Brief ReportImplementation of Behavior Change Techniques in Mobile Applications for Physical Activity
Introduction
Mobile technology has captured the imagination of healthcare workers and patients as a promising vehicle for delivering health-related interventions with potentially greater reach and lower long-term cost than in-person interventions.1 More than 50% of American adults own smartphones and half of those owners use their phone to search for health information.2 Approximately 50% of mobile subscribers use a fitness application (app).3, 4 Apps that increase physical activity levels would be valuable because insufficient physical activity is the second-leading preventable cause of death in the U.S., with links to heightened risk for major non-communicable diseases.5, 6 Despite the popularity of fitness apps, their efficacy for increasing physical activity is largely unknown, in part because their dynamic and evolving nature presents a challenge to the slow pace of conventional evaluation methods.7
In the absence of high-quality evidence from RCTs, clinicians or patients can benefit from an informed review of app features to guide their selections of apps to increase physical activity and prevent health problems. Apps have previously been evaluated on the basis of their theoretical content, potential for behavior change, and consistency with evidence-based clinical practices.8, 9, 10, 11 Understanding which behavior change techniques (BCTs) are implemented can illuminate mechanisms by which using an app might facilitate behavior change as well as the types of patients for whom a given app may work best. One recent study12 found that relatively few BCTs were identified in the marketing materials of fitness apps, and two types of apps—educational and motivational—were identified based on their BCT configurations. That study was limited by its focus on app descriptions in online marketing materials instead of inspecting apps to determine which BCTs were actually implemented. This study addresses this gap by auditing BCTs identified from a user inspection of apps.
Section snippets
Methods
Top consumer-rated physical activity apps in the “health and fitness” category of the Apple iTunes and Google Play marketplaces (N=100) were identified and downloaded for evaluation on November 22, 2013 (25 paid and 25 free apps from each marketplace) and analyzed in 2014. This set included apps from popular developers such as Endomondo, MapMyFitness, Nike, Noom, and Runtastic. Apps that appeared on both free and paid lists (n=8) or were available for both operating systems (n=6) were evaluated
Results
Overall, 39 of 93 possible BCTs were observed in the coded apps. Apps incorporated between one and 21 BCTs with an average of 6.6 in each app (SD=3.3, median=6). Table 1 indicates that the most commonly observed techniques involved providing social support, information about others’ approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. The number of BCTs did not differ significantly between free and paid apps (t[98]=1.43, p=0.08, d
Discussion
At present, BCTs have been only narrowly implemented in physical activity apps and most BCTs in the taxonomy were not observed in any apps. User inspection identified more BCTs in apps than did a review of marketing materials, although the rank ordering of BCTs from both sources was similar.12 Different coding systems were used in these studies; thus, comparisons should be interpreted cautiously.
The most common BCTs in the apps involved social support via online communities (e.g., Facebook,
Acknowledgments
Funding for this work was provided in part by the Pennsylvania Space Grant Consortium. The authors thank Rachel Angstadt, Anthony Augliera, Jazmine Gordon, Paige Kunz, Ashley Jones, Avernelle Maule, Devin Parambo, Sara Pelletier, and Elisabet Polanco for their contributions as coders.
No financial disclosures were reported by the authors of this paper.
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2021, Technological Forecasting and Social ChangeCitation Excerpt :The effect of BCTs on satisfaction is also supported. Previous studies consider BCTs in terms of the effect of their features on user ratings; for example, health apps with tracking scored significantly higher in engagement, aesthetics, and overall mobile app rating scale scores (Bardus et al., 2016; Yang et al., 2015). Studies also found that BCTs can promote users’ physical activity and healthy habits (Dallinga et al., 2015; Walsh et al., 2016).