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Record W4323795207 · doi:10.2196/44462

Exploring User Visions for Modeling mHealth Apps Toward Supporting Patient-Parent-Clinician Collaboration and Shared Decision-making When Treating Adolescent Knee Pain in General Practice: Workshop Study

2023· article· en· W4323795207 on OpenAlexvenueno aff
Simon Kristoffer Johansen, Anne Marie Kanstrup, Kian Haseli, Visti Hildebrandt Stenmo, Janus Laust Thomsen, Michael Skovdal Rathleff

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsVisionmHealthMobile appsPsychologyMedicineMedical educationComputer scienceHuman–computer interactionNursingWorld Wide WebPsychological interventionSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Long-standing knee pain is one of the most common reasons for adolescents (aged 10-19 years) to consult general practice. Generally, 1 in 2 adolescents will continue to experience pain after 2 years, but exercises and self-management education can improve the prognosis. However, adherence to exercises and self-management education interventions remains poor. Mobile health (mHealth) apps have the potential for supporting adolescents' self-management, enhancing treatment adherence, and fostering patient-centered approaches. However, it remains unclear how mHealth apps should be designed to act as tools for supporting individual and collaborative management of adolescents' knee pain in a general practice setting. OBJECTIVE: The aim of the study was to extract design principles for designing mHealth core features, which were both sufficiently robust to support adolescents' everyday management of their knee pain and sufficiently flexible to act as enablers for enhancing patient-parent collaboration and shared decision-making. METHODS: Overall, 3 future workshops were conducted with young adults with chronic knee pain since adolescence, parents, and general practitioners (GPs). Each workshop followed similar procedures, using case vignettes and design cards to stimulate discussions, shared construction of knowledge and elicit visions for mHealth designs. Young adults and parents were recruited via social media posts targeting individuals in Northern Jutland. GPs were recruited via email and cold calling. Data were transcribed and analyzed thematically using NVivo (QSR International) coding software. Extracted themes were synthesized in a matrix to map tensions in the collaborative space and inform a conceptual model for designing mHealth core-features to support individual and collaborative management of knee pain. RESULTS: Overall, 38% (9/24) young adults with chronic knee pain since adolescence, 25% (6/24) parents, and 38% (9/24) GPs participated in the workshops. Data analysis revealed how adolescents, parents, and clinicians took on different roles within the collaborative space, with different tasks, challenges, and information needs. In total, 5 themes were identified: adolescents as explorers of pain and social rules; parents as supporters, advocates and enforcers of boundaries; and GPs as guides, gatekeepers, and navigators or systemic constraints described participants' roles; collaborative barriers and tensions referred to the contextual elements; and visions for an mHealth app identified beneficial core features. The synthesis informed a conceptual model, outlining 3 principles for consolidating mHealth core features as enablers for supporting role negotiation, limiting collaborative tensions, and facilitating shared decision-making. CONCLUSIONS: An mHealth app for treating adolescents with knee pain should be designed to accommodate multiple users, enable them to shift between individual management decision-making, take charge, and engage in role negotiation to inform shared decision-making. We identified 3 silver-bullet principles for consolidating mHealth core features as enablers for negotiation by supporting patient-GP collaboration, supporting transitions, and cultivating the parent-GP alliance.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.178
GPT teacher head0.440
Teacher spread0.261 · 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 designObservational
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

Citations11
Published2023
Admission routes1
Has abstractyes

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