Martin Seth S, Feldman David I, Blumenthal Roger S, Jones Steven R, Post Wendy S, McKibben Rebeccah A, Michos Erin D, Ndumele Chiadi E, Ratchford Elizabeth V, Coresh Josef, Blaha Michael J
Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, MD (S.S.M., D.I.F., R.S.B., S.R.J., W.S.P., R.A.M.K., E.D.M., C.E.N., E.V.R., M.J.B.) Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (S.S.M., W.S.P., R.A.M.K., E.D.M., C.E.N., J.C.).
Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, MD (S.S.M., D.I.F., R.S.B., S.R.J., W.S.P., R.A.M.K., E.D.M., C.E.N., E.V.R., M.J.B.).
J Am Heart Assoc. 2015 Nov 9;4(11):e002239. doi: 10.1161/JAHA.115.002239.
We hypothesized that a fully automated mobile health (mHealth) intervention with tracking and texting components would increase physical activity.
mActive enrolled smartphone users aged 18 to 69 years at an ambulatory cardiology center in Baltimore, Maryland. We used sequential randomization to evaluate the intervention's 2 core components. After establishing baseline activity during a blinded run-in (week 1), in phase I (weeks 2 to 3), we randomized 2:1 to unblinded versus blinded tracking. Unblinding allowed continuous access to activity data through a smartphone interface. In phase II (weeks 4 to 5), we randomized unblinded participants 1:1 to smart texts versus no texts. Smart texts provided smartphone-delivered coaching 3 times/day aimed at individual encouragement and fostering feedback loops by a fully automated, physician-written, theory-based algorithm using real-time activity data and 16 personal factors with a 10 000 steps/day goal. Forty-eight outpatients (46% women, 21% nonwhite) enrolled with a mean±SD age of 58±8 years, body mass index of 31±6 kg/m(2), and baseline activity of 9670±4350 steps/day. Daily activity data capture was 97.4%. The phase I change in activity was nonsignificantly higher in unblinded participants versus blinded controls by 1024 daily steps (95% confidence interval [CI], -580 to 2628; P=0.21). In phase II, participants receiving texts increased their daily steps over those not receiving texts by 2534 (95% CI, 1318 to 3750; P<0.001) and over blinded controls by 3376 (95% CI, 1951 to 4801; P<0.001).
An automated tracking-texting intervention increased physical activity with, but not without, the texting component. These results support new mHealth tracking technologies as facilitators in need of behavior change drivers.
URL: http://ClinicalTrials.gov/. Unique identifier: NCT01917812.
我们假设一种具有跟踪和短信功能的全自动移动健康（mHealth）干预措施会增加身体活动量。
mActive研究招募了马里兰州巴尔的摩市一家门诊心脏病中心年龄在18至69岁的智能手机用户。我们采用序贯随机化方法评估该干预措施的两个核心组成部分。在为期1周的盲法导入期确定基线活动量后，在第一阶段（第2至3周），我们按2:1随机分为非盲法跟踪组与盲法跟踪组。非盲法跟踪允许通过智能手机界面持续获取活动数据。在第二阶段（第4至5周），我们将非盲法参与者按1:1随机分为智能短信组与无短信组。智能短信通过智能手机每天提供3次指导，旨在通过基于理论、由医生编写的全自动算法利用实时活动数据和16项个人因素，以每天10000步为目标进行个性化鼓励并促进反馈循环。48名门诊患者（46%为女性，21%为非白人）入组，平均年龄±标准差为58±8岁，体重指数为31±6kg/m²，基线活动量为每天9670±4350步。每日活动数据捕获率为97.4%。第一阶段，非盲法参与者的活动量变化比盲法对照组每天高1024步，但差异无统计学意义（95%置信区间[CI]，-580至2628；P=0.21）。在第二阶段，接收短信的参与者比未接收短信的参与者每天多走2534步（95%CI，1318至3750；P<0.001），比盲法对照组多走3376步（95%CI，1951至4801；P<0.001）。
一种自动化跟踪-短信干预措施增加了身体活动量，且短信这一组成部分起到作用。这些结果支持将新的移动健康跟踪技术作为需要行为改变驱动因素的促进手段。
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