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Effectiveness of Cognitive Behavioral Therapy for Insomnia on Sleep Quality in Patients with Insomnia: A Meta-Analysis

2023· article· en· W4409845563 on OpenAlexaboutno aff
Cynthia Octaviani, Hanung Prasetya, Bhisma Murti

Bibliographic record

VenueIndonesian Journal of Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInsomniaCognitive behavioral therapy for insomniaSleep qualitySleep (system call)Meta-analysisCognitionPsychologyCognitive behavioral therapyClinical psychologyMedicinePsychiatryInternal medicineComputer science

Abstract

fetched live from OpenAlex

Background: The relationship between sleep quality and sleep quantity is very close and has a significant impact on a person's sleep needs. If someone does not get enough sleep, then this will have an impact on the quality of their sleep, and vice versa. Therefore, maintaining a good quality of sleep is very important to maintain a healthy body and improve quality of life. The aim of the study was to analyze the effect of cognitive behavior therapy for insomnia (CBT-I) on sleep quality in insomnia patients. Subjects and Method: This study is a meta-analysis with PICO. Population: insomnia patients. Intervention: given cognitive behavior therapy for insomnia (CBT-I). Comparison: not given cognitive behavior therapy for insomnia (CBT-I). Outcome: sleep quality. The articles used in this study were obtained from five databases, namely PubMed, Embase, Web of Science, ScienceDirect, and Google Scholar. The keywords used to search for articles use the medical subject heading (MeSH) term and the emtree of the keywords "Insomnia" AND "Cognitive Behavior Therapy for Insomnia" AND "Sleep Quality". The articles used were full text in English from 2013 to 2023. Articles were selected using the 2021 PRISMA flowchart and analyzed using the RevMan 5.3 application. Results: A total of 10 randomized controlled trial study articles came from Iran, Canada, Spain, Texas, Korea, Kansas and the United States. The total sample size is 473 research subjects. Based on the analysis, insomnia patients who were given CBT-I therapy showed an average PSQI score of 1.88 units lower than those not given CBT-I (placebo), and the difference was statistically significant (SMD = -1.88; 95% CI = -2.55 to -1.22; p<0.001). Then insomnia patients who were given CBT-I showed an average PSQI score of 0.52 units lower than those given other insomnia therapies, and the difference was statistically significant (SMD= -0.52; 95% CI= -0.77 to -0.28; p <0.001) . Then when viewed as a whole, it shows that insomnia patients who are given CBT-I therapy on average have or show a PSQI score of 0.78 units lower than other therapies and without CBT-I therapy (placebo), and this difference is statistically significant (SMD= -0.78; 95% CI= -1.13 to -0.42; p < 0.001). Conclusion: Cognitive behavior therapy for insomnia (CBT-I) can improve sleep quality in insomnia patients (decrease the PSQI score). Keywords: insomnia, cognitive behavior therapy for insomnia, sleep quality. Correspondence: Cynthia Octaviani. Master's Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Jawa Tengah, Indonesia. Email: Cynthia.octaviani14@gmail.com. Mobile: +6287812315855.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.096
GPT teacher head0.434
Teacher spread0.338 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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