Balance or movement confidence: exergame targets for people living with dementia
Bibliographic record
Abstract
BACKGROUND: Impaired balanced and cognition increase fall risk of people living with dementia [1], with experience of falls reducing confidence [2]. People living with dementia enjoy exergames, which have the potential to deliver interventions targeting balance and movement confidence [3]. This study examines the impact of a digital bowling game on balance and movement confidence of people living with dementia and explores the applicability of a novel observational approach. METHOD: Sixty-six people attending four adult day programs in Ontario, Canada were recruited to a 20-session, 10-week digital bowling intervention (Figure 1). Pre- and post-intervention, participants completed the Montreal Cognitive Assessment (MoCA), Mini Balance Evaluation Systems Test (MiniBESTest) or the Sharpened Romberg to assess balance. All sessions were video recorded. Two observational measures were developed to assess movement confidence. The first based on an existing video coding scheme was used to identify observable indicators of movement confidence (e.g., movements, tempo, and attention [4]. A second categorical measure was derived from the annotated videos, to produce a summative score out of 16 (with higher scores indicating higher confidence). RESULT: Participants (53% female; mean age=77.85 y; age range=58-94 y) were living with mild to severe dementia, as indicated by their MoCA scores (mean = 12.7/30; range 0-25), with 89% experiencing balance impairments at baseline. After the intervention MiniBESTest completers maintained their scores over time (p >0.05), whilst Sharpened Romberg scores declined significantly (p = 0.01). Movement confidence was high at the start of the intervention and remained so throughout. Participants expressed fears about falling whilst completing the balance assessments but not whilst exergaming. CONCLUSION: Balance and movement confidence diverged in this population, whereby participants performed better (e.g., smoother fluency of movements) in situations where their confidence was higher (i.e., exergaming). The two observational movement confidence measures appear feasible for assessing movement confidence of people living with dementia, when self-report tools are not suitable [5]. The findings suggest that movement confidence plus more ecological approaches to measuring balance, for example whilst playing exergames, could be developed to assess interventions targeting fall risk in people living with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".