Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse. Issue 12 (16th September 2021)

• Record Type: Journal Article
• Authors: Crochiere, Rebecca J; Zhang, Fengqing (Zoe); Juarascio, Adrienne S; Goldstein, Stephanie P; Thomas, J Graham; Forman, Evan M
• Is Part Of: Translational behavioral medicine. Volume 11:Issue 12(2021)
• Page Start: 2099
• Page End: 2109
• Publication Date: 2021-09-16
• Subjects: Sensors -- Ecological momentary assessment -- Weight loss -- Lapses -- Machine learning; Medicine and psychology -- Periodicals; 616.0019
• DOI: 10.1093/tbm/ibab123
• Languages: English
• ISSNs: 1869-6716

Abstract

Abstract: 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.