Sensor-based Just-in Time Adaptive Interventions (JITAIs) Targeting Eating Behavior

针对饮食行为的基于传感器的即时自适应干预措施 (JITAI)

• 批准号: 10160900
• 负责人: EDWARD S SAZONOV
• 金额: $ 58.81万
• 依托单位: UNIVERSITY OF ALABAMA IN TUSCALOOSA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10160900
• 关键词: Achievement; Adult; Behavior; Behavior Therapy; Behavioral; Behavioral Sciences; Body Weight decreased; Body mass index; Cellular Phone; Child; Crossover Design; Data; Data Set; Detection; Development; Devices; Diabetes Mellitus; Eating; Eating Behavior; Eating Disorders; Elderly; Energy Intake; Feedback; Feeding behaviors; Food; Future; Goals; Healthy Eating; Image; Individual; Informal Social Control; Ingestion; Intervention; Learning; Liquid substance; Machine Learning; Mastication; Measurement; Measures; Monitor; Obesity; Overweight; Participant; Patient Self-Report; Pattern; Plant Roots; Population; Questionnaires; Randomized; Running; Sampling; Testing; Time; Weight; Weight maintenance regimen; Work; adaptive intervention; base; behavior change; clinical practice; cost; healthy weight; increased appetite; innovation; intervention effect; nutrition; obese person; personalized intervention; personalized medicine; sensor; simulation; success; sucking; synergism; theories; wearable device; wearable sensor technology; weight maintenance

PROJECT SUMMARY/ABSTRACT Long-term weight control is difficult to achieve and requires permanent changes in eating behavior. Emerging wearable sensor technology enables accurate and objective measurement of ingestive behavior, and real-time analysis of the sensor data paves the way for development of individually tailored and immediately delivered intervention (just-in-time adaptive Intervention; JITAI) to change eating behavior. Grounded in empirically and theoretically supported behavior change strategies for weight control, the proposed project relies on the synergy of wearable sensor technology, machine learning, behavioral science, personalized medicine, and nutrition to deliver and test such JITAIs. We previously developed a wearable sensor, the Automatic Ingestion Monitor (AIM), that automatically and accurately detects eating and characterizes meal microstructure (e.g., eating duration, rate of ingestion). These data can also be used to accurately estimate energy intake. The goals of this project are to: 1) use the AIM to study two common behavioral patterns observed among individuals with overweight/obesity, namely, excessive total daily energy intake (EI) and fast eating rate; 2) define the optimal personalized triggering metrics for two JITAIs targeting these behaviors; and 3) evaluate JITAIs’ effects on daily energy intake and targeted behaviors. In fulfillment of these goals, we will first conduct a study to characterize the target eating behaviors, then simulate and define triggering metrics for personalized JITAIs to change targeted eating behaviors and decrease EI. The JITAIs are rooted in self-regulation theory (SRT): setting a behavioral goal and monitoring progress toward that goal, with feedback to reinforce success. To enable the SRT-informed JITAIs, we will first use the AIM to collect data about ingestive behaviors quantified by objective, sensor-measured metrics from 90 adults with overweight/obesity who will wear the device for one week in free living conditions. Second, using the collected dataset, we will: a) analyze individual curves of cumulative daily EI and rate of eating within eating episodes to define triggering parameters for personalized JITAI delivery, and b) numerically simulate JITAI delivery and effects. We will then conduct a second study to evaluate the immediate effect of JITAIs on EI and ingestive behavior in free living participants. We will conduct a within-subjects trial with 128 adults wearing the AIM for 7 weeks. To personalize JITAIs, the AIM will learn individual eating patterns over a 1-week run-in period. Each JITAI will be delivered for two weeks (weeks 2-3 and 5-6) in a randomized crossover design with the resulting daily EI and ingestive behavior compared to baseline and the acceptability of the JITAIs assessed via questionnaire. On washout weeks 4 and 7, participants will continue to wear the AIM (no JITAIs) to assess persistence of intervention effects. The proposed project is the first step in demonstrating that AIM-based JITAIs can alter a variety of eating behaviors associated with excess EI.

项目总结/摘要 长期的体重控制很难实现，需要饮食行为的永久性改变。新兴 可穿戴传感器技术能够准确、客观地测量摄食行为， 传感器数据的分析为开发个性化定制并立即交付的 及时适应性干预（JITAI）改变饮食行为。以经验为基础， 理论上支持的体重控制行为改变策略，建议的项目依赖于 可穿戴传感器技术、机器学习、行为科学、个性化医疗和 提供和测试这种JITAI的营养。我们以前开发了一种可穿戴传感器，自动摄取 监测器（AIM），自动准确地检测进食并表征膳食微观结构（例如， 进食持续时间、摄入速率）。这些数据也可以用来准确估计能量摄入。的 该项目的目标是：1）使用AIM研究在 超重/肥胖个体，即每日总能量摄入量（EI）过多和进食速度过快; 2） 定义针对这些行为的两个JITAI的最佳个性化触发度量;以及3）评估 JITAI对每日能量摄入和目标行为的影响。为了实现这些目标，我们将首先 一项研究，以表征目标饮食行为，然后模拟和定义个性化的触发指标， JITAIs改变有针对性的饮食行为，降低EI。JITAI植根于自我调节理论 (SRT)设定一个行为目标，并监控朝着这个目标的进展，通过反馈来加强成功。 为了启用SRT通知的JITAI，我们将首先使用AIM收集有关摄食行为的数据 量化的客观，传感器测量的指标，从90名成年人超重/肥胖谁将穿 在自由生活条件下使用一周。其次，使用收集的数据集，我们将：a）分析个人 累积每日EI曲线和进食事件内的进食率，以定义触发参数 个性化JITAI递送，以及B）数值模拟JITAI递送和效果。然后我们将进行一次 第二项研究是评估JITAIs对自由生活参与者的EI和摄食行为的直接影响。 我们将对128名成年人进行一项为期7周的受试者内试验。为了个性化JITAI， AIM将在1周的磨合期内学习个人的饮食模式。每个吉泰将交付两周 （第2-3周和第5-6周），采用随机交叉设计，每日EI和摄食行为 与基线相比，通过问卷调查评估JITAI的可接受性。洗脱期第4周， 7、受试者将继续佩戴AIM（无JITAI），以评估干预效果的持续性。的 拟议的项目是证明基于AIM的JITAI可以改变多种饮食行为的第一步 与过量的EI有关。