Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial.
Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial.
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DOI：
10.2196/mhealth.9117
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发表时间：
2018-01-25
影响因子：
5
通讯作者：
Aswani A
中科院分区：
文献类型：
--
作者：
Zhou M;Fukuoka Y;Mintz Y;Goldberg K;Kaminsky P;Flowers E;Aswani A
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，导致身体活动量不变甚至减少。这项随机对照试验（RCT）的目的是评估使用机器学习的基于自动移动的手机的个性化和自适应目标设定干预与稳定每日步数目标为10，000的主动对照相比的有效性。在这项为期10周的RCT中，通过电子邮件通知招募了64名参与者，并要求他们参加首次面对面会议。在数据收集的导入期后，参与者被随机分为干预组或活性对照组，比例为一比一。研究开发的移动的手机应用程序（使用推送通知提供每日步数目标，并允许实时身体活动监测）安装在每个参与者的移动的手机上，参与者被要求全天将手机放在口袋里。通过该应用程序，干预组接受了完全自动化的自适应个性化每日步数目标，对照组接受了每天10，000步的恒定步数目标。每日步数通过研究开发的移动的手机应用程序进行客观测量。参与者的平均（SD）年龄为41.1（11.3）岁，83%（53/64）的参与者为女性。两组之间的基线人口统计学相似（P> 0.05）。干预组（n=34）的参与者在导入期和10周之间的平均（SD）每日步数减少了390（490）步，而对照组参与者减少了1350（420）步（n=30; P= 0.03）。两组间每日步数的净差异为960步（95% CI 90-1830步）。两组在导入期和10周之间的每日步数都有所减少，因为在导入期也提供了干预措施，并且没有收集自然基线。结果显示了这种干预措施的短期疗效，应该在一个全面的RCT中进行正式评估，并进行更长的随访。ClinicalTrials.gov：NCT 02886871; https://clinicaltrials.gov/ct2/show/NCT02886871（由WebCite在http：//www.webcitation.org/6wM1Be1Ng上存档）。
Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone–based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant’s mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. 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. 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. ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).
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