Tailoring of Health-Promotion Video Messaging for Reproductive-Aged Women at Risk for Developing Cardiometabolic Disease: Qualitative Focus-Groups Study
Notice bibliographique
Résumé
BACKGROUND: Targeting reproductive-aged women at high risk for type 2 diabetes (T2D) provides an opportunity for prevention earlier in the life course. A woman's experiences during her reproductive years may have a large impact on her future risk of T2D. Her risk is 7 to 10 times higher if she has had gestational diabetes (GDM). Despite these risks, T2D is preventable. Evidence-based programs, such as the National Diabetes Prevention Program (DPP), can reduce the risk of developing T2D by nearly 60%. However, only 0.4% of adults with prediabetes have participated in the DPP to date and reproductive-aged women are 50% less likely to participate than older women. In prior work, our team developed a mobile 360° video to address diabetes risk awareness and promote DPP enrollment among at-risk adults; this video was not designed, however, for reproductive-aged women. OBJECTIVE: This study aims to obtain feedback from reproductive-aged women with cardiometabolic disease risk about a 360° video designed to promote enrollment in the DPP, and to gather suggestions about tailoring video messages to reproductive-aged women. METHODS: Focus groups and a qualitative descriptive approach were used. Women with at least 1 previous pregnancy, aged 18 to 40 years, participated in one of three focus groups stratified by the following health risks: (1) a history of GDM or a hypertensive disorder of pregnancy, (2) a diagnosis of prediabetes, or (3) a BMI classified as obese. Focus-group questions addressed several topics; this report shared findings regarding video feedback. The 3 focus-group discussions were conducted via Zoom and were recorded and transcribed for analysis. Deductive codes were used to identify concepts related to the research question and inductive codes were created for novel insights shared by participants. The codes were then organized into categories and themes. RESULTS: The main themes identified were positive feedback, negative feedback, centering motherhood, and the importance of storytelling. While some participants said the video produced a sense of urgency for health-behavior change, all participants agreed that design changes could improve the video's motivating effect on health-behavior change in reproductive-aged women. Participants felt a tailored video should recognize the complexities of being a mother and how these dynamics contribute to women's difficulty engaging in healthy behaviors without stirring feelings of guilt. Women desired a video with a positive, problem-solving perspective, and recommended live links as clickable resources for practical solutions promoting health behavior change. Women suggested using storytelling, both to describe how complications experienced during pregnancy impact long-term health and to motivate health behavior change. CONCLUSIONS: Reproductive-aged women require tailored lifestyle-change messaging that addresses barriers commonly encountered by this population (eg, parenting or work responsibilities). Moreover, messaging should prioritize a positive tone that harnesses storytelling and human connection while offering realistic solutions.
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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 | 0,017 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».