What is the role of smartphones on physical activity promotion? A systematic review and meta-analysis

Abstract

Objectives To identify and evaluate the effect of interventions that used cell phones as a means to promote physical activity (PA). Methods The databases searched were MedLine/PubMed, Scopus, SPORTDiscus, PsycINFO, Science Direct, Lilacs, and SciELO. After removing duplicates, applying exclusion criteria, and checking the reference lists, 45 studies were reviewed. The Downs and Black (D&B) scale measured methodological quality, and a random effect model was used to compute the meta-analysis of PA by the reported unit (minutes per day or steps per day), delivery agent (application (APP), SMS, or other), and PA measurement (questionnaire, accelerometer, pedometer). Results Mobile phone-based PA interventions were efficient in increasing both minutes [10.49;); p = 0.004] and steps per day [735.17; CI (227.72-1242.61); p = 0.005] in adults when compared to baseline. Furthermore, APP-based interventions were able to increase the number of steps (p = 0.04) and minutes per day of PA (p = 0.04) in adults. Also, 85% of included manuscripts were classified as moderate-to high-quality articles. Conclusions Mobile phone-based PA interventions, inclusive those delivery by APP, were effective to increase minutes and steps per day in adults.

Improving prevention strategies for cardiometabolic disease

“…Importantly, building physical activity into the day, using approaches such as active travel 37 or by incorporating multiple small amounts activity throughout the day can provide tangible benefits 38 . Pedometer-supported behaviour change approaches which incorporate setting of progressive physical activity goals, and provide selfmonitoring and feedback of activity levels are effective at increasing physical activity levels 39 , and there is emerging evidence that smartphone-based approaches can be effective for promoting physical activity 40 . However, it is important that targets are personalised and realistic -for example, for someone who is very inactive, initially increasing step count by 1000 per day, equivalent to about 10 extra minutes of walking, can potentially provide benefit and provides a platform for further increases in activity with time.…”

Commercial app use linked with sustained physical activity in two Canadian provinces: a 12-month quasi-experimental study

“…A 2016 systematic review [18] and a 2019 meta-analysis [20] of studies using apps to improve PA found that few stand-alone app interventions reported positive effects. Another recent meta-analysis [21] and systematic review [19] on the other hand found that app-based interventions increased PA. The still limited number of RCTs in this area (n < 10), due in part to the rapid pace of app development and rollout, may help explain the discrepancies [19,20].…”

“…To enhance our understanding of this rapidly evolving field non-RCT alternatives (e.g., quasi-experimental designs) are needed [19,22,23]. Longitudinal designs in particular are warranted given the majority of studies do not exceed three months [19][20][21] even though sustained PA is needed to attain many of the purported health benefits [1]. Rigorous quasi-experimental evaluations of top tier commercial apps (i.e.…”

Examining Responsiveness to an Incentive-Based Mobile Health App: Longitudinal Observational Study

“…The number of mHealth app downloads also increased by 16% from 2016 to 2017 (3.2 to 3.7 billion) [ 7 ]. Although both supply (apps published in stores) and demand (app downloads) is growing, low engagement (with engagement measured as repeated usage, consistent with behavioral science approaches [ 8 , 9 ]) has resulted in small effect sizes and presented hurdles for financial sustainability continues to be a challenge for the industry [ 10 - 13 ]. For instance, 90% of all mHealth apps are uninstalled within 30 days, and 83% of mHealth app companies have fewer than 10,000 monthly active users, a standard industry engagement metric [ 14 ].…”

“…For instance, 90% of all mHealth apps are uninstalled within 30 days, and 83% of mHealth app companies have fewer than 10,000 monthly active users, a standard industry engagement metric [ 14 ]. Systematic reviews of controlled studies on this topic suggest that tailoring content to individual characteristics, regularly updating apps, and incorporating a range of behavior change techniques, for example, may boost engagement [ 10 - 13 ]. To date, however, evaluations of only a few mHealth apps out of the thousands in the app stores have been published in peer-reviewed scientific journals [ 12 ].…”