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

Stephanie P Goldstein, Adam Hoover, E Whitney Evans, J Graham Thomas, Stephanie P Goldstein, Adam Hoover, E Whitney Evans, J Graham Thomas

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://ichgcp.net/clinical-trials-registry/NCT03739151.

Keywords: Dietary lapse; dietary assessment; ecological momentary assessment; passive sensing; weight loss.

Conflict of interest statement

Declaration of conflicting interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: SG, EWE, and JGT declare that they have no conflicts of interest. AH is the co-creator of the Bite Counter. While this study did not use Bite Counters, it benefited from use of the Bite Counter algorithms.

© The Author(s) 2021.

Figures

Figure 1.
Figure 1.
Study schematic and timeline.
Figure 2.
Figure 2.
Example of output from wrist-inferred eating and secondary activities using our algorithms (depicts one participant's data over one day). Downward arrows represent algorithm-identified peaks in wrist motion energy (y axis): colored boxes represent activities classified from wrist motion data and length of time performed in minutes (x axis): blue vertical lines represent EMA-reported lapses and non-lapse eating: red circles enclose the wrist-inferred eating episodes that would be matched to EMA-reported eating.
Figure 3.
Figure 3.
Eating episodes across the 24-week study period by participant: (a) depicts total weekly dietary lapses per participant. (b) depicts total weekly non-lapses per participant, and (c) depicts total weekly wrist-detected eating episodes per participant. Note: Dashed lines are indicative of non-continuous assessment weeks (EMA) and solid lines are indicative of continuous assessment weeks (ActiGraph).
Figure 4.
Figure 4.
Hours between matched EMA-reported and wrist-inferred eating episodes. Negative values indicate EMA eating episode was reported after its wrist-inferred match. Positive values indicate the EMA eating episode was reported before its wrist-inferred match.

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