Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

Abstract

Objectives Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse. Method We are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed. Results While data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work. Conclusion This protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions. Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151

Sensor technology in eating disorders research: A systematic review

“…In addition to more comprehensively integrating sensors into laboratory and naturalistic protocols measuring pathological eating behaviors, novel sensors including eating utensils equipped with sensors, sensors capable of detecting swallowing behaviors, and smart home sensors to measure use and interaction with kitchen appliances should be explored within this population. The fields of obesity and health behavior research have utilized many of these types of sensors in both in‐lab (Kyritsis et al, 2019, 2021) and ambulatory protocols (Alshurafa et al, 2021; Goldstein et al, 2021; Langlet et al, 2020; Maramis et al, 2016, Maramis et al, 2020; Stankoski et al, 2021; Zhang et al, 2020) and offer high potential to inform future application of these sensors to ED research. Furthermore, preliminary evidence suggests that continuous glucose monitors (a wearable sensor commonly used in the management of diabetes mellitus) may be able to detect certain features of eating behaviors based on glucose levels (Banerjee et al, 2004; Wasserman, 2009; Wolever et al, 1991).…”

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

“…First, the JITAI for dietary lapse is currently solely reliant on EMA, which improves the rigor of self-report but also incurs a high level of participant burden. Although, previous work indicates that participants are willing to respond to EMA prompts 6 times per day, there is a high priority for this research to transition to passively sensed dietary lapses or relevant triggers [ 64 , 103 ]. Second, the selected theory-driven intervention options to be evaluated in this MRT are based on the best available, but nonetheless static, model of adherence behavior.…”

“…As in several previous trials conducted by the research team and others, dietary lapses will be assessed via EMA [ 6 , 7 , 64 ]. EMA typically captures naturalistic eating behavior better than lab-based tasks because near real-time reporting has the potential to reduce bias and improve validity [ 13 , 65 , 66 ].…”

Dietary lapses are associated with meaningful elevations in daily caloric intake and added sugar consumption during a lifestyle modification intervention

“…Participants were asked to complete a 7‐day run‐in, in which the minimum criteria for starting treatment included tracking dietary intake (≥2 meals/day for 7 days) and gaining their physician's confirmation of eligibility (cardiovascular disease risk factor diagnosis) and permission to participate in the study. 29 Participants who met the run‐in requirements began their initial treatment session approximately 1 week after their baseline appointment and continued weekly sessions for the first 12 weeks with monthly boosters during the final 12 weeks. Participants were asked to complete regular EMA surveys and phone‐based 24‐h dietary recalls (schedule for each assessment described below).…”

“…As detailed in Goldstein et al. 29 the target sample size ( N = 40) was derived via a Monte Carlo simulation using data from prior work studying lapses and weight loss in the context of lifestyle modification (primary aim of the main trial). According to rules of thumb for multi‐level modeling, the recruited sample of N = 32 (with an average of ∼7–8 repeated observations per person [5 food recall assessment weeks, 3 daily recalls occurring each week, accounting for expected attrition and data loss]) was 80% powered to detect a minimum effect size between 0.25 and 0.26 at alpha = 0.05, assuming a medium (ICC) of 0.3–0.5.…”