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Record W4294845110 · doi:10.1097/gme.0000000000002051

Behavioral interventions for improving sleep outcomes in menopausal women: a systematic review and meta-analysis

2022· review· en· W4294845110 on OpenAlexaff
Christine M. Lam, Leticia Hernández-Galán, Lawrence Mbuagbaw, Joycelyne Ewusie, Lehana Thabane, Alison K. Shea

Bibliographic record

VenueMenopause The Journal of The North American Menopause Society · 2022
Typereview
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsImpactSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsMedicinePsychological interventionMeta-analysisRandomized controlled trialMEDLINEPhysical therapyStrictly standardized mean differenceConfidence intervalActigraphyAdverse effectInsomniaPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

IMPORTANCE: Perimenopausal and postmenopausal women commonly report sleep disruption and insomnia. Behavioral interventions may be safe alternatives for patients who are unwilling to begin pharmacological treatments because of adverse effects, contraindications, or personal preference. OBJECTIVE: The primary objective is to assess the efficacy of behavioral interventions on sleep outcomes among perimenopausal and postmenopausal women, as measured using standardized scales and objective methods (polysomnography, actigraphy). The secondary objective is to evaluate the safety of these methods through occurrence of adverse events. EVIDENCE REVIEW: Searches were performed within MEDLINE (OVID interface, 1946 onward), Embase (OVID interface, 1974 onward), Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, and Web of Science (Core collection) using a search strategy developed in consultation with a health sciences librarian. Title/abstract and full-text screenings were performed in duplicate, and relevant studies were selected based on inclusion and exclusion criteria set to identify randomized controlled trials evaluating the effects of behavioral interventions on sleep quality. Risk of bias assessments were done using the Cochrane Risk of Bias 2 tool, and the Grading of Recommendations Assessment, Development and Evaluation approach was used to assess the certainty of the body of evidence. Data were pooled in a meta-analysis using a random-effects model. FINDINGS: Nineteen articles reporting results from 16 randomized controlled trials were included, representing a total of 2,108 perimenopausal and postmenopausal women. Overall, behavioral interventions showed a statistically significant effect on sleep outcomes (standardized mean difference [SMD], -0.62; 95% confidence interval [CI], -0.88 to -0.35; I2 = 93.4%). Subgroup analyses revealed that cognitive behavioral therapy (SMD, -0.40; 95% CI, -0.70 to -0.11; I2 = 72.7%), physical exercise (SMD, -0.57; 95% CI, -0.94 to -0.21; I2 = 94.0%), and mindfulness/relaxation (SMD, -1.28; 95% CI, -2.20 to -0.37; I2 = 96.0%) improved sleep, as measured using both subjective (eg, Pittsburg Sleep Quality Index) and objective measures. Low-intensity (SMD, -0.91; 95% CI, -1.59 to -0.24; I2 = 96.8) and moderate-intensity exercise (SMD, -0.21; 95% CI, -0.34 to -0.08; I2 = 0.0%) also improved sleep outcomes. No serious adverse events were reported. Overall risk of bias ranged from some concern to serious, and the certainty of the body of evidence was assessed to be of very low quality. CONCLUSIONS AND RELEVANCE: This meta-analysis provides evidence that behavioral interventions, specifically, cognitive behavioral therapy, physical exercise, and mindfulness/relaxation, are effective treatments for improving sleep outcomes among perimenopausal and postmenopausal women.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.033
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.117
GPT teacher head0.410
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations30
Published2022
Admission routes1
Has abstractyes

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