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Record W4406962379 · doi:10.2196/60216

Development of a Mobile-Based Personal Health Record for Pediatric Attention-Deficit/Hyperactivity Disorder Management: Protocol for a Study Based on Action Research Design

2025· article· en· W4406962379 on OpenAlexvenueno aff
Dian Budi Santoso, Martina Sinta Kristanti, Dian Kesumapramudya Nurputra, Retno Sutomo

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintProtocol (science)Action (physics)Electronic health recordMedicinePsychologyMedical educationInternet privacyComputer scienceAlternative medicineHealth careWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is one of the most widespread neurobehavioral problems during childhood. A child's personal health record (PHR) plays an important role in the controlled routine monitoring of ADHD symptom improvement. Along with the advantages, the convenience offered by mobile technology, and the ubiquity of smartphones in contemporary society, there is a compelling need for PHR to be available in the form of a mobile app. OBJECTIVE: This study aims to identify stakeholder needs, followed by designing, developing, testing, and evaluating a mobile-based PHR in the context of pediatric ADHD management. METHODS: This study will adopt an action research design structured into 4 stages: diagnosing, planning, taking, and evaluating action. Stakeholders, including parents, pediatricians, occupational therapists, clinical psychologists, and teachers, will participate actively. In stage 1, stakeholder requirements for the mobile-based PHR will be explored through in-depth interviews, focus group discussions (FGDs), and document reviews. Thematic analysis will be used to identify key needs and challenges. In stage 2, a systematic literature review will be conducted to enhance user requirements analysis by synthesizing insights from existing mobile apps for pediatric ADHD management. A mobile-based PHR prototype will be designed and developed based on user requirements enhanced with systematic review results. In stage 3, the prototype will undergo a 6-week trial with participants to evaluate its functionality and address any identified issues. In stage 4, both quantitative and qualitative methods will be used to assess the app's usability and quality. The System Usability Scale (SUS) and the User Version of the Mobile App Rating Scale (uMARS) will be used for quantitative evaluation, while interviews and FGDs will be conducted for qualitative evaluation. RESULTS: This study commenced in October 2024. As of December 2024, 13 participants (n=5, 38.5%, parents; n=2, 15.4%, pediatricians; n=2, 15.4%, occupational therapists; n=2, 15.4%, clinical psychologists; and n=2, 15.4%, teachers) have been enrolled, meeting the minimum participant requirements for stage 1. Stage 1 was completed at the end of 2024, with stage 2 expected to be completed by September 2025, stage 3 by December 2025, and stage 4 by February 2026. The findings from each stage will inform iterative refinements to the mobile-based PHR. The final results, including usability and quality assessments, are anticipated for publication by the middle of 2026. CONCLUSIONS: This study protocol outlines a pivotal initiative to enhance the management of pediatric ADHD. By using an action research methodology and actively engaging stakeholders, the study aims to contribute significantly to the field. The iterative cycles of the research seek to develop a mobile-based PHR that is not only user friendly but also effective and uniquely attuned to the diverse needs of those involved in pediatric ADHD care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60216.

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)0.110
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.110
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.086
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.003
Science and technology studies0.0070.004
Scholarly communication0.0040.004
Open science0.0040.004
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0520.016

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 head0.455
GPT teacher head0.613
Teacher spread0.158 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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