fitness app research paper

A Systematic Review of Fitness Apps and Their Potential Clinical and Sports Utility for Objective and Remote Assessment of Cardiorespiratory Fitness

Affiliations.

• 1 GICAFE "Physical Activity and Exercise Sciences Research Group", University of Balearic Islands, Balearic Islands, Spain. [email protected].
• 2 PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain. [email protected].
• 3 Chronobiology Research Group, Department of Physiology, Faculty of Biology, University of Murcia, Campus Mare Nostrum, IUIE, IMIB-Arrixaca, Murcia, Spain.
• 4 Ciber Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.
• 5 Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine in New Orleans, New Orleans, LA, USA.
• 6 Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
• 7 School of Kinesiology and Health Studies, Queen's University, Kingston, ON, Canada.
• 8 Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, USA.
• 9 PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain.
• 10 Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden.
• PMID: 30825094
• PMCID: PMC6422959
• DOI: 10.1007/s40279-019-01084-y

Background: Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility. Technological advancements have led to increased digitization within healthcare and athletics. Nevertheless, further investigation is needed to enhance the validity and reliability of existing fitness apps for CRF assessment in both contexts.

Objectives: The present review aimed to (1) systematically review the scientific literature, examining the validity and reliability of apps designed for CRF assessment; and (2) systematically review and qualitatively score available fitness apps in the two main app markets. Lastly, this systematic review outlines evidence-based practical recommendations for developing future apps that measure CRF.

Data sources: The following sources were searched for relevant studies: PubMed, Web of Science ® , ScopusTM, and SPORTDiscus, and data was also found within app markets (Google Play and the App Store).

Study eligibility criteria: Eligible scientific studies examined the validity and/or reliability of apps for assessing CRF through a field-based fitness test. Criteria for the app markets involved apps that estimated CRF.

Study appraisal and synthesis methods: The scientific literature search included four major electronic databases and the timeframe was set between 01 January 2000 and 31 October 2018. A total of 2796 articles were identified using a set of fitness-related terms, of which five articles were finally selected and included in this review. The app market search was undertaken by introducing keywords into the search engine of each app market without specified search categories. A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis.

Results: Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps. Out of the scientific apps reviewed, the SA-6MWTapp showed the best validity against a criterion measure (r = 0.88), whilst the InterWalk app showed the highest test-retest reliability (ICC range 0.85-0.86).

Limitations: Levels of evidence based on scientific validity/reliability of apps and on commercial apps could not be robustly determined due to the limited number of studies identified in the literature and the low-to-moderate quality of commercial apps.

Conclusions: The results from this scientific review showed that few apps have been empirically tested, and among those that have, not all were valid or reliable. In addition, commercial apps were of low-to-moderate quality, suggesting that their potential for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations that apps might potentially offer to objectively and remotely assess CRF as a complementary tool to traditional methods in the clinical and sports settings.

Publication types

• Systematic Review
• Cardiorespiratory Fitness*
• Cardiovascular Diseases / diagnosis
• Exercise Test
• Mobile Applications / standards*
• Monitoring, Physiologic / instrumentation
• Reproducibility of Results
• Risk Factors

Grants and funding

• MINECO/FEDER DEP2016-79512-R/Spanish Ministry of Economy and Competitiveness
• 667302/European Union?s Horizon 2020 research and innovation programme
• DEP2005-00046/ACTI/EXERNET Research Network on Exercise and Health in Special Populations
• PN I+D+I 2017-2021/SAMID III network, RETICS,
• RD16/002/ISCIII- Sub-Directorate General for Research Assessment and Promotion, the European Regional Development Fund
• CB16/10/00239/Ministry of Economy and Competitiveness and the Instituto de Salud Carlos III
• 19899/GERM/15/Seneca Foundation

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Open Access

Peer-reviewed

Research Article

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

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

* E-mail: [email protected]

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

Roles Investigation, Methodology, Writing – review & editing

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

Roles Writing – review & editing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

Author summary

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

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

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

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

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

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

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

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

Introduction

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

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

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

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

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

The secondary aims of the study were to examine:

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

Study design

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

Settings and participants

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

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

Survey development and measures

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

Demographic characteristics.

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

Health behaviors.

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

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

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

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

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

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

COVID-19 related apps

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

Data analysis

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

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

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

Sample description

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

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

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

Mobile apps..

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

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

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

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

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

Fitness trackers.

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

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

The association between technology usage and healthy behaviors.

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

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

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

COVID-19 related apps.

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

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

Qualitative results.

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

Maintaining a sense of normalcy and social connections.

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

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

Technologies as a double-edged sword.

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

Desired features of technology.

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

Principal results

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

Impact of mobile devices on health behaviors

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

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

Differences in app usage between genders

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

Adoption and usage of COVID-19 related apps

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

Strengths and limitations

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

Implications

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

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

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

Supporting information

S1 appendix. strobe checklist..

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

S2 Appendix. Survey.

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

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

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

S4 Appendix. Subgroup analyses.

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

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

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

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

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

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A Systematic Review of Fitness Apps and Their Potential Clinical and Sports Utility for Objective and Remote Assessment of Cardiorespiratory Fitness

• Systematic Review
• Open access
• Published: 01 March 2019
• Volume 49 , pages 587–600, ( 2019 )

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• Adrià Muntaner-Mas 1 , 2 ,
• Antonio Martinez-Nicolas 3 , 4 ,
• Carl J. Lavie 5 ,
• Steven N. Blair 6 ,
• Robert Ross 7 ,
• Ross Arena 8 &
• Francisco B. Ortega 2 , 9  

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Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility. Technological advancements have led to increased digitization within healthcare and athletics. Nevertheless, further investigation is needed to enhance the validity and reliability of existing fitness apps for CRF assessment in both contexts.

The present review aimed to (1) systematically review the scientific literature, examining the validity and reliability of apps designed for CRF assessment; and (2) systematically review and qualitatively score available fitness apps in the two main app markets. Lastly, this systematic review outlines evidence-based practical recommendations for developing future apps that measure CRF.

Data Sources

The following sources were searched for relevant studies: PubMed, Web of Science ® , ScopusTM, and SPORTDiscus, and data was also found within app markets (Google Play and the App Store).

Study Eligibility Criteria

Eligible scientific studies examined the validity and/or reliability of apps for assessing CRF through a field-based fitness test. Criteria for the app markets involved apps that estimated CRF.

Study Appraisal and Synthesis Methods

The scientific literature search included four major electronic databases and the timeframe was set between 01 January 2000 and 31 October 2018. A total of 2796 articles were identified using a set of fitness-related terms, of which five articles were finally selected and included in this review. The app market search was undertaken by introducing keywords into the search engine of each app market without specified search categories. A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis.

Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps. Out of the scientific apps reviewed, the SA-6MWTapp showed the best validity against a criterion measure ( r  = 0.88), whilst the InterWalk app showed the highest test–retest reliability (ICC range 0.85–0.86).

Limitations

Levels of evidence based on scientific validity/reliability of apps and on commercial apps could not be robustly determined due to the limited number of studies identified in the literature and the low-to-moderate quality of commercial apps.

Conclusions

The results from this scientific review showed that few apps have been empirically tested, and among those that have, not all were valid or reliable. In addition, commercial apps were of low-to-moderate quality, suggesting that their potential for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations that apps might potentially offer to objectively and remotely assess CRF as a complementary tool to traditional methods in the clinical and sports settings.

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1 Introduction

Cardiorespiratory fitness (CRF) is a powerful marker of cardiovascular (CV) health [ 1 , 2 , 3 , 4 , 5 , 6 ]. Despite the strong existing evidence linking CRF to CV health, the recent eHealth tools developed to assess CV disease (CVD) risk on the basis of multiple risk factors does not include CRF as a measure [ 7 ]. Further, maximal oxygen uptake ( V O 2max ) is an objective measure of CRF and has been considered a key indicator of sports performance [ 8 , 9 , 10 ]. In fact, V O 2max assessment has been historically recommended in both the clinical and sports settings by the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) [ 11 , 12 ]. Since an incremental maximal or submaximal exercise test is not always possible in clinical or field settings, in part due to feasibility concerns with its routine measurement (e.g., time needed, expensive equipment, expertise required, etc.), estimations of CRF using non-exercise algorithms have a pragmatic importance that may enhance CVD risk and sports performance prediction [ 13 , 14 , 15 , 16 ]. However, the rapid development in smartphone technology might provide a novel alternative to non-exercise algorithms to estimate CRF (i.e., V O 2max ) in the present and future. Such an approach could be useful and meaningful from a clinical point of view as well as from a sports and training landscape.

1.1 Usefulness of Smartphone Apps in Clinical and Sports Context

Technological advancements have led to increased digitization within healthcare and sports [ 17 , 18 ]. The emergence of available smartphone applications (apps) in Google Play and the App Store (iTunes) in September 2008 and June 2009, respectively, have contributed to a better understanding of human health by allowing us to gather vast amounts of medical and fitness data [ 19 ]. Specifically, some improvements in app technology (e.g., a built-in camera for heart rate assessment, accelerometers, etc.) have opened new opportunities for collecting relevant information in the clinical and sports settings.

In fact, successful examples of clinicians and scientists using apps that allow for a flood of new information for better management of a patients’ CV health are already available [ 20 ]. More specifically, the usefulness of apps in clinical practice is supported by current reviews of CV mHealth (healthcare practice supported by mobile devices), which have outlined the potential of these apps to improve access to a large number of people living far from clinical centers, reduce costs, and enhance health outcomes for CVD management [ 21 , 22 , 23 ]. Within this context, the use of apps for telemedicine purposes has demonstrated their potential and effectiveness for remote monitoring of clinical parameters, such as CVD risk factors [ 24 ]. An example of this practice is the AliveCor Kardia device, a clinically validated smartphone-based electrocardiogram recording [ 25 ]. A recent randomized controlled trial has examined the assessment of remote heart rhythm in 1001 ambulatory patients ≥ 65 years of age at increased risk of stroke who were using this device. The results highlight that this approach was significantly more likely to identify incident atrial fibrillation than routine care over a 12-month period [ 26 ]. If these innovative clinical practices are viable with other vital signs, it can be speculated that apps may hold utility in detecting patients with low CRF levels, and in turn allow for a more accurate determination of CVD risk [ 27 ].

The use of apps to collect data has also drawn widespread attention among sports professionals and exercise scientists. In fact, some apps have already been developed to collect physiological, kinanthropometric, and sports performance data [ 28 ]. The use of apps for data collection is likely the most popular in recreational sporting activities, although they are also utilized in a higher performance sporting context [ 29 ]. In high-performance sports, the expertise required to quantify an athlete’s physical performance with traditional methods is often expensive and non-user-friendly, especially for trainers [ 28 ]. However, apps hold great potential by making physical performance measurements for coaches and trainers more affordable in field conditions. A popular recreational example is the various apps designed for tracking distance or pace during endurance sports [ 30 , 31 , 32 ]. A real-world app is Strava , commonly used for individuals practicing recreational endurance activities [ 32 ]. Among the most attractive Strava features is its ability to track all aspects of logged physical activities (e.g., distance, pace, watts, heart rate) and its capability to analyze them on a per-minute basis. Likewise, in a competitive sporting context, there are already validated apps aimed at coaches for assessing sports performance data such as sprint mechanical outputs [ 33 ] and running technique [ 34 ].

1.2 Usefulness of Fitness Apps for Assessing CRF in Clinical and Sports Context

Fitness apps might provide a valuable opportunity for assessing CRF, bringing the laboratory into the pocket, and making fitness assessments feasible as part of routine clinical care [ 35 ]. Furthermore, using apps for CRF self-assessment as part of the clinical workflow might provide clinicians with health information difficult to collect during brief patient visits, furthermore allowing integration of this data into the electronic health record and aid in ongoing care [ 35 , 36 ]. Despite the plethora of existing traditional approaches to CRF assessment, some barriers hamper its use in clinical practice. For instance, the correct selection of a CRF protocol according to a person’s individualized exercise or functional capacity can be challenging at times [ 37 ]. In addition, making this selection often requires professionals with advanced training in CRF measurement not always available in the clinical setting. Another hurdle to performing clinical CRF assessments is the use of specialized equipment (e.g., ECG, pulse oximetry, accelerometers, etc.) that may not be available. In this context, an app-based approach could overcome these challenges associated with traditional approaches, allowing for broader application of CRF assessments. For instance, a valid and reliable fitness app might assist health professionals in the selection of the optimal CRF assessment protocol and integrate the measurement of physiological signals to more objectively assess CRF. Also, these apps could be useful for screening programs identifying individuals with higher versus lower CVD risk based on the app-assessed CRF level. As a current clinical example, the MyHeart Counts app has demonstrated real-world feasibility in assessing CRF on a large scale, incorporating this assessment into the broader evaluation of CV health [ 36 , 38 ].

The assessment of CRF for sports and training purposes is also an important function of health fitness professionals [ 11 ]. In fact, the use of apps for assessing other physical fitness components such as muscular fitness is a current practice in the sports field. As examples, the My Jump and PowerLift apps have shown scientific validity and reliability for measuring distinct aspects of muscular performance [ 39 , 40 ]. Further, both apps are being used by many sports professionals in field settings. However, health fitness professionals are demanding valid tools for the remote and objective assessment of CRF. In this context, the usefulness of apps for CRF assessment might provide coaches with additional data difficult to obtain in field settings with traditional approaches. Specifically, an app-based approach for determining CRF in sports could add value to exercise prescription and monitoring training. For instance, with exercise prescription, these apps could be used before a training session to adjust intensities for training in the appropriate intensity zone and obtain the best physiological adaptations for athletes, reducing the risk of overtraining. In this context, an example is the HRV4Training app that provides analyses on the relationship between physiological parameters (taking measures of heart rate and heart rate variability), training and performance. In brief, the HRV4Training app estimates acute heart rate variability changes in response to acute stressors that affect an athlete’s acute physiology. This data can be used for determining athlete fatigue and thus modifying the training program of the athlete from day to day and in real time. Likewise, apps assessing CRF might hold value in monitoring cardiovascular performance changes throughout a conditioning program and tracking injury risk factors affecting cardiovascular functioning. Also, CRF measurements with fitness apps could advance the research field, making CRF assessment more affordable and potentially self-administered by the athletes.

1.3 Purpose

The purpose of this review article is to facilitate a scientific discussion about the new opportunities that advances in apps offer, such as the ability to objectively and remotely assess CRF as a complementary tool to traditional methods for estimating CVD risk, as well as to assess CRF for improving performance in sports and training. To address the purpose of this review and provide evidence-based practical recommendations for researchers, clinicians, and sports professionals, the following original two-pronged approach has been employed: (1) a systematic review of the available scientific literature, examining the validity and reliability of apps designed for CRF assessment, and (2) a systematic analysis of apps estimating CRF and stored within the two major app markets (Google Play and App Store).

The search strategy, criteria, and related terms used in both the scientific literature and the app market search are presented in Supplemental Tables 1–5 in the electronic supplementary material (ESM).

2.1 Scientific Literature Search

2.1.1 literature search strategy and study selection process.

The literature search was carried out according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [ 41 ]. The search included four major electronic databases (i.e., PubMed, Web of Science ® , Scopus™, and SPORTDiscus) and the timeframe was set between 01 January 2000 and 31 October 2018. Even though the two main markets, Google Play, and the App Store, were launched between 2007 and 2008, native apps (apps developed for use on a specific device) began to appear commercially around 2000; therefore, the timeframe was set based on the emergence of the first native apps. For searching in PubMed, we used Medical Subject Heading (MeSH) terms and a combination of relevant keywords in the field (see Supplemental Table 1 in the ESM). The same search strategy and the combination of terms were repeated in Web of Science ® , Scopus™, and SPORTDiscus, but without using MeSH terms (see Supplemental Tables 2–4 in the ESM). The reference lists of included articles were also searched for additional studies. Included were studies that examined the validity and/or reliability of apps for assessing CRF using a field-based fitness test. Studies were excluded according to the following criteria: (1) studies written in languages other than English and Spanish, and (2) studies from which we could not access the full text. The selection procedure of the 2796 articles initially identified was undertaken following a two-step approach: (1) screening based on the title and abstract; and (2) search of the full text of the articles selected in the previous step. The first two authors (AM-M and AM-N) independently performed the study selection process and disagreements were resolved in a consensus meeting. The selection process for scientific studies is illustrated in Fig.  1 a.

Flow chart: an overview of the review process for the scientific literature search and the app market search

2.1.2 Data Collection Process

For selected articles, data extraction was undertaken by the first author (AM-M) and the second author (AM-N) confirmed accuracy. A standardized data extraction form was utilized and is presented in Table  1 . Data extracted included details on source (authors, year, name of the fitness app, and market platform), information about the participants (age, sample size, and gender), fitness test examined, criterion measure used as gold standard, main outcomes studied, statistical methods, and the validity and/or reliability of results.

2.1.3 Assessment of Methodological Quality

The risk of bias within scientific studies finally included was assessed by using some elements of the Cochrane Collaboration’s tool for assessing the risk of bias [ 42 ]. Specifically, the domains analyzed in the present systematic review were detection bias, attrition bias, reporting bias, and other bias.

2.2 App Markets Search

2.2.1 apps search strategy and selection process.

The app market searches were conducted in the Spanish Google Play and App Store. However, we also performed manual searches for other relevant apps in the United States app market. Apps from Google Play and App Store were screened in October 2018. The apps were identified by introducing keywords into the search engine of each app market without specified search categories (see Supplemental Table 5 in the ESM). The inclusion criteria were fitness apps that assess CRF with physiological signals, integrated algorithms, and/or fitness apps serving as a simple CRF calculator. However, those apps not in English or Spanish, not readily accessible or unable to be downloaded were excluded. The selection procedure of the apps on both app markets was carried out by the first author (AM-M) using the following two-step method: (1) screening apps based on description and screenshots; and (2) 1 week after the first search, a second screening was performed following the identical selection process. Apps identified in the two steps were selected for download and content assessment. The selection process for apps is illustrated in Fig.  1 b.

2.2.2 Data Collection Process and Qualitative Assessment

Selected apps were downloaded to a smartphone (Apple or Android software) by AM-M and AM-N. In those cases in which an app had a free and a paid (premium/more advanced) version, both apps were downloaded and assessed. Further, the data extraction was undertaken using the following two-step method: (1) AM-M and AM-N independently assessed five apps using two qualitative instruments (explained below); this first step was to ensure that both reviewers used the same criteria to fill both qualitative instruments; and (2) AM-M extracted and scored information of all apps stored on App Store and AM-N did the same with apps stored in Google Play.

Two qualitative assessments were carried out with apps ultimately included in the systematic review. First, the Mobile App Rating Scale (MARS) was used to rate app quality [ 43 ]. The selected apps were tested for at least 10 min and rated with this scale. In brief, the MARS has 29 items measured on a 5-point scale grouped into six domains (engagement, functionality, aesthetics, information, subjective and perceived impact). An overall score was computed as a MARS mean considering four domains (engagement, functionality, aesthetics, information). Second, a standardized instrument was developed specifically for this review to evaluate some features of the apps included (score, ratings, downloads, price, fitness test, test instructions, heart rate measurement, GPS, maximal or peak oxygen consumption ( V O 2 ) estimation, external equipment needed, historic measurement, export results, prompt self-monitoring of behavioural outcome, social network, reference values, scientific validation, multiple user, language, and last update date). Twelve of these items were rated as ‘yes’ or ‘no’, for instance, if an app included CRF reference values, the app was rated as ‘yes’ for this item. The sum of total ‘yes’ responses per app was computed as an average of the quality of the apps. Pearson correlations were used to examine the relationships between the cost of apps, their features, and MARS mean score. All statistical analyses were conducted using IBM SPSS Statistics version 22.4 (Armonk, NY, USA), with significance levels set at p  < 0.05.

3.1 Scientific Literature Results

3.1.1 studies’ characteristics.

Figure  1 illustrates the flow chart of the scientific review (according to PRISMA), as well as the flow chart of the review of the current app markets. The scientific studies selected are summarized in Table  1 . The results of the scientific literature’s search revealed that there were five studies [ 44 , 45 , 46 , 47 , 48 ] published in peer-reviewed journals, of which only three apps were available to be downloaded on commercial platforms ( HRV4Training , InterWalk app, and TOHRC Walk Test ). HRV4Training [ 44 ] was the only app stored in both app markets (Google Play and App Store). All the included apps were available in English, except for the InterWalk app, which was only available in Danish [ 46 ]. Four studies [ 45 , 46 , 47 , 48 ] included < 20 participants, and one study [ 44 ] included 48 individuals. Three studies included healthy adults [ 44 , 47 , 48 ], whereas Brinkløv et al. [ 46 ] included participants with type 2 diabetes mellitus and Brooks et al. [ 45 ] included those with congestive heart failure and pulmonary hypertension. The 6-minute walk test was used in two studies [ 45 , 47 ]. Two studies [ 45 , 46 ] were adjudicated to be of low risk of bias and three [ 44 , 47 , 48 ] were considered to have a high risk of bias. The criteria for high risk of bias were (1) the study failed to include the complete methodology to assess validate/reliability of the fitness test with the app; and (2) the study did not entirely report the results or analysis methods of the outcomes studied.

3.1.2 Fitness Test Assessment Methods

Altini et al. [ 44 ] used information from three sets of predictors (models hereafter) for quantifying CRF: (1) anthropometric data (body mass index, age, and gender) taken from the HRV4Training app; (2) physiological data (morning heart rate and heart rate variability) acquired with the HRV4Training app at rest conditions plus model 1; and (3) training data measured as the ratio between running speed (retrieved from the Strava app and linked to HRV4Training ) and morning heart rate (retrieved from HRV4Training ) plus model 1. The criterion CRF was determined as V O 2max , by means of cardiopulmonary exercise testing (CPX) (incremental protocol on a cycle ergometer). Root mean square error (validity results) was 4.2 ± 3.0 mLO 2 ·kg −1 ·min −1 for model 1, 4.1 ± 3.1 mLO 2 ·kg −1 ·min −1 for model 2 and 3.5 ± 2.8 mLO 2 ·kg −1 ·min −1 for model 3. Participant-independent root mean square error decreased by 15% and 18% when model 3 was compared with model 1 and 2, respectively.

Brinkløv et al. [ 46 ] developed the InterWalk app, integrating the InterWalk Fitness Test. The on-board accelerometer of the smartphone was used as a predictor of peak V O 2 during the test. Specifically, the vector magnitude during the last 30 seconds of the test, body weight, height, and gender were used to create a linear regression equation to predict peak V O 2 . The criterion CRF was determined as peak V O 2 assessed by CPX (graded walking test protocol on a treadmill). The overall peak VO 2 prediction of the algorithm ( R 2 ) was 0.60 and 0.45 when the smartphone was placed in the right pocket of the pants (lower position) or jacket (upper position), respectively ( p  < 0.001). No differences were found in peak V O 2 when the test was performed with or without verbal encouragement ( p  = 0.70). The reliability (intraclass correlation coefficient, ICC [95% CI]) was 0.86 [0.64–0.96] of the predicted peak VO 2 for the lower position of the smartphone and 0.85 [0.60–0.96] for the upper position.

Brooks et al. [ 45 ] developed the SA-6MWTapp integrating the 6-minute walk fitness test (6MWT). They developed a distance estimation algorithm for the SA-6MWTapp , considering step counts from an ActiGraph GT3X and measured distance on a pre-measured 6MWT course. The best-fit algorithm was incorporated into the SA-6MWTapp . In addition, self-reported information from the app (age, birth date, height, and weight) and heart rate immediately at the end of the 6MWT was collected. The heart rate was taken using photoplethysmography from the user’s finger placed over the phone’s camera at the end of the test. The validation protocol was undertaken with one smartphone placed in a hip holster and the other smartphone placed in the front pants pocket. The correlation between SA-6MWTapp estimated distance and in-clinic measured distance along a pre-measured course was 0.88 (95% CI 0.87–0.86) and the mean difference ± SD was 7.6 ± 26 m ( p  = 0.30). The smartphone position did not influence the estimation of measured distance ( p  = 0.70). The coefficient of variation from distances estimated from the SA-6MWTapp was 3.2 ± 1 m (home validation phase) and highly correlated with in-clinic measured distance ( r  = 0.88 [95% CI 0.87–0.89]).

Capela et al. [ 47 , 48 ] developed the 2–6MWT app and the TOHRC Walk Test app, respectively. The 2-minute walk fitness test and 6MWT, respectively, were integrated into a Blackberry Z10. They used the accelerometer, gyroscope, and magnetometer of a Blackberry Z10 at approximately 50 Hz and developed an algorithm capable of estimating total distance walked, total number of steps, number of steps per length, cadence, step time (left and right steps), stride time, and step time symmetry (left and right steps). The smartphone was placed around the person’s waist using a belt that included a rear pocket (to fit the smartphone) at the center of the lower back. A digital video recorded from a separate BlackBerry 9900 smartphone was used as a gold standard. Capela et al. [ 48 ] found that the foot strike time measured with the 2–6MWT app was within 0.07 s when compared with gold standard video recordings. Furthermore, the total distance calculated by the 2–6MWT app was within 1 m of the measured distance. Capela et al. [ 47 ] showed that the average difference between the TOHRC Walk Test app and gold standard foot strike timing was 0.014 ± 0.015 s. Also, the total distance calculated by the TOHRC Walk Test app was within 1 m of the measured distance for all but one participant.

3.2 App Markets Results

The app markets search led to a total of 88 apps meeting our inclusion criteria, of which 42 were stored in Google Play and 46 in App Store, with only four apps simultaneously stored on both platforms. The cost of the apps ranged from €0 to €10.99 (mean 1.24, SD 2.15) with more than half offered for free ( n  = 53, 60.22%). Google Play ( n  = 31, 73.80%) market stored more free apps than App Store ( n  = 22, 47.08%). Supplemental Tables 6–7 (in the ESM) show MARS mean and domain scores of the apps rated. Apps were sorted from highest to lowest according to the MARS mean scores (see Supplemental Tables 6–7 in the ESM). The average total MARS score was 2.97 (SD 0.73) out of 5 and 46.59% ( n  = 41) had a minimum acceptability score of 3.00. Regarding the MARS domains, functionality was the highest scoring (mean 3.85, SD 0.76), followed by aesthetics (mean 2.79, SD 1.14), information (mean 2.72, SD 0.95), engagement (mean 2.54, SD 0.86), subjective (mean 1.90, SD 1.07) and perceived impact (mean 1.70, SD 1.01). The top five ranked apps in App Store were HRV4Training , MyHeart Counts , Fitness Test pro , AeroExaminer—Aerobic VO 2 Max Test and Conditioning and CardioCoach , respectively. The top five ranked apps in Google Play were HRV4Training , Fitness Test pro , iWalkAssess , Bruce Treadmill Test Lite , and Bruce Treadmill Test Protocol , respectively.

Supplemental Tables 8–9 (in the ESM) provide the reader with a complete set of apps currently available, including a direct link (by clicking on the app’s name) to each specific app, as well as a qualitative evaluation of each of the apps. The 20-m shuttle run test was the most prevalent field-based fitness test used within Google Play and App Store apps ( n  = 31, 27.28%). Only one app ( HRV4Training ) [ 44 ] included a measure of a physiological signal and four apps calculated the distance by GPS. Sixty-one apps (69.31%) provided V O 2max estimation without considering any physiological signal, 31 (35.22%) included reference values for maximal/peak V O 2 interpretation, 47 (53.40%) allowed assessments to be saved and 30 (34.09%) had the chance to add multiple users. Five apps (5.68%) required external equipment to estimate CRF, 28 (31.81%) enabled the user to export data, 31 (35.22%) enabled the user to share results in major social networks, and 49 apps of 88 (55.68%) provided test instructions to participants.

Figure  2 shows a comparison between apps stored in Google Play and App Store regarding the quality scoring with MARS (A) and the apps’ features (B). MARS mean was slightly higher for App Store apps in comparison with Google Play (3.17 vs. 2.77). Likewise, all the MARS domains obtained higher scores for App Store apps. Regarding the apps’ features, the App Store repository stored more apps than Google Play in all items except for the V O 2 estimation item. A positive association was observed between the cost of apps and the total MARS mean score ( r  = 0.46; p  < 0.001). The total number of features was positively associated with the total MARS mean score ( r  = 0.55; p  < 0.001).

Quality scoring ( MARS , mobile app rating scale) of the apps ( a ) and apps’ futures ( b ). In a , the numbers 0–5 signify the score obtained in each MARS item, whereas in b , the numbers 0–50 refer to the total number of apps that contain such features

4 Discussion

The purpose of this report was three-fold: first, to systematically review the validity and reliability of CRF apps assessment available in the scientific literature and app markets; second, to provide evidence-based practical recommendations; and third, to stimulate a scientific discussion on how the information retrieved from these apps might have clinical relevance for the assessment of CV health, as well as for sports performance and training.

One of our major findings is that, despite having identified 88 fitness apps, only five have been tested scientifically; all five for validity [ 44 , 45 , 46 , 47 , 48 ] and only two for reliability [ 45 , 46 ]. Three of the five apps were scored as having a moderate to good validity [ 44 , 45 , 46 ]. Nevertheless, there were some limitations, for example, (1) none of the five apps were stored on both app markets for free download and two were not for public use; (2) the sample size of the validation studies was small, and (3) four studies [ 45 , 46 , 47 , 48 ] used algorithms designed from data collected by the smartphone’s accelerometer, which could impact applicability to other smartphones since the algorithms may not provide valid data when used with other smartphone models.

Despite these limitations, indicating there is not the ability to reach a consensus on a preferred fitness app, some may be useful, particularly compared with performing no CRF assessment. Among them, the MyHeart Count app currently possesses the greatest clinical utility among the commercial apps reviewed. The MyHeart Count estimates CRF by means of the 6MWT, with more than 400 customer ratings and a current score of 4.5 out of 5 (see Supplemental Tables 8–9 in the ESM). In addition, its feasibility has been published [ 26 ] and the app contains many of the conditions described in Fig.  3 . Notwithstanding, the main limitation is that its validity and reliability have not been tested to date; therefore, caution should be taken with its use. Furthermore, MyHeart Counts is currently available in the United States alone, and only to iPhone users (version 5S and later). Regarding the sports field, for both recreational and high-performance sports purposes, the HRV4training app currently has proven to be the most useful tool. However, coaches and athletes should exercise caution when using this app. Although its validity for determining CRF is good, no reliability data is currently available. Moreover, CRF estimation is exclusively available for runners and cyclists linking the HV4training app to the Strava app and using a heart rate monitor and a power meter during their workouts. Also, the HV4training app is the most expensive within the two main markets analyzed in this review.

Apps identified in the scientific literature search and in the app markets review, future research directions, and key factors to be considered when selecting or developing an app for assessing cardiorespiratory fitness. CRF cardiorespiratory fitness, CVD cardiovascular disease, MARS Mobile App Rating Scale

4.1 Evidence-Based Practical Recommendations

In this review, we sought to contribute to this developing field of research by identifying limitations of current apps and outlining the desirable characteristics that are generalizable to multiple populations with differing needs. Accordingly, we discuss some fundamental points that should be considered when selecting an app among the many options available or when developing a new app. Future research directions based on the knowledge gaps identified herein are also considered.

Most of the apps assessed were based on the maximal CRF test, which, for patient populations, is a clinical standard requiring professionals with specialized training. To broaden applicability, validation of apps using sub-maximal tests to estimate CRF (e.g., 6-minute walk test, 1-mile walk test, etc.) enhance the ability to remotely assess CRF in safer and more feasible conditions. In this regard, a recent major study demonstrated the impact of changes in submaximal CRF on health outcomes [ 49 ]. Additionally, we have found that most apps served as a simple CRF calculator while only one included a physiological signal (e.g., heart rate and heart rate variability) to estimate CRF. The integration of physiological signals into apps might provide more accurate data to better estimate an individual’s CVD risk and/or sports performance. Therefore, technology advancements that incorporate relevant physiological signals into apps is another venue for future research in this area. According to our review, the modes of exercise used for CRF testing include running, walking, stepping, and cycling, with running tests as the most prevalent mode. However, care should be taken when this testing modality is used in individuals with physical limitations and when testing takes place remotely.

Another important aspect to be considered when choosing an app is where the fitness testing will take place. In this context, apps including step tests are a feasible solution when testing is performed in a room with limited resources. Otherwise, the external equipment needed, such as a treadmill or stationary bicycle, is another key factor when selecting an app. Those apps that require additional resources and equipment will make large-scale assessments more challenging and less feasible. Therefore, whenever possible, CRF tests without the need for additional equipment are preferable. It is important to note that submaximal exercise tests with fewer resource and equipment requirements can provide valuable information although they are not as precise as maximal exercise testing [ 37 ].

The cost and language of apps are also important factors, making apps universally accessible to individuals across broad socioeconomic strata. Most apps examined are available in English and half of them are offered at no cost. Another major problem identified in this systematic review was that very few apps (i.e., only four out of 88 [4.54%]) were simultaneously available on both the Google Play and App Store platforms. The low number of apps that are on both platforms can be attributed to different reasons. For instance, apps in the App Store must meet a quality guideline review prior to publication, demanding higher app quality than Google Play; however, the publishing cost is higher than with Google Play. This fact is likely the cause of the higher cost of apps in the App Store. Thus, app developers may choose to publish apps in one or another market depending on these factors. This is an important limitation since, ideally, a researcher, clinician or sports professional would like to use the same app regardless of which type of smartphone the individual may have. Ideally, future efforts in the field should be focused on published apps in the most spoken languages worldwide, that are low-cost, and stored in both app markets for optimal clinical and sports application.

An additional drawback of the scientific and commercial apps reviewed herein is the lack of data on interoperability. Despite the information collected from apps, opportunities for connected healthcare with respect to CRF assessment remains suboptimal; the transmission of patient-generated data, stored in Android and Apple devices, to the patients’ electronic health records has been previously documented [ 50 , 51 ] and should be considered for CRF assessment. In order to achieve app interoperability, a plan is needed to support developments in privacy and data security, as well as interoperability across smartphone devices and app markets, extending data from devices to electronic health records [ 52 ]. As mHealth matures, health information technology interoperability will bring a real integration of patient-generated CRF data into electronic health records, making data device-independent.

In addition, most of the apps already use existing and scientifically validated CRF field-based fitness tests (e.g., 20-m shuttle run test, 6-minute walk test, Cooper Test) transformed into an app format. However, the ability of the resulting apps to assess CRF against a criterion method has rarely been tested. Thus, it is highly recommended, whenever possible, to select scientifically validated apps, and for researchers to test the validity and reliability of existing and newly developed apps. Likewise, other functionalities such as the inclusion of test instructions, the capability to store repeated measurements to later perform longitudinal comparisons, the possibility to export data entered and the main results of the test, the integration of multiple users, and the ability to generate feedback based on results are important factors that should be kept in mind when selecting an app or developing a new one.

4.2 Future Potential for Fitness Apps in a Clinical and Sports Context

Figure  3 presents the main characteristics that would emulate a high-quality fitness app for clinical and sports fields. This information will assist researchers to work together with app developers to design better apps in the future.

In this sense, future potential of the use of apps for CRF testing in a clinical context might encourage patients to seek knowledge about their CRF level, which would, in turn, be translated into the management risk of CVDs [ 53 ]. In addition, high-quality fitness apps would be relatively inexpensive whereas the assessment of well established risk factors for developing a CVD (e.g., cholesterol and blood pressure) requires equipment with a high economic cost. Furthermore, monitoring is usually reserved for individuals with increased risk for or established CVD. Thanks to the universality of apps, people of all ages and socioeconomic status might be encouraged to self-assess their CRF to estimate the lifetime risk of CVD. Low CRF is independent of other CVD risk conditions traditionally controlled in the clinics (e.g., obesity, hypertension, type 2 diabetes, dyslipidemia), therefore the integration of CRF assessments through apps would enhance the traditional method for estimating CVD risk into clinical workflows. Patients do not always recognize themselves as being at CVD risk, hence CRF apps with alarms in case of low CRF would favor the early detection of CV abnormalities.

In the sports context, future apps for CRF testing might allow coaches to integrate this measurement into their routine practice, overcoming the limitations of traditional methods noted above. For instance, a desirable fitness app might have two unique components, one for the athlete to track measurements and the other for coaches to manage data collected from the individual or team as a whole. In this context, coaches might receive athletes’ data remotely, making it more feasible to make adjustments in training programs on a daily basis. Along the same lines, these fitness apps would incorporate a training index, based on daily CRF measurement and other acute stressors, to predict trainability of athletes according to current physiological status. The capability of fitness apps to collect and store CRF measurements effortlessly would make the interpretation of acute and chronic training loads more feasible. Although CRF is per se a recognized indicator of sports performance for recreational and elite athletes, the future ability of fitness apps to collect other physiological and non-physiological parameters may enrich the interpretation of CRF measurements in this field.

5 Limitations and Strengths

The main limitation of this review was the small number of scientific studies identified. A second important limitation was the bias found in some validation manuscripts, which makes it difficult to draw robust conclusions. Further, even though we provided a list of apps currently available in app markets, it is important to note that the volume and turnover of apps are high; thus, it is likely that new applications will appear while others assessed in the current analysis will be defunct in the near future. The strengths of our review include a comprehensive analysis and discussion concerning the opportunities that apps provide for the objective and remote assessment of CRF and their usefulness for clinical and sports/training purposes [ 1 , 2 , 3 , 4 , 5 , 6 , 8 , 9 , 10 ]. Specifically, our review contributes to the field by providing (1) information on the validity and reliability of apps currently available in the scientific literature; (2) a comprehensive list of apps currently available in app markets, including a qualitative rating of each in order to assist readers with selection of the best apps (Supplemental Tables 6–9 in the ESM, which include a direct link to each app); (3) a list of the key characteristics that a fitness app should have in order to assist readers with the selection of apps, as well as app developers to design better apps in the future; and (4) a list of recommendations for future research directions based on knowledge gaps identified during this systematic review.

6 Conclusions

There is no doubt that we are witnessing the beginning of a new technologic era in healthcare and sports; however, the validity/reliability of the CRF assessment should be improved in a manner consistent with technological development. In fact, the results from this review demonstrate that few presently available apps have been empirically evaluated and among those that have, not all are valid or reliable. In addition, commercially available apps are mostly of low-to-moderate quality, suggesting that the potential of apps for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations for the future development of apps that objectively and remotely assess CRF as a complementary tool to traditional methods for estimating lifetime CVD risk and for improving athletes’ performance. Likewise, sports practitioners will be able to take advantage of the opportunities that fitness apps offer to evaluate the CRF level of clients remotely and to monitor fitness changes. Collectively, we believe that expanding digitalization is a key component of the future of healthcare and sports, and in turn capitalizing on digitalization for the refinement of CRF assessment, now considered a vital sign [ 37 ], is an important objective that requires continued inquiry.

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Acknowledgements

We are grateful to Ms Carmen Sainz-Quinn for assistance with the English language.

The views expressed are those of the authors and do not reflect the official policy or position of the institutions they belong to.

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GICAFE “Physical Activity and Exercise Sciences Research Group”, University of Balearic Islands, Balearic Islands, Spain

Adrià Muntaner-Mas

PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain

Adrià Muntaner-Mas & Francisco B. Ortega

Chronobiology Research Group, Department of Physiology, Faculty of Biology, University of Murcia, Campus Mare Nostrum, IUIE, IMIB-Arrixaca, Murcia, Spain

Antonio Martinez-Nicolas

Ciber Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain

Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine in New Orleans, New Orleans, LA, USA

Carl J. Lavie

Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

Steven N. Blair

School of Kinesiology and Health Studies, Queen’s University, Kingston, ON, Canada

Robert Ross

Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, USA

Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden

Francisco B. Ortega

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Adrià Muntaner-Mas, Antonio Martinez-Nicolas, Carl J. Lavie, Steven N. Blair, Robert Ross, Ross Arena, and Francisco B. Ortega declare that they have no conflict of interest.

FBO research activity was supported by the Spanish Ministry of Economy and Competitiveness—MINECO/FEDER DEP2016-79512-R; by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement (No. 667302); by the University of Granada, Plan Propio de Investigación 2016, Unit of Excellence on Exercise and Health (UCEES); by the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and European Regional Development Fund (ERDF), ref. SOMM17/6107/UGR; and by the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005-00046/ACTI); the SAMID III network, RETICS, funded by the PN I+D+I 2017-2021 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/002). AMN was supported by the Ministry of Economy and Competitiveness and the Instituto de Salud Carlos III through the CIBERFES (CB16/10/00239) and by the Seneca Foundation through the unit of excellence Grant 19899/GERM/15.

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Muntaner-Mas, A., Martinez-Nicolas, A., Lavie, C.J. et al. A Systematic Review of Fitness Apps and Their Potential Clinical and Sports Utility for Objective and Remote Assessment of Cardiorespiratory Fitness. Sports Med 49 , 587–600 (2019). https://doi.org/10.1007/s40279-019-01084-y

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Published on 13.7.2021 in Vol 23 , No 7 (2021) : July

Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

Authors of this article:

Original Paper

• Yanxiang Yang, MSc   ; 
• Joerg Koenigstorfer, Prof Dr  

Chair of Sport and Health Management, Technical University of Munich, Munich, Germany

Corresponding Author:

Joerg Koenigstorfer, Prof Dr

Chair of Sport and Health Management

Technical University of Munich

Georg-Brauchle-Ring 60/62

Campus D – Uptown Munich

Munich, 80992

Phone: 49 89 289 24559

Email: [email protected]

Background: Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active.

Objective: Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions.

Methods: Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships.

Results: The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit ( β =.42; P <.001), performance expectancy ( β =.36; P <.001), facilitating conditions ( β =.15; P <.001), price value ( β =.13; P <.001), and effort expectancy ( β =.09; P =.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active ( β =.12; P <.001; R 2 =0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects.

Conclusions: This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity.

Introduction

To date, there are 3.8 billion smartphone users worldwide [ 1 ], and approximately half of them consider their smartphones as something “they could not live without” [ 2 ]. Numerous smartphone apps have been developed to allow users to go beyond basic voice calling and texting to social media, gaming, and managing their health and fitness. In June 2021, 98,406 apps in the Google Play Store and 159,758 apps in the Apple App Store were available to users in the health and fitness category [ 3 , 4 ]. These apps aim to promote physical activity and healthy lifestyles [ 5 , 6 ]. It is important to increase our understanding of the factors that influence users in adopting these apps and subsequent associations with intentions to engage in healthy behaviors—both from the perspective of public health and management (eg, app providers)—because stakeholders in these domains are (or should be) interested in finding ways to promote healthy lifestyles via digitization in general and the use of mobile devices in particular.

The most widely used theoretical frameworks that explain why users adopt or use technology are the technology acceptance model [ 7 ] and the Unified Theory of Acceptance and Use of Technology (UTAUT) [ 8 ]. The two models focus on the organizational context. In consumer settings, the second version of the UTAUT (ie, UTAUT2) has been developed to explain the acceptance of new technology by individuals [ 9 ]. Since the first application of UTAUT2 (studying the acceptance of the mobile internet), it has been used to explain smartphone app adoption and usage [ 10 , 11 ], among other applications. With regard to previous empirical studies on mobile health and fitness apps, important gaps exist in the research. First, previous studies have left out the essential determinants that UTAUT2 incorporates (eg, habit and hedonic motivation). Given the importance of habit [ 12 ] and hedonic motivation [ 13 ], the sole focus on the four determinants proposed by UTAUT seems insufficient [ 14 , 15 ]. Second, the relationship between the intentions to use fitness apps and to be physically active has not been explored. Assessing the downstream effects of intention to use fitness apps is important, because downloaded but unused apps or apps that are unable to motivate people to become or remain physically active will have fewer health benefits [ 5 , 16 ]. Third, understanding whether different fitness app features moderate the relationships of the UTAUT2 determinants and the behavioral intentions of using the app is lacking. Previous research has categorized app features, such as education-related versus motivation-related features [ 17 ], but did not consider their influence on structural relationships that aim to explain app usage intentions and physical activity intentions. Finally, despite the fact that the moderating effects of individual-difference variables (eg, age, gender, and experience) have been theorized and empirically assessed [ 9 ], they have largely been neglected in prior research on mobile health and fitness apps [ 18 - 21 ]. However, their relevance was shown in a post hoc meta-analysis, for example, in which age was a significant moderator [ 22 ].

This study aims to partially fill these gaps and answer four research questions: (1) What are the relationships between the UTAUT2 determinants and behavioral intentions of individuals to use fitness apps? (2) What is the downstream relationship between the behavioral intentions of using fitness apps and being physically active? (3) Do fitness app features moderate the relationships between the UTAUT2 determinants and the intentions of using fitness apps? (4) Are there individual differences regarding age, gender, and user experience in the relationships between the UTAUT2 determinants and intentions to use fitness apps?

To answer the research questions, we applied and extended the UTAUT2 model in the context of smartphone fitness apps. A sample of 839 individuals was surveyed to test our hypotheses. Path modeling was used to test the hypotheses. In the following, we reviewed the extant literature on determinants of fitness app usage, developed the hypotheses, and presented the methodology of our approach.

Literature Review

Smartphone fitness apps.

Along with the growing consensus on the health benefits of physical activity [ 23 ], a myriad of fitness wearables and smartphone fitness apps have been developed to quantify and promote physical activity. Fitness wearables are “devices that offer training plans, assist with activity tracking, and generally collect and process health-related data” [ 24 ], whereas fitness apps refer to “the self-contained programs for smartphones designed for the purpose of getting fit” [ 25 ]. This study focused on smartphone fitness apps.

Despite the potential of smartphone fitness apps to deliver cost-effective physical activity and health promotion, their effectiveness has not been sufficiently established [ 5 , 16 , 26 , 27 ]. In particular, the effectiveness of fitness apps usage or app-based interventions was modest or short-lived [ 5 , 16 ]. In previous studies, only a limited number of factors considered by researchers have been based on theories or behavior change techniques [ 16 , 26 , 27 ]. Furthermore, only a small number of fitness apps have undergone rigorous evidence-based evaluations in controlled trials [ 28 ]. There are some quality concerns in the reporting of these studies, for example, only a few studies have reported whether fitness apps are based on human behavior change theories [ 28 , 29 ]. Herein, we outline the factors that might predict the behavioral intentions of individuals to use fitness apps (and their downstream effects), building upon theories that have been identified as relevant in the information systems literature, particularly UTAUT2.

Determinants of the Behavioral Intentions of Using Fitness Apps

Venkatesh et al [ 8 ] developed the UTAUT by integrating eight theories (ie, technology acceptance model, theory of reasoned action, motivational model, theory of planned behavior, combined technology acceptance model, theory of planned behavior model, model of PC utilization, diffusion of innovation theory, and social cognitive theory). According to UTAUT, performance expectancy, effort expectancy, social influence, and facilitating conditions are the four key determinants of behavioral intentions to use technology. In 2012, three additional factors were identified as part of the UTAUT2, namely hedonic motivation, price value, and habit [ 9 ]. In the UTAUT2, the individual-difference factors of age, gender, and experience have been identified as important moderators of the relationships between the seven determinants and behavioral intentions. Hew et al [ 20 ] applied the UTAUT2 to examine the factors that affect smartphone app adoption in general, considering the moderators of gender and education. They found that all but two factors (ie, social influence and price value) were significant determinants, with habit exerting the strongest influence. Gender and education were nonsignificant moderators. Most important to this research, previous studies used the UTAUT2 to investigate the determinants of behavioral intentions of using fitness-promoting smartwatches [ 18 ] and fitness apps [ 19 , 30 ]. However, none of them considered individual-difference factors as moderators, and none of them considered the effect of app features on the proposed relationships.

Specifically, Beh et al [ 18 ] found positive relationships among performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation and behavioral intention to use smartwatches for fitness and health monitoring purposes. The authors postulated that perceived vulnerability to developing chronic diseases and perceived severity of chronic diseases would moderate the effects but found only weak support for their hypotheses. Dhiman et al [ 19 ] found that effort expectancy, social influence, price value, and habit were positively related to fitness app adoption intentions. They considered self-efficacy to be a predictor of effort expectancy and innovativeness as a predictor of habit; both relationships were significant. Yuan et al [ 30 ] did not consider any mediators and found that performance expectancy, hedonic motivation, price value, and habit were predictors of behavioral intentions to continuously use health and fitness apps; however, effort expectancy, social influence, and facilitating conditions were nonsignificant predictors. These studies have important limitations. First, the downstream effects on intentions of being physically active were not assessed in any of the studies. The linkage of fitness app usage intentions and intentions of being physically active is important, because health benefits can only be realized if intended app usage motivates people to become or remain physically active. Second, none of the studies considered app features to be relevant moderators, despite the fact that previous research showed that app features, such as gamification, might moderate the effects of UTAUT2 determinants on app usage intentions [ 31 ], and despite the fact that the consideration of risk perception factors (instead of app features) was largely unsuccessful [ 18 ]. Third, only one study assessed the moderating roles of age, gender, and experience. However, the authors did not include these variables in the model because of nonsignificant findings [ 30 ]. Thus, important similarities with, and differences to the original UTAUT2 studies regarding the influence of age, gender, and experience remain largely unknown. This study aims to fill these gaps partly.

Building upon UTAUT2, we first propose that the seven UTAUT2 determinants relate positively to individuals’ intentions to use fitness apps. Second, we postulate positive downstream relationships with the intention of being physically active. Third, we pose a research question that considers three prominent app features (ie, education, motivation, and gamification related) as moderators of the relationships between the seven UTAUT2 determinants and behavioral intentions of using the app. Finally, we explore the moderating effects of individual differences (ie, age, gender, and experience) on the relationship between the seven UTAUT2 determinants and behavioral intentions to use the app. We have listed the hypotheses in the following sections.

Hypotheses Development

Performance expectancy.

Performance expectancy is defined as the “degree to which using a technology will provide benefits to consumers in performing certain activities” [ 9 ]. It was the strongest predictor of behavioral intentions in the original UTAUT study [ 8 ] and is a pivotal determinant of new technology acceptance in health care [ 32 , 33 ] and fitness wearables [ 21 , 34 ]. In the context of this study, performance expectancy refers to the degree to which a user believes that using a particular fitness app would help improve their fitness. Previous studies have shown a positive relationship between performance expectancy and intention to use fitness apps [ 15 , 30 ]. As the perception that fitness apps help people reach their fitness-related goals should be of high relevance to users, we propose the following:

Hypothesis 1: performance expectancy is positively related to individuals’ behavioral intentions to use fitness apps.

Effort Expectancy

Effort expectancy refers to “the degree of ease associated with consumers’ use of technology” [ 9 ], similar to the perceived ease of use as described in the technology acceptance model [ 7 ]. In this study, effort expectancy assesses the perceived ease of use of fitness apps. The easier the individuals believe the fitness apps are to use, the higher is their intention to use them. Prior studies have revealed a positive relationship between effort expectancy and behavioral intention to use fitness apps [ 15 , 19 ] and fitness wearables [ 18 , 34 ]. As people should be interested in intuitive and easy app usage, we expect the following:

Hypothesis 2: effort expectancy is positively related to behavioral intentions of individuals to use fitness apps.

Social Influence

Social influence is defined as “the extent to which consumers perceive that important others (eg, friends, peers) believe they should use a particular technology” [ 9 ]. Social influence plays a particular role when users lack information about their usage [ 35 ]. In the context of fitness apps, previous studies have revealed inconsistent results regarding the effect of social influence on behavioral intentions of using fitness apps. It was a positive predictor of usage intentions of students of a Chinese university [ 15 ] and Indian users [ 19 ], although it did not predict the intentions of college-aged US residents [ 30 ]. Given the positive effect of social influence postulated in the original UTAUT2 [ 9 ] and the importance of social support in being physically active [ 36 , 37 ], we assume the following:

Hypothesis 3: social influence is positively related to the behavioral intention of individuals to use fitness apps.

Facilitating Conditions

Facilitating conditions refer to “consumers’ perceptions of the resources and support available to perform a behavior” [ 9 ]. In the context of this research, it reflects the support from resources (eg, ubiquitous internet connection for smartphones) and the required knowledge (eg, experience of smartphone use) to be able to use fitness apps. The original UTAUT2 study [ 9 ], as well as studies considering the acceptance of general apps [ 20 ] and fitness wearables [ 18 ], showed that facilitating conditions increase acceptance. Thus, we postulate the following:

Hypothesis 4: facilitating conditions relate positively to behavioral intentions of individuals to use fitness apps.

Price Value

Price value is defined as “consumers’ cognitive trade-off between the perceived benefits of a technology and the monetary cost of using it” [ 9 ]. Individuals expect a higher quality of services when they have to pay more for them [ 30 , 38 ]. In the fitness app context, providers offer three main patterns of pricing: free, paid, or freemium (ie, free base app use but additional features need to be paid for). Even if an app can be used for free, individuals might nevertheless consider other cost aspects, such as personal time costs or psychological costs. Previous studies have found a positive relationship between price value considerations and behavioral intentions to use the mobile internet [ 9 ], health care wearables [ 39 ], and fitness apps [ 19 , 30 ]. Owing to the fact that a high value for a given price can be assumed to be perceived positively by individuals, we propose the following:

Hypothesis 5: price value relates positively to behavioral intentions of individuals to use fitness apps.

Hedonic Motivation

Hedonic motivation refers to “the fun or pleasure derived from using a technology” [ 9 ]. If the intrinsic motivation of an individual is high, they typically have high levels of hedonic motivation [ 40 ]. A meta-analysis revealed that 58% (53/91) of the included UTAUT2-related empirical studies included hedonic motivation as a factor, whereas 81% (43/53) of the studies found a positive relationship between hedonic motivation and behavioral intentions to use the technology [ 13 ]. Hedonic motivation has a positive effect on the intention to adopt health care wearables [ 18 , 21 ] and fitness apps [ 30 ]. Thus, we suggest that if a user has fun using a fitness app, they are more likely to use it. Hypothesis 6 is as follows:

Hypothesis 6: hedonic motivation is positively related to the behavioral intentions of individuals to use fitness apps.

Habit refers to “the extent to which people tend to perform behavior automatically” and was found to be a positive predictor of behavioral intentions to use the mobile internet [ 9 ]. Approximately 35% (23/66) of UTAUT2-related empirical studies utilized habit as a construct [ 12 ]. Most importantly, 83% (15/18) of the studies revealed positive associations between habit and intention [ 12 ]. In the context of this study, we consider habit to be an important predictor, because smartphones are a central means by which individuals can manage and facilitate their daily lives [ 2 ] and because individuals use their smartphone (and potentially fitness apps [ 19 , 30 ]) by habit. We thus propose the following:

Hypothesis 7: habit relates positively with the behavioral intentions of individuals using fitness apps.

Downstream Consequence of Behavioral Intentions of Using Fitness Apps

Fitness apps aim to promote user fitness levels. As it is assumable that people who download these apps are (at least partly) committed to reaching this goal, we postulate that higher intentions to use fitness apps relate positively to the willingness of people to be physically active in the future. The claim can be substantiated by consistency theories, arguing that cognitive consistency fosters updates on the expectancy regarding an outcome or a state (here, to be physically active) [ 41 ]. However, to date none of the UTAUT2-based studies have examined the relationship between usage intentions of new technology that aims to promote fitness (or health) and the downstream consequence on behavioral intentions to engage in physical activity–related behaviors. Two recent systematic reviews concluded that the effects of fitness apps on physical activity levels are present but are modest in magnitude [ 5 , 16 ]. Previously formed intentions at the individual level might be explanatory variables for these effects. Thus, hypothesis 8 is stated as follows:

Hypothesis 8: behavioral intentions to use fitness apps relate positively to behavioral intentions of being physically active.

Moderating Effects of Fitness App Features

Smartphone apps have certain features, that is, the set of operational functions that an app can perform (eg, gaming). The essence of fitness app features may be summarized within behavior change techniques (eg, goal setting, monitoring, and acquisition of knowledge) [ 42 ]. In addition, various frameworks of features implemented in fitness apps have been proposed. For example, Mollee et al [ 43 ] identified user input, textual or numerical overviews, social sharing, and general instructions as the most implemented features of fitness apps. Rabin and Bock [ 44 ] suggested that fitness tracking, tracking of progress toward fitness goals, and the integration of features that increase enjoyment (eg, music) are user-desired features. Other studies focused on the social features of fitness apps (eg, sharing or comparing steps and receiving social support) [ 45 ], whereas a review concluded that the evidence of social app features to promote fitness was limited [ 36 ].

Conroy et al [ 17 ] used an empirical approach to cluster fitness apps in terms of features and used cluster analysis to identify two broad categories, namely, motivation related and education related. Motivation-related app features emphasize the social and self-regulation of fitness (eg, tracking, feedback, social support, goal setting, and reward features). Education-related app features focus on fitness education (eg, instruction, coaching, and learning) [ 17 ]. These two clusters do not include gamification-related features, which have become relevant in helping individuals improve their health and fitness [ 46 ]. Gamification-related features use game design elements to make the user experience playful and enjoyable [ 47 , 48 ]. In this study, we thus consider gamification-related features besides the motivation- and education-related features of fitness apps.

The literature on apps in general (without a focus on physical activity) has considered app features as moderators of the relationship between acceptance determinants and behavioral intentions of using apps [ 31 , 48 ]. However, it remains unclear whether the UTAUT2 determinants interact with fitness app features to explain the behavioral intentions of using these apps. Such interaction effects might explain the modest effects found in systematic reviews on the effects of fitness apps on physical activity [ 5 , 16 ]. To explore this issue, we formulate the following research question: do fitness app features moderate the relationships between the UTAUT2 determinants and behavioral intentions of using fitness apps?

Moderating Effects of Individual Differences

The moderating effects of age, gender, and experience—individual-difference variables—on the relationships between UTAUT2 determinants and behavioral intentions have been proposed and empirically tested in the original UTAUT2 study [ 9 ]. In particular, it was theorized that age moderated the relationships between the seven UTAUT2 determinants and behavioral intentions such that the effects are stronger among young (vs old) users for performance expectancy, effort expectancy, and hedonic motivation but weaker for social influence, facilitating conditions, price value, and habit [ 8 , 9 ]. Gender was postulated to moderate the relationship between the seven UTAUT2 determinants and behavioral intentions such that the effects are stronger among women (vs men) for effort expectancy, social influence, facilitating conditions, and price value but weaker for performance expectancy, hedonic motivation, and habit [ 8 , 9 ]. Experience was postulated to moderate the relationships between five UTAUT2 determinants and behavioral intentions such that the effects are stronger among users in the early (vs late) stage of experience for effort expectancy, social influence, facilitating conditions, and hedonic motivation but weaker for habit [ 8 , 9 ]. Three- and four-way interactions of age, gender, and experience were included in the original UTAUT2 study [ 9 ]. Despite the fact that the original studies supported these proposed moderator relationships, previous studies on mobile health and fitness apps applying the UTAUT or UTAUT2 did not fully consider them [ 14 , 15 , 18 - 21 , 49 ]. The moderators have been meta-analyzed and suggested as worthy of study [ 22 ] or noted as future work [ 19 ]. To fill this research gap, we state the following research question: are there individual differences in the relationships between the UTAUT2 determinants and intentions to use fitness apps?

Study Design and Procedure

This study applied a cross-sectional web-based survey design, and the results were reported according to the CHERRIES checklist [ 50 ]. Using a convenience sampling technique, we recruited 867 Amazon Mechanical Turk workers in March 2020. This sample size was considered sufficient based on a thumb rule [ 51 ], as well as similar studies on fitness app acceptance [ 19 , 30 ]. Participants were limited to healthy adults who were aged between 18 and 65 years, owned a smartphone, and had downloaded at least one smartphone fitness app. Participants were also required to be able to read and understand English and be located in the United States (ie, US residents). Participants who met the eligibility criteria were invited to participate in the Amazon Mechanical Turk online survey, delivered via Qualtrics. All participants were informed about the study procedures via detailed instructions at the beginning of the survey ( Multimedia Appendix 1 ), including the purpose, inclusion criteria, and estimated time needed to complete the survey. After the instructions were provided, informed consent was obtained from each participant. The survey consisted of UTAUT2-related questions, questions that assessed the dependent variables as well as mediators and moderators, and demographics of participants, which were collected at the end of the survey. Each participant was compensated with US $1.50 for their participation. Once 28 incomplete surveys were eliminated, data from 839 respondents were retained for analysis.

This study was conducted in accordance with the ethical standards of the university faculty board, which acts as the local ethics committee for studies outside the Faculty of Medicine, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

The UTAUT2 items for the seven determinants and behavioral intentions of using apps were adapted to the context of this study [ 9 ]. They were measured on a 7-point rating scale ranging from 1 (strongly disagree) to 7 (strongly agree). The behavioral intentions of being physically active were gauged using two separate measures. First, intentions were measured via an adaptation of the International Physical Activity Questionnaire Short Form [ 52 ], which covers a period of 4 weeks in the future. The sum of the values (measured in metabolic equivalent of task [MET] min/week) was calculated according to established data processing guidelines [ 53 ]. Second, it was measured using a single question: “To what degree do you want to be physically active in the next four weeks?” (1=not at all; 7=very much) [ 54 ]. The individual-difference variables of age and gender were self-reported. Experience was measured with a single item: “When did you download a fitness app for the first time? - () months ago,” as done in the original UTAUT2 study [ 9 ].

Participants also rated the features of their most preferred app with importance ratings (1=not important at all; 7=extremely important). Importance ratings were used because apps typically have multiple features and because the features from the perspective of users are important in this study [ 55 ]. The items for education- and motivation-related app features were formulated in agreement with previous cluster classifications [ 17 ] and substantive content of behavior change techniques [ 42 ]. Gamification-related app features were operationalized based on the extant literature on gamification and fitness apps [ 47 , 56 ]. All three app features were measured using three items each. Examples of items are as follows: “How important to you are app features that motivate you to be physically active?” for motivation-related features; “How important to you are app features that educate yourself about how to exercise best?” for education-related features; and “How important to you are app features to enjoy yourself while exercising?” for gamification-related features.

Statistical Analyses

Normality was evaluated using multivariate skewness and kurtosis [ 57 ]. We conducted a confirmatory factor analysis to evaluate the internal reliability, convergent validity, and discriminant validity of the measurement model [ 58 ]. For internal reliability, we examined the Cronbach α (>.70) and construct reliability (>0.70). We used the average variance extracted (AVE; AVE>0.50) and factor loadings for convergent validity [ 59 ]. Discriminant validity was assessed using the Fornell-Larcker criterion [ 59 ] and the heterotrait-monotrait (HTMT) criteria [ 60 ]. Various model fit indices were applied, including the normed chi-square statistic (χ 2 /df ratio, value<3.0), Tucker-Lewis index (TLI; TLI>0.90), comparative fit index (CFI; CFI>0.90), root mean square error of approximation (RMSEA; RMSEA<0.05), and standardized root mean square residual (SRMR; SRMR<0.05) [ 58 ].

Path modeling (maximum likelihood) was used to test the hypotheses. The variables were mean-centered before the analysis, and gender was coded as a dummy variable (0=female; 1=male). For significant interaction effects between the UTAUT2 determinants and app features, follow-up tests were performed to observe how the moderator changes the hypothesized relationships, as recommended by Dawson [ 61 ]. Data analyses were performed using R (RStudio) and the lavaan package [ 62 ]. The level of significance was set at P <.05.

Participants

A total of 839 participants completed the study. The participants were from 49 US states, with a median of 10 participants per state. They were aged, on average, 37 (SD 10.2) years; 48.3% (405/839) were female; and 51.7% (434/839) were male. Participants were experienced in using fitness apps, as on average they had downloaded the app about 30 months ago. Most participants were White (681/839, 81.2%), employed workers (676/839, 80.6%), married (442/839, 52.7%), and single (322/839, 38.4%). About 66.7% (560/839) reported having a bachelor’s degree or higher, whereas 33.3% (279/839) held an associate’s degree or lower. They were mostly young adults (562/839, 67% aged between 18 and 40 years), and approximately 44.8% (376/839) of them were either overweight or obese. Approximately 76% (638/839) of them had downloaded two or more fitness apps (mean 3.4, SD 2.5). When asked about their preferred fitness app, 14.1% (118/839) stated MyFitnessPal, 13.2% (111/839) stated Fitbit, and 6.2% (52/839) stated Samsung Health (which are among the preferred apps in real-time app rankings under the category of health and fitness in both the Apple App Store and Google Play Store). In total, 159 different apps were mentioned as the preferred apps by the participants. Table 1 shows the sociodemographic characteristics of the participants.

a BMI was classified according to the US Centers for Disease Control and Prevention’s BMI weight status categories: underweight (below 18.5 kg/m 2 ); normal or healthy weight (18.5 to 24.9 kg/m 2 ); overweight (25.0 to 29.9 kg/m 2 ); and obese (over 30.0 kg/m 2 ).

Descriptive Statistics and Assumption Tests

Table 2 provides an overview of the descriptive statistics of the variables. The average ratings of the UTAUT2 determinants ranged from 4.26, for social influence, to 6.02, for facilitating conditions. Education-, motivation-, and gamification-related app features were considered important, with the highest ratings for motivation (mean 5.21) compared with gamification- and education-related app features (mean 5 for both). Participant ratings of their behavioral intentions to use fitness apps were above the midpoint of the scale (mean 5.53); intentions of being physically active in the future were very high for both MET values and the ratings on the seven-point rating scale (mean 4589 MET min/week, SD 3137; and mean 6.07, SD 1.05, respectively). All values of skewness and kurtosis were within the suggested criteria (ie, skewness <2 and kurtosis <7 [ 63 ]), indicating normality of the univariate distribution.

a Model fit was satisfactory: χ 2 564 =2112.2; χ 2 /df=3.8; comparative fit index=0.93; Tucker-Lewis index=0.91; root mean square error of approximation=0.06; and standardized root mean square residual=0.07.

b The criteria for skewness (absolute value <2) and kurtosis (absolute value <7) were fulfilled for a sample size greater than 300 (ie, N=839), indicating normality of the univariate distribution [ 63 ].

c AVE: average variance extracted.

d [xx] refers to the brand name of the specified fitness app.

e BI: behavioral intentions to use the fitness app.

f MO: motivation-related app features.

g ED: education-related app features.

h GA: gamification-related app features.

i PA: Intentions of being physically active. The intentions were measured using the International Physical Activity Questionnaire (metabolic equivalent of task min/week) and a single-item 7-point rating scale. The reported measurement model is based on the first measure.

j N/A: not applicable.

k MET: metabolic equivalent of task.

l EXP: user experience with fitness apps.

Measurement Model

The overall model fit using MET minutes per week values for physical activity intentions as the dependent variable was found to be satisfactory (χ 2 564 =2112.2; χ 2 /df=3.8; CFI=0.93; TLI=0.91; RMSEA=0.06; and SRMR=0.07), after excluding one item for facilitating conditions (ie, “I can get help from others when I have difficulties using the [ brand name ] app” with a factor loading of 0.30). The internal reliability, convergent validity, and discriminant validity of the measurement model were evaluated. All Cronbach α and construct reliability values were ≥.77 (ie, above the suggested threshold of 0.70), indicating internal reliability. The AVE and factor loadings were >0.54, in all cases, above the thresholds of 0.50, suggesting convergent validity ( Table 2 ).

Table 3 shows the results of the discriminant validity. First, no cross-loadings were detected among the measurement items. Second, all the square roots of AVE were greater than the relevant interconstruct correlations with two exceptions (ie, performance expectancy: 0.88; and facilitating conditions: 0.87). The HTMT criteria were fulfilled (ie, all HTMT values were ≤0.85) with one exception (performance expectancy: 0.88), but the value is still within the acceptable range between 0.85 and 0.90 [ 60 ].

a BI: behavioral intentions to use the fitness app.

b PE: performance expectancy.

c EE: effort expectancy.

d SI: social influence.

e FC: facilitating conditions.

f HM: hedonic motivation.

g PV: price value.

h HA: habit.

i MO: motivation-related app features.

j ED: education-related app features.

k GA: gamification-related app features.

l PA: intentions of being physically active.

m GEN: gender.

n EXP: user experience with fitness apps.

o Terms in italics along the diagonal are square roots of average variance extracted. Below the diagonal, the lower left metrics test the discriminant validity according to the Fornell-Larcker criterion. Discriminant validity is fulfilled if the square roots of the average variance extracted are larger than the relevant interconstruct correlations. Furthermore, above the diagonal, the upper right metrics refer to the heterotrait-monotrait ratio, where <0.85 or <0.90 indicates good discriminant validity.

p N/A: not applicable.

Structural Model and Hypotheses Testing

Path modeling was used to test the hypotheses. The model was established by modeling the hypothesized paths among the UTAUT2 determinants, behavioral intentions of using fitness apps, intentions of being physically active, and the three app features ( Figure 1 ). On the basis of the different measures of intention to be physically active, two models were established. The first model (considering physical activity intentions measured in MET min/week) had an excellent fit (χ 2 79.00 =97.74; χ 2 /df=1.2; P= .08; CFI=0.984; TLI=0.968; RMSEA=0.017; SRMR=0.006). The model fit for the second model (taking into account physical activity intentions measured on a single-item rating scale) was also good (χ 2 79.00 =179.07; χ 2 /df=2.3; P <.001; CFI=0.925; TLI=0.849; RMSEA=0.039; SRMR=0.010). Both models explained 76% of the variance in the behavioral intentions to use fitness apps.

In what follows, we first present the results of model 1. Performance expectancy ( β= .36, SE 0.04; P <.001), effort expectancy ( β= .09, SE 0.04; P= .04), facilitating conditions ( β= .15, SE 0.04; P <.001), price value ( β= .13, SE 0.03; P <.001), and habit ( β= .42, SE 0.04; P <.001) were positively related to behavioral intention to use fitness apps, whereas social influence ( β= .03, SE 0.03; P= .37) and hedonic motivation ( β= .02, SE 0.03; P= .63) were nonsignificant predictors. Behavioral intentions to use fitness apps relate positively to intentions of being physically active ( β= .12, SE 0.03; P <.001), explaining 2% of the variance in physical activity intentions. For model 2, the path coefficients between the UTAUT2 determinants and behavioral intentions of using the fitness app were identical to the results obtained from model 1. Behavioral intentions to use fitness apps relate positively to intentions of being physically active ( β= .37, SE 0.03; P <.001), explaining 12% of the variance in physical activity intentions. Thus, hypotheses 1, 2, 4, 6, 7, and 8 were supported, whereas hypotheses 3 and 5 were not supported ( Table 4 ; Figure 2 ).

a Unstandardized path coefficient. See Table 5 for the path coefficients of the individual-difference moderators and their interaction effects.

b UTAUT2: Unified Theory of Acceptance and Use of Technology 2.

c PE: performance expectancy.

d BI: behavioral intentions to use the fitness app.

e EE: effort expectancy.

f SI: social influence.

g FC: facilitating conditions.

h HM: hedonic motivation.

i PV: price value.

j HA: habit.

k PA: intentions of being physically active, measured in metabolic equivalent of task minutes per week.

l ED: education-related app features.

m N/A: not applicable.

n MO: motivation-related app features.

o GA: gamification-related app features.

The testing of the interaction effects of app features and the seven UTAUT2 determinants was performed next ( Table 4 ). Education-related app features moderated the relationships between performance expectancy and behavioral intentions to use fitness apps ( β= −.08, SE 0.03; P =.01), as well as between habit and behavioral intentions of using fitness apps ( β= .08, SE 0.03; P =.009). Motivation-related app features moderated the relationships between performance expectancy and behavioral intentions of using fitness apps ( β= .10, SE 0.03; P =.002), facilitating conditions and behavioral intentions to use fitness apps ( β=− .11, SE 0.04; P =.005), and habit and behavioral intentions to use fitness apps ( β=− .18, SE 0.03; P <.001). Gamification-related app features moderated the relationship between hedonic motivation and behavioral intention to use fitness apps ( β= .07, SE 0.03; P= .006).

The testing of the interaction effects of individual differences and the seven UTAUT2 determinants ( Table 5 ) also revealed that age moderated the relationship between effort expectancy and behavioral intention to use fitness apps ( β =−.11, SE 0.04; P =.008). Gender moderated the relationships among performance expectancy and behavioral intention to use fitness apps ( β= .13, SE 0.06; P =.03), habit, and behavioral intentions ( β=− .12, SE 0.05; P =.02). Experience was a nonsignificant moderator. In addition, the joint moderating tests (three- and four-way effects) taking into account individual differences revealed a significant three-way interaction for age, gender, and hedonic motivation ( β =−.14, SE 0.06; P =.02); a significant three-way interaction for age, experience, and effort expectancy ( β =.09, SE 0.03; P =.007), and a significant three-way interaction of age, experience, and habit on behavioral intentions to use fitness apps ( β =−.12, SE 0.04; P =.004). There were no significant four-way interaction effects.

Subsequently, we conducted follow-up tests to describe how the moderators changed the relationships ( Table 6 ), considering low (−1 SD of the mean) and high (+1 SD of the mean) values of the moderators. First, when education-related features were rated as important, the relationship between performance expectancy and usage intentions was weaker compared with when this feature was rated as unimportant. Second, when education-related features were rated as important, the relationship between habit and usage intentions was stronger compared with when these features were rated as unimportant. Third, when motivation-related features were rated as important, the relationship between performance expectancy and usage intentions was stronger, the relationship between facilitating conditions and usage intentions became nonsignificant, and the relationship between habit and usage intentions was weaker compared with when these features were rated unimportant. Fourth, when gamification-related features were rated as important, the relationship between hedonic motivation and usage intentions was stronger but still nonsignificant compared with when this feature was rated unimportant. Furthermore, the relationship between effort expectancy and usage intentions was positive for younger users but nonsignificant for older users. Finally, the relationship between performance expectancy and usage intentions was stronger among males, whereas the relationship between habit and usage intentions was stronger among females.

a Unstandardized path coefficient. See Table 4 for the path coefficients of the seven UTAUT2 determinants and app-feature moderators.

b BI: behavioral intentions to use the fitness app.

d EE: effort expectancy.

e SI: social influence.

f FC: facilitating conditions.

g HM: hedonic motivation.

h PV: price value.

i HA: habit.

j GEN: gender.

k EXP: user experience with fitness apps.

a Low: low moderators.

b High: high moderators.

c ED: education-related app features.

d PE: performance expectancy.

e HA: habit.

i HM: hedonic motivation.

j EE: effort expectancy.

k GEN: gender. The results for females (dummy: 0) are reported as low moderators; the results for males (dummy: 1) are reported as high moderators.

Principal Findings

The purpose of this study was to examine the influence of the UTAUT2 determinants, as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on the app usage intentions of individuals and their behavioral intentions of being physically active. The results showed that habit and performance expectancy were the two strongest predictors of intentions of individuals to use fitness apps. The effects of performance expectancy were greater when motivation-related features were rated as important and when education-related features were rated as less important. Moreover, the effects of performance expectancy were greater for males. The effects of habit were greater when education-related features were rated as important and when motivation-related features were rated as less important. Furthermore, the effects of habit were greater for females. Age moderated the relationship between effort expectancy and app usage intention. The intentions of individuals to use fitness apps predicted their intentions of being physically active, using two different means of measuring future physical activity.

Theoretical Contribution

We contribute to the literature on mobile health and physical activity in several ways. Answering the first research question ( What are the relationships between the UTAUT2 determinants and intentions to use smartphone fitness apps? ), we found positive relationships among habit, performance expectancy, facilitating conditions, price value, effort expectancy, and behavioral intentions to use fitness apps. Habit and performance expectancy were found to be the most important predictors of intention to use fitness apps, consistent with prior studies (eg, habit [ 19 , 20 , 30 ] and performance expectancy [ 14 , 15 , 30 ]). Positive relationships have also been identified for effort expectancy [ 18 - 20 ], facilitating conditions [ 18 , 20 , 21 ], and price value [ 19 , 21 , 30 ].

Social influence was a nonsignificant predictor of intention [ 18 , 20 , 30 ]. Interestingly, the latter finding is not due to the high domain-specific experience of users (given the nonsignificant interaction effect of social influence and experience), who might have relied less on peer opinions for their evaluations and intentions than low-experience users. Furthermore, in contrast to the original UTAUT2 study [ 9 ] and previous studies [ 18 , 20 , 21 , 30 ], but in agreement with Dhiman et al [ 19 ], we found a nonsignificant relationship between hedonic motivation and app usage intentions. This may be explained by the high demands of fitness app users on app usage to achieve their physical activity goals, compared with the fun or pleasure derived from the apps. However, focusing solely on the four determinants proposed by the first version of UTAUT [ 14 , 15 , 34 ] may be insufficient. Habit, in particular, is the strongest determinant linked to the intention to use fitness apps in this study.

Answering the second research question ( What is the downstream relationship between the behavioral intentions of using fitness apps and of being physically active? ), we contribute to UTAUT2-based research by showing that app usage intentions have important downstream consequences. In particular, individuals have greater intentions of being physically active when they have higher intentions to use fitness apps. Assessing the downstream effect of intention to use fitness apps is important, because downloaded but unused apps or apps unable to motivate people to become or remain physically active will have little health effects [ 5 , 16 ]. The positive relationship between fitness app usage intentions and physical activity intentions indicates that app usage might motivate people to become or remain active. The findings thus contribute to previous research into whether, and when, mobile health and fitness apps may help individuals become physically active [ 64 , 65 ]. However, it should be noted that the intentions of individuals to be physically active are affected by numerous correlates and determinants (eg, self-efficacy, sociodemographic variables, sport club membership, among others) [ 66 ], and the intention-behavior gap is considerable [ 67 ]. Thus, adding these factors and incorporating measurements of actual physical activity may be warranted in the future.

Answering the third research question ( Do fitness apps moderate the relationships between the UTAUT2 determinants and intentions of using fitness apps? ), this study contributes to previous research that categorized app features [ 17 ] yet ignored their influence on the structural relationships proposed by the UTAUT2. On the basis of our exploratory analysis, we identified six relevant interaction effects. One of the most intuitive findings was that when motivation-related features were rated as important, the relationship between performance expectancy and intentions was strong. Research into goal achievement [ 68 , 69 ] might explain the interaction effect: individuals who are interested in improving their physical activity levels, or keeping them at certain levels, might use the app exactly for this purpose. Among the three features, motivational elements aim most directly to help users stick to their goals and plans [ 70 ]; as there is goal congruence, the effect is strong [ 71 ]. When motivation-related features were rated as important, the relationship between facilitating conditions and usage intentions was not significant. This makes sense, because people who lack resources and capacities are more dependent on help from others compared with people who do have these resources and capacities, particularly when motivation features are not considered crucial (ie, motivation might “not be the problem”). In addition, when motivation-related features were important, the relationship between habit and intention was weaker compared with when this feature was unimportant. This finding might indicate that when habits have been formed, features that motivate individuals to be active (eg, reminders) become less important to these app users [ 72 ].

This study also found that performance expectancy had a greater effect on usage intentions when education-related features were rated as unimportant. In this case, individuals might be less interested in being educated—an aspect that might distract them from achieving their goals. In addition, the effect of habit on usage intention was stronger when education-related features were rated as important. This may be explained by the fact that habits of individuals are formed best when they are exposed to education-related cues when using an app (eg, how and when to exercise best) [ 73 ]. Regarding the interaction between hedonic motivation and gamification-related features, no final conclusions can be drawn. Although research into intrinsic motivation [ 74 ] and flow [ 75 ] may lead us to propose that intrinsic motivation, as a principal source of enjoyment, may be enhanced by the gamification app features (eg, apps using incommensurate gamification elements [likes]) [ 76 ], the follow-up tests did not reach significant levels in this study.

Answering the fourth research question ( Are there individual differences in age, gender, and user experience between the relationships of the UTAUT2 determinants and intentions to use fitness apps? ), we found partly significant, partly nonsignificant moderating effects of age, gender, and experience. First, the relationship between effort expectancy and app usage intentions was stronger among younger individuals, which agrees with the original UTAUT2 study [ 8 , 9 ] and a meta-analysis (ie, age group of those aged 25 to 30 years) [ 22 ]. Second, the relationship between performance expectancy and usage intentions was stronger among males, which is consistent with the original UTAUT2 study. In contrast, the relationship between habit and usage intention was stronger among females [ 9 ]. Thus, females were not more sensitive to new cues, which might have weakened the effect of habit on behavioral intentions. In the context of fitness apps, females may indeed be prone to cues that help them form health-related habits, because they are interested in health- and body-appearance-related topics. Finally, in this study, experience was a nonsignificant moderator regarding the interaction effects of the UTAUT2 determinants on app usage intentions. Thus, differences in experiences between users might be less relevant today—a time in which smartphone users can easily add and delete new apps and in which users are technology savvy.

Managerial Implications

This study has implications for smartphone app designers and managers. First, they can be advised to focus on habit formation and performance (eg, goal setting) when designing fitness apps and tailoring them to potential users. Meeting users’ expectations concerning facilitating conditions, price value, and effort expectancy will also increase the likelihood of the app being accepted. Second, practitioners should highlight certain app features that depend on user preferences. For example, motivation-related features are important drivers of app usage intentions for target group users who value performance (education-related features might be less relevant here); habit formation and facilitating conditions are less important to these individuals. Third, health professionals should consider age and gender differences among users with regard to the effects of effort expectancy (age) as well as performance expectancy and habit (gender). Finally, practitioners may also be advised to monitor whether app usage intentions have a positive correlation with intentions of, or even actual, physical activities so that immediate action can be taken when users lose track of their original goals (having already downloaded the app).

Limitations and Outlook

This study has some limitations. First, the generalizability of our findings is limited. We used a nonrepresentative sample of US residents who owned a smartphone and had previously used fitness apps. Future studies may consider inexperienced people with fitness apps to reveal the influence of UTAUT2 determinants on usage intentions at the early- or preadoption stage. Second, given this research design, we did not consider one specific fitness app, but participants stated their preferred app and rated the features of this app. Thus, we considered a variety of apps (which might be beneficial for external validity, given the myriad of apps on the market [ 3 , 4 ]). Researchers might collaborate with certain providers and use real-world app data and objectively measure actual physical activity to validate our findings. Third, we relied on self-reported physical activity intentions using a single measure and the International Physical Activity Questionnaire Short Form. Overreporting is common for the latter (eg, approximately 84% [ 77 ]). Finally, future research could look into the mechanisms of moderation effects on individuals’ behavioral intentions to use apps, incorporate app features into mobile health interventions accordingly, and evaluate their long-term influence on physical activity levels.

Acknowledgments

The authors would like to thank the participants of this study. The authors did not receive any external funding from this study.

Conflicts of Interest

None declared.

Online instructions to participants.

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Abbreviations

Edited by R Kukafka; submitted 26.11.20; peer-reviewed by C Ochoa-Zezzatti, J Offermann-van Heek; comments to author 09.02.21; revised version received 24.03.21; accepted 04.05.21; published 13.07.21

©Yanxiang Yang, Joerg Koenigstorfer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.07.2021.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Adoption of Health and Fitness Apps by Smartphone Users: Interactive Qualitative Analysis

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