Evidence map›Paper›PMID 42230741›Full record

ReviewNPJ digital medicine2026

A clinically actionable framework for personalizing iCBT to improve depression outcomes.

Katie Aafjes-van Doorn, Helen Christensen

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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.

Katie Aafjes-van DoornNew York University, Shanghai, China.
Helen ChristensenUniversity of New South Wales, Sydney, NSW, Australia. h.christensen@blackdog.org.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Internet-based cognitive behavioral therapy (iCBT) is effective for depression, but its impact is constrained by low engagement and modest response rates. Personalization may address these limitations, yet a gap remains between research evidence and clinically actionable implementation. This narrative review synthesizes evidence on personalization in iCBT for depression using a three-stage framework: pre-implementation optimization, stratifying treatment based on patient characteristics, and dynamically adapting therapy using progress monitoring. Evidence was evaluated using principles of evidence grading, with attention to the volume, consistency, and directness of findings for each stage. The strongest evidence supports pre-implementation optimization of engagement and stratification of initial support based on baseline severity, treatment history, and related clinical characteristics. Evidence for dynamic adaptation is promising but less developed, with support for early identification of nonresponse and adjustment of treatment intensity, but limited iCBT-specific trials testing adaptive treatment strategies in depression. Across stages, engagement and clinical outcomes are related but distinct targets for personalization. Emerging research on responsiveness, progress monitoring, and digital biomarkers offers future opportunities for more precise and scalable personalization. More rigorous depression-specific iCBT studies are needed to determine when, how, and for whom personalized interventions improve engagement and clinical outcomes.

Identifiers

PMID42230741
PMCPMC13530205

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.