EFFECT OF KINECT TAI CHI ON OVERALL HEALTH OF DEMENTIA CLIENTS: A FEASIBILITY AND USABILITY STUDY
Bibliographic record
Abstract
The prevalence of dementia is increasing worldwide. Dementia clients experience an increased risk for depression and physical inactivity. Tai Chi can enhance the physical and mental health of healthy older adults, including persons with dementia. However, programs tailored for dementia clients are scarce and barriers, such as transportation and accessibility, further limit participation in Tai Chi. The purpose of this pilot study was to evaluate the usability of a home Kinect-based Tai Chi system (K-TaiChi), and to determine its effect on perceived physical and mental health of dementia clients in preparation for a large-scale study. Using a serious-games methodology, K-TaiChi was developed to guide dementia clients through postures and movements, recognize features of their movement, and provide visual feedback and rewards when movements are performed well. Ten community dwelling individuals with mild to moderate dementia used K-TaiChi in their homes three times per week, for six weeks. Focus groups with dementia clients and their caregivers were conducted to evaluate our system’s feasibility and usability. The Cornell Scale for Depression in Dementia was administered pre and post intervention to evaluate its effectiveness on mental health. The majority of participants successfully used K-TaiChi. Results revealed improvements in depression and physical activity levels of those that completed all 18 sessions. Because of its user-friendliness and its effects on activity levels, depression, and perceived health, K-TaiChi holds promise for community-residing mild to moderate persons with dementia who are unable to participate in traditional Tai Chi programs.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".