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Record W4307425093 · doi:10.1002/oby.23570

Long‐term impact of a behavioral weight management program on depression and anxiety symptoms: 5‐year follow‐up of the <scp>WRAP</scp> trial

2022· article· en· W4307425093 on OpenAlexfundno aff
Rebecca A. Jones, Julia Mueller, Stephen J. Sharp, Simon J. Griffin, Amy L. Ahern

Bibliographic record

VenueObesity · 2022
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
FundersProgramme Grants for Applied ResearchMedical Research Council CanadaMedical Research CouncilNational Institute for Health and Care Research
KeywordsAnxietyDepression (economics)Term (time)MedicineGerontologyWeight managementWeight lossPhysical therapyPsychiatryClinical psychologyInternal medicineObesity

Abstract

fetched live from OpenAlex

OBJECTIVE: Behavioral weight management programs may support short-term mental health; however, limited evidence reports the long-term impacts. This study investigated the impact of behavioral weight management programs on depression and anxiety symptoms at 5 years from baseline. METHODS: to a brief intervention (BI) or commercial behavioral weight management program (WW; formerly Weight Watchers) for 12 or 52 weeks (CP12 and CP52, respectively). Linear regression was used to separately compare 5-year changes in depression and anxiety symptoms (by Hospital Anxiety and Depression Scale) between randomized groups, adjusting for baseline depression/anxiety symptoms, gender, and research center. RESULTS: A total of 643 (51%) participants attended the 5-year study follow-up visit. There was no evidence of a difference between the randomized groups for 5-year changes in depression (BI: -0.08 ± 3.29; CP12: 0.02 ± 3.01; CP52: -0.09 ± 3.41) or anxiety (BI: 0.16 ± 3.50; CP12: -0.05 ± 3.55; CP52: -0.66 ± 3.59) symptoms. CONCLUSIONS: This study found no evidence that commercial weight management programs differed in 5-year changes in depression and anxiety symptoms, compared with BI. These are average effects; some individuals experienced increases or decreases in symptoms. Future research should investigate who is at most risk of mental health declines and investigate how to support them. Future trials should transparently report long-term mental health outcomes to strengthen understanding.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.421
Teacher spread0.378 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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