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Record W4405367055 · doi:10.56808/2586-940x.1114

The effects of the brain training program on cognition among the older adults in Thailand

2024· article· en· W4405367055 on OpenAlexaboutno aff
Chutima Thongwachira, Vilaivan Thongcharoen, Usa Khemthong, Natnarun Kleawklong

Bibliographic record

VenueJournal of Health Research · 2024
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionCognitive trainingGerontologyPsychologyTraining (meteorology)MedicineNeuroscienceGeography

Abstract

fetched live from OpenAlex

Background: Dementia is typically found in older adults, and negatively impacts cognitive function. The current study examined the effects of brain training programs with learning activities on cognitive function in older adults. Methods: We conducted a two-arm quasi-experimental study with participants who were over 60 years old and had Montreal Cognitive Assessment (MoCA) scores < 25 (n=66). The intervention group (n=33) performed a brain training program with learning activities based on social cognitive theory. MoCA scores were evaluated at baseline, immediately after activities were completed at Week 9, and 3 months after the activities were completed. Results: Sixty-six participants showed low MoCA. Mean MoCA scores at baseline in the control group (CG) and intervention group (IG) were 20.09 (SD = 2.89) and 19.82 (SD = 2.33), respectively; whereas MoCA scores immediately after activities were completed (Week 9) were 19.15 (SD = 2.06) for the CG and 24.24 (SD = 3.02) for the IG. Mean scores in the IG were significantly higher than those in the CG at Week 9 and 3 months (p < 0.01). Additionally, the mean cognitive function score in the IG at Week 9 and 3 months after the activities was significantly higher than the baseline (F = 116.87, p < 0.01). Conclusion: The brain training program adopted in this study could be used with older people in the community. Healthcare providers should encourage older people to regularly practice brain training at home.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.142
GPT teacher head0.491
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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