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Record W4391321623 · doi:10.1017/s1352465823000656

Anxiolytic impact of cognitive behavioural therapy for insomnia in patients with co‐morbid insomnia and generalized anxiety disorder

2024· article· en· W4391321623 on OpenAlexafffund
Parky Lau, Elisha Starick, Colleen E. Carney

Bibliographic record

VenueBehavioural and Cognitive Psychotherapy · 2024
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Metropolitan University
FundersCanadian Institutes of Health Research
KeywordsAnxietyGeneralized anxiety disorderInsomniaPsychologyClinical psychologyCognitionMoodPsychiatryAnxiety disorder

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive behavioural therapy for insomnia (CBT-I) is an effective treatment for chronic insomnia that also improves non-sleep symptoms, such as mood and anxiety. Identifying sleep-specific variables that predict anxiety change after CBT-I treatment may support alternative strategies when people with generalized anxiety disorder (GAD) do not improve from standard GAD treatment. AIMS: To investigate CBT-I on changes in anxiety and evaluate whether changes in sleep-specific variables predict anxiety outcomes. METHODS: Seventy-two participants presenting with insomnia and GAD (GAD-I) completed four sessions of CBT-I. Participants completed daily diaries and self-report measures at baseline and post-treatment. RESULTS: CBT-I in a co-morbid GAD-I sample was associated with medium reductions in anxiety, and large reductions in insomnia severity. Subjective insomnia severity and tendencies to ruminate in response to fatigue predicted post-treatment anxiety change, in addition to younger age and lower baseline anxiety. CONCLUSIONS: The findings suggest that younger GAD-I participants with moderate anxiety symptoms may benefit most from the anxiety-relieving impact of CBT-I. Reducing perceived insomnia severity and the tendency to ruminate in response to fatigue may support reductions in anxiety in those with GAD-I.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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.

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

Citations6
Published2024
Admission routes2
Has abstractyes

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