Evidence-based risk assessment and recommendations for physical activity clearance: cognitive and psychological conditions<sup>1</sup>This paper is one of a selection of papers published in this Special Issue, entitled Evidence-based risk assessment and recommendations for physical activity clearance, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Physical activity has established mental and physical health benefits, but related adverse events have not received attention. The purpose of this paper was to review the documented adverse events occurring from physical activity participation among individuals with psychological or cognitive conditions. Literature was identified through electronic database (e.g., MEDLINE, psychINFO) searching. Studies were eligible if they described a published paper examining the effect of changes on physical activity behaviour, included a diagnosed population with a cognitive or psychological disorder, and reported on the presence or absence of adverse events. Quality of included studies was assessed, and the analyses examined the overall evidence by available subcategories. Forty trials passed the eligibility criteria; these were grouped (not mutually exclusively) by dementia (n = 5), depression (n = 10), anxiety disorders (n = 12), eating disorders (n = 4), psychotic disorders (n = 4), and intellectual disability (n = 15). All studies displayed a possible risk of bias, ranging from moderate to high. The results showed a relatively low prevalence of adverse events. Populations with dementia, psychological disorders, or intellectual disability do not report considerable or consequential adverse events from physical activity independent of associated comorbidities. The one exception to these findings may be Down syndrome populations with atlantoaxial instability; in these cases, additional caution may be required during screening for physical activity. This review, however, highlights the relative paucity of the reported presence or absence of adverse events, and finds that many studies are at high risk of bias toward reporting naturally occurring adverse events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.376 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".