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Record W3200631166 · doi:10.17918/etd-6690

A prospective investigation of risk factors for weight gain and increases in loss of control eating

2015· dissertation· en· W3200631166 on OpenAlexaboutno aff
Hallie Espel‐Huynh

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
FundersDrexel UniversityUniversity of PennsylvaniaPsi Chi
KeywordsOvereatingAlexithymiaToronto Alexithymia ScaleEmotional eatingPsychologyBinge eatingWeight gainObesityEating disordersPsychosocialClinical psychologyDevelopmental psychologyInhibitory controlCognitionMedicinePsychiatryInternal medicineEating behaviorBody weight

Abstract

fetched live from OpenAlex

Background: Repeated overeating and binge eating are widespread problems in the United States, are associated with poorer psychosocial and physical health outcomes, and pose increased risk for the development of obesity and eating disorders. Individuals who are obese or engage in recurrent binge eating exhibit elevated hedonic drives for eating, impaired cognitive ability to inhibit impulsive responses to familiar stimuli (inhibitory control), and deficits in both physiological and emotional interoceptive awareness (e.g., accurate detection of satiety cues, visceral sensations such as heartbeat, and emotions). Few studies to date have examined whether these observed deficits precede and contribute to the subsequent development of binge eating and weight gain (potentially leading to obesity), or whether they develop concurrently with progressive worsening of dysregulated eating. Methods: The present study explored the extent to which inhibitory control (measured through a response inhibition task), hedonic hunger (as measured by the Power of Food Scale), impaired interoceptive sensitivity (measured via the Heart Beat Perception Task), and alexithymia (as measured by the Toronto Alexithymia Scale), predict weight gain and changes in LOC among a sample of undergraduate women, who are at elevated risk for such outcomes. Participants (N = 102) were assessed at the beginning of the academic year; 93 participants returned for follow-up seven months later (retention: 91%). It was hypothesized that: (1) weight gain would be negatively predicted by inhibitory control performance, and positively predicted by hedonic hunger, physiological interoceptive deficits, and alexithymia; and (2) increases in LOC eating would be negatively predicted by inhibitory control performance, but positively predicted by hedonic hunger, interoceptive deficits, and alexithymia. Results: For a majority of participants, weight and LOC eating behavior remained relatively stable during the study period. Results from multiple linear regression indicated that the predictors of interest were only weakly associated with weight gain and increases in LOC eating severity. Moreover, individuals who gained or lost substantial amounts of weight (> 3.0 lbs.) did not differ from their weight-stable peers on the predictors of interest at baseline. Compared to participants who experience no binge eating, individuals who endorsed binge episodes at either time point exhibited elevations in LOC severity and hedonic hunger at baseline and follow-up. However, they did not demonstrate significant or meaningful baseline differences on any other predictors of interest. Discussion: Results suggest that undergraduate women experience relative stability in weight and eating habits in the short term. Moreover, hedonic hunger, inhibitory control, interoceptive deficits, and alexithymia, are poor predictors of weight gain and changes in LOC eating severity among those who do experience change. Implications for these null findings among a nonclinical sample are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.317
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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