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.

项目摘要/摘要 能量摄入（EI）在病因和预防流行和衰弱的慢性病中起着关键作用 超重/肥胖和2型糖尿病等疾病。自我监控是自我的基石 减少EI的法规方法，但现行的方法是繁重的，不准确，这限制了我们的 能够理解饮食模式并干预它们以改善健康的能力。显然需要 创新的解决方案可以在日常生活的背景下不明显地监视和可靠地估算EI。对于10+ 多年来，我们的小组研究了腕部观察设备（例如智能手表）的实用性，以被动监视 饮食行为通过测量食物的主导手腕运动的加速和旋转 带到嘴里。通过几项研究，我们完善了使用手腕运动模式的方法 在用餐/零食中识别单个摄入量（饮料的“咬”，饮料的饮料）。我们已经显示了 我们可以使用先进的建模来估算进餐式EI来估计餐级EI 每口（kpb）和每饮料的千瓦时（例如ei = #bites x kpb + #drinks x kpd）。我们是 关于使这种方法广泛用于临床应用的风口，但是我们在传感器方面的最新进展 - 基于EI的估计需要验证该方法在现实世界中真正可行。在这个项目中，我们 将确定地解决3个最终障碍：1）我们的方法必须在各个环境中以及高度验证 代表性样本； 2）我们使用进气手势估算EI的模型必须考虑变化 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的基于传感器的EI估计 在三种环境（自助餐厅，基于家庭和自由生活）的不同样本中的方法，合并 最少的食物和饮料的用户输入（例如，高能量密度，零卡路里饮料）和 上下文（例如食物来源，一天中的时间），并使用两个不同的传感器（商业智能手表和智能 戒指）。我们将进行两个受控的数据收集，其中一顿饭是视频的 戴智能手表和智能戒指：自助餐厅中的n = 300，在参与者的房屋中n = 240。所有参与者 （n = 540）然后将在日常生活的4天内佩戴两种设备并完成远程食品摄影 （自由生活）。我们将评估使用视频捕获的地面真相的基于传感器的估计值（自助餐厅 和家）和远程食品摄影方法（自由生活）。我们将使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自我监控协议的平台 最大化准确性和可接受性（选择腕部与环形传感器，用户输入类型以及自我长度 监视）。我们的平台最终将通过改变我们的发展方式和 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。