Programs Using Stimulation-Regulating Technologies to Promote Physical Activity in People With Intellectual and Multiple Disabilities: Scoping Review
Bibliographic record
Abstract
BACKGROUND: People with intellectual and multiple disabilities tend to engage in very low levels of physical activity. OBJECTIVE: This review paper aims to provide a comprehensive picture of intervention programs using stimulation-regulating technologies to promote forms of physical activity in people with intellectual and multiple disabilities. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, a scoping review was conducted to identify and provide a synthesis of eligible studies published in English between 2010 and 2021. Studies were identified by searching PubMed, Web of Science, PsycINFO, ERIC, and CINAHL as well as by using Google Scholar and manual searches. Studies were included if they involved individuals with intellectual or multiple disabilities, used stimulation-regulating technology systems to help participants engage in physical activity, and reported data on the impact of the intervention. RESULTS: A total of 42 studies met the inclusion criteria. These studies were divided into 2 groups based on whether they pursued the increase in physical activity through technology-aided delivery of brief periods of preferred stimulation contingent on specific responses or the use of video games (exergames) and related auditory and visual stimulation. Subsequently, a narrative synthesis of the studies was provided. CONCLUSIONS: The evidence reported by the 2 groups of studies is encouraging. However, further research is needed to compare the overall applicability and impact of the intervention strategies proposed by these groups of studies.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".