Identifying Behavioral Change Techniques and Mode of Delivery in Yoga Interventions Across Five Neurological Conditions: A Scoping Review
Bibliographic record
Abstract
Objective:To identify behavior change techniques (BCTs) and the mode of delivery used in yoga interventions across five neurological conditions. Methods:This scoping review followed Arksey and O’Malley’s methodological framework for conducting scoping reviews. Medline, CINAHL, EMBASE, PsycINFO, and CENTRAL databases were searched, combining key terms for population and intervention. Covidence software was used for study selection. Template for Intervention Description and Replication checklist was used to report the intervention and study quality was assessed using the Physiotherapy Evidence Database (PEDro) scale. Interventions were coded for BCTs using various taxonomies and delivery approaches were classified using Mode of Delivery Taxonomy Version 0. Result:Among 4805 articles screened, 35 met the inclusion criteria. Parkinson’s disease, multiple sclerosis, and stroke were the most prevalent disability types with fewer studies on spinal cord injury and traumatic brain injury. A total of 134 BCTs were identified, 20 out of the 93 BCTs (25%) were from the BCT v1 taxonomy, while 15 additional BCTs were identified from other taxonomies and some BCTs were defined by the authors based on the need for yoga interventions. The most used BCTs included 12.6 body changes (68.57%, n = 24), 4.1 instruction (57.14%, n = 20), 12.5 adding objects (48.57%, n = 17), 1.2 problem solving (37.14%, n = 13), 6.1 demonstration (34.28%, n = 12), 8.1 behavioral practice and rehearsal (31.42%, n = 11), and 8.7 graded tasks (28.57%, n = 10). The most common delivery approach was face-to-face. The median PEDro score was 6 indicating medium study quality. Conclusion:Clear reporting of the intervention description and use of BCTs may enhance understanding and ability to replicate yoga interventions. This helps to adapt yoga by changing behaviors using specific BCTs to meet the goals and principles of yoga depending on the target population. The review may help inform future research to examine the effectiveness of specific BCTs on desired outcomes.
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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 | 0.030 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".