Can meditation improve attention in older adults? Study protocol for a 4-week proof-of-concept intervention
Bibliographic record
Abstract
BACKGROUND: Falls are a major health care concern for our aging population. Previous research has identified impaired sustained attention as a risk factor for falls. Recently, meditation has been shown to improve different types of attention in various populations. However, there are no studies to date examining whether meditation training can improve sustained attention and mobility in older adults. METHODS: We are conducting a 4-week proof-of-concept meditation intervention. We will recruit community-dwelling older adults. Participants will be randomized into one of two groups: (1) meditation training or (2) music listening (control). All participants will complete three 20-min group sessions per week and will be encouraged to continue their practice independently on the remaining days each week. Our primary outcome measure is behavioral performance on the Sustained Attention to Response Task (SART). Our secondary and tertiary outcomes include electroencephalograms (EEG) to assess attention and cognitive processing, mobility, and executive function. DISCUSSION: Our proof-of-concept intervention aims to examine whether meditation training can improve sustained attention in older adults, who are known to be susceptible to falls. Importantly, our research has the potential to inform future clinical trials aimed at improving mobility and reducing falls risk in our aging population. TRIAL REGISTRATION: Clinicaltrials.gov ID NCT03417635.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".