Assistive Relaxation Therapy for Older Adults With Insomnia and Mild Cognitive Impairment: A Pilot Study
Bibliographic record
Abstract
Abstract Insomnia symptoms are prevalent in older adults with mild cognitive impairment (MCI) and can pose treatment challenges. Our objective was to test the preliminary efficacy of tablet-based assistive relaxation therapy (ART) to improve insomnia symptoms in community-dwelling older adults with MCI. ART involves breath-based relaxation techniques coupled with a physical anchoring task to redirect thoughts and disengage from pre-sleep anxiety-provoking cognitions. Using a pilot randomized controlled non-crossover design, 20 participants recruited from one urban adult day center were allocated in a 1:1 ratio to intervention or education only control group for a treatment period of two weeks. Our final sample (n=20) was balanced on all demographic and clinical variables and consisted of Black (100%), female (75%), older adults (mean age 68.85 ± 7.29) with mean Montreal Cognitive Assessment scores of 21.2 ± 2.48. All participants at baseline had insomnia symptoms (mean Insomnia Severity Index (ISI) score 15.8 ± 3.78) and poor sleep quality (mean Pittsburgh Sleep Quality Index (PSQI) score 12.95 ± 0.70); half had daytime sleepiness (Epworth Sleepiness Scale (ESS) score 10.15 ± 1.07). Compared to baseline, participants improved on ISI (9.83 ± 1.32; p=0.0002), PSQI (9.11 ± 1.02; p=0.0016) and ESS (8.17 ± 0.86; p=0.08). The intervention group had statistically significant mean change scores on ISI compared to the control (-7.5 ± 1.37 vs. -3.88 ± 1.48; p=.0461). There were no statistically significant between group differences on PSQI or ESS. Our preliminary results suggest ART therapy is an effective treatment for insomnia symptoms in older adults with MCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".