authors have also focused on this main area of research.

Additionally, physical activity was the most frequently occurring keyword. “Behavior change” linked to “physical activity” is also an important keyword. Specifically, it refers to concepts such as behavior change theory, behavior change techniques (e.g., goal setting, self-regulation), etc. However, relatively few studies on consumer behavior from a social science perspective have been found. It seems that consumer-related research has mainly focused on analyzing the optimization of the functionalities of mobile applications from a medical or computer science point of view and neglected the aspects intrinsic to consumer behavior such as the motivations for using fitness apps, the attitude towards them, or how social networks influence the choice of the app to be used. The fact that the keyword “motivation” appears only 8 times and all after 2018 is a clear indication of this finding.

Based on the generalization of all the information obtained and the review of the abstract and some of the full text, we found that the performance and function of fitness apps, the benefits for chronic disease treatment, the influence of using fitness app for public health, and factors of motivations of using fitness apps are currently popular research topics in this field. Future research could build on these directions and incorporate relevant issues from a social science perspective (e.g., consumer motivations, consumer engagement, consumer behavior, etc.) to further investigate on fitness applications.

This article is useful in understanding the early state of research in the fitness app field. However, it is necessary to consider several limitations. One of the limitations of this study is the delimitation of the sample search criteria. In essence, the concept of fitness serves as a central reference for the applications that users utilize to perform self-monitoring of health-related factors, particularly the level of physical activity. The control of “diet” is another health factor that overshadows and is superimposed on the concept of fitness, but one that could also be considered as a separate field in future studies, or add it to the keyword search scope for getting more comprehensive results.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Yali Liu: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization. Maria Avello: Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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