ArticlePLOS digital health2026
Advancing foundational models in digital health technology adoption: A systematic literature review of multidisciplinary factors.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Advancing foundational models in digital health technology adoption: A systematic literature review of multidisciplinary factors.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital health technologies (DHTs) such as patient portals, mobile applications, and electronic health records can improve access to healthcare, self-management and care coordination. However, their adoption remains inconsistent. This study systematically reviews the technological, psychological, social, cultural, health-related and environmental factors influencing DHT adoption. This protocol-registered review (PROSPERO: CRD420251056883) was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. A search of multidisciplinary databases was conducted through the EBSCO Discovery Service (EDS) to identify peer-reviewed English primary studies published between 2015 and 18 June 2025. Although studies published from 2015 onward were searched, only studies published from 2020 onward were retained for synthesis. Two researchers independently applied the Sample, Phenomenon of Interest, Design, Evaluation, Research type (SPIDER) framework to screen articles and extract data. As Covidence operationalises screening using the Population, Intervention, Comparison, Outcome, Study type (PICOS) framework, SPIDER elements were mapped to PICOS to ensure consistency across screening and extraction. Methodological quality was appraised using the Mixed Methods Appraisal Tool (MMAT) to inform interpretation. Data were synthesised using a structured thematic analysis workflow informed by the Thematic Analysis Matrix proposed by Zairul, which operationalises the thematic analysis principles described by Braun and Clarke. Coding, category development and theme generation were managed using ATLAS.ti (Version 24). Eighty-two studies published between 2020 and 2025 met the inclusion criteria. Five themes were identified: (1) access, equity and affordability; (2) usability, engagement and user empowerment; (3) trust, privacy and governance; (4) integration, workforce and sustainability; and (5) clinical effectiveness and quality of care. The findings showed that adoption of DHTs is a multi-layered process shaped by multidisciplinary factors. This review provides researchers, policymakers and healthcare providers with theoretical and practical insights to support sustainable, effective and equitable DHT adoption and to guide the development of future strategies.
Identifiers
What OpenQuestion holds
Registered trials
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.