TD‐P‐025: IMPROVING MOVEMENT CONFIDENCE AND BALANCE IN PEOPLE WITH DEMENTIA, MCI AND PHYSICAL IMPAIRMENT USING GROUP MOTION–BASED TECHNOLOGY
Bibliographic record
Abstract
Using motion-based technology (MBT) to provide engaging leisure activities for people with dementia or MCI can offer benefits such as increased social and physical stimulation (Dove & Astell, 2017). MBT is increasingly being used to measure balance (Hsaoi, et al., 2017) and postural stability in health older adults (Dehbandi, et al., 2017). We aimed to use MBT in adult day centres with people with dementia, MCI or physical impairments as an engaging group activity, while also invoking an increase in movement confidence and balance. A 60-minute group bowling intervention was designed using the Xbox Kinect. Each bowling session was facilitated by a member of the research team, and involved participants taking turns bowling. Sessions were recorded by two video cameras and a 3D full-body Kinect-based tracking system. Pre- and post-balance were assessed using the abbreviated Romberg (feet together eyes open [R-EO], feet together eyes closed: [R-EC] and Sharpened Romberg test (heel-to-toe, eyes open [SR-EO], heel-to-toe, eyes closed [SR-EC]; Steffen, 2012) for 30 seconds each. Data were collected across three sites from 38 participants (mean age = 75.39; mean MoCa score = 12.47) collected over 64 sessions from April-October 2017. Comparison of baseline and post-groups Romberg scores showed significant improvement in R-EO (p<0.05) and R-EC (p<0.05). At baseline 7 and 12 participants could not complete SR-EO and SR-EC respectively, and after the group this was 10 and 12. For SR-EO there was a significant decline (p<0.05) but not in SR-EC (p=0.1). At baseline and post-group MoCA and SR combined scores were correlated (p=0.05). Further exploration of these data comprising objective measures of movement confidence and balance drawn from analysis of 3D tracking data, including trunk stability, gait, arm swing, and movement speed are underway for the first site. Study design, setup, proof of concept of multi-modal mixed-methods data collection and preliminary data will be presented. Group interventions using MBT with people with dementia, MCI or physical impairment can be fun and engaging. Early findings from our study suggest that this group intervention may lead to an improvement in movement confidence and balance, determined by both researcher observation and preliminary empirical data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".