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• 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

World Health Organization

https://www.who.int/dietphysicalactivity/pa/en/ . (Accessed 8 June 2020).

Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The physical activity guidelines for AmericansPhysical activity guidelines for AmericansPhysical activity guidelines for Americans. Jama. 2018;320(19):2020–8.

PubMed   Google Scholar  

Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, Veerman JL, Delwiche K, Iannarone ML, Moyer ML, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the global burden of disease study 2013. BMJ. 2016;354:i3857.

PubMed   PubMed Central   Google Scholar  

Abell B, Glasziou P, Hoffmann T. The contribution of individual exercise training components to clinical outcomes in randomised controlled trials of cardiac rehabilitation: a systematic review and meta-regression. Sports Med Open. 2017;3(1):19.

Anderson D, Seib C, Rasmussen L. Can physical activity prevent physical and cognitive decline in postmenopausal women? A systematic review of the literature. Maturitas. 2014;79(1):14–33.

Barbaric M, Brooks E, Moore L, Cheifetz O. Effects of physical activity on cancer survival: a systematic review. Physiother Can. 2010;62(1):25–34.

Barlow PA, Otahal P, Schultz MG, Shing CM, Sharman JE. Low exercise blood pressure and risk of cardiovascular events and all-cause mortality: systematic review and meta-analysis. Atherosclerosis. 2014;237(1):13–22.

CAS   PubMed   Google Scholar  

Aljawarneh YM, Wardell DW, Wood GL, Rozmus CL. A systematic review of physical activity and exercise on physiological and biochemical outcomes in children and adolescents with type 1 diabetes. J Nurs Scholarsh. 2019.

Chastin SFM, De Craemer M, De Cocker K, Powell L, Van Cauwenberg J, Dall P, Hamer M, Stamatakis E. How does light-intensity physical activity associate with adult cardiometabolic health and mortality? Systematic review with meta-analysis of experimental and observational studies. Br J Sports Med. 2019;53(6):370–6.

Abdulla SY, Southerst D, Cote P, Shearer HM, Sutton D, Randhawa K, Varatharajan S, Wong JJ, Yu H, Marchand AA, et al. Is exercise effective for the management of subacromial impingement syndrome and other soft tissue injuries of the shoulder? A systematic review by the Ontario protocol for traffic injury management (OPTIMa) collaboration. Man Ther. 2015;20(5):646–56.

Alanazi MH, Parent EC, Dennett E. Effect of stabilization exercise on back pain, disability and quality of life in adults with scoliosis: a systematic review. Eur J Phys Rehabil Med. 2018;54(5):647–53.

Adsett JA, Mudge AM, Morris N, Kuys S, Paratz JD. Aquatic exercise training and stable heart failure: a systematic review and meta-analysis. Int J Cardiol. 2015;186:22–8.

Adamson BC, Ensari I, Motl RW. Effect of exercise on depressive symptoms in adults with neurologic disorders: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2015;96(7):1329–38.

Abdin S, Welch RK, Byron-Daniel J, Meyrick J. The effectiveness of physical activity interventions in improving well-being across office-based workplace settings: a systematic review. Public Health. 2018;160:70–6.

Albalawi H, Coulter E, Ghouri N, Paul L. The effectiveness of structured exercise in the south Asian population with type 2 diabetes: a systematic review. Phys Sportsmed. 2017;45(4):408–17.

Sellami M, Gasmi M, Denham J, Hayes LD, Stratton D, Padulo J, Bragazzi N. Effects of acute and chronic exercise on immunological parameters in the elderly aged: can physical activity counteract the effects of aging? Front Immunol. 2018;9:2187.

Hagen KB, Dagfinrud H, Moe RH, Østerås N, Kjeken I, Grotle M, Smedslund G. Exercise therapy for bone and muscle health: an overview of systematic reviews. BMC Med. 2012;10(1):167.

Burton DA, Stokes K, Hall GM. Physiological effects of exercise. Contin Educ Anaesth Crit Care Pain. 2004;4(6):185–8.

Google Scholar  

Kraemer WJ, Ratamess NA. Hormonal responses and adaptations to resistance exercise and training. Sports Med. 2005;35(4):339–61.

Nystoriak MA, Bhatnagar A. Cardiovascular effects and benefits of exercise. Front Cardiovasc Med. 2018;5:135.

CAS   PubMed   PubMed Central   Google Scholar  

Vina J, Sanchis-Gomar F, Martinez-Bello V, Gomez-Cabrera MC. Exercise acts as a drug; the pharmacological benefits of exercise. Br J Pharmacol. 2012;167(1):1–12.

Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol. 2017;32(5):541–56.

Excellence NIfHaC: Cardiovascular disease prevention public health guideline [PH25] Published date: June 2010. Available at: https://www.nice.org.uk/guidance/ph25/resources/cardiovascular-disease-prevention-pdf-1996238687173 .

Excellence NIfHaC: Obesity prevention clinical guideline [CG43] published date: December 2006 Last updated: March 2015. Available at: https://www.nice.org.uk/guidance/cg43/resources/obesity-prevention-pdf-975445344709 .

Excellence NIfHaC: Physical activity for children and young people public health guideline [PH17] Published date: January 2009. Available at: https://www.nice.org.uk/guidance/ph17/resources/physical-activity-for-children-and-young-people-pdf-1996181580229 .

Higgins J, Green S. Cochrane Handbook for Systematic Reviews of Interventions. Version 6 [updated September 2018] edition. Available from www.cochrane-handbook.org : The Cochrane Collaboration, 2011. 2011.

Bougioukas KI, Liakos A, Tsapas A, Ntzani E, Haidich A-B. Preferred reporting items for overviews of systematic reviews including harms checklist: a pilot tool to be used for balanced reporting of benefits and harms. J Clin Epidemiol. 2018;93:9–24.

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):18.

Handoll H, Madhok R. Quality of Cochrane reviews. Another study found that most Cochrane reviews are of a good standard. BMJ. 2002;324(7336):545.

Petticrew M, Wilson P, Wright K, Song F. Quality of Cochrane reviews. Quality of Cochrane reviews is better than that of non-Cochrane reviews. BMJ. 2002;324(7336):545.

Shea B, Moher D, Graham I, Pham B, Tugwell P. A comparison of the quality of Cochrane reviews and systematic reviews published in paper-based journals. Eval Health Prof. 2002;25(1):116–29.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, Moher D, Tugwell P, Welch V, Kristjansson E, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Posadzki PP, Bajpai R, Kyaw BM, Roberts NJ, Brzezinski A, Christopoulos GI, Divakar U, Bajpai S, Soljak M, Dunleavy G, et al. Melatonin and health: an umbrella review of health outcomes and biological mechanisms of action. BMC Med. 2018;16(1):18.

Bellou V, Belbasis L, Tzoulaki I, Evangelou E, Ioannidis JP. Environmental risk factors and Parkinson's disease: an umbrella review of meta-analyses. Parkinsonism Relat Disord. 2016;23:1–9.

Walter SD, Cook RJ. A comparison of several point estimators of the odds ratio in a single 2 x 2 contingency table. Biometrics. 1991;47(3):795–811.

Khan H, Sempos CT. Statistical methods in epidemiology. New York: Oxford University Press; 1989.

DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.

CAS   Google Scholar  

Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019): Cochrane; 2019. Available from www.training.cochrane.org/handbook .

Pieper D, Antoine SL, Mathes T, Neugebauer EA, Eikermann M. Systematic review finds overlapping reviews were not mentioned in every other overview. J Clin Epidemiol. 2014;67(4):368–75.

Adeniyi FB, Young T. Weight loss interventions for chronic asthma. Cochrane Database Syst Rev. 2012;7.

Al-Khudairy L, Loveman E, Colquitt JL, Mead E, Johnson RE, Fraser H, Olajide J, Murphy M, Velho RM, O'Malley C, et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese adolescents aged 12 to 17 years. Cochrane Database Syst Rev. 2017;6.

Amorim Adegboye AR, Linne YM. Diet or exercise, or both, for weight reduction in women after childbirth. Cochrane Database Syst Rev. 2013;7.

Anderson L, Nguyen TT, Dall CH, Burgess L, Bridges C, Taylor RS. Exercise-based cardiac rehabilitation in heart transplant recipients. Cochrane Database Syst Rev. 2017;4.

Anderson L, Thompson DR, Oldridge N, Zwisler AD, Rees K, Martin N, Taylor RS. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2016;1.

Andriolo RB, El Dib RP, Ramos L, Atallah Á, da Silva EMK. Aerobic exercise training programmes for improving physical and psychosocial health in adults with Down syndrome. Cochrane Database Syst Rev. 2010;5.

Araujo DN, Ribeiro CTD, Maciel ACC, Bruno SS, Fregonezi GAF, Dias FAL. Physical exercise for the treatment of non-ulcerated chronic venous insufficiency. Cochrane Database Syst Rev. 2016;12.

Ashworth NL, Chad KE, Harrison EL, Reeder BA, Marshall SC. Home versus center based physical activity programs in older adults. Cochrane Database Syst Rev. 2005;1.

Bartels EM, Juhl CB, Christensen R, Hagen KB, Danneskiold-Samsøe B, Dagfinrud H, Lund H. Aquatic exercise for the treatment of knee and hip osteoarthritis. Cochrane Database Syst Rev. 2016;3.

Beggs S, Foong YC, Le HCT, Noor D, Wood-Baker R, Walters JAE. Swimming training for asthma in children and adolescents aged 18 years and under. Cochrane Database Syst Rev. 2013;4.

Bergenthal N, Will A, Streckmann F, Wolkewitz KD, Monsef I, Engert A, Elter T, Skoetz N. Aerobic physical exercise for adult patients with haematological malignancies. Cochrane Database Syst Rev. 2014;11.

Bidonde J, Busch AJ, Schachter CL, Overend TJ, Kim SY, Góes SM, Boden C, Foulds HJA. Aerobic exercise training for adults with fibromyalgia. Cochrane Database Syst Rev. 2017;6.

Bidonde J, Busch AJ, van der Spuy I, Tupper S, Kim SY, Boden C. Whole body vibration exercise training for fibromyalgia. Cochrane Database Syst Rev. 2017;9.

Bidonde J, Busch AJ, Webber SC, Schachter CL, Danyliw A, Overend TJ, Richards RS, Rader T. Aquatic exercise training for fibromyalgia. Cochrane Database Syst Rev. 2014;10.

Braam KI, van der Torre P, Takken T, Veening MA, van Dulmen-den Broeder E, Kaspers GJL. Physical exercise training interventions for children and young adults during and after treatment for childhood cancer. Cochrane Database Syst Rev. 2016;3.

Bradt J, Shim M, Goodill SW. Dance/movement therapy for improving psychological and physical outcomes in cancer patients. Cochrane Database Syst Rev. 2015;1.

Broderick J, Crumlish N, Waugh A, Vancampfort D. Yoga versus non-standard care for schizophrenia. Cochrane Database Syst Rev. 2017;9.

Broderick J, Knowles A, Chadwick J, Vancampfort D. Yoga versus standard care for schizophrenia. Cochrane Database Syst Rev. 2015;10.

Broderick J, Vancampfort D. Yoga as part of a package of care versus standard care for schizophrenia. Cochrane Database Syst Rev. 2017;9.

Brown J, Ceysens G, Boulvain M. Exercise for pregnant women with gestational diabetes for improving maternal and fetal outcomes. Cochrane Database Syst Rev. 2017;6.

Busch AJ, Barber KA, Overend TJ, Peloso PMJ, Schachter CL. Exercise for treating fibromyalgia syndrome. Cochrane Database Syst Rev. 2007;4.

Busch AJ, Webber SC, Richards RS, Bidonde J, Schachter CL, Schafer LA, Danyliw A, Sawant A, Dal Bello-Haas V, Rader T, et al. Resistance exercise training for fibromyalgia. Cochrane Database Syst Rev. 2013;12.

Cameron ID, Dyer SM, Panagoda CE, Murray GR, Hill KD, Cumming RG, Kerse N. Interventions for preventing falls in older people in care facilities and hospitals. Cochrane Database Syst Rev. 2018;9.

Carson KV, Chandratilleke MG, Picot J, Brinn MP, Esterman AJ, Smith BJ. Physical training for asthma. Cochrane Database Syst Rev. 2013;9.

Carvalho APV, Vital FMR, Soares BGO. Exercise interventions for shoulder dysfunction in patients treated for head and neck cancer. Cochrane Database Syst Rev. 2012;4.

Cavalheri V, Granger C. Preoperative exercise training for patients with non-small cell lung cancer. Cochrane Database Syst Rev. 2017;6.

Cavalheri V, Tahirah F, Nonoyama ML, Jenkins S, Hill K. Exercise training undertaken by people within 12 months of lung resection for non-small cell lung cancer. Cochrane Database Syst Rev. 2013;7.

Ceysens G, Rouiller D, Boulvain M. Exercise for diabetic pregnant women. Cochrane Database Syst Rev. 2006;3.

Choi BKL, Verbeek JH, Tam WWS, Jiang JY. Exercises for prevention of recurrences of low-back pain. Cochrane Database Syst Rev. 2010;1.

Colquitt JL, Loveman E, O'Malley C, Azevedo LB, Mead E, Al-Khudairy L, Ells LJ, Metzendorf MI, Rees K. Diet, physical activity, and behavioural interventions for the treatment of overweight or obesity in preschool children up to the age of 6 years. Cochrane Database Syst Rev. 2016;3.

Connolly B, Salisbury L, O'Neill B, Geneen LJ, Douiri A, Grocott MPW, Hart N, Walsh TS, Blackwood B. Exercise rehabilitation following intensive care unit discharge for recovery from critical illness. Cochrane Database Syst Rev. 2015;6.

Cooney GM, Dwan K, Greig CA, Lawlor DA, Rimer J, Waugh FR, McMurdo M, Mead GE. Exercise for depression. Cochrane Database Syst Rev. 2013;9.

Corbetta D, Sirtori V, Castellini G, Moja L, Gatti R. Constraint-induced movement therapy for upper extremities in people with stroke. Cochrane Database Syst Rev. 2015;10.

Cramer H, Lauche R, Klose P, Lange S, Langhorst J, Dobos GJ. Yoga for improving health-related quality of life, mental health and cancer-related symptoms in women diagnosed with breast cancer. Cochrane Database Syst Rev. 2017;1.

Cramp F, Byron-Daniel J. Exercise for the management of cancer-related fatigue in adults. Cochrane Database Syst Rev. 2012;11.

Dal Bello-Haas V, Florence JM. Therapeutic exercise for people with amyotrophic lateral sclerosis or motor neuron disease. Cochrane Database Syst Rev. 2013;5.

Dale MT, McKeough ZJ, Troosters T, Bye P, Alison JA. Exercise training to improve exercise capacity and quality of life in people with non-malignant dust-related respiratory diseases. Cochrane Database Syst Rev. 2015;11.

Daley A, Stokes-Lampard H, Thomas A, MacArthur C. Exercise for vasomotor menopausal symptoms. Cochrane Database Syst Rev. 2014;11.

de Morton N, Keating JL, Jeffs K. Exercise for acutely hospitalised older medical patients. Cochrane Database Syst Rev. 2007;1.

Dobbins M, Husson H, DeCorby K, LaRocca RL. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18. Cochrane Database Syst Rev. 2013;2.

Doiron KA, Hoffmann TC, Beller EM. Early intervention (mobilization or active exercise) for critically ill adults in the intensive care unit. Cochrane Database Syst Rev. 2018;3.

Ekeland E, Heian F, Hagen KB, Abbott JM, Nordheim L. Exercise to improve self-esteem in children and young people. Cochrane Database Syst Rev. 2004;1.

Elbers RG, Verhoef J, van Wegen EEH, Berendse HW, Kwakkel G. Interventions for fatigue in Parkinson's disease. Cochrane Database Syst Rev. 2015;10.

Felbel S, Meerpohl JJ, Monsef I, Engert A, Skoetz N. Yoga in addition to standard care for patients with haematological malignancies. Cochrane Database Syst Rev. 2014;6.

Forbes D, Forbes SC, Blake CM, Thiessen EJ, Forbes S. Exercise programs for people with dementia. Cochrane Database Syst Rev. 2015;4.

Fransen M, McConnell S, Harmer AR, Van der Esch M, Simic M, Bennell KL. Exercise for osteoarthritis of the knee. Cochrane Database Syst Rev. 2015;1.

Fransen M, McConnell S, Hernandez-Molina G, Reichenbach S. Exercise for osteoarthritis of the hip. Cochrane Database Syst Rev. 2014;4.

Freitas DA, Holloway EA, Bruno SS, Chaves GSS, Fregonezi GAF, Mendonça K. Breathing exercises for adults with asthma. Cochrane Database Syst Rev. 2013;10.

Furmaniak AC, Menig M, Markes MH. Exercise for women receiving adjuvant therapy for breast cancer. Cochrane Database Syst Rev. 2016;9.

Giangregorio LM, MacIntyre NJ, Thabane L, Skidmore CJ, Papaioannou A. Exercise for improving outcomes after osteoporotic vertebral fracture. Cochrane Database Syst Rev. 2013;1.

Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, Lamb SE. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. 2012;9.

Gorczynski P, Faulkner G. Exercise therapy for schizophrenia. Cochrane Database Syst Rev. 2010;5.

Grande AJ, Keogh J, Hoffmann TC, Beller EM, Del Mar CB. Exercise versus no exercise for the occurrence, severity and duration of acute respiratory infections. Cochrane Database Syst Rev. 2015;6.

Grande AJ, Reid H, Thomas EE, Nunan D, Foster C. Exercise prior to influenza vaccination for limiting influenza incidence and its related complications in adults. Cochrane Database Syst Rev. 2016;8.

Grande AJ, Silva V, Andriolo BNG, Riera R, Parra SA, Peccin MS. Water-based exercise for adults with asthma. Cochrane Database Syst Rev. 2014;7.

Gross A, Kay TM, Paquin JP, Blanchette S, Lalonde P, Christie T, Dupont G, Graham N, Burnie SJ, Gelley G, et al. Exercises for mechanical neck disorders. Cochrane Database Syst Rev. 2015;1.

Hageman D, Fokkenrood HJP, Gommans LNM, van den Houten MML, Teijink JAW. Supervised exercise therapy versus home-based exercise therapy versus walking advice for intermittent claudication. Cochrane Database Syst Rev. 2018;4.

Han A, Judd M, Welch V, Wu T, Tugwell P, Wells GA. Tai chi for treating rheumatoid arthritis. Cochrane Database Syst Rev. 2004;3.

Han S, Middleton P, Crowther CA. Exercise for pregnant women for preventing gestational diabetes mellitus. Cochrane Database Syst Rev. 2012;7.

Hartley L, Dyakova M, Holmes J, Clarke A, Lee MS, Ernst E, Rees K. Yoga for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2014;5.

Hartley L, Flowers N, Lee MS, Ernst E, Rees K. Tai chi for primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2014;4.

Hartley L, Lee MS, Kwong JSW, Flowers N, Todkill D, Ernst E, Rees K. Qigong for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2015;6.

Hassett L, Moseley AM, Harmer AR. Fitness training for cardiorespiratory conditioning after traumatic brain injury. Cochrane Database Syst Rev. 2017;12.

Hayden J, van Tulder MW, Malmivaara A, Koes BW. Exercise therapy for treatment of non-specific low back pain. Cochrane Database Syst Rev. 2005;3.

Hay-Smith EJC, Herderschee R, Dumoulin C, Herbison GP. Comparisons of approaches to pelvic floor muscle training for urinary incontinence in women. Cochrane Database Syst Rev. 2011;12.

Heine M, van de Port I, Rietberg MB, van Wegen EEH, Kwakkel G. Exercise therapy for fatigue in multiple sclerosis. Cochrane Database Syst Rev. 2015;9.

Heiwe S, Jacobson SH. Exercise training for adults with chronic kidney disease. Cochrane Database Syst Rev. 2011;10.

Hemmingsen B, Gimenez-Perez G, Mauricio D, Roqué i Figuls M, Metzendorf MI, Richter B. Diet, physical activity or both for prevention or delay of type 2 diabetes mellitus and its associated complications in people at increased risk of developing type 2 diabetes mellitus. Cochrane Database Syst Rev. 2017;12.

Herbert RD, de Noronha M, Kamper SJ. Stretching to prevent or reduce muscle soreness after exercise. Cochrane Database Syst Rev. 2011;7.

Heymans MW, van Tulder MW, Esmail R, Bombardier C, Koes BW. Back schools for non-specific low-back pain. Cochrane Database Syst Rev. 2004;4.

Holland AE, Hill CJ, Jones AY, McDonald CF. Breathing exercises for chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2012;10.

Howe TE, Rochester L, Neil F, Skelton DA, Ballinger C. Exercise for improving balance in older people. Cochrane Database Syst Rev. 2011;11.

Howe TE, Shea B, Dawson LJ, Downie F, Murray A, Ross C, Harbour RT, Caldwell LM, Creed G. Exercise for preventing and treating osteoporosis in postmenopausal women. Cochrane Database Syst Rev. 2011;7.

Hurkmans E, van der Giesen FJ, Vliet Vlieland TPM, Schoones J, Van den Ende E. Dynamic exercise programs (aerobic capacity and/or muscle strength training) in patients with rheumatoid arthritis. Cochrane Database Syst Rev. 2009;4.

Hurley M, Dickson K, Hallett R, Grant R, Hauari H, Walsh N, Stansfield C, Oliver S. Exercise interventions and patient beliefs for people with hip, knee or hip and knee osteoarthritis: a mixed methods review. Cochrane Database Syst Rev. 2018;4.

Jones M, Harvey A, Marston L, O'Connell NE. Breathing exercises for dysfunctional breathing/hyperventilation syndrome in adults. Cochrane Database Syst Rev. 2013;5.

Katsura M, Kuriyama A, Takeshima T, Fukuhara S, Furukawa TA. Preoperative inspiratory muscle training for postoperative pulmonary complications in adults undergoing cardiac and major abdominal surgery. Cochrane Database Syst Rev. 2015;10.

Kendrick D, Kumar A, Carpenter H, Zijlstra GAR, Skelton DA, Cook JR, Stevens Z, Belcher CM, Haworth D, Gawler SJ, et al. Exercise for reducing fear of falling in older people living in the community. Cochrane Database Syst Rev. 2014;11.

Kramer MS, McDonald SW. Aerobic exercise for women during pregnancy. Cochrane Database Syst Rev. 2006;3.

Lahart IM, Metsios GS, Nevill AM, Carmichael AR. Physical activity for women with breast cancer after adjuvant therapy. Cochrane Database Syst Rev. 2018;1.

Lane R, Harwood A, Watson L, Leng GC. Exercise for intermittent claudication. Cochrane Database Syst Rev. 2017;12.

Larun L, Brurberg KG, Odgaard-Jensen J, Price JR. Exercise therapy for chronic fatigue syndrome. Cochrane Database Syst Rev. 2017;4.

Larun L, Nordheim LV, Ekeland E, Hagen KB, Heian F. Exercise in prevention and treatment of anxiety and depression among children and young people. Cochrane Database Syst Rev. 2006;3.

Lauret GJ, Fakhry F, Fokkenrood HJP, Hunink MGM, Teijink JAW, Spronk S. Modes of exercise training for intermittent claudication. Cochrane Database Syst Rev. 2014;7.

Lawrence M, Celestino Junior FT, Matozinho HHS, Govan L, Booth J, Beecher J. Yoga for stroke rehabilitation. Cochrane Database Syst Rev. 2017;12.

Lin CWC, Donkers NAJ, Refshauge KM, Beckenkamp PR, Khera K, Moseley AM. Rehabilitation for ankle fractures in adults. Cochrane Database Syst Rev. 2012;11.

Liu CJ, Latham NK. Progressive resistance strength training for improving physical function in older adults. Cochrane Database Syst Rev. 2009;3.

Long L, Anderson L, Dewhirst AM, He J, Bridges C, Gandhi M, Taylor RS. Exercise-based cardiac rehabilitation for adults with stable angina. Cochrane Database Syst Rev. 2018;2.

Loughney LA, West MA, Kemp GJ, Grocott MPW, Jack S. Exercise interventions for people undergoing multimodal cancer treatment that includes surgery. Cochrane Database Syst Rev. 2018;12.

Macedo LG, Saragiotto BT, Yamato TP, Costa LOP, Menezes Costa LC, Ostelo R, Maher CG. Motor control exercise for acute non-specific low back pain. Cochrane Database Syst Rev. 2016;2.

Macêdo TMF, Freitas DA, Chaves GSS, Holloway EA, Mendonça K. Breathing exercises for children with asthma. Cochrane Database Syst Rev. 2016;4.

Martin A, Booth JN, Laird Y, Sproule J, Reilly JJ, Saunders DH. Physical activity, diet and other behavioural interventions for improving cognition and school achievement in children and adolescents with obesity or overweight. Cochrane Database Syst Rev. 2018;3.

McKeough ZJ, Velloso M, Lima VP, Alison JA. Upper limb exercise training for COPD. Cochrane Database Syst Rev. 2016;11.

McNamara RJ, McKeough ZJ, McKenzie DK, Alison JA. Water-based exercise training for chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2013;12.

McNeely ML, Campbell K, Ospina M, Rowe BH, Dabbs K, Klassen TP, Mackey J, Courneya K. Exercise interventions for upper-limb dysfunction due to breast cancer treatment. Cochrane Database Syst Rev. 2010;6.

Mead E, Brown T, Rees K, Azevedo LB, Whittaker V, Jones D, Olajide J, Mainardi GM, Corpeleijn E, O'Malley C, et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese children from the age of 6 to 11 years. Cochrane Database Syst Rev. 2017;6.

Meekums B, Karkou V, Nelson EA. Dance movement therapy for depression. Cochrane Database Syst Rev. 2015;2.

Meher S, Duley L. Exercise or other physical activity for preventing pre-eclampsia and its complications. Cochrane Database Syst Rev. 2006;2.

Mehrholz J, Kugler J, Pohl M. Water-based exercises for improving activities of daily living after stroke. Cochrane Database Syst Rev. 2011;1.

Mehrholz J, Kugler J, Pohl M. Locomotor training for walking after spinal cord injury. Cochrane Database Syst Rev. 2012;11.

Mehrholz J, Thomas S, Elsner B. Treadmill training and body weight support for walking after stroke. Cochrane Database Syst Rev. 2017;8.

Mishra SI, Scherer RW, Geigle PM, Berlanstein DR, Topaloglu O, Gotay CC, Snyder C. Exercise interventions on health-related quality of life for cancer survivors. Cochrane Database Syst Rev. 2012;8.

Mishra SI, Scherer RW, Snyder C, Geigle PM, Berlanstein DR, Topaloglu O. Exercise interventions on health-related quality of life for people with cancer during active treatment. Cochrane Database Syst Rev. 2012;8.

Montgomery P, Dennis JA. Physical exercise for sleep problems in adults aged 60+. Cochrane Database Syst Rev. 2002;4.

Morris NR, Kermeen FD, Holland AE. Exercise-based rehabilitation programmes for pulmonary hypertension. Cochrane Database Syst Rev. 2017;1.

Muktabhant B, Lawrie TA, Lumbiganon P, Laopaiboon M. Diet or exercise, or both, for preventing excessive weight gain in pregnancy. Cochrane Database Syst Rev. 2015;6.

Ngai SPC, Jones AYM, Tam WWS. Tai chi for chronic obstructive pulmonary disease (COPD). Cochrane Database Syst Rev. 2016;6.

Norton C, Cody JD. Biofeedback and/or sphincter exercises for the treatment of faecal incontinence in adults. Cochrane Database Syst Rev. 2012;7.

O'Brien K, Nixon S, Glazier R, Tynan AM. Progressive resistive exercise interventions for adults living with HIV/AIDS. Cochrane Database Syst Rev. 2004;4.

O'Brien K, Nixon S, Tynan AM, Glazier R. Aerobic exercise interventions for adults living with HIV/AIDS. Cochrane Database Syst Rev. 2010;8.

Østerås N, Kjeken I, Smedslund G, Moe RH, Slatkowsky-Christensen B, Uhlig T, Hagen KB. Exercise for hand osteoarthritis. Cochrane Database Syst Rev. 2017;1.

Page MJ, Green S, Kramer S, Johnston RV, McBain B, Chau M, Buchbinder R. Manual therapy and exercise for adhesive capsulitis (frozen shoulder). Cochrane Database Syst Rev. 2014;8.

Page MJ, Green S, McBain B, Surace SJ, Deitch J, Lyttle N, Mrocki MA, Buchbinder R. Manual therapy and exercise for rotator cuff disease. Cochrane Database Syst Rev. 2016;6.

Page MJ, O'Connor D, Pitt V, Massy-Westropp N. Exercise and mobilisation interventions for carpal tunnel syndrome. Cochrane Database Syst Rev. 2012;6.

Panebianco M, Sridharan K, Ramaratnam S. Yoga for epilepsy. Cochrane Database Syst Rev. 2017;10.

Perry A, Lee SH, Cotton S, Kennedy C. Therapeutic exercises for affecting post-treatment swallowing in people treated for advanced-stage head and neck cancers. Cochrane Database Syst Rev. 2016;8.

Radtke T, Nevitt SJ, Hebestreit H, Kriemler S. Physical exercise training for cystic fibrosis. Cochrane Database Syst Rev. 2017;11.

Regnaux JP, Lefevre-Colau MM, Trinquart L, Nguyen C, Boutron I, Brosseau L, Ravaud P. High-intensity versus low-intensity physical activity or exercise in people with hip or knee osteoarthritis. Cochrane Database Syst Rev. 2015;10.

Ren J, Xia J. Dance therapy for schizophrenia. Cochrane Database Syst Rev. 2013;10.

Rietberg MB, Brooks D, Uitdehaag BMJ, Kwakkel G. Exercise therapy for multiple sclerosis. Cochrane Database Syst Rev. 2005;1.

Risom SS, Zwisler AD, Johansen PP, Sibilitz KL, Lindschou J, Gluud C, Taylor RS, Svendsen JH, Berg SK. Exercise-based cardiac rehabilitation for adults with atrial fibrillation. Cochrane Database Syst Rev. 2017;2.

Romano M, Minozzi S, Bettany-Saltikov J, Zaina F, Chockalingam N, Kotwicki T, Maier-Hennes A, Negrini S. Exercises for adolescent idiopathic scoliosis. Cochrane Database Syst Rev. 2012;8.

Ryan JM, Cassidy EE, Noorduyn SG, O'Connell NE. Exercise interventions for cerebral palsy. Cochrane Database Syst Rev. 2017;6.

Saragiotto BT, Maher CG, Yamato TP, Costa LOP, Menezes Costa LC, Ostelo R, Macedo LG. Motor control exercise for chronic non-specific low-back pain. Cochrane Database Syst Rev. 2016;1.

Saunders DH, Sanderson M, Hayes S, Kilrane M, Greig CA, Brazzelli M, Mead GE. Physical fitness training for stroke patients. Cochrane Database Syst Rev. 2016;3.

Schulzke SM, Kaempfen S, Trachsel D, Patole SK. Physical activity programs for promoting bone mineralization and growth in preterm infants. Cochrane Database Syst Rev. 2014;4.

Seron P, Lanas F, Pardo Hernandez H, Bonfill Cosp X. Exercise for people with high cardiovascular risk. Cochrane Database Syst Rev. 2014;8.

Shaw KA, Gennat HC, O'Rourke P, Del Mar C. Exercise for overweight or obesity. Cochrane Database Syst Rev. 2006;4.

Shepherd E, Gomersall JC, Tieu J, Han S, Crowther CA, Middleton P. Combined diet and exercise interventions for preventing gestational diabetes mellitus. Cochrane Database Syst Rev. 2017;11.

Sibilitz KL, Berg SK, Tang LH, Risom SS, Gluud C, Lindschou J, Kober L, Hassager C, Taylor RS, Zwisler AD. Exercise-based cardiac rehabilitation for adults after heart valve surgery. Cochrane Database Syst Rev. 2016;3.

Silva IS, Fregonezi GAF, Dias FAL, Ribeiro CTD, Guerra RO, Ferreira GMH. Inspiratory muscle training for asthma. Cochrane Database Syst Rev. 2013;9.

States RA, Pappas E, Salem Y. Overground physical therapy gait training for chronic stroke patients with mobility deficits. Cochrane Database Syst Rev. 2009;3.

Strike K, Mulder K, Michael R. Exercise for haemophilia. Cochrane Database Syst Rev. 2016;12.

Takken T, Van Brussel M, Engelbert RH, van der Net JJ, Kuis W, Helders P. Exercise therapy in juvenile idiopathic arthritis. Cochrane Database Syst Rev. 2008;2.

Taylor RS, Sagar VA, Davies EJ, Briscoe S, Coats AJS, Dalal H, Lough F, Rees K, Singh SJ, Mordi IR. Exercise-based rehabilitation for heart failure. Cochrane Database Syst Rev. 2014;4.

Thomas D, Elliott EJ, Naughton GA. Exercise for type 2 diabetes mellitus. Cochrane Database Syst Rev. 2006;3.

Ussher MH, Taylor AH, Faulkner GEJ. Exercise interventions for smoking cessation. Cochrane Database Syst Rev. 2014;8.

Valentín-Gudiol M, Mattern-Baxter K, Girabent-Farrés M, Bagur-Calafat C, Hadders-Algra M, Angulo-Barroso RM. Treadmill interventions in children under six years of age at risk of neuromotor delay. Cochrane Database Syst Rev. 2017;7.

van der Heijden RA, Lankhorst NE, van Linschoten R, Bierma-Zeinstra SMA, van Middelkoop M. Exercise for treating patellofemoral pain syndrome. Cochrane Database Syst Rev. 2015;1.

Vloothuis JDM, Mulder M, Veerbeek JM, Konijnenbelt M, Visser-Meily JMA, Ket JCF, Kwakkel G, van Wegen EEH. Caregiver-mediated exercises for improving outcomes after stroke. Cochrane Database Syst Rev. 2016;12.

Voet NBM, van der Kooi EL. Riphagen, II, Lindeman E, van Engelen BGM, Geurts ACH: strength training and aerobic exercise training for muscle disease. Cochrane Database Syst Rev. 2013;7.

White CM, Pritchard J, Turner-Stokes L. Exercise for people with peripheral neuropathy. Cochrane Database Syst Rev. 2004;4.

Wieland LS, Skoetz N, Pilkington K, Vempati R, D'Adamo CR, Berman BM. Yoga treatment for chronic non-specific low back pain. Cochrane Database Syst Rev. 2017;1.

Williams AD, Bird ML, Hardcastle SGK, Kirschbaum M, Ogden KJ, Walters JAE. Exercise for reducing falls in people living with and beyond cancer. Cochrane Database Syst Rev. 2018;10.

Williams MA, Srikesavan C, Heine PJ, Bruce J, Brosseau L, Hoxey-Thomas N, Lamb SE. Exercise for rheumatoid arthritis of the hand. Cochrane Database Syst Rev. 2018;7.

Yamamoto S, Hotta K, Ota E, Matsunaga A, Mori R. Exercise-based cardiac rehabilitation for people with implantable ventricular assist devices. Cochrane Database Syst Rev. 2018;9.

Yamato TP, Maher CG, Saragiotto BT, Hancock MJ, Ostelo R, Cabral CMN, Menezes Costa LC, Costa LOP. Pilates for low back pain. Cochrane Database Syst Rev. 2015;7.

Yang ZY, Zhong HB, Mao C, Yuan JQ, Huang YF, Wu XY, Gao YM, Tang JL. Yoga for asthma. Cochrane Database Syst Rev. 2016;4.

Young J, Angevaren M, Rusted J, Tabet N. Aerobic exercise to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev. 2015;4.

Zainuldin R, Mackey MG, Alison JA. Optimal intensity and type of leg exercise training for people with chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2011;11.

Mok A, Khaw K-T, Luben R, Wareham N, Brage S. Physical activity trajectories and mortality: population based cohort study. Bmj. 2019;365:l2323.

Ekelund U, Brown WJ, Steene-Johannessen J, Fagerland MW, Owen N, Powell KE, Bauman AE, Lee IM. Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850 060 participants. Br J Sports Med. 2019;53(14):886–94. https://doi.org/10.1136/bjsports-2017-098963 . Epub 2018 Jul 10.

Article   PubMed   Google Scholar  

Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, Bauman A, Lee IM. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302–10.

Lear SA, Hu W, Rangarajan S, Gasevic D, Leong D, Iqbal R, Casanova A, Swaminathan S, Anjana RM, Kumar R, et al. The effect of physical activity on mortality and cardiovascular disease in 130000 people from 17 high-income, middle-income, and low-income countries: the PURE study. Lancet. 2017;390(10113):2643–54.

Sattelmair J, Pertman J, Ding EL, Kohl HW 3rd, Haskell W, Lee IM. Dose response between physical activity and risk of coronary heart disease: a meta-analysis. Circulation. 2011;124(7):789–95.

Heyman E, Gamelin FX, Goekint M, Piscitelli F, Roelands B, Leclair E, Di Marzo V, Meeusen R. Intense exercise increases circulating endocannabinoid and BDNF levels in humans--possible implications for reward and depression. Psychoneuroendocrinology. 2012;37(6):844–51.

Horton R. Offline: the gravy train of systematic reviews. Lancet. 2019;394(10211):1790.

Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health. 2018;6(10):e1077–86.

Fletcher GF, Landolfo C, Niebauer J, Ozemek C, Arena R, Lavie CJ. Promoting physical activity and exercise: JACC health promotion series. J Am Coll Cardiol. 2018;72(14):1622–39.

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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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Systematic review article, trends in physical fitness among school-aged children and adolescents: a systematic review.

• 1 Faculty for Sport and Physical Education, University of Montenegro, Niksic, Montenegro
• 2 Montenegrosport, Podgorica, Montenegro
• 3 Montenegrin Sports Academy, Podgorica, Montenegro
• 4 Faculdade de Motricidade Humana, Centro Interdisciplinar para o Estudo da Performance Humana, Universidade de Lisboa, Lisboa, Portugal
• 5 Faculdade de Medicina, Instituto de Saúde Ambiental, Universidade de Lisboa, Lisboa, Portugal
• 6 Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany

Introduction and Objective: This systematic review aimed to analyse the international evolution of fitness with its distributional changes in the performance on tests of physical fitness among school-aged children and adolescents.

Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the search was undertaken in four international databases (ERIC, PubMed, Scopus, and Web of Science) to identify the studies reporting temporal trends in the physical fitness among school-aged children and adolescents.

Results: A total of 485 potential articles were identified, of which 19 articles were relevant for the qualitative synthesis; 1,746,023 children and adolescents from 14 countries (China, Finland, Sweden, Belgium, New Zealand, Denmark, Spain, Norway, Mozambique, Poland, USA, Lithuania, Portugal, Canada), for the period between 1969 and 2017 were included. The subjects were tested using 45 motor tests from eight battery tests. The quality of the study in eight articles was rated as strong, while in 11 articles it was rated as moderate.

Discussion: The vast majority of studies show a constant decline in strength and endurance. Three Chinese studies show an increase in strength from 1985 to 1995 and then a decline until 2014. For endurance, similar patterns were found in the two most comprehensive Chinese studies. The decline in flexibility is also evident in European countries. For agility, speed, balance, and coordination, the trend differs among populations.

Introduction

Physical fitness is a multicomponent construct that is closely related to the ability to perform physical activity ( 1 , 2 ). It is considered to be an important health marker, because high levels of fitness during childhood and adolescence have a positive impact on adult health ( 3 , 4 ). Additionally, higher levels of physical fitness enable participation in a variety of physical activities and decrease the risk of health problems ( 5 – 7 ).

Physical fitness is determined by genetic factors and the level of regular exercise and physical activity ( 8 ). The modern era has brought changes in ways of life and work that are associated with lower levels of physical activity ( 9 ). From a traditionally active lifestyle in which physical fitness was necessary to manage daily tasks, most people switched to the more sedentary lifestyles. In the previous four decades, studies have indicated an association between the lower physical activity levels ( 10 ) and change in body composition and somatotype ( 11 , 12 ). Also, declines in physical fitness are often recorded ( 13 ), which are likely influenced by the decreasing trend in physical activity and changes in body composition ( 14 , 15 ). The decline in cardiorespiratory fitness has been extensively documented ( 16 – 18 ). Moreover, a decline in flexibility ( 19 ), repetitive strength and running speed were also recorded ( 20 ). Given the reported declining trends in physical activity and consequently physical fitness, some researchers predict the emergence of serious public health concerns ( 6 , 21 ). Contrary to this evidence, in some countries, an increase has been registered on certain components of physical fitness. Examples of this include an increase of muscular fitness in Finland ( 22 ) and cardiorespiratory fitness and strength in Canada ( 23 ).

These differences in physical fitness among countries have not been examined in detail to date ( 3 ). Furthermore, previous studies have only analyzed trends of single physical fitness components (e.g., cardiorespiratory fitness, muscular fitness), which underscores the need for a comprehensive review study that covers more than one physical fitness component and gives a general picture of its trends, which would facilitate conclusion drawing and monitoring. Thus, the objective of this systematic review was to gather the available information on all components of physical fitness among children and adolescents and to analyse the international trends of their performances on the respective physical fitness tests.

Data selection, collection, and analyses were performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 24 ).

Inclusion Criteria

Scientific articles containing data on temporal trends in physical fitness published up to April 2020 were included. Eligibility criteria were the following: (1) cross-sectional, longitudinal, and interventional studies (design criterion); (2) articles published in scientific journals, book chapters, books, conference proceedings, and theses (publish criterion); (3) fitness battery, physical fitness, cardiovascular fitness, speed, flexibility, agility, muscular strength, body composition (outcome measure criterion); (4) children and adolescents aged 10 to 18 years (participant criterion); (5) articles published in English, French, Portuguese, Spanish, or German (language criterion).

Search Strategy

The literature search was undertaken in four international databases: the Education Resources Information Center (ERIC), PubMed, Scopus, and Web of Science. The search was conducted on April 17, 2020. In each database, a search was conducted by title, taking a predefined combination of keywords (through discussion among the research team) into account. The combination of used keywords was the following: “field-based test” OR fit * OR “physical performance” OR “sport performance” OR “physical condition” OR “aerobic capacity” OR “maximum oxygen consumption” OR strength OR flexibility OR motor OR endurance OR speed OR agility OR balance OR “body composition” OR anthropometry OR “body mass index” OR BMI OR skinfolds OR “waist circumference” AND trend * OR tendenc * AND adolescent * OR child * OR young * OR “school age” OR school-age OR youth. The keywords were selected through discussion among the research team, finally defined by the consensus of all authors.

Data Extraction and Selection

After performing a search in the databases, the necessary data were transferred to a software tool for publishing and managing bibliographies. The process of data extraction was performed based on PRISMA guidelines ( 24 ). The articles were downloaded from the databases, after which duplicates, identified by title and author, were removed. Two researchers screened titles and abstracts of the remaining records. Then, the full-text of relevant articles were read and examined according to the inclusion criteria, to decide whether or not to include them in the systematic review. The following information was extracted from each study: author's name and year of publication, study design, country, sample characteristics (number of participants, gender, and age), the instrument/battery for assessing physical fitness, main results, and study quality.

Study Quality and Risk of Bias

The methodological quality of the studies was assessed using the Quality Assessment Tool for Quantitative Studies ( 25 ), which is a 19-item checklist, assessing eight methodological domains: selection bias, study design, confounders, blinding, data collection methods, withdrawals and dropouts, intervention integrity, and analyses. Each section was graded as being of strong, moderate, or weak methodological quality. A global rating is determined based on the scores of each component. Two researchers rated the studies in each domain, as well as the overall quality of each study. Discrepancies were resolved by consensus.

Figure 1 shows the flow chart of records selection. A total of 485 potential articles were identified through the electronic database search (three from ERIC; 145 from PubMed; 166 from Scopus; 171 from Web of Science). After exclusion of the duplicates (279), the title and abstract of 206 were assessed for eligibility. After elimination at the title and abstract level 157 articles, the remaining 49 articles were subsequently read. After reading, another 30 articles were eliminated, leaving 19 relevant articles that satisfied the inclusion criteria and were included in the qualitative synthesis.

Figure 1 . Flow diagram of study selection.

Study Characteristics

In Table 1 , the studies' characteristics are presented. From 19 studies included in the qualitative synthesis, a total sample of 1,746,023 children and adolescents from 14 countries was represented. All studies had a cross-sectional design with two, three, or more samples from the same number of time-points. Three studies were performed in China and another three in Finland, two in Sweden, and one in each of the following countries: Belgium, New Zealand, Denmark, Spain, Norway, Mozambique, Poland, the United States of America, Lithuania, Portugal, and Canada. The period covered by the studies was between 1969 and 2017. Overall, participants' performance in 45 fitness tests from eight different fitness tests batteries was recorded. Strength was most frequently tested, followed by endurance, flexibility, agility, speed, balance, and coordination. The quality of the studies in eight articles was rated as strong, while in 11 articles it was rated as moderate.

Table 1 . Characteristics of the studies.

Strength was mostly tested topographically (arm and shoulder belt strength in 10 papers for boys and eight for girls; lower limb strength in 10 papers for boys and 11 for girls; abdomen strength in five papers for boys and nine for girls). With the exception of one study, which applied a test assessing the strength of the entire body, eight tests were used to evaluate arm and shoulder strength: handgrip strength (kg), bent-arm hang (s), arm pull (kg), bench-press (n): all four for both boys and girls; pull-ups (n), oblique body pull-ups (n), pull-ups (n/min): three for boys only, and flexed-arm hang (s): only for girls. Three tests were used to evaluate the strength of the lower limbs in both sexes: standing long jump (cm), vertical jump (cm), and leg lifts. The sit-ups test was used for both sexes to evaluate the strength of the abdomen. In one study, a two-hand lift (N) test, which assessed the strength of the entire body, was used for boys and girls.

For the strength of the arms and shoulder belt, a declining trend was found in nine studies for boys and seven for girls. In three Chinese studies ( 34 , 36 , 37 ), a growth trend from 1985 to 1995 was initially found for all cohorts of boys, followed by a trend of decline until 2014. In the additional six studies for boys ( 3 , 22 , 23 , 26 , 27 , 33 ) and seven for girls ( 3 , 3 , 23 , 26 , 27 , 30 , 31 , 33 ), a steady decline was observed between 1969 and 2017. For a cohort of Chinese 12-year-olds, the results of upper-body strength showed an increase until 2005, followed by a decline ( 34 ). Significant changes were not shown in the cohort of Canadians aged 15 to 19 ( 23 ), in Finnish adolescents aged 13 to 16 ( 22 ), in boys in Belgium ( 27 ), and in Spanish boys and girls on a static strength test ( 3 ). Finally, a growth in strength was noted in Mozambique children and adolescents between 1992 and 2012 ( 30 ), in Canadian girls aged between 11 and 14 ( 23 ), and in Polish girls aged 15.5 years on an absolute strength test ( 31 ).

For the explosive strength of the lower extremities, a declining trend was found in six studies for boys and eight for girls. Three Chinese studies for boys and girls ( 34 , 36 , 37 ) found an initial growth trend from 1985 to 1995 (it peaked in 1995), followed by a downward trend until 2014. In three studies for boys and girls ( 3 , 28 , 33 ) and two girls only ( 27 , 31 ), a steady decline was noted between 1969 and 2013. No significant changes for explosive strength were noted in the study by Huotari et al. ( 22 ) of Finnish boys, the study by Westerstahl et al. ( 26 ) for Swedish girls, and in the study with Portuguese adolescents of both sexes ( 19 ). Furthermore, no significant changes were noted for the repetitive strength of the subjects of both sexes in Belgium ( 27 ). Progress was recorded for a cohort of Swedish adolescents aged 16.4 between 1974 and 1995 ( 26 ), which is consistent with the results of studies in China in which a decline in strength occurs after that period. The growth trend was recorded for a cohort of Finnish adolescents aged 13–16 between 1976 and 2001 ( 22 ).

The trend of abdomen strength decline in both sexes was found in Swedish adolescents between 1974 and 1995 ( 26 ), and in Polish girls ( 31 ) between 2006 and 2013. A cohort of Portuguese school-aged boys from 1993 to 2008 and girls from 1993 to 2003 found a trend of decline, followed by a growth trend until 2013 ( 19 ). Studies in China have noted a trend of decline in repetitive strength in girls from 1985 to 1995, followed by a growth trend ( 34 , 36 , 37 ). The growth of repetitive strength in both sexes was recorded in New Zealand from 1991 to 2003 ( 28 ) and in Finland between 1996 and 2002 ( 22 ). In contrast, between 1992 and 2012, only Lithuanian girls showed an increase, while there were no significant changes among the boys ( 33 ).

Westerstahl et al. ( 26 ) examined complete body strength and found progress for both sexes of a cohort of Swedish adolescents aged 16.4 years, for the period between 1974 and 1995, which is a similar result to those in Chinese studies indicating fitness growth also up to 1995 and reaching a peak in that period ( 34 , 36 , 37 ).

Endurance was assessed in boys within 14 research studies and in girls within 15 studies. Eleven tests were used for both sexes: 10 × 50 m run (s), 1-mile run-walk (s), 20 m shuttle run ( n stages), 3,000 m running (s), 550 m run (s), 8 × 50 m shuttle run (s), Bruce protocol treadmill test, Astrand-Rhyming submaximal bicycle test, cycle test with progressively increasing workload, mCAFT step, run-walk (m/9 min). Two tests were used only for boys [1,000 m run (s) and 2,000 m run (s)] and two for girls only [1,500 m run (s) and 800 m running (s)]. Two studies have applied a vital capacity (ml) test.

The constant trend of decline in boys' and girls' endurance is indicated by the results of eight studies covering the period between 1969 and 2017 [(Huotari et al., 2009), ( 15 , 23 , 26 , 28 , 30 , 33 )]. Studies in China did not show significant changes in results for both sexes from 1985 to 1995 followed by a period of continuous decline ( 36 , 37 ). Dyrstad et al. ( 17 ) pointed to the growth of endurance of Norwegian adolescents from 1969 to 1989 and for girls from 1980 to 2000, and its decline between 1990 and 2009 for boys and between 2000 and 2009 for girls. A study in Canada ( 23 ) for an older cohort of boys and girls (15–19 years) showed no significant changes between 2007 and 2017, as well as in Danish adolescents of both sexes between 1983 and 2003 ( 29 ). Some studies pointed to the growth in the endurance of some cohorts, such as Chinese 12-year-olds ( 34 ) of both sexes (Han ethnicity) between 1985 and 2014, Spanish adolescents of both sexes aged 12.5 to 17.5 years between 2001 and 2007. ( 3 ), and girls in Poland (15.5 years) from 2006 to 2013 ( 31 ).

Two studies have shown a trend for forced vital capacity (ml) that shows functional ability, as people with high lung capacity are predisposed to endurance-type sports. The results for this parameter were calculated together for both sexes for children and adolescents aged 7–18 years and show inconsistent results. For the entire population of China, forced vital capacity decreased from 1985 to 2005, after which it increased until 2014 ( 36 ), while for residents in Xinjiang (China) it increased until 1995 (reached a peak in 1995), then declines until 2005 after which it remains at a similar level until 2014 ( 37 ).

Flexibility

Flexibility was tested within nine research studies for both sexes. For boys, a sit-and-reach test was used, and a sit-and-reach test and standing trunk flexion were used for girls. Tests in Belgium ( 27 ), Lithuania ( 33 ), Portugal ( 19 ), and testing of girls in Poland ( 31 ) show a decrease in the flexibility between 1969 and 2013. In China, from 1985 to 1995, progress was seen (reaching a peak in 1995), after which the results decreased until 2014 for both sexes ( 37 ). The opposite trend is cited by another Chinese study showing a decline in the flexibility of boys and girls until 1995, followed by an increase to the year 2000 and retention at similar values until 2010, and finally a slight decline until 2014 ( 36 ). Colley et al. ( 23 ) found a decrease in flexibility for the period between 2007 and 2009 and then an increase until 2017 in a sample of Canadian boys aged 15–19, while for boys between 11 and 14 years they found no significant change in the results of the flexibility test. The same authors demonstrate girls' progress of flexibility for both ages. The growth of flexibility for both sexes is reasonable in the cohort of school children (10–14 years) of New Zealand ( 28 ) for the period between 1991 and 2003 and children (8–15 years) in Mozambique ( 30 ) for the period between 1992 and 2012.

Agility was tested within six studies for boys and within seven studies for girls. Three tests for both sexes were used for assessment: a 10 × 5 m shuttle run (s), a 4 × 10 m shuttle run (s), and a 4 × 9 m run (s). In both sexes, agility declines in Mozambique ( 30 ), and New Zealand ( 28 ), moreover, for girls in Belgium ( 27 ) and Poland ( 31 ). The growth of agility for both sexes was found in Spain ( 3 ) and Finland ( 22 ) and only for boys in Lithuania ( 33 ), while the girls showed no significant changes. In Belgium, there is a trend of increasing results for boys between 1969 and 2005, but there were no significant changes between parents and their children ( 27 ).

Speed testing was performed within six studies. For the evaluation of both sexes, the following tests were used: 50 m sprint (s), 40 m sprint (s), and the speed of movement frequency was measured by the Plate tapping test ( n /20 s). Tests show a negative trend in the speed of boys and girls in the cohort of Flemish subjects in Belgium ( 27 ) and in girls in Poland ( 31 ). In a study by Ao et al. ( 34 ), Chinese girls' (Han ethnicity) speed decreased from 1985 to 1991 and increased thereafter but only in rural areas, while in boys a constant decrease was recorded. In the Chinese province of Xinjiang, the speed decreased for both sexes from 1985 to 1995, then increased until 2005, after which it remained stable ( 37 ). In contrast, the results for the whole of China for both sexes ( 36 ) show an increase in speed until 1995, then a decrease until 2005 and retention at a similar level until 2010, followed by a slight increase in 2014. The increase in speed was also visible in urban areas of China (Han ethnicity) for 12-year-olds of both sexes ( 34 ), and Portugal ( 19 ).

Balance was tested for both sexes as part of two research studies with the Flamingo balance ( n /min) test. Balance tests show that in the cohort of Flemish subjects in Belgium (12–18 years) there are no significant changes when comparing the results of parents and their children later when they reached the same age; however, for the entire female population there is a decrease in balance ( 27 ). In Lithuania, greater improvement in balance scores was found in the previous decade and still more in girls 11–18 ( 33 ).

Coordination

Only one study shows coordination results for both sexes using three tests: motor coordination track (s), lateral jumping ( n /15 s), and Figure 8 ( n /min). The only study that examined coordination was conducted by Huotari et al. ( 35 ) for a cohort of Finnish adolescents (15–16 years) in the period between 2003 and 2010. The motor coordination track (s) test shows a decrease during the tracked period for both sexes. Lateral jumping test ( n /15 s) which evaluates dynamic balance, quickness, and the explosive strength of lower limbs with coordination indicates that there are no significant changes for both sexes, while the Figure 8 test ( n /min) test, which evaluates object control skills with coordination, shows that there are no significant changes in boys, while girls have made progress in results.

This study provides a comprehensive overview of longitudinal changes in the physical fitness of children and adolescents and thereby indicates relevant knowledge to develop appropriate public health strategies. The articles that were included in the qualitative synthesis describe the temporal trends for seven physical fitness attributes. Overall, a declining trend was found for one or all three topological areas of strength (9 of 10 studies for boys and in 8 in 11 for girls), endurance (9 in 14 studies for boys and in 8 in 15 for girls), flexibilities (4 in 9 studies), agilities (4 in 6 studies for boys and 4 in 7 for girls), and speed (2 in 6 studied).

Based on the analysis of all three topological areas of strength (arm and shoulder belt strength, lower limb strength, abdomen strength), it can be noted that most studies indicate a declining trend, which is not surprising given the changing lifestyle of children and adolescents, physical inactivity, and increasing screen-time around the world in the previous three decades ( 38 ). However, in some studies, in certain cohorts a trend of increase in strength has been noticed whereas in other cohorts a decrease in strength was not evident ( 19 , 22 , 23 ). It should be emphasized that these are studies with children and adolescents in which (according to the decisions of the governments in these countries) special programmes of additional exercise were applied. Possibly, even in these cohorts there would have been a declining trend in strength levels if there had been only the regular school curriculum, without additional exercise. In several studies, the handgrip strength test shows a growth trend in strength for girls ( 23 , 31 , 36 ). These results are in line with previous research, since absolute strength is proportional to the size of the muscle cross-sectional area ( 39 ) and body height, and which increased over the last 40 years ( 11 , 12 ).

Noteworthy is the fact that the majority of studies point to a trend of declining levels of endurance, which can be explained by the same causes as for the trend of declining strength. A small number of cohorts in certain studies show that there is no decline in endurance levels or even a growth trend. However, these are cohorts for which intervention programmes mirror the longitudinal effects ( 23 , 31 ) or studies covering insufficient periods ( 3 ), or studies with non-representative populations ( 29 , 34 ). In summary, the results of these studies must be interpreted with caution, when drawing conclusions about longitudinal trends.

Notably, research conducted in Europe has recorded a trend of declining levels of flexibility. In other parts of the world, growth trends have been reported, for example, in Canada ( 23 ), where additional exercise programmes have been conducted. However, the reasons for the growth trend in New Zealand ( 28 ) and Mozambique ( 30 ) have not yet been determined.

Given the diversity of agility trends in the available studies, it is not possible to discuss a specific direction of movement for this motor ability in children and adolescents. The speed trends are very difficult to interpret because results indicate differences. In particular, the three Chinese studies are quite contradictory. The trend of increasing speed in Portugal ( 19 ) probably results from the government's policy on additional exercise, although it is known that speed is highly genetically determined.

Two studies examining the balance are not enough to define the trajectory of the global trend. The results of studies conducted in Belgium ( 27 ) and Lithuania ( 33 ) are contradictory at first glance, but they are focused on different time periods, and it is possible that in identical periods differences would not exist.

Only one study that have examined coordination ( 35 ) does not provide a possibility to discuss global trends.

Generally, the decline of physical fitness of children and adolescents around the world is caused by various factors. In recent years, several studies showed that weight gain is related to physical fitness ( 14 , 15 , 31 ). As body weight increased, so did BMI, which influenced this trend in China ( 37 ), Sweden ( 26 ), and New Zealand ( 28 ). In addition to these two components, the increase in the thickness of the skin folds caused a decline in physical fitness in Belgium ( 27 ). Two Chinese studies showed that the decline in physical fitness was caused by changing lifestyles, characterized by higher media and fast food consumption ( 34 , 36 ). Morales-Demori et al. ( 32 ) linked the trend of declining endurance to a sedentary lifestyle, and Venckunas et al. ( 33 ) with additional risk factors of smoking, alcohol consumption, non-active lifestyle and long-term television viewing.

This study also has certain limitations. One of the most significant is the insufficient differentiation of the samples by age (e.g., 10–12; 12–14; 14–16; 16–18) which would show the most accurate data. However, the results for the whole population are mostly combined. Also, the authors themselves are self-critical and reported the following limitations in their works: some studies combined results for both sexes; field tests although used worldwide with this age, carry the possibility of individual errors of measurement performers; when testing aerobic fitness, the results will have real values only if all subjects are highly motivated; in some studies urban i rural area are divided, but did not take into account the urbanization of certain rural areas, which likely cause a certain contradiction; results of non-representative samples of some studies can't be generalized for the whole population. Finally, it should be noted, that causes and determinants of physical fitness trends have not been fully identified by researchers. However, weight gain and higher BMI levels are mainly the cause of the declining trend in physical fitness, which facilitates the need to monitor several health-related factors (e.g., food intake) among several settings of the living environment.

Studies from Finland ( 22 ), Canada ( 23 ), and Portugal ( 19 ) show that changing policies, including the development and implementation of health-enhancing programmes, can reduce the negative impact of a sedentary lifestyle, which have taken precedence in our population.

Finally, consistent conclusions about the development of strength and endurance were drawn, because these dimensions were assessed with the largest number of tests, while the other motor skills (flexibility, agility, speed, balance, coordination) were less frequently and insufficiently tested to draw a meaningful conclusion. We encourage researchers to develop a comprehensive and easily applicable test battery enabling the assessment of all motor skills. Doing so would clearly enhance the evidence regarding the longitudinal trends of physical fitness among children and adolescents around the world.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

BM wrote the manuscript, collected the data, and performed analyses. JG wrote the manuscript, overviewed previous studies, and discussed the results. AM performed analyses. MP discussed the results. YD revised manuscript. DS revised manuscript. SP discussed the results and revised manuscript. All authors contributed to the article and approved the submitted version.

This systematic review was supported by a grant from the Education, Audiovisual and Culture Executive Agency (EACEA), by the Erasmus Plus Sports Programme, within the European Fitness Monitoring System (EUFITMOS) (613324-EPP-1-2019-1-PT-SPO-SCP).

Conflict of Interest

The 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.

The reviewer AB declared a past co-authorship with the authors YD and DS.

1. Aslan A, Salci Y, Guvenc A. The effects of weekly recreational soccer intervention on the physical fitness level of sedentary young men. Montenegrin J Sports Sci Med. (2019) 8:51–9. doi: 10.26773/mjssm.190308

CrossRef Full Text | Google Scholar

2. Vukasevic V, Mitrovic M, Zivanovic N, Masanovic B. Comparative study of motor ability between basketball players from second leagues in Montenegro and Serbia. J Anthropol Sport Phys Edu. (2019) 3:13–7. doi: 10.26773/jaspe.190103

3. Moliner-Urdiales D, Ruiz JR, Ortega FB, Jiménez-Pavón D, Vicente-Rodriguez G, Rey-López JP, et al. Secular trends in health-related physical fitness in Spanish adolescents: the AVENA and HELENA Studies. J Sci Med Sport. (2010) 13:584–8. doi: 10.1016/j.jsams.2010.03.004

4. Silventoinen K, Maia J, Jelenkovic A, Pereira S, Gouveia É, Antunes A, et al. Genetics of somatotype and physical fitness in children and adolescents. Am J Hum Biol. (2020). doi: 10.1002/ajhb.23470. [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Boraczynski M, Boraczynski T, Podstawski R, Mankowski S, Choszcz D, Honkanen A. Physical fitness classification standards for Polish early education teachers. S Afr J Res Sport Phys Educ Recreat. (2015) 37:113–30.

Google Scholar

6. Kao SC, Cadenas-Sanchez C, Shigeta TT, Walk AM, Chang YK, Pontifex MB, et al. A systematic review of physical activity and cardiorespiratory fitness on P3b. Psychophysiology. (2019) 57:e13425. doi: 10.1111/psyp.13425

7. Marttinen RN, Fredrick R III, Silverman SS. Middle school students' free-living physical activity on physical education days, non-physical education days, and weekends. Montenegrin J Sports Sci Med. (2018) 7:5–12. doi: 10.26773/mjssm.180301

8. Erikssen G, Liestøl K, Bjørnholt J, Thaulow E, Sandvik L, Erikssen J. Changes in physical fitness and changes in mortality. Lancet. (1998) 352:759–62. doi: 10.1016/S0140-6736(98)02268-5

9. Katzmarzyk PT, Mason C. The physical activity transition. J Phys Act Health. (2009) 6:269–80. doi: 10.1123/jpah.6.3.269

10. Gray C, Gibbons R, Larouche R, Sandseter E, Bienenstock A, Brussoni M, et al. What is the relationship between outdoor time and physical activity, sedentary behaviour, and physical fitness in children? A systematic review. Int J Environ Res Public Health. (2015) 12:6455–74. doi: 10.3390/ijerph120606455

11. NCD Risk Factor Collaboration. A century of trends in adult human height. Elife . (2016) 5:e13410. doi: 10.7554/eLife.13410

12. NCD Risk Factor Collaboration. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. (2017) 390:2627–42. doi: 10.1016/S0140-6736(17)32129-3

13. Malina RM. Physical fitness of children and adolescents in the United States: status and secular change. Med. Sport Sci. 50:67–90. doi: 10.1159/000101076

14. Huotan PRT, Nupponen H, Laakso L, Kujale UM. Secular trends in aerobic fitness performance in 13-18-year-old adolescents from 1976 to 2001. Br J Sports Med. (2009) 44:968–72. doi: 10.1136/bjsm.2008.055913

15. Johansson L, Brissman M, Morinder G, Westerståhl M, Marcus C. Reference values and secular trends for cardiorespiratory fitness in children and adolescents with obesity. Acta Paediatr Int J Paediatr . (2020) 109:1665–71. doi: 10.1111/apa.15163

16. Tomkinson GR, Olds TS, Kang SJ, Kim DY. Secular trends in the aerobic fitness test performance and body mass index of Korean children and adolescents (1968-2000). Int J Sports Med. (2007) 28:314–20. doi: 10.1055/s-2006-924357

17. Dyrstad SM, Berg T, Tjelta LI. Secular trends in aerobic fitness performance in a cohort of Norwegian adolescents. Scand J Med Sci Sports. (2012) 22:822–7. doi: 10.1111/j.1600-0838.2011.01315.x

18. Tomkinson GR, Lang JJ, Tremblay MS. Temporal trends in the cardiorespiratory fitness of children and adolescents representing 19 high-income and upper middle-income countries between 1981 and 2014. Br J Sports Med. (2017) 2017:097982. doi: 10.1136/bjsports-2017-097982

19. Costa AM, Costa MJ, Reis AA, Ferreira S, Martins J, Pereira A. Secular trends in anthropometrics and physical fitness of young Portuguese school-aged children. Acta Med Port. (2017) 30:108–14. doi: 10.20344/amp.7712

20. Li YH. Physical fitness secular trends in guangzhou children, China: 1995 to 2005. Med Sci Sports Exerc. (2010) 42:554–554. doi: 10.1249/01.MSS.0000385367.67999.35

CrossRef Full Text

21. Johnson U, Ivarsson A, Parker J, Andersen MB, Svetoft I. Connection in the fresh air: A study on the benefits of participation in an electronic tracking outdoor gym exercise programme. Montenegrin J Sports Sci Med. (2019) 8:61–7. doi: 10.26773/mjssm.190309

22. Huotari PRT, Laakso L, Kujala UM, Nupponen H. Secular trends in muscular fitness among Finnish adolescents. Scand J Public Health. (2010) 38:739–47. doi: 10.1177/1403494810384425

23. Colley RC, Clarke J, Doyon CY, Janssen I, Lang JJ, Timmons BW, et al. Trends in physical fitness among Canadian children and youth. Health Reports. (2019) 30:3–13. doi: 10.25318/82-003-x201901000001-eng

24. Moher D. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. (2009) 151:264. doi: 10.7326/0003-4819-151-4-200908180-00135

25. National Collaborating Centre for Methods and Tools. Quality Assessment Tool for Quantitative Studies Method. McMaster University. (2016). Retrieved from: http://www.nccmt.ca/resources/search/14

26. Westerstahl M, Barnekow-Bergkvist M, Hedberg G, Jansson E. Secular trends in body dimensions and physical fitness among adolescents in Sweden from 1974 to 1995. Scand J Med Sci Sports. (2003) 13:128–37. doi: 10.1034/j.1600-0838.2003.10274.x

27. Matton L, Duvigneaud N, Wijndaele K, Philippaerts R, Duquet W, Beunen G, et al. Secular trends in anthropometric characteristics, physical fitness, physical activity, and biological maturation in Flemish adolescents between 1969 and 2005. Am J Hum Biol. (2007) 19:345–57. doi: 10.1002/ajhb.20592

28. Albon HM, Hamlin MJ, Ross JJ. Secular trends and distributional changes in health and fitness performance variables of 10-14-year-old children in New Zealand between 1991 and 2003. Br J Sports Med. (2010) 44:263–9. doi: 10.1136/bjsm.2008.047142

29. Andersen LB, Froberg K, Kristensen PL, Moller NC, Resaland GK, Anderssen SA. Secular trends in physical fitness in Danish adolescents. Scandinavian J Med Sci Sports. (2010) 20:757–63. doi: 10.1111/j.1600-0838.2009.00936.x

30. Dos Santos FK, Prista A, Gomes T, Daca T, Madeira A, Katzmarzyk PT, et al. Secular trends in physical fitness of mozambican school-aged children and adolescents. Am J Hum Biol. (2015) 27:201–6. doi: 10.1002/ajhb.22638

31. Karpowicz K, Karpowicz M, Strzelczyk R. Structure of physical fitness among young female basketball players (Trends of Changes in 2006-2013). J Strength Cond Res. (2015) 29:2745–57. doi: 10.1519/JSC.0000000000000943

32. Morales-Demori R, Jamil O, Serratto M. Trend of endurance level among healthy inner-city children and adolescents over three decades. Pediatr Cardiol. (2017) 38:123–7. doi: 10.1007/s00246-016-1492-6

33. Venckunas T, Emeljanovas A, Mieziene B, Volbekiene V. Secular trends in physical fitness and body size in Lithuanian children and adolescents between 1992 and 2012. J Epidemiol Commun Health. (2017) 71:181–7. doi: 10.1136/jech-2016-207307

34. Ao D, Wu F, Yun CF, Zheng XY. Trends in physical fitness among 12-year-old children in urban and rural areas during the social transformation period in China. Journal of Adolescent Health. (2019) 64:250–7. doi: 10.1016/j.jadohealth.2018.08.021

35. Huotari P, Heikinaro-Johansson P, Watt A, Jaakkola T. Fundamental movement skills in adolescents: Secular trends from 2003 to 2010 and associations with physical activity and BMI. Scand J Med Sci Sports. (2018) 28:1121–9. doi: 10.1111/sms.13028

36. Dong Y, Lau PWC, Dong B, Zou Z, Yang Y, Wen B, et al. Trends in physical fitness, growth, and nutritional status of Chinese children and adolescents: a retrospective analysis of 1·5 million students from six successive national surveys between 1985 and 2014. The Lancet Child and Adolescent Health. (2019) 3:871–80. doi: 10.1016/S2352-4642(19)30302-5

37. Bi C, Zhang F, Gu Y, Song Y, Cai X. Secular trend in the physical fitness of Xinjiang children and adolescents between 1985 and 2014. Int J Environ Res Public Health. (2020) 17:2195. doi: 10.3390/ijerph17072195

38. Masanovic B, Popovic S, Bjelica D, Gardasevic J. The 2018 national report on children's and adolescents' physical activity and physical fitness for Montenegro. Iran J Public Health. (2020) 49:1992–2000. doi: 10.18502/ijph.v49i10.4705

39. Singer KP, Breidahl P. The use of computed tomography in assessing muscle cross-sectional area, and the relationship between cross-sectional area and strength. Aust J Physiother . (1987) 33:75–82. doi: 10.1016/S0004-9514(14)60585-7

Keywords: secular trends, physical fitness, fitness changes, physical performance, youngsters

Citation: Masanovic B, Gardasevic J, Marques A, Peralta M, Demetriou Y, Sturm DJ and Popovic S (2020) Trends in Physical Fitness Among School-Aged Children and Adolescents: A Systematic Review. Front. Pediatr. 8:627529. doi: 10.3389/fped.2020.627529

Received: 09 November 2020; Accepted: 25 November 2020; Published: 11 December 2020.

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Copyright © 2020 Masanovic, Gardasevic, Marques, Peralta, Demetriou, Sturm and Popovic. 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: Jovan Gardasevic, jovan@ucg.ac.me

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• Published: 17 August 2023

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 .

Aboelmaged M, Ali I, Hashem G (2022) Mobile apps use forwellness and fitness and university students’ subjective wellbeing. Inform Develop 38(4):672–687. https://doi.org/10.1177/02666669211020498

Article   Google Scholar  

Acikgoz F, Filieri R, Yan M (2022) Psychological predictors of intention to use fitness apps: the role of subjective knowledge and innovativeness. Int J Hum Comp Interact. https://doi.org/10.1080/10447318.2022.2074668

Ahn H, Park E (2023) Motivations for user satisfaction of mobile fitness applications: an analysis of user experience based on online review comments. Humanit Soc Sci Commun 10(1). https://doi.org/10.1057/s41599-022-01452-6

Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs, NJ

Google Scholar  

Angosto S, García-Fernández J, Valantine I, Grimaldi-Puyana M (2020) The intention to use fitness and physical activity apps: a systematic review. Sustainability 12(16):16. https://doi.org/10.3390/su12166641

Angosto S, Lara-Bocanegra A, Bohorquez MR, García-Fernández J, Zhang JJ (2022) Challenges in sport marketing: a systematic review of digital sport marketing. In: Byon KK, Yim BH, Zhang JJ (eds) Marketing analysis in sport business. Global perspectives, 1st edn. Routledge, London, pp. 70–92

Chapter   Google Scholar  

Ardern CL, Büttner F, Andrade R, Weir A, Ashe MC, Holden S, Impellizzeri FM, Delahunt E, Dijkstra HP, Mathieson S, Rathleff MS, Reurink G, Sherrington C, Stamatakis E, Vicenzino B, Whittaker JL, Wright AA, Clarke M, Moher D, Page MJ, Khan KM, Winters M (2022) Implementing the 27 PRISMA 2020 Statement items for systematic reviews in the sport and exercise medicine, musculoskeletal rehabilitation and sports science fields: the PERSiST (implementing Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science) guidance. Br J Sports Med 56(4):175–195. https://doi.org/10.1136/bjsports-2021-103987

Article   PubMed   Google Scholar  

Baubonytė S, Deliautaitė K, Narkevičienė KM, Valantinė I (2021) The use of sports apps for engaging in physical activity. Balt J Sport Health Sci 121(2):28–33

Beh PK, Ganesan Y, Iranmanesh M, Foroughi B (2021) Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behav Inf Technol 40(3):282–299. https://doi.org/10.1080/0144929X.2019.1685597

Beldad AD, Hegner SM (2018) Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: a structural equation modeling approach. Int J Hum–Comput Interact 34(9):882–893. https://doi.org/10.1080/10447318.2017.1403220

Byun H, Chiu W, Bae J (2018) Exploring the adoption of sports brand apps: an application of the modified technology acceptance model. Int J Asian Bus Inf Manag 9(1):52–65. https://doi.org/10.4018/IJABIM.2018010105

Cai J, Zhao Y, Sun J (2022) Factors influencing fitness app users’ behavior in China. Int J Hum–Comput Interac 38(1):53–63. https://doi.org/10.1080/10447318.2021.1921483

Chang CJ, Yang SC, Wolzok E (2023) Examining the use of fitness apps in sports centers in Taiwan: incorporating task–technology fit into a technology readiness acceptance model. Manag Sport Leis. https://doi.org/10.1080/23750472.2023.2165532

Cheng LK, Huang HL, Lai CC (2021) Continuance intention in running apps: the moderating effect of relationship norms. Int J Sports Mark Spons 23(1):132–154. https://doi.org/10.1108/IJSMS-08-2020-0143

Chen MF, Lin NP (2018) Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Res 28:351–373. https://doi.org/10.1108/IntR-03-2017-0099

Chiu W, Cho H (2021) The role of technology readiness in individuals’ intention to use health and fitness applications: a comparison between users and non-users. Asia Pac J Mark Logist 33(3):807–825. https://doi.org/10.1108/APJML-09-2019-0534

Chiu W, Cho H, Chi CG (2021) Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Inf Technol People 34(3):978–998. https://doi.org/10.1108/ITP-09-2019-0463

Cho H, Chi C, Chiu W (2020) Understanding sustained usage of health and fitness apps: incorporating the technology acceptance model with the investment model. Technol Soc 63:101429. https://doi.org/10.1016/j.techsoc.2020.101429

Cho J, Kim SJ (2015) Factors of leading the adoption of diet/exercise apps on smartphones: application of channel expansion. J Internet Comput Serv 16(1):101–108. https://doi.org/10.7472/jksii.2015.16.1.101

Clement J (2020) Health and fitness App downloads worldwide from 1st quarter 2019 to 2nd quarter 2020. Statista. Available via STATISTA. https://www.statista.com/statistics/1127248/health-fitness-Apps-downloadsworldwide Accessed 19 Dec 2022

Damberg S (2021) Predicting future use intention of fitness apps among fitness app users in the United Kingdom: the role of health consciousness. Int J Sports Mark Spons 23(2):369–384. https://doi.org/10.1108/IJSMS-01-2021-0013

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Davis FD, Bagozzi RP, Wharshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35(8):982–1003. https://doi.org/10.1287/mnsc.35.8.982

Dhiman N, Arora N, Dogra N, Gupta A (2020) Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective. J Indian Bus Res 12(3):363–388. https://doi.org/10.1108/JIBR-05-2018-0158

Dillon A (2001) User acceptance of information technology. In: Karwowski K (ed) 555 Encyclopedia of human factors and ergonomics. Routledge, London, pp. 556–561

Dutta DK, Gwebu KL, Wang J (2015) Personal innovativeness in technology, related knowledge and experience, and entrepreneurial intentions in emerging technology industries: a process of causation or effectuation? Int Entrep Manag J 11(3):529–555. https://doi.org/10.1007/s11365-013-0287-y

Eshet E, Bouwman H (2015) Addressing the context of use in mobile computing: a survey on the state of the practice. Interact Comput 27(4):392–412. https://doi.org/10.1093/iwc/iwu002

Farooq MS, Salam M, Jaafar N, Fayolle A, Ayupp K, Radovic-Markovic M, Sajid A (2017) Acceptance and use of lecture capture system (LCS) in executive business studies: extending UTAUT2. Interact Technol Smart Educ 14(4):329–348. https://doi.org/10.1108/ITSE-06-2016-0015

Ferreira H, García-Fernández J, Pedragosa V, Cepeda-Carrión G (2021) The use of fitness centre apps and its relation to customer satisfaction: a UTAUT2 perspective. Int J Sports Mark Spons 23(5):966–985. https://doi.org/10.1108/IJSMS-01-2021-0010

Ferreira H, García-Fernández J, Cepeda-Carrión G (2023) The mediating role of e-Lifestyles to use the fitness center app. Int J Human Comp Interact. https://doi.org/10.1080/10447318.2023.2204273

Ferrer I (2021) Países Bajos impone un confinamiento estricto hasta el 14 de enero para frenar los contagios por la variante ómicron. El País. Available via ELPAIS. https://elpais.com/sociedad/2021-12-18/paises-bajos-impone-un-confinamiento-estricto-hasta-el-14-de-enero-para-frenar-los-contagios-por-la-variante-omicron.html Accessed 20 Dec 2022

Fox S, Duggan M (2021) Mobile fact sheet. Pew Research Center. Available via PRC. http://www.pewinternet.org/data-trend/mobile Accessed 20 Dec 2022

Gao T, Rohm AJ, Sultan F, Huang S (2012) Antecedents of consumer attitudes toward mobile marketing: a comparative study of youth markets in the United States and China. Thunderbird Int Bus Rev 54(2):211–224. https://doi.org/10.1002/tie.21452

García-Fernández J, Gálvez-Ruiz P, Grimaldi-Puyana M, Angosto S, Fernández-Gavira J, Bohórquez MR (2020) The promotion of physical activity from digital services: influence of E-lifestyles on intention to use fitness apps. Int J Environ Res Public Health 17(18):6839. https://doi.org/10.3390/ijerph17186839

Article   PubMed   PubMed Central   Google Scholar  

Gómez-Ruiz AA, Gálvez-Ruiz P, Grimaldi-Puyana M, Lara-Bocanegra A, García-Fernández J (2022) Investigating the intention to use fitness app: the role of the perceived attractiveness of fitness center customers. Sport Bus Manag 12(4):537–553. https://doi.org/10.1108/SBM-12-2021-0145

Gopinath K, Selvam G, Narayanamurthy G (2022) Determinants of the adoption of wearable devices for health and fitness: a meta-analytical study. Commun Assoc Inform Syst 50(1):23. https://doi.org/10.17705/1CAIS.05019

Gu P, Liang Z, Zhang H, Zhang D (2022) Effects of attitudes towards exercise behaviour, use of sports apps and COVID-19 on intentions to exercise. J Person Med 12(9):1434. https://doi.org/10.3390/jpm12091434

Gu P, Zhang H, Liang Z, Zhang D (2022) Impact of public risk perception in China on the intention to use sports APPs during COVID-19 pandemic. Int J Environ Res Public Health 19(19):11915. https://doi.org/10.3390/ijerph191911915

Gunasinghe A, Hamid JA, Khatibi A, Azam SF (2020) The adequacy of UTAUT-3 in interpreting academician’s adoption to e-Learning in higher education environments. Interact Technol Smart Educ 17(1):86–106. https://doi.org/10.1108/ITSE-05-2019-0020

Guo J (2022) Influencing factors of college students’ use of sports apps in mandatory situations: based on UTAUT and SDT. BioMed Res Int 2022:9378860. https://doi.org/10.1155/2022/9378860

Gupta S, Mathur N, Narang D (2022) E-leadership and virtual communication adoption by educators: an UTAUT3 model perspective. Glob Knowl Mem Commun. https://doi.org/10.1108/GKMC-01-2022-0001

Hu J, He W, Zhang J, Song J (2023) Examining the impacts of fitness app features on user well-being. Inf Manag 60(5):103796. https://doi.org/10.1016/j.im.2023.103796

Huang G, Ren Y (2020) Linking technological functions of fitness mobile apps with continuance usage among Chinese users: moderating role of exercise self-efficacy. Comput Human Behav 103:151–160. https://doi.org/10.1016/j.chb.2019.09.013

Jeong SH, Chung JY (2022) Integrated technology acceptance model (TAM) of sports team smartphone application (STSA) in the stimulus organism response (SOR) framework. J Phys Educ Sport 22(9):2152–2161

Jin CH (2020) Predicting the use of brand application based on a TRAM. Int J Hum–Comput Interact 36(2):156–171. https://doi.org/10.1080/10447318.2019.1609227

Jones P, Ratten V, Hayduk T (2020) Sport, fitness, and lifestyle entrepreneurship. Int Entrep Manag J 16(3):783–793. https://doi.org/10.1007/s11365-020-00666-x

Kercher VM, Kercher K, Bennion T, Levy P, Alexander C, Amaral PC, Li YM, Han J, Liu Y, Wang R, Huang HY, Gao BH, Batrakoulis A, Gómez Chávez LFJ, Haro JL, Zavalza ARP, Rodríguez LEA, Veiga OL, Valcarce-Torrente M, Romero-Caballero A (2022) Fitness trends from around the globe. ACSM’s Health Fit J 26(1):21–37. https://doi.org/10.1249/FIT.0000000000000737

Kim HM (2022) Social comparison of fitness social media postings by fitness app users. Comput Hum Behav 131:107204. https://doi.org/10.1016/j.chb.2022.107204

Kim B, Lee E (2022) What Factors Affect a User as Intention to Use Fitness Applications? The Moderating Effect of Health Status: A Cross-Sectional Study. INQUIRY J Health Care Organiz Prov Financ 59:1–13. https://doi.org/10.1177/0046958022109582

Kim SC, Yoon D, Han EK (2016) Antecedents of mobile app usage among smartphone users. J Mark Commun 22(6):653–670. https://doi.org/10.1080/13527266.2014.951065

Kim T, Chiu W (2019) Consumer acceptance of sports wearable technology: The role of technology readiness. Int J Sports Mark Spons 20(1):109–126. https://doi.org/10.1108/IJSMS-06-2017-0050

Kim Y, Kim S, Rogol E (2017) The effects of consumer innovativeness on sport team applications acceptance and usage. J Sport Manag 31(3):241–255. https://doi.org/10.1123/jsm.2015-0338

Koenig-Lewis N, Marquet M, Palmer A, Zhao AL (2015) Enjoyment and social influence: predicting mobile payment adoption. Serv Ind J 35(10):537–554. https://doi.org/10.1080/02642069.2015.1043278

Lim JS, Noh GY (2017) Effects of gain-versus loss-framed performance feedback on the use of fitness apps: mediating role of exercise self-efficacy and outcome expectations of exercise. Comput Hum Behav 77:249–257. https://doi.org/10.1016/j.chb.2017.09.006

Lin CT, Shen CC, Mao TY, Yang CC (2019) Empirical investigation of sports management, behavior growth and usage of sports APP: new learning perspective. Pol J Manag Stud 19(1):225–234. https://doi.org/10.17512/pjms.2019.19.1.17

Lin CW, Mao TY, Huang YC, Sia WY, Yang CC (2020) Exploring the adoption of Nike+ Run Club App: an application of the theory of reasoned action. Math Probab Eng 2020:e8568629. https://doi.org/10.1155/2020/8568629

Litman L, Robinson J, Rosenzweig C (2015) The relationship between motivation, monetary compensation, and data quality among US- and India-based workers on Mechanical Turk. Behav Res Methods 47(2):519–528. https://doi.org/10.3758/s13428-014-0483-x

Liu XD, Ly XY, Ren PR (2017) Research on user adoption behavior of fitness App. Enterp Econ 36(11):48–54. https://doi.org/10.13529/j.cnki.enterprise.economy.2017.11.007

Marquez A, Cianfrone BA, Kellison T (2020) Factors affecting spectators’ adoption of digital ticketing: the case of interscholastic sports. Int J Sports Mark Spons 21(3):527–541. https://doi.org/10.1108/IJSMS-07-2019-0080

Middelweerd A, Mollee JS, van der Wal CN, Brug J, Velde SJ (2014) Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nut Phys Act 11(1):97. https://doi.org/10.1186/s12966-014-0097-9

Mohammadi S, Isanejad O (2018) Presentation of the extended technology acceptance model in sports organizations. Ann Appl Sport Sci 6(1):75–86. https://doi.org/10.29252/aassjournal.6.1.75

Newzoo (2021) Global mobile market report. Newzoo. Available via DIALG. https://newzoo.com/key-numbers/ . Accessed 19 Dec 2022

Núñez Sánchez JM, Gómez Chacón R, Jambrino-Maldonado C, García Fernández J (2022) Can a corporate well-being programme maintain the strengths of the healthy employee in times of COVID-19 and extensive remote working? An empirical case study. European J Govern Econ 11(1):51–72. https://doi.org/10.17979/ejge.2022.11.1.8978

Official State Bulletin (2021) Royal Decree-Law 30/2021, of December 23, adopting urgent prevention and containment measures to address the health crisis caused by COVID-19 (No. 30; pp. 159762–159770). Off State Bull. Available via BOE. https://www.boe.es/diario_boe/txt.php?id=BOE-A-2021-21307

Ong AKS, Prasetyo YT, Robas KPE, Persada SF, Nadlifatin R, Matillano JSA, Macababbab DCB, Pabustan JR, Taningco KAC (2023) Determination of factors influencing the behavioral intention to play “Mobile Legends: Bang-Bang” during the COVID-19 pandemic: integrating UTAUT2 and system usability scale for a sustainable E-sport business. Sustainability 15(4):3170. https://doi.org/10.3390/su15043170

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinnes LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10(1):89. https://doi.org/10.1186/s13643-021-01626-4

Parasuraman A (2000) Technology Readiness Index (Tri): a multiple-item scale to measure readiness to embrace new technologies. J Serv Res 2(4):307–320. https://doi.org/10.1177/109467050024001

Perez-Aranda J, González EM, Alarcón P (2023) Understanding antecedents of continuance and revisit intentions: the case of sport apps. J Retail Cons Serv 72:103288. https://doi.org/10.1016/j.jretconser.2023.103288

Perez-Aranda J, González Robles EM, Alarcón P (2021) Sport-related physical activity in tourism: an analysis of antecedents of sport based applications use. Inf Technol Tour 23(1):97–120. https://doi.org/10.1007/s40558-019-00161-2

Pinto AS, Abreu A, Costa E, Paiva J (2022) Using UTAUT-3 to understand the adoption of Mobile Augmented Reality in Tourism (MART). In: Carvalho JVD, Liberato P, Peña A (eds) Advances in tourism, technology and systems. Smart innovation, systems and technologies, vol 284. Springer

Rahman SA, Taghizadeh SK, Ramayah T, Alam MMD (2017) Technology acceptance among micro-entrepreneurs in marginalized social strata: the case of social innovation in Bangladesh. Technol Forecast Soc Change 118:236–245. https://doi.org/10.1016/j.techfore.2017.01.027

Raman P, Aashish K (2022) Gym users: an enabler in creating an acceptance of sports and fitness wearable devices in India. Int J Sport Mark Spons 23(4):707–726. https://doi.org/10.1108/IJSMS-08-2021-0168

Rivera M, Gregory A, Cobos L (2015) Mobile application for the timeshare industry: the influence of technology experience, usefulness, and attitude on behavioral intentions. J Hosp Tour Technol 6(3):242–257. https://doi.org/10.1108/JHTT-01-2015-0002

Ruth J, Willwacher S, Korn O (2022) Acceptance of digital sports: a study showing the rising acceptance of digital health activities due to the SARS-CoV-19 pandemic. Int J Environ Res Public Health 19(1):596. https://doi.org/10.3390/ijerph19010596

Article   CAS   PubMed   PubMed Central   Google Scholar  

Schillewaert N, Ahearne MJ, Frambach RT, Moenaert RK (2005) The adoption of information technology in the sales force. Ind Mark Manag 34(4):323–336. https://doi.org/10.1016/j.indmarman.2004.09.013

Schomakers EM, Lidynia C, Vervier LS, Calero Valdez A, Ziefle M (2022) Applying an extended UTAUT2 model to explain user acceptance of lifestyle and therapy mobile health apps: survey study. JMIR mHealth uHealth 10(1):e27095. https://doi.org/10.2196/27095

Schreurs K, Quan-Haase A, Martin K (2017) Problematizing the digital literacy paradox in the context of older adults’ ICT use: aging, media discourse, and self-determination. Can J Commun 42(2):359–377. https://doi.org/10.22230/cjc.2017v42n2a3130

Schulz KF, Altman DG, Moher D (2010) CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. J Pharmacol Pharmacother 1(2):100–107. https://doi.org/10.4103/0976-500X.72352

Thatcher JB, Perrewé PL (2002) An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Q 26(4):381–396

Thompson WR (2022) Worldwide survey of fitness trends for 2022. ACSM’s Health Fit J 26(1):11–20. https://doi.org/10.1249/FIT.0000000000000732

Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):4. https://doi.org/10.1038/s41591-020-0824-5

Article   CAS   Google Scholar  

Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178. https://doi.org/10.2307/41410412

Venkatesh V, Thong JY, Xu X (2016) Unified theory of acceptance and use of technology: A synthesis and the road ahead. J Assoc Inform Syst 17(5):328–376. https://ssrn.com/abstract=2800121

Vinnikova A, Lu L, Wei J, Fang G, Yan J (2020) The use of smartphone fitness applications: the role of self-efficacy and self-regulation. Int J Environ Res Public Health 17(20):1–16. https://doi.org/10.3390/ijerph17207639 . Scopus

Vogler C, Lyonette C, Wiggins RD (2008) Money, power and spending decisions in intimate relationships. Sociol Rev 56(1):117–143. https://doi.org/10.1111/j.1467-954X.2008.00779.x

Wang C, Wu G, Zhou X, Lv Y (2022) An empirical study of the factors influencing user behavior of fitness software in college students based on UTAUT. Sustainability 14(15):9720. https://doi.org/10.3390/su14159720

Wei J, Vinnikova A, Lu L, Xu J (2021) Understanding and predicting the adoption of fitness mobile apps: evidence from China. Health Commun 36(8):950–961. https://doi.org/10.1080/10410236.2020.1724637

Won D, Chiu W, Byun H (2023) Factors influencing consumer use of a sport-branded app: the technology acceptance model integrating app quality and perceived enjoyment. Asia Pac J Mark Logist 35(5):1112–1133. https://doi.org/10.1108/APJML-09-2021-0709

Yang F, Ren L, Gu C (2022) A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon 8(9):e10562. https://doi.org/10.1016/j.heliyon.2022.e10562

Yang Y, Koenigstorfer J (2021) Determinants of fitness app usage and moderating impacts of education-, motivation-, and gamification-related app features on physical activity intentions: cross-sectional survey study. J Med Internet Res 23(7):e26063. https://doi.org/10.2196/26063

Yeoh R, Kim HK, Kang H, Lin YA, Ho AD, Ho KF (2022) What determines intentions to use mobile fitness apps? The independent and joint influence of social norms. Int J Hum Comput Interact. https://doi.org/10.1080/10447318.2022.2111040

Yin S, Cai X, Wang Z, Zhang Y, Luo S, Ma J (2022) Impact of gamification elements on user satisfaction in health and fitness applications: a comprehensive approach based on the Kano model. Comput Hum Behav 128:107106. https://doi.org/10.1016/j.chb.2021.107106

Yu CY, Jeffe DB, Kenna MA, Germiller JA, Lieu JEC (2021) Validation of a parent proxy Quality-of-Life measure for young children with hearing loss. Laryngoscope 131(3):663–670. https://doi.org/10.1002/lary.28891

Yuan S, Ma W, Kanthawala S, Peng W (2015) Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model. Telemed E-Health 21(9):735–741. https://doi.org/10.1089/tmj.2014.0148

Zhang X, Xu X (2020) Continuous use of fitness apps and shaping factors among college students: a mixed-method investigation. Int J Nur Sci 7:S80–S87. https://doi.org/10.1016/j.ijnss.2020.07.009

Zhu Y, Wang R, Zeng R, Pu C (2023) Does gender really matter? Exploring determinants behind consumers’ intention to use contactless fitness services during the COVID-19 pandemic: a focus on health and fitness apps. Internet Res 33(1):280–307. https://doi.org/10.1108/INTR-07-2021-0454

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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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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.

• Adhikari, R., Richards, D., Scott, K., 2014. Security and privacy issues related to the use of mobile health apps, in: Proceedings of the 25th Australasian Conference on Information Systems. ACIS, Auckland, New Zealand.
• S. Adriaanse L., Rensleigh C. Web of science, scopus and google scholar: a content comprehensiveness comparison. Electron. Library. 2013; 31 (6):727–744. [ Google Scholar ]
• Alvarado R.U. El crecimiento de la literatura sobre la ley de Bradford. Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información. 2016; 30 (68):51–72. [ Google Scholar ]
• Anderson P.M., Butcher K.F., Schanzenbach D.W. Understanding recent trends in childhood obesity in the United States. Econ. Human Biol. 2019; 34 :16–25. [ PubMed ] [ Google Scholar ]
• Bardus M., van Beurden S.B., Smith J.R., Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int. J. Behav. Nutr. Phys. Act. 2016; 13 (1) doi: 10.1186/s12966-016-0359-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bartoli A., Medvet E. Bibliometric evaluation of researchers in the internet age. Inf. Soc. 2014; 30 :349–354. [ Google Scholar ]
• Beldad A.D., Hegner S.M. Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of german users’ willingness to continue using a fitness app: a structural equation modeling approach. Int. J. Human–Computer Interact. 2018; 34 (9):882–893. [ Google Scholar ]
• Blackman K.C.A., Zoellner J., Berrey L.M., Alexander R., Fanning J., Hill J.L., Estabrooks P.A. Assessing the internal and external validity of mobile health physical activity promotion interventions: a systematic literature review using the RE-AIM framework. J. Med. Internet Res. 2013; 15 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bohé I., Willocx M., Naessens V. 2019 IEEE International Congress on Internet of Things (ICIOT). IEEE. 2019. An extensible approach for integrating health and activity wearables in mobile IoT apps; pp. 69–75. [ Google Scholar ]
• Bonato, M., Turrini, F., DE Zan, V., Meloni, A., Plebani, M., Brambilla, E., Giordani, A., Vitobello, C., Caccia, R., Piacentini, M.F., LA Torre, A., Lazzarin, A., Merati, G., Galli, L., Cinque, P., 2019. A Mobile Application for Exercise Intervention in People Living with HIV. Med. Sci. Sports Exerc. https://doi.org/10.1249/MSS.0000000000002125. [ PubMed ]
• Bradford S.C. Sources of information on specific subjects. Engineering. 1934; 137 :85–86. [ Google Scholar ]
• Broadus R. Toward a definition of “bibliometrics.” Scientometrics. 1987; 12 :373–379. [ Google Scholar ]
• Burke L.E., Wang J., Sevick M.A. Self-monitoring in weight loss: a systematic review of the literature. J. Am. Diet. Assoc. 2011; 111 :92–102. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cheng C.H., Chen C.-H. Developing a mobile APP-supported learning system for evaluating health-related physical fitness achievements of students. Mob. Inf. Syst. 2018; 2018 :8960968. doi: 10.1155/2018/8960968. [ CrossRef ] [ Google Scholar ]
• Cotton Victor, Patel M.S. Gamification use and design in popular health and fitness mobile applications. Am. J. Health Promot. 2019; 33 (3):448–451. doi: 10.1177/0890117118790394. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corbin C.B., Pangrazi R.P., Franks B.D. Definitions: health, fitness, and physical activity. Pres. Counc. Phys. Fit. Sports Res. Dig. 2000:1–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan L.T., Van Wagenen S.A., Brown B.A., Hedin R.J., Seino-Stephan Y., Hall P.C., West J.H. Apps of steel: are exercise apps providing consumers with realistic expectations? Heal. Educ. Behav. 2013; 40 :133–139. doi: 10.1177/1090198112452126. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cummiskey M. There's an app for that smartphone use in health and physical education. J. Phys. Educ. Recreat. Danc. 2011; 82 :24–30. [ Google Scholar ]
• de Oliveira, O.J., da Silva, F.F., Juliani, F., Barbosa, L.C.F.M., Nunhes, T.V., 2019. Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects, in: Scientometrics Recent Advances. IntechOpen, pp. 1–20. https://doi.org/http://dx.doi.org/10.5772/intechopen.85856.
• Direito Artur, Dale L.P., Shields E., Dobson R., Whittaker R., Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health. 2014; 14 (1):646. doi: 10.1186/1471-2458-14-646. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duran-Sanchez A., Alvarez-GarcIa J., de la Cruz M., Maldonado-Erazo C.P. Collaborative economy: analysis of scientific production in academic magazines. Revista De Gestao E Secretariado-Gesec. 2016; 7 (3):1–20. [ Google Scholar ]
• Edwards E.A., Lumsden J., Rivas C., Steed L., Edwards L.A., Thiyagarajan A., Sohanpal R., Caton H., Griffiths C.J., Munafò M.R., Taylor S., Walton R.T. Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps. BMJ Open. 2016 doi: 10.1136/bmjopen-2016-012447. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gaviria-Marin M., Merigó J.M., Baier-Fuentes H. Knowledge management: a global examination based on bibliometric analysis. Technol. Forecast. Soc. Change. 2019; 140 :194–220. [ Google Scholar ]
• Hew J.-J. Hall of fame for mobile commerce and its applications: a bibliometric evaluation of a decade and a half (2000–2015) Telemat. Informatics. 2017; 34 :43–66. [ Google Scholar ]
• Honary M., Bell B.T., Clinch S., Wild S.E., McNaney R. Understanding the role of healthy eating and fitness mobile apps in the formation of maladaptive eating and exercise behaviors in young people. JMIR mHealth uHealth. 2019; 7 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kao H.-Y., Wei C.-W., Yu M.-C., Liang T.-Y., Wu W.-H., Wu Y.J. Integrating a mobile health applications for self-management to enhance Telecare system. Telemat. Informatics. 2018; 35 :815–825. [ Google Scholar ]
• Lee A.M., Biang J., Thompson L.A., Gurka M.J., Williamson V.G., Modave F. Efficacy and effectiveness of mobile health technologies for facilitating physical activity in adolescents: scoping review. JMIR mHealth and uHealth. 2019; 7 (2):e11847. doi: 10.2196/11847. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lewis Z.H., Swartz M.C., Martinez E., Lyons E.J. Social support patterns of middle-aged and older adults within a physical activity app: secondary mixed method analysis. JMIR Aging. 2019; 2 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Li Y., Ding J., Wang Y., Tang C., Zhang P. Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality. JMIR mHealth and uHealth. 2019; 7 (7):e13261. doi: 10.2196/13261. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisée C., Larivière V., Archambault É. Conference proceedings as a source of scientific information: a bibliometric analysis. J. Am. Soc. Inf. Sci. Technol. 2008; 59 :1776–1784. [ Google Scholar ]
• Lister C., West J.H., Cannon B., Sax T., Brodegard D. Just a fad? Gamification in health and fitness apps. J. Med. Internet Res. 2014; 16 doi: 10.2196/games.3413. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lotka A.J. The frequency distribution of scientific productivity. J. Washingt. Acad. Sci. 1926; 16 :317–323. [ Google Scholar ]
• Lupton D. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit. Public Health. 2013; 23 :393–403. [ Google Scholar ]
• Mas, A.M., Sampol, P.P., Conti, J.V., 2016. Efectos de un programa de entrenamiento presencial vs prescripción a través de una aplicación móvil en personas mayores. RETOS.Nuevas Tendencias en Educ. Física, Deport. y Recreación 32–37.
• Mata F., Torres-Ruiz M., Zagal R., Guzman G., Moreno-Ibarra M., Quintero R. A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning. Telemat. Informatics. 2018; 35 :837–853. [ Google Scholar ]
• Michels C., Fu J.-Y. Systematic analysis of coverage and usage of conference proceedings in web of science. Scientometrics. 2014; 100 :307–327. [ Google Scholar ]
• Moral-Munoz J.A., Esteban-Moreno B., Herrera-Viedma E., Cobo M.J., Pérez I.J. Smartphone applications to perform body balance assessment: a standardized review. J. Med. Syst. 2018 doi: 10.1007/s10916-018-0970-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pérez-Cruzado D., Cuesta-Vargas A.I. Improving adherence physical activity with a smartphone application based on adults with intellectual disabilities (APPCOID) BMC Public Health. 2013; 13 :1173. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Priesterroth L., Grammes J., Holtz K., Reinwarth A., Kubiak T. Gamification and behavior change techniques in diabetes self-management apps. J. Diabetes Sci. Technol. 2019 doi: 10.1177/1932296818822998. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Price D.J. Columbia University Press; New York: 1963. Little Science, Big Science. [ Google Scholar ]
• Price D.J. Citation measures of hard science, soft science, technology, and nonscience. In: Nelson C.E., Pollak D.K., editors. Communication among Scientists and Engineers. Heath Lexington; MA: 1970. pp. 3–22. [ Google Scholar ]
• Rose J., Jiang Y., Mangematin V. Technological innovation mediated by business model innovation: app developers moving into health. Int. J. Technol. Manage. Inderscience Publishers. 2017:6–27. doi: 10.1504/IJTM.2017.085702. [ CrossRef ] [ Google Scholar ]
• Schoeppe S., Alley S., Rebar A.L., Hayman M., Bray N.A., Lippevelde W. Van, Gnam J.-P., Bachert P., Direito A., Vandelanotte C. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. Int. J. Behav. Nutr. Phys. Act. 2017; 14 :n/a. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tang J., Abraham C., Stamp E., Greaves C. How can weight–loss app designers' best engage and support users? A qualitative investigation. Br. J. Health Psychol. 2015; 20 :151–171. [ PubMed ] [ Google Scholar ]
• Tripicchio G.L., Ammerman A.S., Neshteruk C., Faith M.S., Dean K., Befort C., Ward D.S., Truesdale K.P., Burger K.S., Davis A. Technology components as adjuncts to family-based pediatric obesity treatment in low-income minority youth. Child. Obes. 2017; 13 :433–442. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Van Eck N., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84 :523–538. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Veloutsou C., Mafe C.R. Brands as relationship builders in the virtual world: a bibliometric analysis. Electron. Commer. Res. Appl. 2020; 39 [ Google Scholar ]
• West J.H., Hall P.C., Hanson C.L., Barnes M.D., Giraud-Carrier C., Barrett J. There’s an app for that: content analysis of paid health and fitness apps. J. Med. Internet Res. 2012; 14 doi: 10.2196/jmir.1977. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• WHO Non-communicable diseases [WWW Document] 2018. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases accessed 7.2.20.
• Zanjirchi S.M., Abrishami M.R., Jalilian N. Four decades of fuzzy sets theory in operations management: application of life-cycle, bibliometrics and content analysis. Scientometrics. 2019; 119 :1289–1309. [ Google Scholar ]
• Zhou M., Fukuoka Y., Mintz Y., Goldberg K., Kaminsky P., Flowers E., Aswani A. Evaluating machine learning–based automated personalized daily step goals delivered through a mobile phone app: randomized controlled trial. JMIR mHealth uHealth. 2018; 6 [ PMC free article ] [ PubMed ] [ Google Scholar ]