The Effect of Visual Deprivation During Cognitive Motor Dual Task Training on Cognitive Function in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background Diabetes Mellitus (DM) is a chronic metabolic disorder caused by hyperglycemia, impaired insulin secretion, and insulin resistance. Type 2 diabetes mellitus (T2DM) is associated with an increased risk for cognitive dysfunction. Cognitive motor dual task blindfold training (CMDBT) forces the brain to process motor tasks in one of the four procedural memory centers: the basal ganglia, cerebellum, supplementary motor area, and premotor cortex. Hence, it helps improve cognition in patients with T2DM. Methods A randomized controlled trial was conducted with 62 participants diagnosed with type 2 diabetes mellitus. Baseline cognition was assessed using the Montreal Cognitive Assessment (MoCA). The experimental group (n=31) underwent cognitive-motor dual-task blindfold training combined with aerobic and resistance exercises, while the control group (n=31) received conventional aerobic and resistance training only. Both groups participated in their respective interventions three times weekly for 12 weeks. Post-intervention cognitive function was re-assessed using the MoCA scale. Results Statistical analysis of the data revealed that there was a significant improvement in cognitive function in experimental group A subjects, with a significant mean difference observed in group A compared to group B. The P value of MoCA was 0.0001 in experimental group A subjects. Conclusion CMDBT is more effective than conventional exercise in enhancing cognitive function in individuals with T2DM and may serve as a valuable intervention to mitigate diabetes-associated cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".