SYSTEMATIC REVIEW OF THE EFFECTS OF NON-VR EXERGAMING ON MOBILITY OUTCOMES OF OLDER ADULTS LIVING IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Introduction: Institutionalized older adults have high-rates of mobility decline resulting in reduced quality of life and increased dependency. Given the ageing population, there has been a proliferation of exergaming technology targeting older adults to maintain their physical activity (PA) levels and prevent decline. However, it is unclear if exergaming is effective to maintain or improve the PA of institutionalized older adults. Method: Four databases (MEDLINE/CINAHL/PsycINFO/Compendex) were systematically searched (key terms like “nursing homes”, exergaming”). Quantitative manuscripts examining the effects of exergaming on PA measures of institutionalized older adults published in English between 2006-present were eligible. Virtual reality was excluded from the search. No meta-analysis was conducted due to hetereogeneity of the results. Results: 11 studies were included from a search that yielded 208 results. The exergaming platforms that were used the most were the Kinect and Wii. The most commonly used PA measures were the Berg Balance Scale and the Timed-up-and-Go (n=4 studies) with no other measures being used in more than one study. Interventions ranged in exercise (e.g. cognitive-motor training, strength training, balance, etc), frequency, duration, and modality. Study designs were also heterogeneous. Articles were of very poor to poor quality. There was minimal reporting on adverse events. Older adults with cognitive impairment were commonly excluded. Challenges in current technology and studying this group are highlighted. Conclusion: Exergaming may be promising to maintain PA but more robust research is needed. More exergaming technology designed for long-term care to meet the specific complex needs of this population is warranted.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".