Background Over the past several decades, numerous large cohort studies have attempted to quantify the protective effect of physical activity on cardiovascular ...

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.

The Global Physical Activity Questionnaire (GPAQ) was originally designed to be interviewer-administered by the World Health Organization in assessing physical activity. The main aim of this study was to compare the psychometric properties of a self-administered GPAQ with the original interviewer-administered approach. Additionally, this study explored whether using different accelerometry-based physical activity bout definitions might affect the questionnaire’s validity. A total of 110 participants were recruited and randomly allocated to an interviewer- (n = 56) or a self-administered (n = 54) group for test-retest reliability, of which 108 participants who met the wear time criteria were included in the validity study. Reliability was assessed by administration of questionnaires twice with a one-week interval. Criterion validity was assessed by comparing against seven-day accelerometer measures. Two definitions for accelerometry-data scoring were employed: (1) total-min of activity, and (2) 10-min bout. Participants had similar baseline characteristics in both administration groups and no significant difference was found between the two formats in terms of validity (correlations between the GPAQ and accelerometer). For validity, the GPAQ demonstrated fair-to-moderate correlations for moderate-to-vigorous physical activity (MVPA) for self-administration (r_s = 0.30) and interviewer-administration (r_s = 0.46). Findings were similar when considering 10-min activity bouts in the accelerometer analysis for MVPA (r_s = 0.29 vs. 0.42 for self vs. interviewer). Within each mode of administration, the strongest correlations were observed for vigorous-intensity activity. However, Bland-Altman plots illustrated bias toward overestimation for higher levels of MVPA, vigorous- and moderate-intensity activities, and underestimation for lower levels of these measures. Reliability for MVPA revealed moderate correlations (r_s = 0.61 vs. 0.63 for self vs. interviewer). Our findings showed comparability between both self- and interviewer-administration modes of the GPAQ. The GPAQ in general but especially the self-administered version may offer a relatively inexpensive method for measuring physical activity of various types and at different domains. However, there may be bias in the GPAQ measurements depending on the overall physical activity. It is advisable to incorporate accelerometers in future studies, particularly when measuring different intensities of physical activity.