Effects of a Group-Based 8-Week Multicomponent Cognitive Training on Cognition, Mood and Activities of Daily Living among Healthy Older Adults: A One-Year Follow-Up of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Cognitive interventions have the potential to enhance cognition among healthy older adults. However, little attention has been paid to the effect of cognitive training (CT) on mood and activities of daily living (ADL). OBJECTIVES: To assess the effectiveness of a multicomponent CT using a training program of executive functions, attention, memory and visuospatial functions (TEAM-V Program) on cognition, mood and instrumental ADL. DESIGN: A randomized, single-blinded, treatment-as-usual controlled trial. SETTING: Geriatric clinic in Bangkok, Thailand. PARTICIPANTS: 77 nondemented community-dwelling older adults (mean age 65.7±4.3 years). INTERVENTION: The CT (TEAM-V) program or the treatment-as-usual controlled group. The TEAM-V intervention was conducted over 5 sessions, with a 2-week interval between each session. Of 77 participants randomized (n=40 the TEAM-V program; n=37 the control group). MEASUREMENTS: The Thai version of Montreal Cognitive Assessment (MoCA), The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog), Thai version of Hospital Anxiety and Depression Scale (HADS) and The Chula ADL were used to assess at baseline, 6 months and 1 year. RESULTS: Compared with the control arm, the TEAM-V Program was associated with reducing anxiety (P = 0.004). Compared with the baseline, participants receiving the TEAM-V Program were associated with significantly improved general cognition (MoCA, P < 0.001), immediate recall (word recall task, P = 0.01), retrieval and retention of memory process (word recognition task, P = 0.01), attention (number cancellation part A, P < 0.001) and executive function (maze test, P = 0.02) at 1 year. No training effects on depression (P = 0.097) and IADL (P = 0.27) were detected. CONCLUSIONS: The TEAM-V Program was effective in reducing anxiety. Even though, the program did not significantly improve cognition, depression and ADL compared with the control group, global cognition, memory, attention and executive function improved in the intervention group compared with baseline. Further studies incorporating a larger sample size, longitudinal follow-up and higher-intensity CT should be conducted.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".