Evidence map›Paper›PMID 41323092›Full record

ArticleDigital health

Trends in digital mental health interventions: A 20-year bibliometric analysis.

Boxiang Zhang, Lucy Yue Lau, Yi Chen

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

3 authors.

Boxiang ZhangCancer Research Institute, The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.ORCID https://orcid.org/0009-0009-1919-0290
Lucy Yue LauDepartment of Public Health, Harvard Medical School, Boston, MA, USA.
Yi ChenCancer Research Institute, The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.ORCID https://orcid.org/0009-0002-7413-2463

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rising prevalence of mental health disorders such as depression and anxiety challenges traditional treatments limited by resource shortages, poor accessibility, and low adherence. Digital health technologies-particularly digital mental health interventions-offer innovative, scalable, and personalized solutions. Mobile health applications, online cognitive behavioral therapy (CBT), and AI-driven tools are becoming essential in mental health care. Methods: This study employs bibliometric methods to examine research trends and thematic evolution in digital mental health self-management interventions, using data retrieved from the Web of Science Core Collection (WoSCC). CiteSpace, VOSviewer, and Bibliometrix were applied to quantify research output, collaboration networks, and influential topics. The analysis covered English-language publications from 2006 to 2025, with internal consistency checks and sensitivity analyses within the WoSCC dataset ensuring robustness. Results: A total of 2262 eligible publications were retrieved, showing a clear growth trajectory. The United States led with 932 publications, followed by the United Kingdom and Australia. Citations surged after 2016, peaking in 2023, reflecting increasing academic and clinical relevance. Research has shifted from feasibility studies to AI-enhanced and personalized interventions. Keywords such as "artificial intelligence," "digital CBT," and "personalized care" showed notable growth. International and interdisciplinary collaborations also expanded, underscoring the field's global integration. Conclusion: Digital mental health interventions are evolving from traditional models to intelligent, personalized solutions, providing scalable solutions to global mental health challenges. This study offers insights into future research directions, focusing on technology integration, ethical issues, and clinical validation to drive global application.

Indexed as

bibliometricscognitive behavioral therapydepressive disorderDigital healthmental health

Identifiers

PMID41323092
PMCPMC12663068

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.