Faculty of Medicine and Health, Westmead Applied Research Centre, The University of Sydney, Sydney, New South Wales, Australia
Centre for Health Informatics - Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Br J Sports Med. 2021 Apr;55(8):422-432. doi: 10.1136/bjsports-2020-102892. Epub 2020 Dec 21.
To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.
Systematic review and meta-analysis.
PubMed and seven additional databases, from 2007 to 2020.
Randomised controlled trials in adults (18-65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.
We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.
Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.
Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I=69%, =0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression.
Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.
确定涉及具有自动和连续自我监测及反馈功能的移动应用程序(app)或追踪器的身体活动干预措施的有效性。
系统评价和荟萃分析。
PubMed 及另外 7 个数据库，检索时间为 2007 年至 2020 年。
在没有慢性病的成年人（18-65 岁）中进行的随机对照试验，测试移动应用程序或活动追踪器，任何比较的主要结果都是身体活动测量。独立进行筛选。
我们进行了随机效应荟萃分析，所有的效应量都转化为标准化均数差(SDM)。我们进行了探索性荟萃回归分析，其中连续和离散的调节变量在亚组分析中被确定为具有统计学意义。
身体活动：日常步数、中等至剧烈体力活动每周分钟数、每周运动天数、总体力活动每周分钟数、代谢当量。
35 项研究符合纳入标准，28 项研究被纳入荟萃分析（n=7454 名参与者，28%为女性）。荟萃分析显示，身体活动测量有较小到中等程度的积极影响（SDM 0.350，95%置信区间 0.236 至 0.465，I²=69%，p=0.051），相当于每天增加 1850 步（95%置信区间 1247 至 2457）。在亚组分析和荟萃回归中，包括短信和个性化功能的干预措施明显更有效。
使用应用程序或追踪器的干预措施似乎能有效促进身体活动。需要进行更长时间的研究来评估不同干预措施对长期参与和效果的影响。
