Evidence map›Paper›PMID 40143404›Full record

ArticleJMIR formative research2025

Mobile Health Tool to Capture Social Determinants of Health and Their Impact on HIV Treatment Outcomes Among People Who Use Drugs: Pilot Feasibility Study.

Rachel E Gicquelais, Caitlin Conway, Olivia Vjorn, Andrew Genz, Gregory Kirk, Ryan Westergaard

Abstract read
In one paragraph

Article in JMIR formative research, 2025. 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

6 authors.

Rachel E GicquelaisSchool of Medicine and Public Health, University of Wisconsin-Madison, 603 WARF Office Building, 610 Walnut Street, Madison, WI, 53726, United States, 1 608-890-1837.ORCID 0000-0002-7022-6385
Caitlin ConwayUniversity of Wisconsin-Madison School of Nursing, Madison, WI, United States.ORCID 0009-0000-2842-5607
Olivia VjornCenter for Health Enhancement Systems Studies, University of Wisconsin-Madison College of Engineering, Madison, WI, United States.ORCID 0000-0002-3085-6067
Andrew GenzDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0001-5636-6827
Gregory KirkDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-7829-1405
Ryan WestergaardSchool of Medicine and Public Health, University of Wisconsin-Madison, 603 WARF Office Building, 610 Walnut Street, Madison, WI, 53726, United States, 1 608-890-1837.ORCID 0000-0001-5701-4516

Funding

The AIDS Linked to the Intravenous Experience (ALIVE) StudyU01DA036297 · NIDA · JOHNS HOPKINS UNIVERSITY · PI Gregory D Kirk, Shruti H Mehta · 2014 to 2026
$28.9M
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 DA042424NIDA NIH HHS U01 DA036297
6 · The paper itself

Abstract

Background: Active substance use, food or housing insecurity, and criminal legal system involvement can disrupt HIV care for people living with HIV and opioid use disorder (OUD). These social determinants of health are not routinely captured in clinical settings. Objective: We evaluated whether real-time reports of social and behavioral factors using a smartphone app could predict viral nonsuppression and missed care visits to inform future mobile health interventions. Methods: We enrolled 59 participants from the AIDS Linked to the Intravenous Experience (ALIVE) Study in Baltimore, Maryland, into a 12-month substudy between February 2017 and October 2018. Participants were eligible if they had OUD and had either a measured HIV RNA ≥1000 copies/mL or a ≥1-month lapse in antiretroviral therapy in the preceding 2 years. Participants received a smartphone and reported HIV medication adherence, drug use or injection, and several disruptive life events, including not having a place to sleep at night, skipping a meal due to lack of income, being stopped by police, being arrested, or experiencing violence on a weekly basis, through a survey on a mobile health app. We described weekly survey completion and investigated which factors were associated with viral nonsuppression (HIV RNA ≥200 copies/mL) or a missed care visit using logistic regression with generalized estimating equations adjusted for age, gender, smartphone comfort, and drug use. Results: Participants were predominantly male (36/59, 61%), Black (53/59, 90%), and had a median of 53 years old. At baseline, 16% (6/38) were virally unsuppressed. Participants completed an average of 23.3 (SD 16.3) total surveys and reported missing a dose of antiretroviral therapy, using or injecting drugs, or experiencing any disruptive life events on an average of 13.1 (SD 9.8) weekly surveys over 1 year. Reporting use of any drugs (adjusted odds ratio [aOR] 2.3, 95% CI 1.4-3.7), injecting drugs (aOR 2.3, 95% CI 1.3-3.9), and noncompletion of all surveys (aOR 1.6, 95% CI 1.1-2.2) were associated with missing a scheduled care visit over the subsequent 30 days. Missing ≥2 antiretroviral medication doses within 1 week was associated with HIV viral nonsuppression (aOR 3.7, 95% CI: 1.2-11.1) in the subsequent 30 days. Conclusions: Mobile health apps can capture risk factors that predict viral nonsuppression and missed care visits among people living with HIV who have OUD. Using mobile health tools to detect sociobehavioral factors that occur prior to treatment disengagement may facilitate early intervention by health care teams.

Indexed as

HIV InfectionsMobile ApplicationsOpioid-Related DisordersSocial Determinants of HealthAdultBaltimoreFeasibility StudiesFemaleHumansMaleMiddle AgedPilot ProjectsSmartphoneTelemedicineTreatment Outcomedrug useHIVmHealthmobile healthsmartphonesocial determinants of health

Identifiers

PMID40143404
PMCPMC11964955

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.