Physical Activity Interventions for Individuals With a Mild Traumatic Brain Injury:
Bibliographic record
Abstract
OBJECTIVE: To document the characteristics, measured outcomes, and effectiveness of physical activity (PA) interventions designed to improve health-related outcomes in individuals with a mild traumatic brain injury (mTBI) to assist in rehabilitation quality improvement efforts of a TBI rehabilitation program. METHODS: A scoping review following a 6-step iterative framework search across 5 databases (MEDLINE, CINAHL, PsycINFO, SPORTDiscuss, and EMBASE) and the gray literature (Google) was performed. Selected PA interventions were designed for individuals of all ages and any mechanism of injury (eg, sports-related and falls). Data were charted, collated, and summarized according to the Consensus on Exercise Reporting Template checklist and domains of the International Classification of Functioning, Disability and Health. Involvement of clinical experts ensured tailoring of the knowledge synthesis to meet clinical needs. RESULTS: Thirty-five articles and 14 gray literature records were retained. Five types of PA interventions were identified with the majority being multimodal. Reporting of PA intervention characteristics was highly variable across studies; many details necessary for intervention replication are missing. Study outcomes focused primarily on improving body functions and symptoms of mTBI, and less frequently on activities, participation, and health-related quality of life. The methodological quality of studies varies. CONCLUSIONS: Identified PA intervention types offer various management options for healthcare providers. PA interventions may improve a wide range of health-related outcomes supporting the inclusion of PA in the management of individuals of all ages with mTBI. Higher-quality research and better reporting about intervention characteristics is however needed.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".