Cognitive effects of calligraphy therapy for older people: a randomized controlled trial in Hong Kong
Bibliographic record
Abstract
BACKGROUND: This pilot study investigated the effects of calligraphy therapy on cognitive function in older Hong Kong Chinese people with mild cognitive impairment. METHODS: A single-blind, randomized controlled trial was carried out in a sample of 31 adults aged 65 years or older with mild cognitive impairment. They were randomly assigned to receive either intensive calligraphy training led by a trained research assistant for eight weeks (calligraphy group, n = 14) or no calligraphy treatment (control group, n = 17). Participants' cognitive function was assessed by the Chinese version of the Mini-Mental State Examination (CMMSE) before and after calligraphy treatment. Repeated measures analysis of variance and paired samples t-tests were used to analyze the data. RESULTS: A significant interaction effect of time and intervention was detected [F (1, 29) = 9.11, P = 0.005, η(2) = 0.24]. The calligraphy group was found to have a prominent increase in CMMSE global score, and scores in the cognitive areas of orientation, attention, and calculation after two months (ΔM = 2.36, P < 0.01), whereas their counterparts in the control group experienced a decline in CMMSE score (ΔM = -0.41, P < 0.05). CONCLUSION: Calligraphy therapy was effective for enhancing cognitive function in older people with mild cognitive impairment and should be incorporated as part of routine programs in both community and residential care settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".