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Record W2372598609 · doi:10.2196/games.5687

Epic Allies: Development of a Gaming App to Improve Antiretroviral Therapy Adherence Among Young HIV-Positive Men Who Have Sex With Men

2016· article· en· W2372598609 on OpenAlexvenueno aff
Sara LeGrand, Kathryn E. Muessig, Tobias McNulty, Karina Soni, Kelly Knudtson, Alex Lemann, Nkechinyere Nwoko, Lisa Hightow‐Weidman

Bibliographic record

VenueJMIR Serious Games · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental Health
KeywordsUsabilitymHealthMen who have sex with menInteractivityPopulationComputer sciencePsychologyInternet privacyPsychological interventionMedicineApplied psychologyWorld Wide WebHuman immunodeficiency virus (HIV)Human–computer interactionFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: In the United States, the human immunodeficiency virus (HIV) disproportionately affects young men who have sex with men (YMSM). For HIV-positive individuals, adherence to antiretroviral therapy (ART) is critical for achieving optimal health outcomes and reducing secondary transmission of HIV. However, YMSM often struggle with ART adherence. Novel mobile phone apps that incorporate game-based mechanics and social networking elements represent a promising intervention approach for improving ART adherence among YMSM. OBJECTIVE: This study used a multiphase, iterative development process to create an ART adherence app for YMSM. METHODS: The three-phase development process included: (1) theory-based concept development jointly by public health researchers and the technology team, (2) assessment of the target population's ART adherence needs and app preferences and development and testing of a clickable app prototype, and (3) development and usability testing of the final app prototype. RESULTS: The initial theory-based app concept developed in Phase One included medication reminders, daily ART adherence tracking and visualization, ART educational modules, limited virtual interactions with other app users, and gamification elements. In Phase Two, adherence needs, including those related to information, motivation, and behavioral skills, were identified. Participants expressed preferences for an ART adherence app that was informational, interactive, social, and customizable. Based on the findings from Phase Two, additional gaming features were added in Phase Three, including an interactive battle, superhero app theme, and app storyline. Other features were modified to increase interactivity and customization options and integrate the game theme. During usability testing of the final prototype, participants were able to understand and navigate the app successfully and rated the app favorably. CONCLUSIONS: An iterative development process was critical for the development of an ART adherence game app that was viewed as highly acceptable, relevant, and useful by YMSM.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.295
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations86
Published2016
Admission routes1
Has abstractyes

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