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Record W4323529115 · doi:10.2196/45301

Digital Adherence Technologies and Mobile Money Incentives for Management of Tuberculosis Medication Among People Living With Tuberculosis: Mixed Methods Formative Study

2023· article· en· W4323529115 on OpenAlexvenueno aff
Angella Musiimenta, Wilson Tumuhimbise, Esther C. Atukunda, Aaron Mugaba, Sebastian Linnemayr, Jessica E. Haberer

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of Mental HealthFogarty International CenterNational Institutes of Health
KeywordsTuberculosismHealthMobile phonePsychological interventionShort Message ServiceIntervention (counseling)IncentiveMedicineFocus groupeHealthQualitative researchFormative assessmentFamily medicineReferralNursingPsychologyHealth careBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Although there is an increasing use of digital adherence technologies (DATs), such as real-time monitors and SMS reminders in tuberculosis medication adherence, suboptimal patient engagement with various DATs has been reported. Additionally, financial constraints can limit DAT's utility. The perceived usefulness and the design mechanisms of DATs linked to mobile money financial incentives for tuberculosis medication management remain unclear. OBJECTIVE: The aim of this study is to describe the perceived usefulness and design mechanisms for a DAT intervention called My Mobile Wallet, which is composed of real-time adherence monitors, SMS reminders, and mobile money incentives to support tuberculosis medication adherence in a low-income setting. METHODS: This study used mixed methods approaches among persons with tuberculosis recruited from the Tuberculosis Clinic in the Mbarara Regional Referral Hospital. We purposively sampled 21 persons with tuberculosis aged 18 years and older, who owned cell phones and were able to use SMS text messaging interventions. We also enrolled 9 participants who used DATs in our previous study. We used focus group discussions with the 30 participants to solicit perceptions about the initial version of the My Mobile Wallet intervention, and then iteratively refined subsequent versions of the intervention following a user-centered design approach until the beta version of the intervention that suited their needs was developed. Surveys eliciting information about participants' cell phone use and perceptions of the intervention were also administered. Content analysis was used to inductively analyze qualitative data to derive categories describing the perceived usefulness of the intervention, concerns, and design mechanisms. Stata (version 13; StataCorp) was used to analyze survey data. RESULTS: Participants expressed the perceived usefulness of the My Mobile Wallet intervention in terms of being reminded to take medication, supported with transport to the clinic, and money to meet other tuberculosis medication-related costs, all of which were perceived to imply care, which could create a sense of connectedness to health care workers. This could consequently cause participants to develop a self-perceived need to prove their commitment to adherence to health care workers who care for them, thereby motivating medication adherence. For fear of unintended tuberculosis status disclosure, 20 (67%) participants suggested using SMS language that is confidential-not easily related to tuberculosis. To reduce the possibilities of using the money for other competing demands, 25 (83%) participants preferred sending the money 1-2 days before the appointment to limit the time lag between receiving the money and visiting the clinic. CONCLUSIONS: DATs complemented with mobile money financial incentives could potentially provide acceptable approaches to remind, support, and motivate patients to adhere to taking their tuberculosis medication. TRIAL REGISTRATION: ClinicalTrials.gov NCT05656287; https://clinicaltrials.gov/ct2/show/NCT05656287.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.505
Teacher spread0.438 · 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 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

Citations15
Published2023
Admission routes1
Has abstractyes

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