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Record W3021389381 · doi:10.2196/16165

Describing the Process and Tools Adopted to Cocreate a Smartphone App for Obesity Prevention in Childhood: Mixed Method Study

2020· article· en· W3021389381 on OpenAlexvenueno aff
Paolo Giorgi Rossi, Francesca Ferrari, Sergio Amarri, Andrea Bassi, Laura Bonvicini, Luca Dall'Aglio, Claudia Della Giustina, Alessandra Fabbri, A. Ferrari, Elena Ferrari, Marta Fontana, Marco Foracchia, Teresa Gallelli, Giulia Ganugi, Barbara Ilari, Sara Lo Scocco, Gianluca Maestri, Veronica Moretti, Costantino Panza, Mirco Pinotti, Riccardo Prandini, Simone Storani, Maria Elisabeth Street, Marco Tamelli, Hayley Trowbridge, Francesco Venturelli, Alessandro Volta, Anna Davoli

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupChildhood obesityStakeholderPublic healthmHealthWorkloadCommunity-based participatory researchMedicineMedical educationPsychologyPublic relationsNursingPsychological interventionObesityBusinessPolitical scienceParticipatory action researchSociologyComputer scienceMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood obesity prevention is a public health priority in industrialized countries. The Reggio Emilia Local Health Authority has implemented a program involving primary and secondary prevention as well as the care of obese children. There are many health-promoting mobile apps, but few are targeted to children and very few are sponsored by public health agencies. OBJECTIVE: The goal of the research was to describe the process and tools adopted to cocreate a mobile app sponsored by the Reggio Emilia Local Health Authority to be installed in parents' phones aimed at promoting child health and preventing obesity. METHODS: After stakeholder mapping, a consulting committee including relevant actors, stakeholders, and users was formed. Key persons for childhood obesity prevention were interviewed, focus groups with parents and pediatricians were conducted, and community reporting storytelling was collected. The results of these activities were presented to the consulting committee in order to define the functionalities and contents of the mobile app. RESULTS: Three key trends emerged from community reporting: being active, playing, and being outdoors; time for oneself, family, and friends; and the pressures of life and work and not having time to be active and socialize. In focus groups, interviews, and labs, mothers showed a positive attitude toward using an app to manage their children's weight, while pediatricians expressed concerns that the app could increase their workload. When these findings were explored by the consulting committee, four key themes were extracted: strong relationships with peers, family members, and the community; access to safe outdoor spaces; children's need for age-appropriate independence; and professional support should be nonjudgmental and stigma-free. It should be a dialogue that promotes family autonomy. The app functions related to these needs include the following: (1) newsletter with anticipatory guidance, recipes, and vaccination and well-child visit reminders; (2) regional map indicating where physical activity can be done; (3) information on how to manage emergencies (eg, falls, burns, fever); (4) module for reinforcing the counseling intervention conducted by pediatricians for overweight children; and (5) a function to build a balanced daily diet. CONCLUSIONS: The pilot study we conducted showed that cocreation in health promotion is feasible, with the consulting committee being the key co-governance and cocreation tool. The involvement of stakeholders in this committee made it possible to expand the number of persons and institutions actively contributing to the project.

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)0.031
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.186
GPT teacher head0.484
Teacher spread0.298 · 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 designQualitative
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

Citations20
Published2020
Admission routes1
Has abstractyes

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