Evidence map›Paper›PMID 28025735›Full record

ArticleAIDS and behavior2017

Examining the Acceptability of mHealth Technology in HIV Prevention Among High-Risk Drug Users in Treatment.

Roman Shrestha, Tania B Huedo-Medina, Frederick L Altice, Archana Krishnan, Michael Copenhaver

Registry-linked trialAbstract read
In one paragraph

Article in AIDS and behavior, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03282890 (Testing an Integrated Bio-Behavioral Primary HIV Prevention Intervention Among High-Risk People Who Use Drugs), which is not on this map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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.

NCT03282890 nacompletednot on this map

Testing an Integrated Bio-Behavioral Primary HIV Prevention Intervention Among High-Risk People Who Use Drugs

TypeinterventionalSponsorUniversity of ConnecticutRan2017 to 2023Enrolled237ConditionsRisk Behavior, HIV/AIDS, Medication Adherence, Substance AbuseArmsCHRP-BB
3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 37 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Roman ShresthaDepartment of Community Medicine & Health Care, University of Connecticut Health Center, 263 Farmington Avenue, MC 6325, Farmington, CT, 06030-6325, USA. Roman.Shrestha@UConn.edu.ORCID http://orcid.org/0000-0003-4340-3101
Tania B Huedo-MedinaInstitute for Collaboration on Health, Intervention, and Policy, University of Connecticut, Storrs, CT, USA.
Frederick L AlticeInstitute for Collaboration on Health, Intervention, and Policy, University of Connecticut, Storrs, CT, USA.
Archana KrishnanInstitute for Collaboration on Health, Intervention, and Policy, University of Connecticut, Storrs, CT, USA.
Michael CopenhaverInstitute for Collaboration on Health, Intervention, and Policy, University of Connecticut, Storrs, CT, USA.
University of Connecticut · USAlbany State University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Intervention of HIV, Drug Use and the Criminal Justice System in MalaysiaR01DA025943 · NIDA · YALE UNIVERSITY · PI ALTICE, FREDERICK LEWIS · 2008 to 2012
$2.7M
Enhancing Health Outcomes Among HIV+ Substance AbusersK24DA017072 · NIDA · YALE UNIVERSITY · PI ALTICE, FREDERICK LEWIS · 2003 to 2012
$1.5M
HIV Prevention and Adherence Among Priority Drug Using PopulationsK02DA033139 · NIDA · UNIVERSITY OF CONNECTICUT STORRS · PI COPENHAVER, MICHAEL · 2013 to 2017
$697k
NCATS NIH HHS UL1 TR001863NIDA NIH HHS K02 DA033139NIDA NIH HHS K24 DA017072NIDA NIH HHS R01 DA025943
6 · The paper itself

Abstract

Despite promising trends of the efficacy of mobile health (mHealth) based strategies to a broad range of health conditions, very few if any studies have been done in terms of the examining the use of mHealth in HIV prevention efforts among people who use drugs in treatment. Thus, the goal of this study was to gain insight into the real-world acceptance of mHealth approaches among high-risk people who use drugs in treatment. A convenience sample of 400 HIV-negative drug users, who reported drug- and/or sex-related risk behaviors, were recruited from a methadone clinic in New Haven, Connecticut. Participants completed standardized assessments of drug- and sex-related risk behaviors, neurocognitive impairment (NCI), and measures of communication technology access and utilization, and mHealth acceptance. We found a high prevalence of current ownership and use of mobile technologies, such as cell phone (91.5%) including smartphone (63.5%). Participants used mobile technologies to communicate mostly through phone calls (M = 4.25, SD = 1.24), followed by text messages (M = 4.21, SD = 1.29). Participants expressed interest in using mHealth for medication reminders (72.3%), receive information about HIV (65.8%), and to assess drug-related (72.3%) and sex-related behaviors (64.8%). Furthermore, participants who were neurocognitively impaired were more likely to use cell phone without internet and show considerable interest in using mHealth as compared to those without NCI. The findings from this study provide empirical evidence that mHealth-based programs, specifically cell phone text messaging-based health programs, may be acceptable to this high-risk population.

Indexed as

Patient Acceptance of Health CareSexual BehaviorAcquired Immunodeficiency SyndromeAdultCell PhoneConnecticutDelivery of Health CareDrug UsersFemaleHIV InfectionsHumansInternetMaleMethadoneNeurocognitive DisordersOpiate Substitution TreatmentMethadonemHealthNeurocognitive impairmentPeople who use drugsSubstance abuseText messaging

Identifiers

PMID28025735
PMCPMC5484759
OpenAlexW2561933682

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.