Evidence map›Paper›PMID 31816017›Full record

ArticleTranslational behavioral medicine2021

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

Chelsea E Canan, Tabor E Flickinger, Marika Waselewski, Alexa Tabackman, Logan Baker, Samuel Eger, Ava Lena D Waldman, Karen Ingersoll, Rebecca Dillingham

Abstract read
In one paragraph

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.

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.

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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

9 authors.

Chelsea E CananDepartment of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Tabor E FlickingerDepartment of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Marika WaselewskiDepartment of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Alexa TabackmanUniversity of Virginia School of Medicine, Charlottesville, VA, USA.
Logan BakerUniversity of Virginia's College at Wise, Wise, VA, USA.
Samuel EgerUniversity of Virginia's College at Wise, Wise, VA, USA.
Ava Lena D WaldmanDepartment of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Karen IngersollDepartment of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
Rebecca DillinghamDepartment of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.

Funding

UVa MHIRT: Training Future Leaders to Address Global Rural Health DisparitiesT37MD008659 · NIMHD · UNIVERSITY OF VIRGINIA · PI DILLINGHAM, REBECCA A., KOOLS, SUSAN MCEVILLY · 2014 to 2018
$1.3M
Biodefense Short-Term Training for Minority StudentT35AI060528 · NIAID · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI HALME, ADRIAN J, PETRI, WILLIAM A · 2004 to 2024
$1.3M
NIAID NIH HHS T35 AI060528NIMHD NIH HHS T37 MD008659
6 · The paper itself

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.

Indexed as

HIV InfectionsMobile ApplicationsTelemedicineHumansLatent Class AnalysisSmartphoneLatent class analysisMobile healthPLWHViral suppression

Identifiers

PMID31816017
PMCPMC7877298

What OpenQuestion holds

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LicenceCC BY-NC
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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.