Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention. (1st August 2022)

• Record Type: Journal Article
• Authors: Goldstein, Stephanie P.; Hoover, Adam; Thomas, J. Graham
• Is Part Of: Appetite. Volume 175(2022)
• Publication Date: 2022-08-01
• Subjects: Dietary lapse -- Lifestyle modification -- Overweight/obesity -- Dietary assessment -- Passive sensing -- Ecological momentary assessment; Food habits -- Periodicals; Appetite -- Periodicals; Appetite disorders -- Periodicals; Electronic journals; 306.4613
• DOI: 10.1016/j.appet.2022.106090
• Languages: English
• ISSNs: 0195-6663

Abstract

Abstract: Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites ( B = −0.16, p < .05), were shorter ( B = −0.54, p < .05), or had a slower rate ( B = 1.27, p < .001). Statistically significant interactions between eating characteristics ( B s = −0.30 to −0.08, p s < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. Clinical trials registry number: NCT03739151.