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Record W2982141328 · doi:10.2196/12618

Designing a Mobile App to Enhance Parenting Skills of Latinx Parents: A Community-Based Participatory Approach

2019· article· en· W2982141328 on OpenAlexvenueno aff
J. Doty, Sonya S. Brady, Javiera Monardez Popelka, Laura Rietveld, Diego Garcia‐Huidobro, Matthew J Doty, Roxana Linares, María Verónica Svetaz, Michele Allen

Bibliographic record

VenueJMIR Formative Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersHealth Resources and Services AdministrationFamily Process InstituteUniversity of MinnesotaU.S. Department of Health and Human Services
KeywordsmHealthCommunity-based participatory researcheHealthFacilitatorPsychologyContext (archaeology)Medical educationParticipatory action researchMobile phoneOperationalizationInternet privacyApplied psychologyComputer scienceHealth careMedicineSocial psychologySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Latinx families are among the highest users of smartphones, yet few health-focused Web programs have been developed for this audience. Parent-based smartphone apps designed for Latinx families may help increase access to evidence-informed parenting programming and ultimately reduce health disparities among children and adolescents. To maximize uptake of such apps, the Center for eHealth Research and Disease Management (CeHRes) Roadmap for electronic health (eHealth) development recommends 5 phases of development: (1) contextual inquiry, (2) value specification, (3) design, (4) operationalization, and (5) evaluation. OBJECTIVE: Guided by the CeHRes Roadmap, our objective was to apply a community-based participatory research (CBPR) approach to mobile app development. We present a formative evaluation to inform the design of an eHealth mobile app for Latinx parents of adolescents based on a face-to-face parenting program, Padres Informados/Jovenes Preparados (PIJP). METHODS: Community participants in the process included Latinx parents and stakeholders. We conducted a parent survey (N=115) and interviews (N=20) to understand the context and obtain feedback on a mockup and prototype of the app, facilitator workshops to streamline content, and stakeholder interviews (N=4) to discuss values and app requirements. RESULTS: We report results from the first 3 phases of the CeHRes Roadmap. In the survey, 96.5% (111/115) of parents reported they had access to a cell phone, 85.6% (89/104) reported they would use a parenting app in the next month if they had access, and 80.2% (89/111) reported intentions to use a stress reduction app. Parents reported that setting goals about parenting and tracking those goals were important potential features of an app. In logistic regression analyses, technology attitudes and barriers were not related to parent's intentions to use a parenting mobile app (95% CI 0.51-1.17 and 95% CI 0.28-2.12, respectively). Qualitative interviews confirmed Latinx parents' technology engagement and desire for education and child development information online. Stakeholder interviews identified 3 community values: familism, the promotion of adolescent health, and delivery of economic value. Community stakeholders participated in defining the mobile app requirements. On the basis of community and parent input, the mobile app prototype was designed with 3 sections: (1) 8 modules of video-based parenting skills instruction with content from the face-to-face PIJP program, (2) breath rate information from a wearable device to support awareness of stress levels that could affect parenting, and (3) goal setting and tracking capacities. CONCLUSIONS: The findings of this study highlight the utility of an iterative, participatory design process. The CBPR approach and community collaboration enhanced the CeHRes Roadmap by promoting power sharing, facilitating recruitment, and building trust among community members. Experiences applying community research to the initial 3 phases of the CeHRes Roadmap in a Latinx community are discussed, along with plans for the 2 final phases.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.002

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 head0.175
GPT teacher head0.549
Teacher spread0.375 · 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 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

Citations27
Published2019
Admission routes1
Has abstractyes

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