Stimulating the Uptake of Preconception Care by Women With a Vulnerable Health Status Through mHealth App–Based Nudging (Pregnant Faster): Cocreation Design and Protocol for a Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Women with a low socioeconomic status often have a vulnerable health status due to an accumulation of health-deteriorating factors such as poor lifestyle behaviors, including inadequate nutrition, mental stressors, and impaired health literacy and agency, which puts them at an unnecessary high risk of adverse pregnancy outcomes. Adequately preparing for pregnancy through preconception care (PCC) uptake and lifestyle improvement can improve these outcomes. We hypothesize that nudging is a successful way of encouraging engagement in PCC. A nudge is a behavioral intervention that changes choice behavior through influencing incentives. The mobile health (mHealth) app-based loyalty program Pregnant Faster aims to reward women in an ethically justified way and nudges to engage in pregnancy preparation by visiting a PCC consultation. OBJECTIVE: Here, we first describe the process of the cocreation of the mHealth app Pregnant Faster that aims to increase engagement in pregnancy preparation by women with a vulnerable health status. Second, we describe the cohort study design to assess the feasibility of Pregnant Faster. METHODS: The content of the app is based on the eHealth lifestyle coaching program Smarter Pregnancy, which has proven to be effective in ameliorating preconceptional lifestyle behaviors (folic acid, vegetables, fruits, smoking, and alcohol) and an interview study pertaining to the preferences of the target group with regard to an mHealth app stimulating PCC uptake. For moral guidance on the design, an ethical framework was developed based on the bioethical principles of Beauchamp and Childress. The app was further developed through iterative cocreation with the target group and health care providers. For 4 weeks, participants will engage with Pregnant Faster, during which opportunities will arise to earn coins such as reading informative blogs and registering for a PCC consultation. Coins can be spent on small fun rewards, such as folic acid, fruits, and mascara. Pregnant Faster's feasibility will be tested in a study including 40 women aged 18 to 45 years, who are preconceptional or <8 weeks pregnant, with a low educational level, and living in a deprived neighborhood. The latter 2 factors will serve as a proxy of a low socioeconomic status. Recruitment will take place through flyers, social media, and health care practices. After finalization, participants will evaluate the app through the "mHealth App Usability Questionnaire" and additional interviews or questionnaires. RESULTS: Results are expected to be published by December 2023. CONCLUSIONS: Pregnant Faster has been designed through iterative cocreation with the target group and health care professionals. With the designed study, we will test Pregnant Faster's feasibility. If overall user satisfaction and PCC uptake is achieved, the app will be further developed and the cohort will be continued with an additional 400 inclusions to establish effectiveness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45293.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».