Heart rate variability after bariatric surgery: The add‐on value of exercise
Bibliographic record
Abstract
ABSTRACT Purpose To assess the impact of bariatric surgery and an added supervised exercise training programme on heart rate variability (HRV) in patients with severe obesity. Methods Fifty‐nine patients who underwent bariatric surgery were randomised in the post‐operative period to a 12‐week supervised exercise training programme (moderate intensity combination aerobic/resistance exercise training programme) or a control group. Indices of HRV including time‐domain, spectral‐domain, and nonlinear parameters were measured preoperatively, and at 3, 6, and 12 months. Results After the surgical procedure, both groups improved anthropometric parameters. Type 2 diabetes, hypertension, and dyslipidemia resolutions were similar between groups. Total body weight loss at 6 and 12 months were also comparable between groups (6 months: 28 ± 6 vs. 30 ± 6%; 12 months: 38 ± 9 vs. 38 ± 10%; control vs. intervention group respectively). Bariatric surgery improved HRV parameters at 12 months compared to the pre‐operative values in the intervention group: standard deviation of R‐R interval (SDNN) (156.0 ± 46.4 vs. 122.6 ± 33.1 ms), low frequency (LF) (6.3 ± 0.8 vs. 5.8 ± 0.7 ms 2 ), and high frequency (HF) (5.1 ± 0.8 vs. 4.7 ± 0.9 ms 2 ) (all p <0.001). For the control patients, similar improvements in SDNN (150.0 ± 39.4 vs. 118.8 ± 20.1 ms), LF (6.1 ± 0.9 vs. 5.7 ± 0.8 ms 2 ), and HF (5.0 ± 0.9 vs. 4.7 ± 0.9 ms 2 ) were obtained (all p <0.001). However, there was no add‐on impact of the supervised exercise training programme on HRV after 12 months ( p >0.05 for all HRV parameters). Conclusion Bariatric surgery is associated with an improvement in HRV. A supervised exercise training programme in the post‐operative period did not modulate further the benefits of bariatric surgery regarding HRV parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".