Evidence map›Paper›PMID 41424507›Full record

ArticleFrontiers in sleep2024

Duration of insomnia and success expectancy predict treatment outcome of iCBT for insomnia.

Polina Pchelina, Mikhail Poluektov

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in sleep, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04300218 (Effectiveness and Cost-effectiveness of iCBT-I in Clinical Settings), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04300218 nacompletednot on this map

Effectiveness and Cost-effectiveness of iCBT-I in Clinical Settings

TypeinterventionalSponsorI.M. Sechenov First Moscow State Medical UniversityRan2020 to 2023Enrolled107ConditionsChronic InsomniaArmsinternet-delivered cognitive-behavioral therapy for insomnia (iCBT-I), Care as usual (CAU)
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Polina PchelinaCereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland.
Mikhail PoluektovDepartment of Neurology and Neurosurgery, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Identifying prognostic factors of treatment outcome may assist in customizing an intervention to a patient's needs. Hence, we conducted a secondary analysis of data from a randomized controlled trial to investigate the effectiveness of an internet-based cognitive behavioral therapy for insomnia (iCBT-I) to find patient characteristics that may predict the change of insomnia severity after treatment. Materials and methods: In this exploratory analysis involving 94 chronic insomnia patients, we examined the predictive value of several self-reported measures, medical history, and sociodemographic variables to psychological distress with separate linear regression models. The main outcome was the Insomnia Severity Index score improvement from pre- to post-treatment. Results: The study found that duration of insomnia, Conclusion: Our results suggest that iCBT-I might be particularly beneficial for patients with higher expectations from the therapy and those who have a shorter duration of insomnia. Some pronounced personality traits, such as attention seeking, grandiosity, distractibility, and rigid perfectionism, may predict worse outcomes. However, because this was a Clinical trial registration: https://clinicaltrials.gov/study/NCT04300218?cond=NCT04300218&rank=1, Identifier NCT04300218.

Indexed as

chronic insomniaInsomnia Severity Indexinternet-based cognitive behavioral therapy for insomniamultiple imputationpredictorsresponders

Identifiers

PMID41424507
PMCPMC12713890

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.