High-Intensity Interval Training Improves Cognitive Flexibility in Older Adults
Bibliographic record
Abstract
Introduction: Regular aerobic exercise is associated with better executive function in older adults. It is unclear if high-intensity-interval-training (HIIT) elicits moderate-intensity continuous training (MICT) or resistance training (RT). We hypothesized that HIIT would augment executive function more than MICT and RT. Methods: Sixty-nine older adults (age: 68 ± 7 years) performed six weeks (three days/week) of HIIT (2 × 20 min bouts alternating between 15 s intervals at 100% of peak power output (PPO) and passive recovery (0% PPO); n = 24), MICT (34 min at 60% PPO; n = 19), or whole-body RT (eight exercise superior improvements in executive function of older adults than moderate-intensity-continuous-training, 2 × 10 repetitions; n = 26). Cardiorespiratory fitness (i.e., V˙O2max) and executive function were assessed before and after each intervention via a progressive maximal cycle ergometer protocol and the Stroop Task, respectively. Results: The V˙O2max findings revealed a significant group by time interaction (p = 0.001) in which all groups improved following training, but HIIT and MICT improved more than RT. From pre- to post-training, no interaction in the naming condition of the Stroop Task was observed (p > 0.10). However, interaction from pre- to post-training by group was observed, and only the HIIT group exhibited a faster reaction time (from 1250 ± 50 to 1100 ± 50 ms; p < 0.001) in switching (cognitive flexibility). Conclusion: Despite similar improvements in cardiorespiratory fitness, HIIT, but not MICT nor RT, enhanced cognitive flexibility in older adults. Exercise programs should consider using HIIT protocols in an effort to combat cognitive decline in older adults.
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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) | 0.000 | 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".