@文章{信息:doi/10.2196/26699，作者="M{\" nninghoff, Annette and Kramer, Jan Niklas and Hess, Alexander Jan and Ismailova, Kamila and Teepe, Gisbert W and Tudor Car, Lorainne and M{" \"u}ller-Riemenschneider, Falk and Kowatsch, Tobias"，标题="移动健康体育活动干预的长期有效性:随机对照试验系统综述与meta分析”，期刊=“J Med Internet Res”，年=“2021”，月=“Apr”，日=“30”，卷=“23”，数=“4”，页数=“e26699”，关键词=“mHealth;身体活动;系统评价;荟萃分析;背景:移动健康(mHealth)干预可以增加身体活动(PA);然而，它们的长期影响还没有得到很好的理解。目的:本研究的主要目的是了解移动健康干预对PA的即时和长期影响。第二目标是探索潜在的效应调节因子。方法:我们根据Cochrane和PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)指南进行了本研究。 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. ", issn="1438-8871", doi="10.2196/26699", url="//www.mybigtv.com/2021/4/e26699", url="https://doi.org/10.2196/26699", url="http://www.ncbi.nlm.nih.gov/pubmed/33811021" }