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Record W2336898845 · doi:10.2196/mhealth.5287

Creating Effective Mobile Phone Apps to Optimize Antiretroviral Therapy Adherence: Perspectives From Stimulant-Using HIV-Positive Men Who Have Sex With Men

2016· article· en· W2336898845 on OpenAlexvenueno aff
Keith J. Horvath, Dawit Alemu, Thu Danh, Jason V. Baker

Bibliographic record

VenueJMIR mhealth and uhealth · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on Drug AbuseNational Institutes of Health
KeywordsStimulantmHealthPsychological interventionThematic analysisMobile phoneMen who have sex with menFocus groupMedicineInternet privacyApp storePsychologyHuman immunodeficiency virus (HIV)Qualitative researchFamily medicineWorld Wide WebComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The use of stimulant drugs among men who have sex with men (MSM) with human immunodeficiency virus (HIV) is associated with decreased odds of antiretroviral therapy (ART) adherence and elevated risk of forward HIV transmission. Advancing tailored and innovative mobile phone-based ART adherence app interventions for stimulant-using HIV-positive MSM requires greater understanding of their needs and preferences in this emerging area. OBJECTIVE: The purpose of this study is to (1) assess reasons that stimulant-using HIV-positive MSM download and sustain their use of mobile phone apps in general, and (2) obtain feedback on features and functions that these men prefer in a mobile phone app to optimize their ART adherence. METHODS: Focus groups were conducted with stimulant-using HIV-positive MSM (24-57 years of age; mostly non-Hispanic white; 42% once a week or more frequent stimulant drug use) in San Francisco and Minneapolis. Our aim was to explore the mobile phone app features and functions that they considered when deciding to download and sustain their use of general apps over time, as well as specific features and functions that they would like to see incorporated into an ART adherence mobile app. Focus groups were audiorecorded and transcribed verbatim. Thematic analysis was applied to transcripts using line-by-line open coding and organizing codes into meaningful themes. RESULTS: Men reported that they currently had a variety of health and wellness, social media and networking, gaming and entertainment, and utility apps on their mobile phones. Downloading apps to their mobile phones was influenced by the cost of the app, recommendations by a trusted source, and the time it takes to download. In addition, downloading and sustained use of apps was more likely to occur when men had control over most features of the app and apps were perceived to be useful, engaging, secure, and credible. Participants suggested that ART adherence mobile phone apps include social networking features, connections to local resources and their medical chart, and breaking HIV news and updates. Although some men expressed concerns about daily self-monitoring of HIV medication doses, many appreciated receiving a summary of their medication adherence over time and suggested that feedback about missed doses be delivered in an encouraging and humorous manner. CONCLUSIONS: In this study, we were able to recruit a relatively high proportion (42%) of HIV-positive MSM reporting weekly or more stimulant use. These results suggest critical design elements that may need to be considered during development of ART adherence-related mobile phone apps for this, and possibly other, high-risk groups. In particular, finding the optimal balance of security, engagement, usefulness, control capabilities, and credibility will be critical to sustained used of HIV treatment apps.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.389
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2016
Admission routes1
Has abstractyes

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