Mobile Device Ownership, Current Use, and Interest in Mobile Health Interventions Among Low-Income Older Chinese Immigrants With Type 2 Diabetes: Cross-sectional Survey Study
Notice bibliographique
Résumé
BACKGROUND: Chinese immigrants suffer a disproportionately high type 2 diabetes (T2D) burden and tend to have poorly controlled disease. Mobile health (mHealth) interventions have been shown to increase access to care and improve chronic disease management in minority populations. However, such interventions have not been developed for or tested in Chinese immigrants with T2D. OBJECTIVE: This study aims to examine mobile device ownership, current use, and interest in mHealth interventions among Chinese immigrants with T2D. METHODS: In a cross-sectional survey, Chinese immigrants with T2D were recruited from Chinese community centers in New York City. Sociodemographic characteristics, mobile device ownership, current use of social media software applications, current use of technology for health-related purposes, and interest in using mHealth for T2D management were assessed. Surveys were administered face-to-face by bilingual study staff in the participant's preferred language. Descriptive statistics were used to characterize the study sample and summarize technology use. RESULTS: The sample (N=91) was predominantly female (n=57, 63%), married (n=68, 75%), and had a high school education or less (n=58, 64%); most participants had an annual household income of less than US $25,000 (n=63, 69%) and had limited English proficiency (n=78, 86%). The sample had a mean age of 70 (SD 11) years. Almost all (90/91, 99%) participants had a mobile device (eg, basic cell phones, smart devices), and the majority (n=83, 91%) reported owning a smart device (eg, smartphone or tablet). WeChat was the most commonly used social media platform (65/91, 71%). When asked about their top source for diabetes-related information, 63 of the 91 participants (69%) reported health care providers, followed by 13 who reported the internet (14%), and 10 who reported family, friends, and coworkers (11%). Less than one-quarter (21/91, 23%) of the sample reported using the internet to search for diabetes-related information in the past 12 months. About one-third of the sample (34/91, 37%) reported that they had watched a health-related video on their cell phone or computer in the past 12 months. The majority (69/91, 76%) of participants reported interest in receiving an mHealth intervention in the future to help with T2D management. CONCLUSIONS: Despite high mobile device ownership, the current use of technology for health-related issues remained low in older Chinese immigrants with T2D. Given the strong interest in future mHealth interventions and high levels of social media use (eg, WeChat), future studies should consider how to leverage these existing low-cost platforms and deliver tailored mHealth interventions to this fast-growing minority group.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 ».