Effect of the strategy game Mangala on cognitive function, anxiety, depression, and fine motor skills in individuals with Alzheimer’s disease: A randomized controlled trial
Bibliographic record
Abstract
Cognitive impairment, changes in mood, and decrease in fine motor skills are some of the most common symptoms experienced by individuals with Alzheimer’s Disease (AD). This study aimed to investigate the effects of the Turkish intelligence and strategy game Mangala on cognitive functions, anxiety, depression, and fine motor skills in individuals with AD. In this randomized controlled study, 37 participants were divided into Mangala Group (MG) and Control Group (CG). Both the MG (n = 18) and the CG (n = 19) attended daily physiotherapy and rehabilitation sessions. Additionally, the MG played Mangala 2 sessions/week for six weeks. The mood state was evaluated using the Hospital Anxiety and Depression Scale (HAD), cognitive functions were examined using the Montreal Cognitive Assessment Scale (MoCA), and fine motor skills were measured using the Nine Hole Peg Test (NHPT). There was no significant difference between the groups pretreatment in terms of MoCA, depression and NHPT values (p > .05), except anxiety levels (p= .009) The MG showed significant improvement post-treatment in terms of MoCA and HAD anxiety (p =.0001 and p =.0008), HAD depression and fine motor skills (p =.043 and p =.0001). There were significant improvements in favor of MG in MoCA (p =.014) and NHPT (p =.004), but not in HAD anxiety (p =.782) and depression (p =.514) scores in terms of delta (difference between pre and post treatment) values. To prevent cognitive decline, reduce depression, and improve fine motor skills in patients with AD, The Mangala game may be a good alternative.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".