Journal article
Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse
Translational behavioral medicine, v 11(12), pp 2099-2109
14 Dec 2021
PMID: 34529044
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse
- Creators
- Rebecca J Crochiere - Drexel UniversityFengqing Zoe Zhang - Brown UniversityAdrienne S Juarascio - Drexel UniversityStephanie P Goldstein - Brown UniversityJ Graham Thomas - Brown UniversityEvan M Forman - Drexel University
- Publication Details
- Translational behavioral medicine, v 11(12), pp 2099-2109
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology); Center for Weight, Eating and Lifestyle Science (WELL) [Historical]
- Web of Science ID
- WOS:000745653400004
- Scopus ID
- 2-s2.0-85122770770
- Other Identifier
- 991019168128104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health