@Article{信息:doi 10.2196 / /移动医疗。9117，作者=“Zhou, Mo和Fukuoka, Yoshimi和Mintz, Yonatan和Goldberg, Ken和Kaminsky, Philip和Flowers, Elena和Aswani, Anil”，标题=“评估机器学习——通过手机应用程序实现的基于自动化个性化的每日步数目标:随机对照试验”，期刊=“JMIR移动健康Uhealth”，年=“2018”，月=“Jan”，日=“25”，卷=“6”，数=“1”，页=“e28”，关键词=“身体活动;手机;健康跟踪;背景:越来越多的证据表明，固定的、非个性化的每日步数目标会使人气馁，导致身体活动不变甚至减少。目的:本随机对照试验(RCT)的目的是评估使用机器学习的基于手机的自动个性化和自适应目标设定干预的效果，与每日稳定步数目标为10,000的主动对照相比。方法:在这项为期10周的随机对照试验中，64名参与者通过电子邮件通知被招募，并被要求参加最初的面对面会议。经过一段数据收集的磨合期后，参与者被随机分为干预组或积极对照组，比例为1:1。研究人员在每个参与者的手机上安装了一款研究开发的手机应用程序(该应用程序通过推送通知提供每日步数目标，并允许实时监测身体活动)，参与者被要求一整天都把手机放在口袋里。通过应用程序，干预组获得了完全自动化的自适应个性化的每日步数目标，对照组获得了每天10,000步的固定步数目标。 Daily step count was objectively measured by the study-developed mobile phone app. Results: The mean (SD) age of participants was 41.1 (11.3) years, and 83{\%} (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95{\%} CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. Conclusions: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. Trial Registration: ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng). ", issn="2291-5222", doi="10.2196/mhealth.9117", url="http://mhealth.www.mybigtv.com/2018/1/e28/", url="https://doi.org/10.2196/mhealth.9117", url="http://www.ncbi.nlm.nih.gov/pubmed/29371177" }