Identifying what works in mental health apps through meta-regression analyses of 169 trials.

Jannis Kraiss, Felix Fiß, Farid Chakhssi, Fatma Betül Aktas, Jurrijn Alexander Koelen, Jorge Piano Simões

Abstract read

In one paragraph

Article 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.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jannis KraissDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands. j.t.kraiss@utwente.nl.

Felix FißDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.

Farid ChakhssiDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.

Fatma Betül AktasDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.

Jurrijn Alexander KoelenDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.

Jorge Piano SimõesDepartment of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This meta-analysis aimed to code active cognitive behavioral elements in mental health apps and to examine the association between these elements and improvements in depression and anxiety. Trials evaluating mental health apps were coded based on 34 pre-registered elements. 169 trials with 1137 timepoints were included (N = 41,807; mean age = 34.3 years; 72.9% female). Psychoeducation, relaxation, mindfulness, and self-monitoring were used most frequently. Bivariate mixed-effect meta-regression models showed that many elements were weakly to moderately effective. Desensitization, stimulus control, and activity scheduling were most strongly and robustly associated with improvements in depression and exposure-based elements with improvements in anxiety. Ineffective elements included graded tasks and personal strengths, but in sum, there was considerable variation in the frequency and impact of active elements. Interventions incorporating a greater number of elements were more effective. This meta-analysis provides insight into how active elements in mental health apps are associated with therapeutic change, informing future interventions.

Identifiers

PMID41807539

DOI10.1038/s41746-026-02466-z

PMCPMC13111678

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LicenceCC BY

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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.