Background Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood. Objective The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators. Methods We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool. Results Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence. Conclusions mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects.

Background Insufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking. Objective We aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs. Methods We conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences. Results We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median −1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system. Conclusions Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change. Trial Registration ClinicalTrials.gov NCT04505124; https://clinicaltrials.gov/ct2/show/NCT04505124

BACKGROUND Insufficient physical activity and unhealthy diets are contributing to the rise in non-communicable diseases. Preventative mobile health (mHealth) interventions may enable reversing this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence whether such interventions can change health behavior is lacking. OBJECTIVE Our primary objectives are to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior, and to examine the feasibility of a novel automated nutrition tracking system. We also aim to understand how this intervention impacts related attitudinal and motivational constructs. METHODS We conducted a 12-week parallel randomized-controlled trial (RCT), followed by semi-structured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland, and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention—a physical activity and food purchase tracking mobile phone application that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional, text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition leveraging digital receipts from loyalty card data analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals and non-parametric tests were conducted to test for within- and between-group differences. RESULTS We recruited 167 participants; 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44.00 years (IQR 19.00), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power of our study. The FutureMe intervention led to directional, small increases in physical activity (median +242 steps/day) and to directional improvements in the nutritional quality of food purchases (median –1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased for the FutureMe group, but decreased for the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and an accepted fully automated nutrition tracking system. CONCLUSIONS Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change. CLINICALTRIAL The trial has been registered on ClinicalTrials.gov(NCT04505124)

BACKGROUND Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood. OBJECTIVE The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators. METHODS We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up &gt;6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool. RESULTS Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; <i>P</i>&lt;.001; MVPA SMD 0.28, 95% CI 0.21-0.35; <i>P</i>&lt;.001; TPA SMD 0.34, 95% CI 0.20-0.47; <i>P</i>&lt;.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; <i>P</i>=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; <i>P</i>=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; <i>P</i>=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; <i>P</i>=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; <i>P</i>=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; <i>P</i>&lt;.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence. CONCLUSIONS mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects.