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Record W3114647711 · doi:10.2196/19677

Development and Feasibility of an App to Decrease Risk Factors for Type 2 Diabetes in Hispanic Women With Recent Gestational Diabetes (Hola Bebé, Adiós Diabetes): Pilot Pre-Post Study

2020· article· en· W3114647711 on OpenAlexvenueno aff
Ellen W. Seely, Patricia Flynn Weitzman, Dharma E. Cortés, Sara Romero Vicente, Sue E. Levkoff

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institutes of Health
KeywordsGestational diabetesMedicineType 2 diabetesPsychological interventionmHealthGerontologyDiabetes mellitusPhysical therapyPregnancyNursingEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Hispanic women have increased risk of gestational diabetes mellitus (GDM), which carries an increased risk for future type 2 diabetes, compared to non-Hispanic women. In addition, Hispanic women are less likely to engage in healthy eating and physical activity, which are both risk factors for type 2 diabetes. Supporting patients to engage in healthy lifestyle behaviors through mobile health (mHealth) interventions is increasingly recognized as a viable, underused tool for disease prevention, as they reduce barriers to access frequently experienced in face-to-face interventions. Despite the high percentage of smartphone ownership among Hispanics, mHealth programs to reduce risk factors for type 2 diabetes in Hispanic women with prior GDM are lacking. OBJECTIVE: This study aimed to (1) develop a mobile app (¡Hola Bebé, Adiós Diabetes!) to pilot test a culturally tailored, bilingual (Spanish/English) lifestyle program to reduce risk factors for type 2 diabetes in Hispanic women with GDM in the prior 5 years; (2) examine the acceptability and usability of the app; and (3) assess the short-term effectiveness of the app in increasing self-efficacy for both healthy eating and physical activity, and in decreasing weight. METHODS: Social cognitive theory provided the framework for the study. A prototype app was developed based on prior research and cultural tailoring of content. Features included educational audiovisual modules on healthy eating and physical activity; personal action plans; motivational text messages; weight tracking; user-friendly, easy-to-follow recipes; directions on building a balanced plate; and tiered badges to reward achievements. Perceptions of the app's acceptability and usability were explored through four focus groups. Short-term effectiveness of the app was tested in an 8-week single group pilot study. RESULTS: In total, 11 Hispanic women, receiving care at a federally qualified community health center, aged 18-45 years, and with GDM in the last 5 years, participated in four focus groups to evaluate the app's acceptability and usability. Participants found the following sections most useful: audiovisual modules, badges for completion of activities, weight-tracking graphics, and recipes. Suggested modifications included adjustments in phrasing, graphics, and a tiering system of badges. After app modifications, we conducted usability testing with 4 Hispanic women, with the key result being the suggestion for a "how-to tutorial." To assess short-term effectiveness, 21 Hispanic women with prior GDM participated in the pilot. There was a statistically significant improvement in both self-efficacy for physical activity (P=.003) and self-efficacy for healthy eating (P=.007). Weight decreased but not significantly. Backend process data revealed a high level of user engagement. CONCLUSIONS: These data support the app's acceptability, usability, and short-term effectiveness, suggesting that this mHealth program has the potential to fill the gap in care experienced by Hispanic women with prior GDM following pregnancy. Future studies are needed to determine the effectiveness of an enhanced app in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04149054; https://clinicaltrials.gov/ct2/show/NCT04149054.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.092
GPT teacher head0.398
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2020
Admission routes1
Has abstractyes

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