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Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage

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...

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Autores principales: Canan, Chelsea E, Flickinger, Tabor E, Waselewski, Marika, Tabackman, Alexa, Baker, Logan, Eger, Samuel, Waldman, Ava Lena D, Ingersoll, Karen, Dillingham, Rebecca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877298/
https://www.ncbi.nlm.nih.gov/pubmed/31816017
http://dx.doi.org/10.1093/tbm/ibz180
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author Canan, Chelsea E
Flickinger, Tabor E
Waselewski, Marika
Tabackman, Alexa
Baker, Logan
Eger, Samuel
Waldman, Ava Lena D
Ingersoll, Karen
Dillingham, Rebecca
author_facet Canan, Chelsea E
Flickinger, Tabor E
Waselewski, Marika
Tabackman, Alexa
Baker, Logan
Eger, Samuel
Waldman, Ava Lena D
Ingersoll, Karen
Dillingham, Rebecca
author_sort Canan, Chelsea E
collection PubMed
description 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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spelling pubmed-78772982021-02-17 Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage Canan, Chelsea E Flickinger, Tabor E Waselewski, Marika Tabackman, Alexa Baker, Logan Eger, Samuel Waldman, Ava Lena D Ingersoll, Karen Dillingham, Rebecca Transl Behav Med Original Research 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. Oxford University Press 2019-12-09 /pmc/articles/PMC7877298/ /pubmed/31816017 http://dx.doi.org/10.1093/tbm/ibz180 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of the Society of Behavioral Medicine. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Research
Canan, Chelsea E
Flickinger, Tabor E
Waselewski, Marika
Tabackman, Alexa
Baker, Logan
Eger, Samuel
Waldman, Ava Lena D
Ingersoll, Karen
Dillingham, Rebecca
Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title_full Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title_fullStr Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title_full_unstemmed Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title_short Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage
title_sort toward understanding the impact of mhealth features for people with hiv: a latent class analysis of positivelinks usage
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877298/
https://www.ncbi.nlm.nih.gov/pubmed/31816017
http://dx.doi.org/10.1093/tbm/ibz180
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