Effects of computer-based cognitive training combined with physical training for older adults with cognitive impairment: A four-arm randomized controlled trial
Bibliographic record
Abstract
Objective Combined physical (PHY) and cognitive (COG) training in sequential (SEQ) and simultaneous (SIMUL) sessions may delay the progression of cognitive impairment. To date, no study has directly compared in older adults with cognitive impairment the effects of COG training, PHY training, SEQ motor-cognitive training and SIMUL motor-cognitve training on specific indices of cognitive performance and activities of daily living (ADL). The purpose of this study was to determine whether SEQ and SIMUL motor-cognitive training can improve treatment outcomes compared with PHY or COG training alone. We also aimed to compare the effects of SEQ versus SIMUL motor-cognitive training on cognitive functions and instrumental ADL (IADL) in older adults with cognitive impairment. Methods A cluster randomized controlled trial was conducted. Eighty older adults with cognitive impairment were randomly assigned to COG, PHY, SEQ or SIMUL training groups. The intervention consisted of 90-min training sessions, totaling 36 sessions. Outcome measures were the Montreal Cognitive Assessment, three subtests of the Wechsler Memory Scale (WMS) and the Lawton IADL scale. Results Significant interaction effects between group and time were found in WMS-spatial span ( p = 0.04) and WMS-word lists ( p = 0.041). For WMS-spatial span, the SIMUL group showed outperformed the COG ( p = 0.039), PHY ( p = 0.010) and SEQ groups ( p = 0.017). For WMS-word lists, the SEQ group improve more than COG ( p = 0.013), PHY ( p = 0.030) and SIMUL ( p = 0.019) groups. No significant differences were found in IADL performance among four groups ( p = 0.645). Conclusions Our study showed SEQ and SIMUL motor-cognitive training led to more pronounced improvements in visuospatial working memory or verbal memory compared with isolated COG or PHY training for community-based older adults with cognitive impairment. For enhancing effects on IADL, we suggest the use of sensitive measurement tools and context-enriched cognitive training involving real-life task demands.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".