Four weeks of meditation training improves sustained attention in community-dwelling older adults: a proof-of-concept randomized controlled trial
Bibliographic record
Abstract
Introduction: With our rapidly expanding population of older adults, identifying effective intervention strategies to improve cognitive functioning is an increasing priority. This study sought to examine whether 4 weeks of thrice-weekly meditation training can improve attention in older adults, as well as whether such benefits may extend to other domains of cognition as well as mobility. Methods: Forty-three participants (mean age 68 years) were randomized into either the focused attention meditation group or the music listening control group ( Clinicaltrials.gov ID NCT03417635). Participants completed three 20-minute guided group sessions per week for four consecutive weeks. Our primary outcome measure was behavioural performance on the Sustained Attention to Response Task (SART). Secondary and tertiary outcome measures included event-related potentials (ERPs) during the SART task, measures of executive functioning, and measures of mobility. Results: We found that meditation training significantly improved attention, as demonstrated by improved SART accuracy and changes in N2 ERP amplitude and latency. Discussion: These findings suggest that meditation may lead to changes in attention and underlying cognitive processing in older adults, although a full-scale definitive trial is needed. Future research on the long-term benefits with real world applications is warranted.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".