Acute effects of high-intensity interval training and moderate-intensity continuous training on executive functions in healthy older adults
Bibliographic record
Abstract
Numerous studies have demonstrated that executive functions benefit from high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT). However, the immediate effects of HIIT and MICT on these functions in older adults have not been compared. This study aimed to examine the acute impact of HIIT and MICT on executive function components in this demographic. Twenty-five healthy community-dwelling older adults (15 females; average age 67.1 ± 4.5 years) participated. The study involved three sessions: an initial session with cognitive assessments (Stroop Task: Naming, Inhibition, and Switching) and a maximal continuous graded exercise test, followed by two sessions involving HIIT (15s at 100% peak power output, 15s rest, 2 × 20 min) or MICT (34 min at 60% peak power output) training protocols in random order. Cognitive tests were administered immediately after and 45 min post-training. The results showed a significant difference in Switching reaction times between MICT and HIIT, with HIIT showing a greater reduction in Switching times after 45 min (p = 0.019). In conclusion, our study indicates that HIIT's beneficial effects on executive functions demonstrated a larger effect size than those of MICT. This suggests that brief, high-intensity exercise could be more effective in enhancing executive functions among 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 | 0.001 |
| 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".