Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity and cardiovascular disease (CVD). Yet, about half of patients fall short of expected outcomes and most experience gradual weight regain, thus negating the benefits over time. Lapses (i.e., self-reported eating instances that deviate from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it is difficult to assess in-lab and via self-report. The investigators therefore propose to study lapses using a multimethod approach with the following tools: ecological momentary assessment (EMA; repeated sampling method via mobile device), a wrist-worn device that automatically detects eating behavior and various eating characteristics (frequency, rate, and duration of eating episodes), and 24-hour dietary recalls. The investigators will recruit participants (n=40) with overweight/obesity and one additional CVD risk factor to enroll in a 12-week BOT program and an additional 12-week period of weight loss maintenance. Participants will complete a biweekly 7-day EMA protocol to self-report on eating behavior, including the occurrence of dietary lapse. Participants will continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Lastly, participants will complete 24-hour dietary recalls via structured interview (split between days with and without lapses) at 6-week intervals to measure the composition of all food and beverages consumed. This study aims to 1) identifying characteristics of lapse behavior by measuring passively-sensed timing, duration, frequency, and rate of eating amongst known lapse episodes, 2) test the association between dietary lapse frequency and weight change, and 3) estimate nutrition composition of dietary lapses. The study approach is consistent with priorities of NHLBI to optimize clinical research and diagnostic strategies to improve CVD and related risk factors.
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Sensor-based Dietary Lapse
Timeframe: 24-week frequency of sensor-based dietary lapse