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Effect of a Self-Directed Lifestyle-based Weight Management Program Among Patients With Comorbid COPD and Sleep Apnea

2025· article· en· W4410268712 on OpenAlexaff
A.G. Leonhard, Scott Coggeshall, Emily Gleason, C. Margaret, Anna E. Barón, Igor Barjaktarević, Rebecca Bascom, Jessica Bon, A.P. Comellas, Philip T. Diaz, N.A. Hanania, M.K. Han, N.N. Hansel, Travis Hee Wai, Rachel Hunter‐Merrill, Jerry A. Krishnan, Stephen C. Lazarus, Jun Ma, Veeranna Maddipati, M. Jeffrey Mador, Barry J. Make, Carmen McEvoy, Catherine A. Meldrum, Mark Millard, Marilyn L. Moy, C.S. Pirozzi, P.Y. Robert, Loretta G. Que, Robert M. Reed, Frank C. Sciurba, Sanjay Sethi, Paul Simonelli, K. Sumino, Aseem Kumar Tiwari, Kirk Voelker, Christine Wendt, Stephen R. Wisniewski, David H. Au, Lucas M. Donovan, Laura C. Feemster

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineObstructive sleep apneaCOPDSleep apneaSleep (system call)ComorbidityPhysical therapyWeight lossApneaIntensive care medicineObesityInternal medicine

Abstract

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Abstract Rationale: Excess weight contributes to impaired physical function among individuals with chronic obstructive pulmonary disease (COPD) and comorbid sleep apnea. Self-directed lifestyle-based weight management programs are an accessible option to promote weight loss and improve physical function, but their effectiveness in this population is unclear. We sought to test whether a self-directed lifestyle program improves 6-minute walk test (6MWT) distance among individuals with COPD and comorbid sleep apnea. Methods: INSIGHT-COPD was a randomized trial to test whether a self-directed video-based lifestyle intervention would lead to better physical function in participants with excess weight and COPD. We performed a retrospective subset analysis restricted to participants with a self-reported clinician-diagnosis of sleep apnea. Our primary outcome was between-group differences for change in 6MWT distance, which we measured using a linear mixed effects model. We also assessed secondary outcomes including weight and SF-12 physical function related quality of life. Finally, we assessed whether participants met meaningful thresholds for improvement and decline in 6MWT distance (30m) and weight loss (5% total body weight) using generalized linear regression models for binary outcomes. Results: Among the 684 participants randomized in INSIGHT-COPD, 285 had self-reported sleep apnea (141 randomized to intervention, 144 to control). At 12 months, those randomized to intervention could walk further relative to those randomized to control (adjusted between group difference for 6MWT distance +25.5 m [95% CI 8.2-42.9 m] for individuals with OSA in the intervention group compared to the control group). We did not observe a between-group difference in the likelihood of achieving meaningful improvement in 6MWT distance, though fewer intervention participants experienced a meaningful decline in 6MWT (Figure). Intervention participants also experienced a greater reduction in weight, (adjusted between-group difference of -2.4 kg, 95% CI -3.9 to -0.9 kg) and were more likely to experience meaningful weight loss (Figure). Participants in the intervention arm also experienced greater improvements in physical-function related quality-of-life (adjusted between-group difference: SF-12 PCS 1.78 pts, 95% CI 0.10 to 3.49. Conclusions: Among individuals with COPD and self-reported sleep apnea, a self-directed video-based weight management intervention did not lead to clinically meaningful improvements in 6MWT distance relative to usual care. However, fewer participants experienced a meaningful reduction in 6MWT distance, and a greater proportion of participants in the intervention group experienced clinically meaningful weight loss. Additional interventions beyond self-directed weight management will be needed to meaningfully improve function among patients with COPD and comorbid sleep apnea.

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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)0.001
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 score0.201
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.384
Teacher spread0.375 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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