Effect of Exercise on Mental Health and Health-related Quality of Life in Adults With Spinal Cord Injury: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of exercise interventions on mental health and health-related quality of life (HRQoL) in individuals with SCI. DATA SOURCES: We searched Embase, CINAHL, Medline, PsychINFO, and SPORTDiscus from inception to September 2023. STUDY SELECTION: We included randomized controlled trials that (1) involved participants ≥18 years old with a SCI; (2) administered an exercise intervention; and (3) measured subjective well-being, psychological well-being, social well-being, and/or HRQoL as outcomes. We reported standardized means differences (d) with a 95% confidence interval (CI), assessed the risk of bias by using the Revised Cochrane Risk-of-bias Tool for Randomized Trials (RoB 2), and the certainty of the evidence using GRADE. DATA SYNTHESIS: Nineteen studies (797 participants, mean age <65 years in every study) were included. Exercise improved overall well-being (d=0.494; 95% CI 0.268, 0.720; low certainty evidence), subjective well-being (d=0.543; 95% CI 0.270, 0.816; low certainty evidence), psychological well-being (d=0.499; 95% CI 0.193, 0.805; low certainty evidence), social well-being (d=0.452; 95% CI 0.151, 0.752; low certainty evidence), and HRQoL (d=0.323; 95% CI 0.072, 0.574; low certainty evidence). Four serious adverse events probably attributable to the interventions were reported in 3 studies. CONCLUSIONS: Exercise interventions can improve well-being and HRQoL in adults with SCI <65 years of age. Additional research is needed to determine effectiveness in adults ≥65 years of age.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".