Effects Of Aquatic High Intensity Interval Training (AHIIT) DWR On Cardiometabolic Health In Elderly Women
Bibliographic record
Abstract
Aquatic High Intensity Interval Training has the potential to improve cardiometabolic health and cognition, while minimizing musculoskeletal strain compared to land-based training. Deep water running (DWR) has emerged as a promising method, offering a reduced risk of injuries and greater affordability for inactive elderly women. PURPOSE: To investigate the effects of an 8-week AHIIT- DWR intervention compared to land-based HIT training (LHIIT) on cardiometabolic health and cognitive outcomes in elderly women. METHODS: Sixty-nine inactive elderly women aged 60 or above were randomly assigned into two groups: AHIIT and LHIIT. The AHIIT group engaged in DWR sessions comprising 30-minutes of interval training, consisting of ten 2-minute exercise bouts at 80-90% of their maximal heart rate (HR max), with 1-minute active recovery at 70% HR max between bouts. The LHIIT group performed treadmill running at the same intensity. Both groups trained twice a week. Aerobic capacity including VO2 max, oxygen pulse, respiratory exchange ratio (RER), minute ventilation (VE) and HR max were assessed using a metabolic analyzer. Lipid profiles were measured through blood analysis. Cognitive function was evaluated using the Montreal Cognitive Assessment (MOCA) and Mini- mental status examinations (MMSE). RESULTS: Both groups showed similar cardiovascular fitness (VO2max) improvement (AHIIT-DWR: 20.88 ± 4.49 to 23.29 ± 5.14; LHIIT: 22.84 ± 6.6 to 25.82 ± 4.66 mL kg−1 min−1, p < 0.05) and RER (AHIIT-DWR: 1.01 1.42 to 1.04 ± 0.10; LHIIT: 0.99 ± 0.10 to 1.16 ± 0.09, p < 0.05) over the 8- week intervention. Compared to baseline, significant differences were identified in VE between AHIIT-DWR and LHIIT group. (AHIIT-DWR: 64.64 ± 11.60; LHIIT: 55.11 ± 12.64, p < 0.05). No significant group differences were observed for cardiometabolic blood markers such as lipid profiles, fasting glucose or cognitive function measured by MOCA and MMSE. AHIIT-DWR showed a high unsupervised adherence rate (>90%). CONCLUSION: Our findings suggest that AHIIT-DWR can elicit a similar improvement in cardiometabolic fitness as LHIIT in inactive older women. AHIIT-DWR and LHIIT had no differences in cardiometabolic blood markers or cognitive function.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".