Evidence map›Paper›PMID 39953626›Full record

Trial reportAddiction science & clinical practice2025

Individual differences in treatment effects of internet-based cognitive behavioral therapy in primary care: a moderation analysis of a randomized clinical trial.

Karin Hyland, Danilo Romero, Sven Andreasson, Anders Hammarberg, Erik Hedman-Lagerlöf, Magnus Johansson

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Addiction science & clinical practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
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

6 authors.

Karin Hyland *Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden. karin.hyland@ki.se.ORCID 0000-0001-6261-0224
Danilo Romero *Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Sven AndreassonCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Anders HammarbergCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Erik Hedman-LagerlöfDivision of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Magnus JohanssonCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.

Funding

Forskningsrådet om Hälsa, Arbetsliv och Välfärd 2015-00415Forskningsrådet om Hälsa, Arbetsliv och Välfärd 2021-01319Stockholms Läns Landsting FoUI-950129
6 · The paper itself

Abstract

BACKGROUND AND

aimsLittle is known regarding predictors of outcome in treatment of alcohol dependence via the internet and in primary care. The aim of the present study was to investigate the role of socio-demographic and clinical factors for outcomes in internet-based cognitive behavioral treatment (ICBT) added to treatment as usual (TAU) for alcohol dependence in primary care.

designSecondary analyses based on data from a randomized controlled trial in which participants were randomized to ICBT + TAU or to TAU only.

settingThe study was conducted in collaboration with 14 primary care centers in Stockholm, Sweden.

participantsThe randomized trial included 264 adult primary care patients with alcohol dependence enrolled between September 2017 and November 2019.

interventionsPatients in the parent trial were randomized to ICBT that was added to TAU (n = 132) or to TAU only (n = 132). ICBT was a 12-week intervention based on motivational interviewing, relapse prevention and behavioral self-control training. MEASURES: Primary outcome was number of standard drinks last 30 days. Sociodemographic and clinical predictors were tested in separate models using linear mixed effects models.

findingsSeverity of dependence, assessed by ICD-10 criteria for alcohol dependence, was the only predictor for changes in alcohol consumption and the only moderator of the effect of treatment. Participants with severe dependence showed a larger reduction in alcohol consumption between baseline and 3-months follow-up compared to participants with moderate dependence. The patients with moderate dependence continued to reduce their alcohol consumption between 3- and 12-months follow-up, while patients with severe dependence did not.

conclusionsDependence severity predicted changes in alcohol consumption following treatment of alcohol dependence in primary care, with or without added ICBT. Dependence severity was also found to moderate the effect of treatment. The results suggest that treatment for both moderate and severe alcohol dependence is viable in primary care. CLINICAL

trial registrationThe study was approved by the Regional Ethics Board in Stockholm, no. 2016/1367-31/2. The study protocol was published in Trials 30 December 2019. The trial identifier is ISRCTN69957414, available at http://www.isrctn.com , assigned 7 June 2018, retrospectively registered.

Indexed as

AlcoholismCognitive Behavioral TherapyInternet-Based InterventionAdultFemaleHumansIndividualityInternetMaleMiddle AgedMotivational InterviewingPrimary Health CareSwedenTreatment OutcomeAlcohol dependenceIndividual differencesInternet-based cognitive behavioral therapyModeration analysisPrimary careSeverity of dependence

Identifiers

PMID39953626
PMCPMC11827356

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.