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Record W2795826733 · doi:10.2196/formative.8248

User-Centered Design of a Mobile App for Weight and Health Management in Adolescents With Complex Health Needs: Qualitative Study

2018· article· en· W2795826733 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJMIR Formative Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of TorontoHealth Sciences CentreHospital for Sick ChildrenHolland Bloorview Kids Rehabilitation HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsSoftware deploymentPsychological interventionQualitative researchWeight managementmHealthWork (physics)Mobile appsHealth careMobile technologyMobile devicePsychologyGerontologyMedicineMedical educationNursingComputer scienceEngineeringWorld Wide WebSociologyWeight lossObesity

Abstract

fetched live from OpenAlex

BACKGROUND: Growing research has been conducted into the deployment and evaluation of mobile technology interventions for weight management in adolescents. However, no work has yet been conducted toward the development of these technologies for adolescents with complex health needs receiving specialized tertiary-level health care. OBJECTIVE: The aim of this study was to conduct a user-centered needs assessment of adolescents interested in weight management with complex health needs requiring specialized health care services, their parents, and health care providers (HCPs) to inform the design and development of a mobile app for weight and health management. METHODS: A qualitative study design was employed. Participants were recruited from two tertiary health care centers. Separate audiotaped focus group interviews were conducted with adolescents aged 12 to 18 years, parents, and HCPs. Interviews were transcribed, and field notes were collected by research staff. Iterative simple content analysis was performed independently by 4 research team members using computer software NVivo (QSR International) 10.0. RESULTS: A total of 19 adolescents, 16 parents, and 21 HCPs were interviewed. Qualitative analysis revealed seven major themes related to app functionality: healthy eating, social support, self-monitoring, communicating with HCPs, supporting mental health, gamification and incentives, and user interface (UI) design. Adolescents provided several ideas related to each feature, whereas parents' views focused on assistance with meal planning and greater access to HCPs. HCPs viewed the app as a novel and more acceptable platform to connect remotely with adolescents than conventional methods. They also strongly endorsed the value of social support capabilities and the ability to connect with an HCP. CONCLUSIONS: This is the first study to conduct a qualitative needs assessment in adolescents receiving specialized health care services toward the design of a mobile app for weight and health management. Our results indicate that core components of the app should include tailored meal recommendations and assistance with meal planning, social networking for peer support, customized and convenient tracking, remote access to HCPs, features to support mental health, and an attractive and engaging UI. These findings will be used to develop and evaluate a mobile app targeting adolescents with complex health needs.

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)0.011
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.291
GPT teacher head0.596
Teacher spread0.305 · 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