Quality participation in technology-mediated exercise interventions in chronic neurological conditions: a scoping review
Bibliographic record
Abstract
Background Technology tools offer an innovative approach to delivering exercise interventions for people with chronic neurological conditions (CNCs), with beneficial effects on patient outcomes. .Objective To determine the extent to which technology-mediated exercise interventions targeting people with CNCs include elements of and strategies to foster quality participation in their design and/or delivery.Methods We conducted a scoping review of technology-mediated exercise trials published from inception till date in CINAHL (EBSCO), MEDLINE (OVID), Cochrane Central, EMBASE (OVID), and Web of Science Core Collection. Covidence was used to facilitate the review. We coded studies for quality participation elements and strategies using the Quality Participation Framework (QPF) and a published matrix of 86 strategies. A narrative synthesis was performed to summarize the main results.Results Sixty-seven studies were included. Of the 67 studies, most targeted persons with stroke (n = 26, 39%), followed by Parkinson’s disease (n = 16, 24%), and multiple sclerosis (n = 12, 18%). Across the studies, none explicitly mentioned ‘quality participation’ or used the QPF when describing the interventions. Nevertheless, we identified 80 matrix strategies and 13 additional strategies across studies.Conclusions There is an opportunity for researchers and interventionists to target other intermittent (e.g. epilepsy) and stable with/without age-related degeneration (e.g. cerebral palsy) CNCs, and to utilize the QPF in the context of technology-mediated interventions across CNCs. Further empirical research is required to evaluate possible interrelatedness between quality participation elements, investigate the applicability of un/under-reported strategies, and the potential for additional strategies for fostering quality participation within the domain of technology-mediated exercise interventions.
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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 | 0.055 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 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".