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: 10425265
• 负责人: EDWARD S SAZONOV
• 金额: $ 58.47万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10425265
• 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 delivery; 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.

项目概要/摘要 长期的体重控制很难实现，需要永久改变饮食行为。 可穿戴传感器技术能够准确、客观地测量摄入行为，并实时测量 传感器数据的分析为开发个性化定制并立即交付铺平了道路 干预（及时适应性干预；JITAI）以经验和基础为基础来改变饮食行为。 理论上支持体重控制的行为改变策略，所提出的项目依赖于 可穿戴传感器技术、机器学习、行为科学、个性化医疗和 我们之前开发了一种可穿戴传感器“自动摄入”。 监测器 (AIM)，自动准确地检测饮食并表征膳食微观结构（例如， 进食时间、摄入速率）这些数据也可用于准确估计能量摄入量。 该项目的目标是：1）使用 AIM 研究观察到的两种常见行为模式 超重/肥胖的个体，即每日总能量摄入量(EI)过多和进食速度过快2) 为针对这些行为的两个 JITAI 定义个性化最佳触发指标；3) 评估； JITAI 对日常能量摄入和目标行为的影响 为了实现这些目标，我们将首先进行。 一项研究来表征目标饮食行为，然后模拟和定义个性化的触发指标 JITAI 旨在改变有针对性的饮食行为并降低 EI JITAI 植根于自我调节理论。 (SRT)：设定行为目标并监控该目标的进展情况，并提供反馈以强化成功。 为了启用基于 SRT 的 JITAI，我们将首先使用 AIM 收集有关摄入行为的数据 通过对 90 名超重/肥胖的成年人进行客观的、传感器测量的指标进行量化，这些成年人将佩戴 其次，使用收集到的数据集，我们将： a) 分析个人。 累积每日 EI 和进食次数内进食速率的曲线，以定义触发参数 个性化 JITAI 交付，以及 b) 对 JITAI 交付和效果进行数字模拟。 第二项研究评估 JITAI 对自由生活参与者的 EI 和摄取行为的直接影响。 我们将对 128 名佩戴 AIM 的成年人进行为期 7 周的受试者内试验，以实现 JITAI 的个性化。 AIM 将在 1 周的磨合期内学习个人饮食模式 每个 JITAI 将交付两周。 （第 2-3 周和第 5-6 周）采用随机交叉设计，得出每日 EI 和摄入行为 与基线进行比较，并在第 4 周和第 4 周通过问卷评估 JITAI 的可接受性。 7、参与者将继续佩戴AIM（无JITAI）以评估干预效果的持久性。 拟议的项目是证明基于 AIM 的 JITAI 可以改变各种饮食行为的第一步 与EI过多有关。