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Efficacy of Telephone-Based Cognitive Behavioral Therapy for Weight Loss, Disordered Eating, and Psychological Distress After Bariatric Surgery

2023· article· en· W4385514335 on OpenAlexafffund
Sanjeev Sockalingam, Samantha Leung, Clement Ma, George Tomlinson, Raed Hawa, Susan Wnuk, Timothy Jackson, David R. Urbach, Allan Okrainec, Jennifer Brown, Daniella Sandre, Stephanie E. Cassin

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsOttawa HospitalWomen's College HospitalPublic Health OntarioToronto Metropolitan UniversityUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental Health
FundersObesity CanadaCanadian Institutes of Health ResearchBausch HealthNovo Nordisk
KeywordsMedicinePsychosocialWeight lossPsychological interventionDistressPhysical therapyQuality of life (healthcare)AnxietyRandomized controlled trialCognitive behavioral therapyWeight Loss SurgeryBinge-eating disorderWeight managementDisordered eatingObesityPsychiatryEating disordersClinical psychologySurgeryInternal medicineBulimia nervosa

Abstract

fetched live from OpenAlex

Importance: Weight regain after bariatric surgery is associated with recurrence of obesity-related medical comorbidities and deterioration in quality of life. Developing efficacious psychosocial interventions that target risk factors, prevent weight regain, and improve mental health is imperative. Objective: To determine the efficacy of a telephone-based cognitive behavioral therapy (tele-CBT) intervention at 1 year after bariatric surgery in improving weight loss, disordered eating, and psychological distress. Design, Setting, and Participants: This multisite randomized clinical trial was conducted at 3 hospital-based bariatric surgery programs, with recruitment between February 2018 and December 2021. Eligibility for participation was assessed among 314 adults at 1 year after bariatric surgery who were fluent in English and had access to a telephone and the internet. Patients with active suicidal ideation or poorly controlled severe psychiatric illness were excluded. Primary and secondary outcome measures were assessed at baseline (1 year after surgery), after the intervention (approximately 15 months after surgery), and at 3-month follow-up (approximately 18 months after surgery). Data were analyzed from January to February 2023. Interventions: The tele-CBT intervention consisted of 6 weekly 1-hour sessions and a seventh booster session 1 month later. The control group received standard postoperative bariatric care. Main Outcomes and Measures: The primary outcome was postoperative percentage total weight loss. Secondary outcomes were disordered eating (Binge Eating Scale [BES] and Emotional Eating Scale [EES]) and psychological distress (Patient Health Questionnaire-9 item scale [PHQ-9] and Generalized Anxiety Disorder-7 item scale [GAD-7]). The hypotheses and data-analytic plan were developed prior to data collection. Results: Among 306 patients 1 year after bariatric surgery (255 females [83.3%]; mean [SD] age, 47.55 [9.98] years), there were 152 patients in the tele-CBT group and 154 patients in the control group. The group by time interaction for percentage total weight loss was not significant (F1,160.61 = 2.09; P = .15). However, there were significant interactions for mean BES (F2,527.32 = 18.73; P < .001), EES total (F2,530.67 = 10.83; P < .001), PHQ-9 (F2,529.93 = 17.74; P < .001), and GAD-7 (F2,535.16 = 15.29; P < .001) scores between the tele-CBT group and control group across all times. Conclusions and Relevance: This study found that tele-CBT delivered at 1 year after surgery resulted in no change in short-term weight outcomes but improved disordered eating and psychological distress. The impact of these psychosocial improvements on longer-term weight outcomes is currently being examined as part of this longitudinal multisite randomized clinical trial. Trial Registration: ClinicalTrials.gov Identifier: NCT03315247.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.351
Teacher spread0.294 · 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 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

Citations28
Published2023
Admission routes2
Has abstractyes

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