Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Stephanie P Goldstein; Adam Hoover; J Graham Thomas

doi:10.1016/j.appet.2022.106090

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Appetite. 2022 Aug 1:175:106090. doi: 10.1016/j.appet.2022.106090. Epub 2022 May 20.

Authors

Stephanie P Goldstein¹, Adam Hoover², J Graham Thomas³

Affiliations

¹ Weight Control and Diabetes Research Center, The Miriam Hospital, 196 Richmond St., Providence, RI, 02903, USA; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, 222 Richmond St., Providence, RI, 02903, USA. Electronic address: stephanie_goldstein@brown.edu.
² Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, 29634, USA.
³ Weight Control and Diabetes Research Center, The Miriam Hospital, 196 Richmond St., Providence, RI, 02903, USA; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, 222 Richmond St., Providence, RI, 02903, USA.

PMID: 35598718
DOI: 10.1016/j.appet.2022.106090

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 (Bs = -0.30 to -0.08, ps < .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.

Keywords: Dietary assessment; Dietary lapse; Ecological momentary assessment; Lifestyle modification; Overweight/obesity; Passive sensing.

Associated data

ClinicalTrials.gov/NCT03739151

Grants and funding

R01 HL153543/HL/NHLBI NIH HHS/United States