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Record W2804697560 · doi:10.2196/mhealth.9031

Acceptability and Feasibility of Real-Time Antiretroviral Therapy Adherence Interventions in Rural Uganda: Mixed-Method Pilot Randomized Controlled Trial

2018· article· en· W2804697560 on OpenAlexvenueno aff
Angella Musiimenta, Esther C. Atukunda, Wilson Tumuhimbise, Emily E. Pisarski, Melanie Tam, Monique A. Wyatt, Norma C. Ware, Jessica E. Haberer

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Institute of Mental HealthFogarty International CenterNational Institutes of Health
KeywordsPsychological interventionAntiretroviral therapyMedicineRandomized controlled trialmHealtheHealthViremiaHuman immunodeficiency virus (HIV)Physical therapyViral loadFamily medicineHealth careNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Wireless electronic adherence monitors can detect antiretroviral therapy (ART) adherence lapses and trigger interventions in real time, thus potentially avoiding unnecessary HIV viremia. Evidence about the acceptability and feasibility of these monitors and associated interventions, however, is limited. OBJECTIVE: The aim of this study was to assess the acceptability and feasibility of real-time adherence monitoring linked to text messaging (short message service, SMS) reminders and notifications to support adherence among individuals living with HIV who are taking ART in rural southwestern Uganda. METHODS: Individuals living with HIV who were initiating ART were enrolled in a pilot randomized controlled trial and followed up for 9 months. Participants received a real-time adherence monitor and were randomized to one of the following study arms: (1) scheduled SMS, (2) SMS triggered by missed or delayed doses, or (3) no SMS. SMS notifications were also sent to 45 patient-identified social supporters for sustained adherence lapses in the scheduled SMS and triggered SMS arms. Study participants and social supporters participated in qualitative semistructured in-depth interviews on acceptability and feasibility of this technology. An inductive, content analytic approach, framed by the unified theory of acceptance and use of technology model, was used to analyze qualitative data. Quantitative feasibility data, including device functionality and SMS tracking data, were recorded based upon device metrics collected electronically and summarized descriptively. RESULTS: A total of 63 participants participated in the study. Participants reported that real-time monitoring intervention linked to SMS reminders and notifications are generally acceptable; the predominant feedback was perceived utility-the intervention was beneficial in motivating and reminding patients to take medication, as well as enabling provision of social support. The intervention was found to be technically feasible, as data were obtained from most participants as expected most of the time. Potential challenges included the impact of the technology on confidentiality, shared phone ownership, usability skills, and availability of electricity. CONCLUSIONS: Real-time adherence monitoring integrated with SMS reminders and social support notifications is a generally acceptable (based primarily on perceived utility) and feasible intervention in a resource-limited country. Future efforts should focus on optimized device design, user training to overcome the challenges we encountered, cost effectiveness studies, as well as studying the monitoring aspect of the device without accompanying interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT01957865; https://clinicaltrials.gov/ct2/show/NCT01957865 (Archived by WebCite at http://www.webcitation.org/6zFiDlXDa).

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.506
Teacher spread0.393 · 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.

Study designRandomized trial
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

Citations73
Published2018
Admission routes1
Has abstractyes

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