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Jul 21, 2021

JMIR mHealth and uHealth This paper is in the following e-collection/theme issue: September 16, 2020 . Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years Authors of this article: 1Research Group: Self-Regulation and Health, Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg 2USC mHealth Collaboratory, Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States 3ALAN – Maladies Rares Luxembourg, Kockelscheuer, Luxembourg 4Research Support Department, University of Luxembourg, Esch-sur-Alzette, Luxembourg Corresponding Author: Department of Behavioural and Cognitive Sciences University of Luxembourg Fax:352 46 66 44 39389 Abstract Background: Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. Objective: The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. Methods: We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. Results: Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. Conclusions: Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions. JMIR Mhealth Uhealth 2021;9(7):e24308 Collaborative Adaptive Interactive Technology framework However, these designs have rarely been implemented. According to the most recent review of PA apps, only 2 of 111 included studies used rapid research designs [ 32 ]. The methodology of the most recent rapid design, MRT, is currently being developed, and the first protocols and trials have been recently published [ 115 , 191 ]. Qualitative Studies Although there is no one recommended methodology, the most commonly reported methods in identified studies include surveys, questionnaires, interviews, and focus group discussions. On the basis of this review, we cannot recommend any specific method, yet there is a clear need for more systematic reporting of results. Nevertheless, the studies summarized in Tables S7-S9 in Multimedia Appendix 1 provide some indication of the most efficacious and user-attractive features in mobile apps aimed at PA promotion. Design simplicity: Ease of use and navigation through the app, absence of unnecessary features, unambiguous information, and a structured layout were all listed as features that positively affected participants’ engagement. Apps with excessive data entry for sign up, presenting features that required instructions, and complicated operating procedures were negatively perceived by users [ 28 , 29 , 69 - 71 , 81 , 82 , 116 ]. Personal approach for each user- tailored coaching, goals, feedback, and notifications: Users perceive a personalized approach as an important factor for motivation and engagement. Therefore, it is important to consider sociodemographic user differences [ 117 , 118 ]. Moreover, the users themselves prefer to be in control of the app’s features, having the ability to hide or add them [ 28 , 69 , 71 , 72 , 81 , 82 , 116 ]. Reward: A transparent reward system was positively recognized by users [ 28 , 71 , 82 , 116 ]. Self-monitoring and goal setting: These app features were the key features enjoyed or rated positively by app users [ 68 , 70 , 72 , 82 ]. Gamification: This feature can positively affect user engagement by bringing more enjoyment to exercise or activity [ 119 , 120 ]. Social networking: This feature was perceived differently in various apps: Peer-to-peer influence was delivered through encouragement, praise, and competition with the participants’ peers. As indicated by Klasnja and Pratt [ 121 ], the results presented by different researchers are inconclusive: studies report both positive and null effects [ 120 - 125 ]. Social support from family and friends: As the reviews show, the effect on participants depends on the behavioral goals of friends or family members: if the goals differ, the effect of social support seems to be low [ 121 ]. Social modeling (eg, tips for health-related resources from successful peers) seems to have a positive effect on participants [ 121 ]. Integration with social networks (eg, Facebook) was perceived negatively by app users [ 69 , 71 , 81 , 82 ]. These findings demonstrate that a chosen method of social support can significantly affect the acceptability and usefulness of the app among users. Overall, it is important to underline the necessity of pretesting the app with a specific target audience to optimally refine the app’s features and components. Devices and Primary Outcomes Used for Data Collection and Analysis in Smartphone-Based PA Interventions: Current Situation and Recommendations Smartphone-based interventions can be divided into stand-alone interventions, where only the app is used and multicomponent interventions, where the app is one of several intervention components. The choice of intervention components affects the intervention outcomes, and, if a multicomponent approach is chosen, may lead to the inclusion of various devices as additional components of the intervention. For the majority of researchers, the selection of smartphone-based intervention components depends on several factors, such as the accuracy of data collection, device compatibility with the user, and durability. As can be seen from Table S6 in Multimedia Appendix 1 , all smartphone-based intervention data collection components can be divided into three groups: smartphones, commercial activity trackers, and medical-grade activity trackers. The selection of a data collection device is usually well aligned with the chosen outcome measures. As presented in Table S6 of Multimedia Appendix 1 , a stand-alone intervention that includes only a smartphone with the installed app and inbuilt accelerometer can track the most common PA outcome measures, that is, minutes spent in MVPA and SB, and daily step count [ 126 ]. These data can also be collected with a range of precision levels (depending, for instance, on the use of built-in GPS sensors, which can provide data that are more accurate) [ 127 - 129 ]. The drawbacks of solely using smartphones include the short battery life of the device, only moderate accuracy levels, moderate durability, and limited exposure time (the user will not usually carry the phone during certain periods of the day) [ 26 , 121 ]. The validation reviews presented in Table S4 of Multimedia Appendix 1 demonstrate that commercially available, usually wrist-worn activity trackers can help collect similar data with higher accuracy levels, although still in a moderate range [ 56 , 60 ]. In addition, some built-in sensors (eg, heart rate [HR]) can provide supplementary data and improve the accuracy of MVPA measures. They avoid most of the drawbacks of smartphone devices, as they provide a long battery life for the device, high device durability, and extended exposure time. Commercial activity trackers show good potential in the implementation of theory-based practices and improve the data collection procedure in human physiology research for both adults and adolescents [ 58 , 120 , 130 - 132 , 192 ]. Medical-grade activity trackers (hip, waist, or wrist worn), for example, ActiGraph devices, provide the highest measurement accuracy levels; however, they also have certain drawbacks. The hip and waist location can lead to low user compatibility levels and reduced exposure, whereas HR can only be measured with a wireless HR monitor [ 193 ]. Consequently, while developing PA interventions, researchers should consider these factors and choose the device according to the characteristics most suitable for their projects. It is important to note the findings of a recent review, which confirms that multicomponent interventions tend to be associated with higher intervention efficacy [ 24 ]. Although some researchers chose to use simplistic outcome measures such as a daily step count, studies show that a multidimensional approach with several outcome measures is more comprehensive [ 107 ]. Advancing mHealth Further: Technological Advances Applied in Smartphone-Based mHealth Interventions Researchers working in the smartphone-based mHealth field often face problems with participants’ engagement: the long-term retention levels are usually quite low at 18 months follow-up measurements [ 133 ]. One way to solve this problem is to make the intervention more attractive to the participants by personalizing it. Personalized smartphone-based PA mHealth interventions may be more effective and preferred by participants over interventions with a generic program and advices or notifications [ 28 , 51 ]. Various researchers have suggested that personalization or tailoring of PA interventions will positively affect participants’ perception and engagement [ 59 , 134 - 136 ]. Some studies have attempted to personalize the intervention components manually and using automated approaches [ 137 , 138 ]. Manual automation (where the researcher inputs a large amount of collected data individually into every participant’s profile) has shown positive trends. Nevertheless, depending on the number of variables, the number of entries required for each participant, and the number of participants, this approach might be too time-consuming to become impractical [ 139 , 140 ]. More automated approaches, specifically machine learning or data mining, require minimum assistance during the utilization period, and are therefore promising for solving big data challenges, including behavior change interventions [ 113 , 194 ]. Rabbi et al [ 138 , 195 ] have already successfully implemented machine-learning solutions in various smartphone-based mHealth interventions, demonstrating their potential. However, machine learning science is in its early stage of development, and some questions still need to be answered and tested. One of them concerns the level of automation: Which one should the researcher choose for his or her particular intervention? Although full manual tracking is considered outdated and has a high data collection burden, fully automated tracking requires high data collection accuracy and may lower participants’ self-awareness; therefore, semiautomated tracking is currently the best solution [ 141 , 142 ]. As the articles listed in Table S10 of Multimedia Appendix 1 show, numerous technological advances are increasingly being used in smartphone-based PA mHealth interventions. One example concerns HR monitoring. Previously, HR monitoring PA interventions used separate devices. Currently, commercial activity trackers include built-in HR sensors, which can increase participants’ acceptance of the intervention. Another example concerns the use of a smartphone’s inbuilt GPS sensor, which can provide high accuracy of movement speed and location, among others; however, it is highly energy consuming and drains smartphone batteries very fast. The latest power management algorithms help to reduce the resource demands of continuous sensing, which ensures longer usability time, providing researchers with additional data collection opportunities [ 143 ]. Implications for Future Research On the basis of this review and in light of the widely used international reporting guidelines, several recommendations for future research can be inferred: Support uniformity of reporting by describing interventions and procedures in an adequate and consistent manner, using industry-recognized reporting guidelines, such as PRISMA, CONSORT, and SPIRIT [ 196 ]. Develop and code interventions in a more systematic way, using recommended practices while taking into account new models that offer additional opportunities in behavior research [ 186 ]. Currently, the systematic approach is either not applied or various frameworks are being used (eg, different versions of taxonomy by Michie), which slows or even prevents knowledge transfer and evidence accumulation. After the first results will be yielded in the development of a methodology for linking BCTs to theoretical mechanisms and the Human Behavior-Change Project, more systematic solutions will become available [ 88 , 194 ]. Meta-analyses, including modern mHealth solutions (eg, smartphones) and excluding outdated devices or methods (intervention based solely on SMS, PDAs, etc), provided there is a sufficient number of studies meeting the inclusion criteria. Profit from interdisciplinary collaboration while developing mHealth interventions. Various researchers and research groups working on the development of PA mHealth interventions have underlined the positive effect of collaboration between related stakeholders and experts in the domains of behavior change, software development, machine learning or data science, physiology, and public health [ 29 , 65 , 70 , 94 , 102 , 106 , 113 , 135 , 144 ]. A recent systematic review demonstrated that the collaboration of experts from various research domains greatly enhances the quality of the produced publications and research work in general [ 145 ]. Perform more studies designed for adolescents, accounting for differences in levels of motivation and lifestyle compared with adults. Implement rapid study designs while evaluating the intervention (eg, MRT, Multiphase Optimization Strategy, Sequential Multiple Assignment Randomized Trial, etc) [ 32 ]. Implement wearable activity monitors with built-in sensors (eg, HR and GPS) will provide more opportunities for data collection. Both commercial and research-grade trackers are advantageous. However, the collaboration of two domains, for instance ActiGraph and Garmin, is yet to bring fruitful results [ 197 ]. Implement the latest findings of machine learning or data mining and artificial intelligence domains into behavior change interventions [ 88 , 138 , 194 ]. Improve engagement with smartphone-based mHealth interventions by testing and implementing meaningful gamification and social networking features [ 120 ]. Build the reward and engagement engine of the app in a way that users will become autonomously physically active over time and do not depend on an app, a tracker, or an intervention in perpetuity. Strengths and Limitations The strength of this scoping review is the comprehensive search strategy, which allows the majority of published related articles to be included. Therefore, the scope of the review is wider than the scope of systematic reviews on smartphone-based mHealth interventions for PA promotion. However, a scoping review does not consider the methodological quality assessment of the included studies. Consequently, several studies had moderate methodological quality, which calls for their findings into question. It is important to emphasize that the included interventions developed and evaluated apps and activity trackers that provide sensor-based feedback on PA. Smartphone-based interventions related to chronic diseases other than cardiovascular diseases and obesity (eg, diabetes mellitus), preventive health issues (eg, alcohol abuse, smoking, and sports injuries), weight loss, diet, and nutrition were not included in this review. Finally, yet most importantly, only smartphone-based mHealth interventions were included in this review. Conclusions Smartphone-based mHealth interventions aimed at PA promotion in adolescents and adults show promising results for effective behavior change. Although there is a plethora of published studies with adults, the number of studies and, consequently, the evidence base for adolescents is very limited. In the past few years, a growing number of researchers have developed multicomponent mHealth interventions that, in addition to the app, include commercial or research-grade activity trackers, which can provide additional insight into a participant’s lifestyle. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach to developing, reporting, and coding of the interventions. Specifically, researchers should aim to develop theory-based rather than theory-inspired interventions, which is currently challenging, as there is no consensus on development, evaluation, or coding practice. Finally, the current stage of behavior science advocates an interdisciplinary approach to the development of behavior change interventions, including innovative approaches such as machine learning and data mining. Acknowledgments The first author is supported by an Industrial Fellowship from the Luxembourg National Research Fund (reference number 12674722) and by Actimage Luxembourg SA. The authors are grateful for the support of the graduate psychology student Claudia Manuela Vila Verde Gonçalves for reviewing the literature. Conflicts of Interest References Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Lancet Physical Activity Series Working Group. The pandemic of physical inactivity: global action for public health. Lancet 2012 Jul 21;380(9838):294-305. [ CrossRef ] [ Medline ] Lee I, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, Lancet Physical Activity Series Working Group. 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