Validating Sensor-based Approaches for Monitoring Eating Behavior and Energy Intake by Accounting for Real-World Factors that Impact Accuracy and Acceptability

通过考虑影响准确性和可接受性的现实因素来验证基于传感器的饮食行为和能量摄入监测方法

• 批准号: 10636986
• 负责人: Stephanie Paige Goldstein
• 金额: $ 67.8万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10636986
• 关键词: Acceleration; Accounting; Address; Adult; Affect; Behavior; Behavioral; Beverages; Bite; Calories; Cellular Phone; Characteristics; Chronic Disease; Clinical; Data; Data Collection; Devices; Diet Records; Eating; Eating Behavior; Ecological momentary assessment; Energy Intake; Ensure; Etiology; Family; Fingers; Food; Gestures; Hand; Health; Health Benefit; Home; Household; Image; Individual; Informal Social Control; Intake; Intervention; Length; Life; Liquid substance; Location; Measurement; Measures; Methods; Modeling; Monitor; Morbidity - disease rate; Motion; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Oral cavity; Overweight; Participant; Patient Self-Report; Patients; Pattern; Performance; Photography; Play; Prevention; Procedures; Protocols documentation; Public Health; Research; Research Personnel; Role; Rotation; Sampling; Source; Technology; Testing; Time; Validation; Video Recording; Work; Wrist; clinical application; contextual factors; cost; design; dietary; energy density; implementation barriers; improved; innovation; mathematical model; member; mortality; obesity treatment; precision nutrition; sensor; smart watch; tool; validation studies; wrist motion

Project Summary/Abstract Energy intake (EI) plays a critical role in the etiology and prevention of prevalent and debilitating chronic diseases such as overweight/obesity and type 2 diabetes. Self-monitoring is the cornerstone of the self- regulation approach for reducing EI, but prevailing methods are burdensome and inaccurate which limits our ability to understand eating patterns and intervene on them to improve health. There is a clear need for innovative solutions that can unobtrusively monitor and reliably estimate EI in the context of daily life. For 10+ years, our group has researched the utility of a wrist-watch device (e.g., smartwatch) to passively monitor eating behavior by measuring the acceleration and rotation of dominant-hand wrist motion of food being brought to the mouth. Through several studies we have refined our approach for using patterns of wrist motion to identify individual intake gestures ("bite" of food, "drink" of beverage) during meals/snacks. We have shown that we can use intake gesture count to estimate meal-level EI by using advanced modeling to estimate kilocalories per bite (KPB) and kilocalories per drink (KPD) (e.g., EI = #bites x KPB + #drinks x KPD). We are on the cusp of making this approach widely available for clinical application, but our latest advances in sensor- based EI estimation require validation before the method is truly viable in real-world settings. In this project we will definitively address 3 final barriers: 1) Our approach must be validated across settings and among a highly representative sample; 2) Our models that use intake gestures to estimate EI must account for varying contexts, such as different types of foods or food sources, that could influence EI; and 3) We must maximize acceptability of the measurement methods. The proposed study will validate our sensor-based EI estimation methods among a diverse sample, across three settings (cafeteria, home-based, and free-living), incorporating minimal user input on foods and beverages (e.g., high energy density foods, zero calorie beverages) and contexts (e.g., food source, time of day), and using two different sensors (commercial smartwatch and smart ring). We will conduct two controlled data collections in which a single meal is video recorded while participants wear the smartwatch and smart ring: N=300 in a cafeteria and N=240 in participant homes. All participants (N=540) will then wear both devices and complete remote food photography during 4 days of everyday life (free living). We will evaluate sensor-based estimates of EI against ground truth captured using video (cafeteria and home) and remote food photography method (free-living). We will use our findings to create a practical platform to guide researchers/clinicians implementing a sensor-based EI self-monitoring protocol that maximizes accuracy and acceptability (selecting wrist vs. ring sensor, type of user input, and length of self- monitoring). Our platform will ultimately support work in precision nutrition by transforming how we develop and evaluate health-related interventions, and ultimately improve the quality of interventions targeting EI.

项目概要/摘要 能量摄入 (EI) 在流行性和衰弱性慢性病的病因学和预防中起着至关重要的作用 超重/肥胖和 2 型糖尿病等疾病。自我监控是自我监控的基石 降低EI的监管方法，但现行方法繁琐且不准确，限制了我们 了解饮食模式并对其进行干预以改善健康的能力。有一个明确的需要 创新的解决方案可以在日常生活中不引人注目地监测和可靠地估计 EI。适合 10 岁以上 多年来，我们的小组研究了手表设备（例如智能手表）被动监控的实用性 通过测量食物的惯用手手腕运动的加速度和旋转来进行饮食行为 送到嘴边。通过多项研究，我们改进了使用手腕运动模式的方法 识别进餐/零食期间的个人摄入姿势（“咬”食物、“喝”饮料）。我们已经展示了 我们可以使用摄入手势计数来估计进餐水平 EI，通过使用高级建模来估计 每口千卡热量 (KPB) 和每杯饮料千卡热量 (KPD)（例如，EI = #bites x KPB + #drinks x KPD）。我们是 即将使这种方法广泛应用于临床，但我们在传感器方面的最新进展 基于 EI 估计的方法在实际环境中真正可行之前需要进行验证。在这个项目中我们 将明确解决最后 3 个障碍：1）我们的方法必须在各种环境和高度评价中进行验证 代表性样本； 2) 我们使用摄入手势来估计 EI 的模型必须考虑到不同的因素 可能影响EI的环境，例如不同类型的食物或食物来源； 3）我们必须最大化 测量方法的可接受性。拟议的研究将验证我们基于传感器的 EI 估计 跨三种环境（自助餐厅、家庭和自由生活）的不同样本中的方法，包括 用户对食品和饮料的投入最少（例如，高能量密度食品、零卡路里饮料）以及 环境（例如食物来源、一天中的时间），并使用两种不同的传感器（商用智能手表和智能手表） 戒指）。我们将进行两次受控数据收集，其中一次膳食被视频记录，而参与者 佩戴智能手表和智能戒指：在自助餐厅 N=300，在参与者家里 N=240。所有参与者 (N=540) 将在 4 天的日常生活中佩戴这两种设备并完成远程食物摄影 （自由生活）。我们将根据使用视频捕获的地面实况来评估基于传感器的 EI 估计（自助餐厅 和家）和远程美食摄影方法（自由生活）。我们将利用我们的发现来创建一个实用的 指导研究人员/临床医生实施基于传感器的 EI 自我监测协议的平台 最大限度地提高准确性和可接受性（选择腕式传感器与环式传感器、用户输入的类型以及自我识别的长度） 监控）。我们的平台最终将通过改变我们的开发和利用方式来支持精准营养工作 评估与健康相关的干预措施，并最终提高针对 EI 的干预措施的质量。