Developing physical activity interventions for adults with spinal cord injury. Part 3: A pilot feasibility study of an intervention to increase self-managed physical activity.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this pilot study was to test the efficacy and feasibility of a group-mediated cognitive-behavioral training (GMCB) intervention for increasing self-managed leisure-time physical activity (LTPA) among people with spinal cord injury (SCI) who are already somewhat active. METHODS: Participants were 13 members of a supervised exercise program for adults with SCI. They took part in a 9-week, evidence-based, theoretically framed, GMCB intervention designed to promote self-regulatory skills and to increase the amount of time spent in self-managed LTPA, outside of the supervised program. Minutes/week of self-managed and supervised LTPA were measured pre- and postintervention, along with measures of social-cognitive variables. Participants' and the interventionist's perceptions of the intervention were also assessed. RESULTS: Participants nearly doubled their total min/week of LTPA, as the result of a significant increase in self-managed LTPA from baseline (M = 42.00 ± 69.57 min/week) to postintervention (M = 197.50 ± 270.86 min/week; p < .05), at no cost to supervised LTPA. Consistent with the GMCB and counseling of self-regulatory skills, self-regulatory efficacy was sustained and action planning increased from pre- (M = 4.63 ± 3.25) to postintervention (M = 6.83 ± 2.40; p = .06). The intervention materials and protocol were perceived as usable by the interventionist and participants and had good intervention fidelity. CONCLUSIONS: Persons with SCI can voluntarily increase their self-managed LTPA after learning and practicing self-regulatory skills. GMCB training interventions are a feasible approach for teaching these skills.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".