Impact of a mobile cognitive activity program on patients with mild cognitive impairment post-stroke: case study
Bibliographic record
Abstract
Objective : This study aimed to investigate the effects of a mobile cognitive activity program on patients with mild cognitive impairment (MCI) caused by stroke, using a single-case study design. Methods: An case study design was employed in the study: Baseline A Traditional cognitive activity pro gram, Intervention Phase B: Mobile cognitive activity program, Return-to-Baseline A’: Reversion to the traditional program, Three stroke patients with MCI participated in the program for 30 minutes per session, five days per week, over three weeks (October 14–November 1, 2024). Assessments using online MoCA-K and CERAD-K were conducted at the end of each phase. Data were analyzed using SPSS 25.0 for descriptive statistics, including means and standard deviations. Results: The mean MoCA-K score improved from 20.33 (±2.08) during baseline A to 22.33 (±2.51) during intervention B and remained at 22.33 (±2.51) during the return-to-baseline A’. For the CERAD-K Trail Making Test A (TMA), the mean time improved from 129.66 seconds (±69.97) in baseline A to 108.00 seconds (±48.53) in intervention B, and further improved to 100.33 seconds (±38.68) in return-to-baseline A’. Conclusion: The mobile cognitive activity program had positive effects on patients with mild cognitive impairment caused by stroke, particularly in improving cognitive abilities related to language. This study provides foundational data for the application of mobile cognitive activity programs in stroke patients. Future research should further validate its effectiveness, allowing for broader use of such programs in the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".