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Record W3159562496 · doi:10.1093/sleep/zsab072.348

349 Cognitive Behavioral Therapy for Insomnia in Patients with Chronic Pain - A Systematic Review and Meta-Analysis

2021· review· en· W3159562496 on OpenAlexaff
Janannii Selvanathan, Chi Pham, Mahesh Nagappa, Philip Peng, Marina Englesakis, Colin A. Espie, Charles M. Morin, Frances Chung

Bibliographic record

VenueSLEEP · 2021
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Western HospitalUniversité LavalUniversity Health NetworkWestern UniversityUniversity of Toronto
FundersIdorsia Pharmaceuticals
KeywordsMeta-analysisInsomniaMedicineChronic painRandomized controlled trialCognitive behavioral therapy for insomniaAnxietyCognitive behavioral therapyPhysical therapyComorbidityPittsburgh Sleep Quality IndexInternal medicinePsychiatrySleep quality

Abstract

fetched live from OpenAlex

Abstract Introduction Patients with chronic non-cancer pain often report insomnia as a significant comorbidity. Cognitive behavioral therapy for insomnia (CBT-I) is recommended as the first line of treatment for insomnia, and several randomized controlled trials (RCTs) have examined the efficacy of CBT-I on various health outcomes in patients with comorbid insomnia and chronic non-cancer pain. We conducted a systematic review and meta-analysis on the effectiveness of CBT-I on sleep, pain, depression, anxiety and fatigue in adults with comorbid insomnia and chronic non-cancer pain. Methods A systematic search was conducted using ten electronic databases. The duration of the search was set between database inception to April 2020. Included studies must be RCTs assessing the effects of CBT-I on at least patient-reported sleep outcomes in adults with chronic non-cancer pain. Quality of the studies was assessed using the Cochrane risk of bias assessment and Yates quality rating scale. Continuous data were extracted and summarized using standard mean difference (SMD) with 95% confidence intervals (CIs). Results The literature search resulted in 7,772 articles, of which 14 RCTs met the inclusion criteria. Twelve of these articles were included in the meta-analysis. The meta-analysis comprised 762 participants. CBT-I demonstrated a large significant effect on patient-reported sleep (SMD = 0.87, 95% CI [0.55–1.20], p < 0.00001) at post-treatment and final follow-up (up to 9 months) (0.59 [0.31–0.86], p < 0.0001); and moderate effects on pain (SMD = 0.20 [0.06, 0.34], p = 0.006) and depression (0.44 [0.09–0.79], p= 0.01) at post-treatment. The probability of improving sleep and pain following CBT-I at post-treatment was 81% and 58%, respectively. The probability of improving sleep and pain at final follow-up was 73% and 57%, respectively. There were no statistically significant effects on anxiety and fatigue. Conclusion This systematic review and meta-analysis showed that CBT-I is effective for improving sleep in adults with comorbid insomnia and chronic non-cancer pain. Further, CBT-I may lead to short-term moderate improvements in pain and depression. However, there is a need for further RCTs with adequate power, longer follow-up periods, CBT for both insomnia and pain, and consistent scoring systems for assessing patient outcomes. Support (if any):

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)0.013
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 score0.017
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.033
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.067
GPT teacher head0.379
Teacher spread0.312 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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