HIITing the brain with exercise: mechanisms, consequences and practical recommendations
Bibliographic record
Abstract
The increasing number of older adults has seen a corresponding growth in those affected by neurovascular diseases, including stroke and dementia. Since cures are currently unavailable, major efforts in improving brain health need to focus on prevention, with emphasis on modifiable risk factors such as promoting physical activity. Moderate-intensity continuous training (MICT) paradigms have been shown to confer vascular benefits translating into improved musculoskeletal, cardiopulmonary and cerebrovascular function. However, the time commitment associated with MICT is a potential barrier to participation, and high-intensity interval training (HIIT) has since emerged as a more time-efficient mode of exercise that can promote similar if not indeed superior improvements in cardiorespiratory fitness for a given training volume and further promote vascular adaptation. However, randomised controlled trials (RCTs) investigating the impact of HIIT on the brain are surprisingly limited. The present review outlines how the HIIT paradigm has evolved from a historical perspective and describes the established physiological changes including its mechanistic bases. Given the dearth of RCTs, the vascular benefits of MICT are discussed with a focus on the translational neuroprotective benefits including their mechanistic bases that could be further potentiated through HIIT. Safety implications are highlighted and components of an optimal HIIT intervention are discussed including practical recommendations. Finally, statistical effect sizes have been calculated to allow prospective research to be appropriately powered and optimise the potential for detecting treatment effects. Future RCTs that focus on the potential clinical benefits of HIIT are encouraged given the prevalence of cognitive decline in an ever-ageing population.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".