Prevalence and Predictors of Health-Related Internet and Digital Device Use in a Sample of South Asian Adults in Edmonton, Alberta, Canada: Results From a 2014 Community-Based Survey
Notice bibliographique
Résumé
BACKGROUND: South Asian Canadians are at high risk of developing cardiovascular disease and diabetes. Consumer-oriented health information technology may help mitigate lifestyle risk factors and improve chronic disease self-management. OBJECTIVE: This study aims to explore the prevalence, patterns, and predictors of the use of the internet, digital devices, and apps for health purposes as well as preferences for future use of eHealth support in South Asian Canadians. METHODS: We conducted a cross-sectional, mixed-mode survey in a convenience sample of 831 South Asian adults recruited at faith-based gathering places, health care settings, and community events in Edmonton, Alberta, in 2014. The 706 responders (mean age 47.1, SD 17.6 years; n=356, 50.4% female; n=509, 72.1% Sikh) who provided complete sociodemographic information were included in the analysis, and the denominators varied based on the completeness of responses to each question. Multivariate logistic regression was used to determine sociodemographic and health status predictors of internet use, being a web-based health information seeker, smartphone or tablet ownership, health app use, and willingness to use various modes of eHealth support. RESULTS: Of all respondents, 74.6% (527/706) were internet users and 47.8% (336/703) were web-based health information seekers. In addition, 74.9% (527/704) of respondents owned a smartphone or tablet and 30.7% (159/518) of these had a health and fitness app. Most internet users (441/527, 83.7%) expressed interest in using ≥1 mode of eHealth support. Older age, being female, having less than high school education, preferring written health information in languages other than English, and lacking confidence in completing medical forms predicted lack of internet use. Among internet users, factors that predicted web-based health information seeking were being female, use of the internet several times per day, being confident in completing medical forms, and preferring health information in English. Predictors of not owning a smartphone or tablet were being older, preferring health information in languages other than English, having less than high school education, living in Canada for <5 years, having a chronic health condition, and having diabetes. Increasing age was associated with lower odds of having a health app. Preferring health information in languages other than English consistently predicted lower interest in all modes of eHealth support. CONCLUSIONS: eHealth-based chronic disease prevention and management interventions are feasible for South Asian adults, but digital divides exist according to language preference, education, age, sex, confidence in completing medical forms, and number of years lived in Canada. Community-based, culturally tailored strategies targeting these factors are required to address existing divides and increase the uptake of credible web-based and app-based resources for health purposes.
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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 | 0,007 | 0,010 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».