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Record W2802421135 · doi:10.2196/mhealth.8155

A Mobile Gaming Intervention to Increase Adherence to Antiretroviral Treatment for Youth Living With HIV: Development Guided by the Information, Motivation, and Behavioral Skills Model

2018· article· en· W2802421135 on OpenAlexvenueno aff
Laura Whiteley, Larry K. Brown, Michelle Lally, Nicholas C. Heck, Jacob J. van den Berg

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCenter for AIDS Research, University of Washington
KeywordsIntervention (counseling)mHealthHuman immunodeficiency virus (HIV)PsychologyBehavior changeApplied psychologyPsychological interventionClinical psychologyMedicineSocial psychologyPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Highly active combination antiretroviral treatment has been shown to markedly improve the health of HIV-infected adolescents and young adults. Adherence to antiretroviral treatment leads to decreased morbidity and mortality and decreases the number of hospitalizations. However, these clinical achievements can only occur when young persons with HIV are adherent to care. Unfortunately, adolescents and young adults have poorer rates of adherence to antiretroviral medications and poorer rates of retention in care than older adults. Novel and engaging digital approaches are needed to help adolescents and young adults living with HIV be adherent to treatment. OBJECTIVE: The aim of this study was to develop an immersive, action-oriented iPhone gaming intervention to improve adherence to antiretroviral medication and treatment. METHODS: Game development was guided by social learning theory, taking into consideration the perspectives of adolescents and young adults living with HIV. A total of 20 adolescents and young adults were recruited from an HIV care clinic in Rhode Island, and they participated in qualitative interviews guided by the information-motivation-behavioral skills model of behavior change. The mean age of participants was 22 years, 60% (12/20) of the participants identified as male, and 60% (12/20) of the sample reported missing a dose of antiretroviral medication in the previous week. Acceptability of the game was assessed with client service questionnaire and session evaluation form. RESULTS: A number of themes emerged that informed game development. Adolescents and young adults living with HIV desired informational game content that included new and comprehensive details about HIV, details about HIV as it relates to doctors' visits, and general health information. Motivational themes that emerged were the desire for enhancement of future orientation; reinforcement of positive influences from partners, parents, and friends; collaboration with health care providers; decreasing stigma; and increasing personal relevance of HIV care. Behavioral skills themes centered on self-efficacy and strategies for medical adherence and self-care. On the client service questionnaire, 10 out of the 11 participants indicated they were "satisfied with the game activities," and 9 out of 11 "would recommend it to a friend." On the session evaluation form, 9 out of 11 agreed that they "learned a lot from the game." CONCLUSIONS: We utilized youth feedback, social learning theory (information-motivation-behavioral skills), and agile software development to create a multilevel, immersive, action-oriented iPhone gaming intervention to measure and improve treatment adherence for adolescents and young adults living with HIV. There is a dearth of gaming interventions for this population, and this study is a significant step in working toward the development and testing of an iPhone gaming app intervention to promote adherence to antiretroviral treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT01887210; http://clinicaltrials.gov/ct2/show/NCT01887210 (Archived by WebCite at http://www.webcitation.org/6xHMW0NI1).

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 score0.801
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.071
GPT teacher head0.417
Teacher spread0.346 · 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

Citations56
Published2018
Admission routes1
Has abstractyes

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