ArticleTranslational behavioral medicine2021
Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage.
Article in Translational behavioral medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Study Protocol(s) for Antiretroviral Improvement among Medicaid EnrolleeS (AIMS): A Cluster-Randomized Controlled Trial Leveraging Real-time Administrative Claims to Support Antiretroviral Prescription Adherence.Contemporary clinical trials · 2025Article
- Curating an HIV mHealth App for People Living with HIV in Washington, DC Through Think-Aloud Usability Testing and Beta Testing.AIDS and behavior · 2025Article
- Three-Year Follow-up of PositiveLinks: Higher Use of mHealth Platform Associated with Sustained HIV Suppression.AIDS and behavior · 2024Article
- Use of a Mobile Health Intervention by Older Versus Younger People with HIV: Analysis of Usage, Social Support, and Network Interactions.Telemedicine reports · 2022Article
- PositiveLinks and the COVID-19 Response: Importance of Low-Barrier Messaging for PLWH in Non-urban Virginia in a Crisis.AIDS and behavior · 2021Article
- Implementation of a Mobile Health Strategy to Improve Linkage to and Engagement with HIV Care for People Living with HIV, Tuberculosis, and Substance Use in Irkutsk, Siberia.AIDS patient care and STDs · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
PositiveLinks (PL) is a multi-feature smartphone-based platform to improve engagement-in-care and viral suppression (VS) among clinic patients living with HIV. Features include medication reminders, mood/stress check-ins, a community board, and secure provider messaging. Our goal was to examine how PL users interact with the app and determine whether usage patterns correlate with clinical outcomes. Patients (N = 83) at a university-based Ryan White clinic enrolled in PL from June 2016 to March 2017 and were followed for up to 12 months. A subset (N = 49) completed interviews after 3 weeks of enrollment to explore their experiences with and opinions of PL. We differentiated PL members based on 6-month usage of app features using latent class analysis. We explored characteristics associated with class membership, compared reported needs and preferences by class, and examined association between class and VS. The sample of 83 PL members fell into four classes. "Maximizers" used all app features frequently (27%); "Check-in Users" tended to interact only with daily queries (22%); "Moderate All-Feature Users" used all features occasionally (33%); and "As-Needed Communicators" interacted with the app minimally (19%). VS improved or remained high among all classes after 6 months. VS remained high at 12 months among Maximizers (baseline and 12-month VS: 100%, 94%), Check-in Users (82%, 100%), and Moderate All-Feature Users (73%, 94%) but not among As-Needed Communicators (69%, 60%). This mixed-methods study identified four classes based on PL usage patterns that were distinct in characteristics and clinical outcomes. Identifying and characterizing mHealth user classes offers opportunities to tailor interventions appropriately based on patient needs and preferences as well as to provide targeted alternative support to achieve clinical goals.
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