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Record W4306772155 · doi:10.1177/00914150221132163

Assisted Relaxation Therapy for Insomnia in Older Adults With Mild Cognitive Impairment: A Pilot Study

2022· article· en· W4306772155 on OpenAlexaboutno aff
Miranda McPhillips, Junxin Li, Darina Petrovsky, Glenna Brewster, Estee Ward, Nancy Hodgson, Nalaka S. Gooneratne

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Nursing ResearchUniversity of Pennsylvania
KeywordsInsomniaAttritionMedicineRandomized controlled trialPhysical therapyRelaxation TherapyCognitionRelaxation (psychology)Cognitive impairmentMontreal Cognitive AssessmentClinical trialIntervention (counseling)PsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Insomnia symptoms are prevalent in older adults with mild cognitive impairment (MCI) and can pose treatment challenges. We tested the feasibility, acceptability, and preliminary efficacy of assisted relaxation therapy (ART) to improve insomnia symptoms in community-dwelling older adults with MCI. In this pilot RCT, 25 participants were assigned to intervention or control groups for 2 weeks. The final sample ( n = 20) consisted of all Black, primarily female (70%) older adults (mean age 69.10; SD = 7.45) with mean Montreal Cognitive Assessment scores of 21.10 ( SD = 2.49). Recruitment was timely; attrition was low (80%). Participants were able to use ART (average use 7.00; SD = 5.07 days). Participants in the ART group improved on Insomnia Severity Index (ISI) (− 7.10; 95% CI [−11.63, −2.55]; p = .004) compared to baseline. There were clinically meaningful mean change scores on ISI for the intervention group compared to the control (− 7.10 vs. − 4.33). Results provide justification for testing ART in a fully powered clinical trial.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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 designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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