Technology-based group exercise interventions for people living with dementia or mild cognitive impairment: A scoping review
Bibliographic record
Abstract
Older people living with dementia or mild cognitive impairment (MCI) are more vulnerable to experiencing social isolation and loneliness due to their cognitive and physical impairments. Increasingly integrating technology into group exercises contributed to the improved resilience and well-being of older adults living with dementia and MCI. The purpose of this scoping review was to identify the various types, feasibility, outcome measures, and impacts of technology-based group exercise interventions for people with dementia or MCI. We utilized the Joanna Briggs Institute approach, a three-step process. A comprehensive literature search on five databases-CINAHL, MEDLINE, Embase, Web of Science, and PsycInfo-until January 2024 yielded 1,585 publications; the final review included 14 publications that recruited a total of 379 participants, with mean age of 69 (SD = 4.21) years to 87.07 (SD = 3.92) years. Analysis of data showed three types of technology-based group exercise interventions for people with dementia or MCI: (a) exergames, (b) virtual cycling or kayak paddling, and (c) video-conferencing platforms. In addition, we identified three key impacts: (a) feasibility and accessibility; (b) physical, psychosocial, and cognitive benefits; and (c) adaptations necessary for persons with dementia or MCI. Our study suggests that technology-based group exercise interventions are feasible and acceptable to persons with dementia or MCI. Future studies should involve individuals with dementia and their caregivers in the design and implementation of technology-based group exercise programs.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".