Evidence map›Paper›PMID 35598718›Full record

ArticleAppetite2022

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

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Appetite, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03739151 (Applying Novel Passive Sensing Technology to Target Adherence to Diet in Behavioral Obesity Treatment for Patients With Cardiovascular Disease Risk), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03739151 nacompletednot on this map

Applying Novel Passive Sensing Technology to Target Adherence to Diet in Behavioral Obesity Treatment for Patients With Cardiovascular Disease Risk

TypeinterventionalSponsorThe Miriam HospitalRan2018 to 2020Enrolled32ConditionsOverweight and ObesityArmsBehavioral Obesity Treatment
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stephanie P GoldsteinWeight 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.
Adam HooverHolcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, 29634, USA.
J Graham ThomasWeight 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.

Funding

Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: A Micro-randomized TrialR01HL153543 · NHLBI · MIRIAM HOSPITAL · PI GOLDSTEIN, STEPHANIE PAIGE · 2020 to 2023
$2.6M
Applying Novel Passive Sensing Technology to Target Adherence to Diet in Behavioral Obesity Treatment for Patients with Cardiovascular Disease RiskF32HL143954 · NHLBI · MIRIAM HOSPITAL · PI GOLDSTEIN, STEPHANIE PAIGE · 2018 to 2019
$119k
NHLBI NIH HHS F32 HL143954NHLBI NIH HHS R01 HL153543
6 · The paper itself

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.

Indexed as

Dietary assessmentDietary lapseEcological momentary assessmentLifestyle modificationOverweight/obesityPassive sensing

Identifiers

PMID35598718
PMCPMC13097070

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.