Center for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, Philadelphia, PA 19104, USA.
The Miriam Hospital's Weight Control and Diabetes Research Center, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Transl Behav Med. 2021 Dec 14;11(12):2099-2109. doi: 10.1093/tbm/ibab123.
Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.
生态瞬时评估（EMA；简短的自我报告调查）的饮食失误风险因素（例如，渴望）已显示出预测和预防饮食失误（不遵守饮食规定）的潜力，这可以改善减肥干预措施。被动传感器也可以测量失误风险因素，并且可能比 EMA 具有优势（例如，客观，自动，半连续的数据采集），但目前只能测量少数失误预测因素，这是一个显著的局限性。本研究初步比较了商业可用传感器与既定 EMA 在失误预测中的负担和准确性。N = 23 名超重/肥胖成年人完成了为期 6 周的基于商业应用的减肥计划。参与者佩戴 Fitbit，启用 GPS 跟踪，完成 EMA，并通过 5 点李克特量表报告 EMA 和传感器负担。感知到的风险因素是身体活动和睡眠（加速度计），地理位置（GPS）和时间，从中提取了 233 个特征（传感器信号的可测量特征）。EMA 测量了 19 个风险因素，失误和分类 GPS 到有意义的地理位置。创建了两个监督二进制分类模型（LASSO）：传感器模型以 63％的敏感性（失误的真实预测率）和 60％的特异性（非失误的真实预测率）预测失误，而 EMA 模型的敏感性为 59％，特异性为 72％。EMA 模型的准确性更高，但自我报告的 EMA 负担（M = 2.96，SD = 1.02）也更高（M = 1.50，SD = 0.94）。EMA 模型的准确性更高，但 EMA 负担高于传感器负担。研究结果强调了传感器在预测失误方面的潜力，未来的研究可能会根据准确性与参与者负担的优先级使用 EMA、传感器或两者。
