The Evaluation of Yoga Interventions for Individuals With Limited Mobility: Pain, Psychological Variables, and Mindfulness
Bibliographic record
Abstract
Objective: The aim of this dissertation was to evaluate specialized yoga interventions for populations with complex chronic health conditions involving chronic pain and limited mobility. \nMethod: Three research trials were conducted at two rehabilitation hospitals in Toronto. In Study 1, participants (N = 10) admitted to Bridgepoint Health were recruited to participate in an 8-week, Hatha yoga program. In Study 2, participants with spinal cord injury (SCI, N = 12) were recruited to participate in an 8-week, Hatha yoga program at the Lyndhurst Centre. In Study 3, participants with SCI (N = 23) were randomized to a 6-week, Iyengar yoga group (IY, n = 11) or to a wait-list control group (WLC, n = 12). Questionnaires on pain, psychological variables, and mindfulness, were collected at two or three points in time. \nResults: In Study 1, repeated measures ANOVAs revealed a main effect of time for anxiety, self-compassion, and the magnification aspect of pain catastrophizing, such that anxiety and pain catastrophizing decreased and self-compassion increased from pre- to post-intervention. In Study 2, there were no significant changes in the quantitative measures but qualitative analysis of the semi-structured interviews revealed main themes regarding benefits along emotional, mental and physical domains. In Study 3, linear mixed effects growth models were conducted to evaluate main effects of group at T2, controlling for T1 scores. Depression scores were lower and self-compassion scores were higher at T2 in the IY group compared to the WLC group. The two groups were combined and analyzed across time by comparing pre- and post-intervention scores. Main effects of time were found for depression scores, self-compassion, mindfulness (total score and subscale scores for mindful observing and mindful non-reactivity), such that depressive symptoms decreased and self-compassion and the various facets of mindfulness increased from pre- to post-intervention. \nDiscussion: The results from these studies show that a yoga program reduces depressive symptoms and increases self-compassion for individuals with SCI, and may also decrease anxiety and pain catastrophizing, and increase mindfulness for populations experiencing pain and limited mobility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".