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Record W3029668958 · doi:10.1093/sleep/zsaa056.533

0536 Motivation at Pretreatment and its Correlates in a Trial of Digital CBT For Insomnia: Preliminary Findings

2020· article· en· W3029668958 on OpenAlexaffabout
Xiaowen Ji, Janet M. Y. Cheung, Hans Ivers, Charles M. Morin

Bibliographic record

VenueSLEEP · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
FundersSage Therapeutics
KeywordsCognitive behavioral therapy for insomniaContext (archaeology)PsychologySleep diaryCronbach's alphaInsomniaClinical psychologyAnxietyCognitive behavioral therapyRandomized controlled trialActigraphyPhysical therapyPsychiatryMedicinePsychometricsInternal medicine

Abstract

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Abstract Introduction Pretreatment motivation is a critical variable in any intervention seeking to modify behaviors. Lack of motivation may hamper the effects of cognitive-behavioral therapy for insomnia (CBT-I), especially when delivered online. This study aims to investigate baseline correlates of pretreatment motivation and its influence on treatment outcomes in the context of digitalized CBT-I. Methods This is a secondary analysis of an ongoing pragmatic trial conducted in primary care clinics of Québec City, Canada. The trial was designed to assess the efficacy of a stepped-care intervention for chronic insomnia in which participants received a digital CBT-I (SHUTi), alone or in addition to sleep medication they were already using. Pre-treatment motivation was measured using two items based on the perceived importance of improving sleep and readiness to change behaviors to improve sleep (Score range: 0 to 20; cronbach’s alpha 0.79). Baseline questionnaires included an extended version of Insomnia Severity Index (ISI), Fatigue Severity Scale (FFS), Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9). Treatment outcome was measured by a change in ISI scores (i.e. ISI post - ISI pre). Results A total of 28 participants were included in the analysis. All participants preferentially selected ISI either as monotherapy (n=13)or in combination with their usual sleep medication (n=15). Participants’ motivation before treatment was high (Mean: 18.04; SD: 1.93). We did not find any associations between motivation and ISI score change or incidence of dropout. However, baseline fatigue was positively correlated with pretreatment motivation (r = 0.51, p = 0.005) and more severe insomnia symptoms were also associated with higher motivation (r=0.43, p=0.03). Specifically, perceived importance was associated with both nighttime and daytime insomnia symptoms while readiness for behavioral change was only associated with daytime impairments on energy, mood and social activities (all p = 0.01). Baseline anxiety and depressive symptoms were not correlated with motivation. Conclusion Insomnia-related daytime impairments and elevated fatigue levels appear to be linked to pretreatment motivation, especially for behavioral changes. Further study with greater statistic power is warranted to understand the relationship between participants’ motivation and treatment adherence or outcomes. Support CIHR0083000212

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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