Smartphone sensors to detect shifts toward healthy behavior during alcohol treatment

摘要 酗酒（女性/男性4+/5+杯/次）会增加可预防的酒精相关疾病的风险 特别是在年轻人（18-25岁）中。作为我们的NIAAA资助的机制的一部分， 酒精治疗变更（MATCH）短信干预随机对照试验（R 01 AA 023650）， 我们得到资助（CTSI试点）收集（但不分析）智能手机传感器（例如，全球定位系统、通信日志） 以及MATCH子样本（N=108）中关于饮酒行为的生态瞬时评估（EMA）数据。 这种辅助数据分析R21将利用在R21中收集的电话传感器数据的独特组合。 酒精临床试验的背景下，以获得对行为改变潜在过程的新见解。手机传感器 MATCH期间收集的数据提供了对个人日常旅行的精细客观测量 模式和访问的地方，和社交性（通信模式）。这些精细的数字痕迹或数字 表型提供了年轻成年人日常生活（例如，旅行，社交）的变化 与TM干预的反应有关。手机传感器数据提供了一种客观地确定何时以及 行为变化如何与治疗效果相关，这将告知基于手机传感器的个人- 下一次数字干预迭代的实现。拟定的二次分析侧重于MATCH 子样本（n=93; 71%女性，范围18-25），EMA数据和电话传感器数据。电话传感器（例如， GPS、加速计、通信日志）和EMA数据收集超过14周（2周导入+ 12- 周干预）。2周的“磨合期”提供了一个基线的日常“例程”，由手机传感器推断， 干预在我们以前的工作中，我们确定了干预“响应者”和“非响应者”类;或探索 使用世界卫生组织风险饮酒水平的降低来定义响应。目标1比较治疗 数字表型上的应答者和非应答者（例如，在TM干预之前进行旅行、通信）。 目的2比较TM干预期间数字表型的应答者和非应答者。探索性 aim使用组迭代多模型估计（GIMME）同时估计 特定于个人（具体分析）和组（响应者/非响应者）的选定电话传感器功能 TM干预期间的水平（以补充目标1和2中基于人群的分析）。探索性 分析提供详细的个人层面信息，以指导干预内容的个性化， 还指示在组级共享的关联。分析还将探讨性别差异， 时间效应（例如，这些创新的次要数据分析， 符合NIAAA推进个性化医疗的战略计划，将（1）确定何时以及如何 年轻人的饮酒行为发生变化与TM干预有关，揭示了替代健康 响应程序;以及（2）为R 01项目提供基础，该项目使用电话传感器数据来个性化 酒精短信干预提出的建议，以优化数字干预效果。

ABSTRACT Binge drinking (4+/5+ drinks/occasion for females/males) increases risk for preventable alcohol-related consequences, particularly among young adults (ages 18-25). As part of our NIAAA-funded Mechanisms of Alcohol Treatment Change (MATCH) text message intervention randomized controlled trial (R01 AA023650), we were funded (CTSI pilot) to collect (but not analyze) smartphone sensor (e.g., GPS, communication logs) and Ecological Momentary Assessment (EMA) data on drinking behavior in a MATCH subsample (N=108). This secondary data analysis R21 will leverage the unique combination of phone sensor data collected in the context of an alcohol clinical trial to gain new insight into processes underlying behavior change. Phone sensor data collected during MATCH provides fine-grained objective measures of a person's daily routine in travel pattern and places visited, and sociability (communication pattern). These fine-grained digital traces or digital phenotypes provide objective markers of how a young adult's daily routine (e.g., travel, sociability) changes in relation to response to TM intervention. Phone sensor data provide a means to objectively determine when and how shifts in behavior occur in relation to treatment effects, which will inform phone-sensor-based personal- ization of the next iteration of the digital intervention. Proposed secondary analyses focus on the MATCH subsample (n=93; 71% female, range 18-25]) with EMA data and phone sensor data. Phone sensor (e.g., GPS, accelerometer, communication logs) and EMA data were collected over 14 weeks (2-week run-in + 12- week intervention). The 2-week "run-in" provides a baseline daily "routine" inferred by phone sensors prior to intervention. As in our prior work, we identify intervention "responder" and "non-responder" classes; or explore defining response using reduction in World Health Organization risk drinking level. Aim 1 compares treatment responders and non-responders on digital phenotypes (e.g., travel, communication) prior to TM intervention. Aim 2 compares responders and non-responders on digital phenotypes during TM intervention. An exploratory aim uses group iterative multiple model estimation (GIMME) to simultaneously estimate associations between selected phone sensor features at person-specific (idiographic analysis) and group (responder/non-responder) levels during TM intervention (to complement population-based analyses in Aims 1 and 2). Exploratory analyses provide detailed individual-level information to guide personalization of intervention content, while also indicating associations that are shared at the group-level. Analyses also will explore gender differences, time effects (e.g., weekend/weekday), and treatment arm. These innovative secondary data analyses, which are in line with NIAAA's strategic plan to advance personalized medicine, will (1) determine when and how shifts in young adults' drinking behavior occur in relation to TM intervention, revealing alternative healthy routines in responders; and (2) provide the basis for an R01 project that uses phone sensor data to personalize the recommendation made by an alcohol text message intervention to optimize digital intervention effects.