Obesity in Cardiac Rehabilitation: Considerations in Offering Weight Management As Part of Cardiac Rehabilitation Programs
Bibliographic record
Abstract
Despite its relevance to cardiovascular health, obesity is rarely targeted during cardiac rehabilitation (CR). The objective of this paper was to review evidence regarding whether measures to address excess body fat should be offered as a standard component of CR for patients with obesity. We organize the paper around three themes: 1) outcomes of obesity management, 2) the complexity of obesity management, and 3) patient attitudes, experiences, and preferences. Our discussion of each theme was informed by a narrative literature review and a survey of Canadian CR healthcare providers (n=80). We consider literature regarding available approaches to obesity management including behavioural weight loss, pharmacological, and surgical treatments. We go on to assess concerns relating to the complexity of obesity intervention, and the importance of CR patients' lived experiences and goals. Finally, we summarize obesity management considerations in the context of the goals and interventions of CR. Although most (71%) CR providers support integrating obesity management into CR, there are concerns about training, weight bias, and unrealistic expectations for weight loss within time-limited programs. Efforts to incorporate obesity management into CR must address these barriers while considering evidence-based strategies to optimize treatment outcomes (e.g., adjunctive pharmacotherapy; long-term follow-up). Whether CR should offer obesity management depends on provider competency and program resources. More research is needed to clarify patient preferences and to establish the feasibility, long-term efficacy, and cost-effectiveness of obesity management approaches in CR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".