Cost-effectiveness of High-Intensity Interval Training (HIIT) vs Moderate Intensity Steady-State (MISS) Training in UK Cardiac Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To perform a cost-effectiveness analysis of high-intensity interval training (HIIT) compared with moderate intensity steady-state (MISS) training in people with coronary artery disease (CAD) attending cardiac rehabilitation (CR). DESIGN: Secondary cost-effectiveness analysis of a prospective, assessor-blind, parallel group, multi-center RCT. SETTING: Six outpatient National Health Service cardiac rehabilitation centers in England and Wales, UK. PARTICIPANTS: 382 participants with CAD (N=382). INTERVENTIONS: Participants were randomized to twice-weekly usual care (n=195) or HIIT (n=187) for 8 weeks. Usual care was moderate intensity continuous exercise (60%-80% maximum capacity, MISS), while HIIT consisted of 10 × 1-minute intervals of vigorous exercise (>85% maximum capacity) interspersed with 1-minute periods of recovery. MAIN OUTCOME MEASURES: We conducted a cost-effectiveness analysis of the HIIT or MISS UK trial. Health related quality of life was measured with the EQ-5D-5L to estimate quality-adjusted life years (QALYs). Costs were estimated with health service resource use and intervention delivery costs. Cost-utility analysis measured the incremental cost-effectiveness ratio (ICER). Bootstrapping assessed the probability of HIIT being cost-effective according to the UK National Institute for Health and Care Excellence (NICE) threshold value (£20,000 per QALY). Missing data were imputed. Uncertainty was estimated using probabilistic sensitivity analysis. Assumptions were tested using univariate/1-way sensitivity analysis. RESULTS: 124 (HIIT, n=59; MISS, n=65) participants completed questionnaires at baseline, 8 weeks, and 12 months. Mean combined health care use and delivery cost was £676 per participant for HIIT, and £653 for MISS. QALY changes were 0.003 and -0.013, respectively. For complete cases, the ICER was £1448 per QALY for HIIT compared with MISS. At a willingness-to-pay threshold of £20,000 per QALY, the probability of HIIT being cost-effective was 96% (95% CI, 0.90 to 0.95). CONCLUSION: For people with CAD attending CR, HIIT was cost-effective compared with MISS. These findings are important to policy makers, commissioners, and service providers across the health care sector.
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".