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Record W2584610886 · doi:10.2196/humanfactors.7133

A Self-Regulation Theory–Based Asthma Management Mobile App for Adolescents: A Usability Assessment

2017· article· en· W2584610886 on OpenAlex
Adam Sage, Courtney Roberts, Lorie L. Geryk, Betsy Sleath, Deborah F. Tate, Delesha M. Carpenter

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Human Factors · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAmerican Lung Association
KeywordsUsabilityMobile appsPersonalizationSelf-managementComputer scienceApplied psychologyAsthmaControl (management)PsychologyHuman–computer interactionMultimediaWorld Wide WebMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Self-regulation theory suggests people learn to influence their own behavior through self-monitoring, goal-setting, feedback, self-reward, and self-instruction, all of which smartphones are now capable of facilitating. Several mobile apps exist to manage asthma; however, little evidence exists about whether these apps employ user-centered design processes that adhere to government usability guidelines for mobile apps. OBJECTIVE: Building upon a previous study that documented adolescent preferences for an asthma self-management app, we employed a user-centered approach to assess the usability of a high-fidelity wireframe for an asthma self-management app intended for use by adolescents with persistent asthma. METHODS: Individual interviews were conducted with adolescents (ages 11-18 years) with persistent asthma who owned a smartphone (N=8). Adolescents were asked to evaluate a PDF app wireframe consisting of 76 screen shots displaying app features, including log in and home screen, profile setup, settings and info, self-management features, and graphical displays for charting asthma control and medication. Preferences, comments, and suggestions for each set of screen shots were assessed using the audio-recorded interviews. Two coders reached consensus on adolescent evaluations of the following aspects of app features: (1) usability, (2) behavioral intentions to use, (3) confusing aspects, and (4) suggestions for improvement. RESULTS: The app wireframe was generally well received, and several suggestions for improvement were recorded. Suggestions included increased customization of charts and notifications, reminders, and alerts. Participants preferred longitudinal data about asthma control and medication use to be displayed using line graphs. All participants reported that they would find an asthma management app like the one depicted in the wireframe useful for managing their asthma. CONCLUSIONS: Early stage usability tests guided by government usability guidelines (usability.gov) revealed areas for improvement for an asthma self-management app for adolescents. Addressing these areas will be critical to developing an engaging and effective asthma self-management app that is capable of improving adolescent asthma outcomes.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.467
Teacher spread0.418 · 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