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Record W4415046569 · doi:10.2196/77538

Evaluating the Impact of Telehealth Exercise Prehabilitation on Cardiometabolic Health in Bariatric Surgery Candidates: Protocol for the BARI-Prehab Randomized Controlled Trial

2025· article· en· W4415046569 on OpenAlexvenueno aff
Belinda Jayne Durey, Alison M. Coates, Kade Davison, Brett Tarca, Jessica Mok, Chetan Parmar, Naiara Fernandez-Munoz, Mariam Adeleke, Zoe Lugg, Daniel Martín

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPrehabilitationTelehealthRandomized controlled trialProtocol (science)TelemedicineeHealthmHealthPhysical activity

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity affects over one billion people globally and is a leading contributor to chronic disease. For those with clinically severe obesity, metabolic and bariatric surgery (MBS) is the most effective intervention for long-term weight loss. However, surgery is often delayed due to systemic barriers, during which time patients may experience further health decline. Low cardiorespiratory fitness is a known risk factor for perioperative complications, prompting recommendations for prehabilitation to target readiness for surgery. Despite this, few patients meet physical activity guidelines, and supervised preoperative exercise programs are rarely offered in routine care. Telehealth-delivered exercise programs offer a promising solution, but evidence of their feasibility, acceptability, and impact in the MBS setting remains limited. OBJECTIVE: This study (BARI-Prehab) aims to assess the efficacy and acceptability of a telehealth-delivered prehabilitation exercise program in improving cardiometabolic health among patients awaiting MBS. METHODS: in mL/kg/min at the anaerobic threshold), measured using cardiopulmonary exercise testing. Secondary outcomes include resting heart rate, heart rate variability, resting metabolic rate, body composition, grip strength, and 7-day physical activity. Intervention acceptability will also be evaluated. RESULTS: Data collection and analysis are ongoing. This trial, funded in September 2020, will evaluate the capacity of a telehealth exercise program to improve cardiometabolic health and determine its suitability for implementation in the MBS preoperative pathway. Following initial protocol development, ethics approval, and trial setup, the clinical phase formally commenced with registration on June 16, 2023. Enrollment is ongoing, with a projected end date of March 2026. As of May 2025, a total of 220 patients have been screened for participation, of whom 30 have enrolled in the trial. The first results are expected to be submitted for publication by mid-2026. CONCLUSIONS: The BARI-Prehab trial will provide evidence on the acceptability and impact of a remotely delivered exercise intervention in the context of MBS. These findings will have implications for the design of accessible, scalable preoperative care models. The significance of this research lies in its potential to guide clinical practice, inform policy, and improve health outcomes for patients undergoing MBS. TRIAL REGISTRATION: ClinicalTrials.gov NCT05235945; https://clinicaltrials.gov/study/NCT05235945. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77538.

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)0.034
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.067
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.033
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0100.007
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0670.011

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 head0.199
GPT teacher head0.608
Teacher spread0.409 · 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 designRandomized trial
Domainnot available
GenreProtocol

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
Published2025
Admission routes1
Has abstractyes

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