Psychological benefits of Exercise for Individuals with Sci
Bibliographic record
Abstract
Chronic pain is a frequent and debilitating comorbidity of SCI (Ravenscroft et al., 2000). Although exercise is an effective strategy for managing pain in other chronic pain populations (e.g., Ettinger et al., 1997), exercise training has not been previously examined in the SCI population. In a RCT of 34 sedentary men and women with traumatic SCI, the effects of exercise on perceived pain and physical and psychological well-being were examined. Additionally, the efficacy of exercise as a pain management strategy was assessed. Exercisers performed aerobic and resistance training twice weekly over 9-months. Controls maintained their usual level of activity. Measures of pain (Ware & Sherbourne, 1992), physical well-being (Reboussin et al., 2000), stress (Cohen et al., 1992), depression (Radlof, 1977) and subjective well-being (Cantril 1965; Patrick et al., 1988) were administered at baseline and at the 3, 6 and 9 months points of the intervention. A series of ANCOV As adjusted for baseline scores revealed a significant group main effect for the measures of pain, stress, depression and subjective well-being which reflected improvement in all of these domains for the exercisers (i.e., decreased pain, stress and depression and increased subjective well-being) and decrement in all of these domains for the controls (ps<.05). Hierarchical linear regression analyses (cf. Baron & Kenny, 1986) revealed that change in physical well-being partially mediated change in pain, change in pain mediated change in stress and subjective well-being and change in stress mediated change in depression. These findings suggest that variables mediating exercise-induced change should be targeted to maximize the effectiveness of exercise as a pain management strategy for individuals with SCI. The therapeutic and theoretical implications of these findings are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".