Progressive Overload in Long-Term Exercise Interventions Targeting Executive Function: A Scoping Review
Bibliographic record
Abstract
Executive function (EF), including inhibitory control, working memory, and cognitive flexibility, is essential for goal-directed behavior across the lifespan. Regular exercise improves EF, with benefits moderated by frequency, intensity, time, and type. Progressive overload—systematic increases in exercise demands—may further optimize EF outcomes, yet its application in EF interventions remains unclear. The purpose of this review was to explore how progressive overload has been implemented in exercise interventions targeting EF. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews processes were used to systematically search, select, and chart data from 44 studies published between 2014 and 2024. Findings were synthesized using quantitative summaries and qualitative thematic analysis. Most studies involved children, adolescents, or older adults. Moderate to large effect sizes were more commonly reported in interventions that progressively overloaded intensity and complexity, as well as in studies specifically targeting working memory and inhibitory control. Reporting inconsistencies and limited experimental manipulation remain key gaps. Future research should systematically manipulate progressive overload and explore occupational and resistance-training contexts.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".