Smartphone sensors to detect shifts toward healthy behavior during alcohol treatment

智能手机传感器可检测酒精治疗期间健康行为的转变

• 批准号: 10700036
• 负责人: Tammy Chung
• 金额: $ 19.12万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10700036
• 关键词: Accelerometer; Age; Alcohols; Behavior; Cellular Phone; Clinical Trials; Communication; Complement; Data; Data Analyses; Ecological momentary assessment; Feedback; Female; Frequencies; Funding; Funding Mechanisms; Goals; Grain; Heterogeneity; Individual; Intervention; Location; Measures; Modeling; National Institute on Alcohol Abuse and Alcoholism; Outcome; Participant; Patient Self-Report; Pattern; Persons; Phenotype; Process; Randomized; Randomized, Controlled Trials; Recommendation; Reporting; Risk; Running; Strategic Planning; Telephone; Text Messaging; Time; Travel; United States National Institutes of Health; Universities; Visit; Work; World Health Organization; alcohol abuse therapy; alcohol intervention; alcohol related consequences; alcohol response; behavior change; binge drinking; digital; digital intervention; drinking; drinking behavior; gender difference; innovation; insight; intervention delivery; intervention effect; male; personalized intervention; personalized medicine; population based; responders and non-responders; response; secondary analysis; sensor; text messaging intervention; treatment arm; treatment comparison; treatment effect; treatment responders; young adult

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.

抽象的 酗酒（女性/男性饮酒 4+/5+ 次/场合）会增加可预防的酒精相关疾病的风险 后果，尤其是年轻人（18-25 岁）。作为我们 NIAAA 资助机制的一部分 酒精治疗改变（MATCH）短信干预随机对照试验（R01 AA023650）， 我们获得资助（CTSI 试点）来收集（但不分析）智能手机传感器（例如 GPS、通信日志） MATCH 子样本中饮酒行为的生态瞬时评估 (EMA) 数据 (N=108)。 R21 的二次数据分析将利用在 R21 中收集的手机传感器数据的独特组合。 酒精临床试验的背景，以获得对行为改变背后的过程的新见解。手机传感器 MATCH 期间收集的数据提供了对个人日常旅行的细粒度客观测量 模式和访问过的地方，以及社交性（沟通模式）。这些细粒度的数字痕迹或数字 表型提供了年轻人日常生活（例如旅行、社交）如何变化的客观标记 与对 TM 干预的反应有关。手机传感器数据提供了一种客观确定何时和 行为变化如何与治疗效果相关，这将告知基于电话传感器的个人 数字干预的下一次迭代的化。建议的二次分析集中在 MATCH 上 子样本（n=93；71% 女性，范围 18-25]），包含 EMA 数据和手机传感器数据。手机传感器（例如， GPS、加速计、通信日志）和 EMA 数据在 14 周内收集（2 周磨合 + 12 周） 周干预）。为期 2 周的“磨合”提供了在使用之前由手机传感器推断出的每日基准“例程” 干涉。与我们之前的工作一样，我们确定了干预“响应者”和“非响应者”类别；或探索 通过降低世界卫生组织风险饮酒水平来定义应对措施。目标 1 比较治疗 TM 干预前对数字表型（例如旅行、交流）有反应的人和无反应的人。 目标 2 比较 TM 干预期间响应者和无响应者的数字表型。一个探索性的 Target 使用组迭代多模型估计 (GIMME) 来同时估计之间的关联 针对特定人员（具体分析）和组（响应者/非响应者）选择的手机传感器功能 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的信息来指导干预内容的个性化，同时 还指示在组级别共享的关联。分析还将探讨性别差异， 时间影响（例如周末/工作日）和治疗组。这些创新的二手数据分析， 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 年轻人饮酒行为的变化与 TM 干预有关，揭示了替代健康 响应者的例程； (2) 为使用手机传感器数据进行个性化的 R01 项目提供基础 通过酒精短信干预提出的建议，以优化数字干预效果。

项目成果

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.

利用手机传感器、机器学习和可解释的人工智能来预测即将发生的当天酗酒事件，以支持及时的自适应干预：算法开发和验证研究。

• 发表时间: 2023-05-04
• 影响因子: 2.2
• 作者: Bae, Sang Won;Suffoletto, Brian;Zhang, Tongze;Chung, Tammy;Ozolcer, Melik;Islam, Mohammad Rahul;Dey, Anind K
• 通讯作者: Dey, Anind K

Trajectory classes of engagement with an alcohol text message intervention and predictors of intervention engagement.

酒精短信干预的参与轨迹类别和干预参与的预测因子。

• 发表时间: 2023-09
• 影响因子: 4.4
• 作者: Chung, Tammy;Ahn, Curie;Suffoletto, Brian P
• 通讯作者: Suffoletto, Brian P

Prediction Rules Identify Which Young Adults Have Higher Rates of Heavy Episodic Drinking After Exposure to 12-Week Text Message Interventions.

预测规则可识别哪些年轻人在接受 12 周短信干预后，偶尔酗酒的比例较高。

• 发表时间: 2024-01
• 作者: Chung, Tammy;Suffoletto, Brian;Feldstein Ewing, Sarah W;Bhurosy, Trishnee;Jiang, Yanping;Valera, Pamela
• 通讯作者: Valera, Pamela