Evidence map›Paper›PMID 41538771›Full record

ArticleJMIR formative research2026

Support Community Formation on a Mobile App for People Living With HIV and Substance Use Disorder: A Computer-Mediated Discourse Analysis.

Adati Tarfa, Kristen Pecanac, Olayinka Shiyanbola, Cameron Liebert, Sarah Dietz, Rebecca Miller, Ryan P Westergaard

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Adati TarfaYale School of Medicine, New Haven, CT, United States.ORCID 0000-0002-8831-2396
Kristen PecanacSchool of Nursing, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0001-8466-1890
Olayinka ShiyanbolaCollege of Pharmacy, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-6018-2104
Cameron LiebertSchool of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, United States, 1 6082632880.ORCID 0009-0003-6126-7243
Sarah DietzSchool of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, United States, 1 6082632880.ORCID 0009-0004-9249-3072
Rebecca MillerSchool of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, United States, 1 6082632880.ORCID 0000-0002-5635-0726
Ryan P WestergaardSchool of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, United States, 1 6082632880.ORCID 0000-0001-5701-4516

Funding

Optimizing HIV care for patients with substance use disorders using predictive analytics in a mobile health applicationDP2DA042424 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI WESTERGAARD, RYAN PATRICK · 2016 to 2016
$2.3M
NIDA NIH HHS DP2 DA042424
6 · The paper itself

Abstract

Background: People living with HIV and substance use disorders (SUDs) have complex health care needs requiring adaptive and effective support systems. While mobile health apps can foster virtual communities grounded in shared lived experiences, little is known about the dynamics within these digital spaces. Objective: We examined the formation of a virtual community on the Addiction Comprehensive Health Enhancement Support System (A-CHESS; The Center for Health Enhancement Systems Studies, University of Wisconsin-Madison College of Engineering) message board, a mobile app designed to support HIV care engagement among individuals living with HIV and SUD. Methods: We conducted a computer-mediated discourse analysis of A-CHESS message board posts to examine communication patterns, interaction structures, and engagement dynamics. Quantitative comparisons were used to assess differences between posters and nonposters using t tests and chi-square tests. We then applied qualitative coding to categorize messages by type, speaker, and function to understand how staff and participants coconstructed a supportive virtual environment. Results: Among 208 participants, 87 (42%) posted at least once on the A-CHESS message board, contributing 1834 messages between April 2019 and May 2021. Posters and nonposters did not differ significantly in age (t206=-0.64; P=.52), gender (χ²1=0.14; P=.71), or race (χ²1=0.52; P=.47). We identified 3 message types: premeditated, adlib, and participant-driven. Staff initially led with premeditated messages (eg, recovery stories, HIV risk information, and "Thought of the Day" inspiration), which participants often interpreted and adapted to their own SUD recovery. Over time, staff incorporated adlib messaging styles using personalized narratives and polls to sustain engagement. Participants then developed their own posts using similar formats, incorporating Alcoholics Anonymous literature, sharing legal and personal challenges, and suggesting new app features (eg, medication check-ins to support adherence). Conclusions: A-CHESS staff adapted communication styles to increase engagement, while participants appropriated the app's message board to reflect personal goals and lived experiences. Mobile health interventions may benefit from design elements that support participant-led discourse and customization, fostering ownership, support, and relevance within virtual care communities.

Indexed as

HIV InfectionsMobile ApplicationsSubstance-Related DisordersAdultFemaleHumansMaleMiddle Agedcomputer-mediateddiscourse analysisHIVHIV caremHealthmobile appmobile healthonline communitiesonline supportopioid use disorderOUDpeople living with HIVqualitative analysissocial supportsubstance use disordersSUDvirtual community

Identifiers

PMID41538771
PMCPMC12807402

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.