Improving Cognition through Dance in Older Filipinos with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: People with mild cognitive impairment (MCI) are considered a high-risk population for developing dementia and therefore potential targets for preventive interventions. So far, no pharmacological interventions have proven to be effective. Latest evidence has laid the groundwork for the hypothesis that dancing can have beneficial effect on cognition by improving neuroplasticity. OBJECTIVE: This study aimed to examine whether a structured modular ballroom dance intervention (INDAK) could improve cognition among Filipino older persons with MCI. METHODS: A two-armed, single-blinded, quasi-experimental study was conducted in a community-based population at Marikina City, Philippines. Two hundred and seven participants older than 60 years old with MCI participated through self-assigned allocation to dance (N=101) and control (N=106) groups. The intervention group received INDAK consisting eight types of ballroom dances with increasing complexity lasting one hour, twice a week for 48 weeks. Neurologists and psychologists blinded to the group allocation administered baseline and post intervention assessments using Alzheimer's Disease Assessment Scale - Cognitive (ADAS-Cog), Filipino version of the Montreal Cognitive Assessment (MoCA-P), Boston Naming Test (BNT), Geriatric Depression Scale (GDS), Instrumental Activities of Daily Living (IADL) and Disability Assessment for Dementia (DAD). RESULTS: Baseline sociodemographic and clinical characteristics did not differ between groups. The mean differences between baseline and 48-week assessments were compared between dancers and controls, showing that the intervention group improved in ADAS-Cog, MoCA-P, BNT and GDS. CONCLUSION: INDAK is potentially a novel, ecological and inexpensive non-pharmacological intervention that can improve cognition among older Filipinos with MCI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".