Stress and Well-Being Intervention and mHealth Delivery Adaptation for Latinx Millennial Caregivers: Qualitative User-Centered Design Approach
Notice bibliographique
Résumé
BACKGROUND: The study aimed to adapt a stress and well-being intervention delivered via a mobile health (mHealth) app for Latinx Millennial caregivers. This demographic, born between 1981 and 1996, represents a significant portion of caregivers in the United States, with unique challenges due to higher mental distress and poorer physical health compared to non-caregivers. Latinx Millennial caregivers face additional barriers, including higher uninsured rates and increased caregiving burdens. OBJECTIVE: We used a community-informed and user-centered design approach to tailor an existing mHealth app to better meet the stress and well-being needs of Latinx Millennial caregivers. METHODS: We employed a two-step, multi-feedback approach. In step one, Latinx Millennial caregivers participated in focus groups to evaluate wireframes for the proposed mHealth app. In step two, participants engaged in usability testing for one week, concluding with short interviews for feedback. Participants were recruited through various channels, including social media and community clinics. Data were analyzed inductively using a rapid qualitative content analysis approach. RESULTS: A total of 29 caregivers (69% women, mean age 31) participated in the study. Participants had a mean age of 31 (SD=4.10), with most (n=28, 96%) caring for an adult and one (4%) caring for children with chronic conditions. All participants completed the step one focus groups, with a subset of 3 caregivers completing usability testing in step two. The most liked features included the: 1) stress rating scale because it helped them understand stress and mental health, 2) mindfulness options because it allowed for flexible timing of activities, 3) journaling prompts because it was a way to address daily challenges and contemplate positives, and 4) resource list for its employment and financial content. One concern was that the journaling prompts may take too much time or effort to complete after a long and hard day. Some suggestions for improvement included: a better tracking system, gamification, caregiving education, a checklist of emotions to use on the journal, tailored resources, and ways to connect with a community of similar caregivers. During step two, participants noted the app was user-friendly but had some glitches and unclear privacy policies. Participants liked the meditation options, resource variety, and daily stress log but wanted more journaling space, longer meditations, and additional relaxation activities. CONCLUSIONS: Caregivers highlighted the need for tailored resources and additional stress-relief activities. Future iterations should consider integrating more personalized and community-specific resources, leveraging platforms like podcasts for broader engagement, and the use of information-based videos to support caregiver skill acquisition. Caregivers expressed needs beyond the scope of the app, such as resource access, demonstrating the need for upstream and downstream interventions. The study reinforces that user-informed design is an ongoing and iterative process, which requires balancing the needs of stakeholders and the feasibility of recommended adaptations.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».