Evidence map›Paper›PMID 41691290›Full record

ArticleChild and adolescent psychiatry and mental health2026

Comparative efficacy of digital health interventions for depression and anxiety symptoms in adolescents and young adults: a systematic review and bayesian network meta-analysis.

Haozhe Wang, Jiayi Yao, Shiguan Jia, Wenjia Chen, Yujie Guan, Jiahao Liu, Mingyu Liao, Enliang Hu, Qinghua Zhang

Abstract read
In one paragraph

Article in Child and adolescent psychiatry and mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Youth Digital Mental Health Training Needs in Academic and Industry Sectors.The journal of behavioral health services & research · 2026
    Article
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

9 authors.

Haozhe WangSchool of Physical Education, China University of Mining and Technology, Xuzhou, 221116, China.
Jiayi YaoSchool of Physical Education, China University of Mining and Technology, Xuzhou, 221116, China.
Shiguan JiaSchool of Physical Education, China University of Mining and Technology, Xuzhou, 221116, China.
Wenjia ChenSchool of Physical Education, China University of Mining and Technology, Xuzhou, 221116, China.
Yujie GuanThe Second Clinical College, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Jiahao Liu, Shanghai, China.
Mingyu LiaoDepartment of Physical Training, Institute of Aviation Safety and Security, China Civil Aviation Flight Academy, Chengdu, China.
Enliang HuDepartment of Physical Training, Institute of Aviation Safety and Security, China Civil Aviation Flight Academy, Chengdu, China.
Qinghua ZhangSchool of Physical Education, China University of Mining and Technology, Xuzhou, 221116, China. 4093@cumt.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression and anxiety symptoms in adolescents and young adults represent a significant global public health challenge. Digital health interventions (DHIs) offer potential solutions to supplement traditional mental health services, though the relative efficacy of different types of interventions remains unclear.

objectiveThis study aims to systematically compare the treatment effects of digital health interventions driven by different mechanisms on depression and anxiety symptoms in this population through a Bayesian network meta-analysis.

methodsA systematic search was conducted in major databases such as PubMed, Embase, and PsycINFO (up to September 2025), including randomized controlled trials (RCTs) targeting depression or anxiety symptoms in individuals aged 12-25 years. Interventions were categorized based on treatment mechanisms into four types: cognitive behavioral therapy-based digital interventions (CBT-DI), third-wave digital therapies (TWDT), general digital mental health support (GDMHS), and technology-enhanced innovative interventions (TEII). The primary outcome measure was the standardized mean difference (SMD), with the cumulative ranking probability assessed using the surface under the cumulative ranking curve (SUCRA).

resultsA total of 18 RCTs involving 5, 821 participants were included. Network meta-analysis indicated that CBT-DI achieved the highest surface under the cumulative ranking curve (SUCRA) values for both depression (79.3%) and anxiety (83.4%). In pairwise comparisons with no intervention controls, CBT-DI demonstrated a statistically significant improvement in anxiety symptoms (SMD = 0.33, 95% CrI: 0.05 to 0.69). However, for depression, the improvement associated with CBT-DI did not reach statistical significance (SMD = 0.44, 95% CrI: -0.02 to 0.91), suggesting that the high ranking probability reflects a potential trend rather than confirmatory evidence of superiority. TWDT and GDMHS demonstrated moderate efficacy for both symptoms, ranking above usual care and no intervention controls. The evidence quality assessment (GRADE) indicated that the primary outcomes were of low to moderate quality.

conclusionDigital health interventions, particularly CBT-based interventions (CBT-DI), were associated with statistically significant improvements in anxiety symptoms. For depression, while CBT-DI ranked highest in probability, it did not demonstrate statistical superiority over controls. Given the imprecision in effect estimates, CBT-DI may be considered a potential complementary measure within a stepped-care mental health system. Results should be interpreted with caution due to wide credible intervals, and further high-quality studies are required to confirm these findings.

Indexed as

AdolescentsAnxietyDepressionDigital health interventionsNetwork meta-analysisYoung adults

Identifiers

PMID41691290
PMCPMC12918560

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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.