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Record W4396520812 · doi:10.1016/j.appet.2024.107386

Proof-of-concept testing of a brief virtual ACT workshop for emotional eating

2024· article· en· W4396520812 on OpenAlexafffund
Jessica Di Sante, Mallory Frayn, Andreea Angelescu, Bärbel Knaüper

Bibliographic record

VenueAppetite · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsPsychologyProof of conceptCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Emotional eating, or eating in response to negative emotions, is a commonly reported short-term emotion regulation strategy but has been shown to be ineffective in the long term. Most emotional eating interventions based on Acceptance and Commitment Therapy (ACT) have been delivered in the context of weight loss trials, highlighting a need for ACT-based emotional eating interventions in weight-neutral contexts. AIMS: This proof-of-concept study aimed to test the acceptability and efficacy potential of a brief virtual ACT workshop for emotional eating in a small sample of adults identifying as emotional eaters. METHODS: Twenty-six adult emotional eaters completed an ACT workshop delivered in two 1.5-h sessions over two weeks. The workshop targeted awareness and acceptance of emotions and eating urges, and valued actions around eating. RESULTS: The acceptability of the workshop was demonstrated by high participant satisfaction. Significant improvements on all outcome measures were found and maintained up to 3 months follow-up. CONCLUSIONS: These proof-of-concept findings suggest that a brief virtual ACT workshop may improve emotional eating and associated ACT processes. Results from this study can inform a future randomized controlled trial to test the efficacy of the workshop and the role of theoretical processes of change. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04457804. LEVEL OF EVIDENCE: Level IV, evidence obtained from multiple time series with the intervention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.334
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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