Evidence map›Paper›PMID 40680174›Full record

ArticleJMIR mHealth and uHealth2025

Current Practice and Expert Perspectives on Cultural Adaptations of Digital Health Interventions: Qualitative Study.

Vasileios Nittas, Sarah J Chavez, Paola Daniore

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
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.

Vasileios NittasDepartment of Behavioral and Social Sciences, Brown University, 121 South Main Street, Providence, RI, 02912, United States, 1 6944624408.ORCID 0000-0002-6685-8275
Sarah J ChavezBrown University Center for Alcohol and Addiction Studies, Brown University, Providence, RI, United States.ORCID 0000-0001-9547-6164
Paola DanioreInstitute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.ORCID 0000-0003-3319-1125

Funding

ALCOHOL INTERVENTION/TREATMENT OUTCOME RESEARCH TRAININGT32AA007459 · NIAAA · BROWN UNIVERSITY · PI ROBERT MIRANDA, PETER M. MONTI · 1986 to 2026
$13.3M
NIAAA NIH HHS T32 AA007459Swiss National Science Foundation 202914
6 · The paper itself

Abstract

Background: Some people are less likely to benefit from digital health interventions (DHIs) than others. Culture, along with other factors, contributes to these differences. DHIs that do not address a population's cultural norms or concerns are likely to be less effective. One way to create culturally sensitive DHIs is through cultural adaptations. Yet, there is currently little evidence-based guidance on when and how to adapt DHIs. Objective: We aimed to capture the experiences of experts to understand the (1) current practices, (2) challenges, and (3) recommendations around culturally adapting DHIs. Methods: We conducted semistructured interviews (n=15) via Zoom (Zoom Video Communications, Inc) between May and August 2023, with academic experts who have previously undertaken cultural adaptations of DHIs. Experts were identified through publications and snowball sampling. We used a thematic analytical approach, beginning with a preliminary deductive codebook and then following a three-stage analysis. All transcripts were coded with the MAXQDA (VERBI Software GmbH) software. Codes were reviewed, and similar or related codes were categorized into broader themes, consolidating one or multiple codes into a single topic. Results: Our analysis produced 30 codes, which were categorized into (1) defining culture, (2) justifying the adaptation, (3) choosing the adaptation elements, (4) implementing the adaptation, (5) understanding the challenges, and (6) recommendations. Based on their experiences, experts recommended that (1) the adaptation team is multiprofessional, digitally competent, and culturally sensitive; (2) DHI users and (3) all other relevant stakeholders are continuously involved; and (4) the adaptations incorporate evaluations and knowledge exchange. They further emphasized that culturally adapted DHIs must be understandable, relatable, appealing, and easy to adhere to, ensuring that health technology and content reflect the target population's lived experiences, sociodemographic characteristics, and digital literacy. When asked which elements of cultural DHI adaptations, the most common responses were language, lived experience, and technology. Responses revealed five common DHI-relevant challenges, including (1) technology, (2) uncertainty, (3) user involvement, (4) communication, and (5) evaluation and sustainability. Conclusions: The cultural adaptation of DHIs was described as an iterative, often unstructured, and resource-intensive process that requires careful justification and a solid understanding of the culture and the specific cultural group for which it is implemented. Our interviews confirmed the absence of technology-specific frameworks to guide the cultural adaptations of DHIs. Based on our findings, such a framework should guide the choice of the correct definition of culture and the criteria for assessing the need to adapt. It should also offer tools to drive stakeholder engagement, prioritize adaptation elements, and address common challenges.

Indexed as

Culturally Appropriate TechnologyDigital HealthHumansInterviews as TopicQualitative Researchcultural adaptationcultural tailoringdigital healthdigital health interventionexperienceexperiencesexpertexpertshealth equityinterventioninterventionsinterviewinterviewsmHealthmobile healthperspectiveperspectivespracticepracticesqualitative studythematic analysisweb-based

Identifiers

PMID40680174
PMCPMC12294640

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.