The Effect of Information and Communication Technologies Utilization Patterns on Self-Rated Health
Notice bibliographique
Résumé
Background: An increasing number of older adults are using information and communication technologies (ICTs), and ICTs have become a major resource for older adults for improving health-related quality of life. ICTs possibly help older adults seek health information online, collaborate with other users in their decision making process, and receive social support. Although there have been some studies highlighting positive associations between ICT utilization and health, there is still limited knowledge about various patterns of ICT utilization among older adults. Objective: This study aims to extend the empirical evidence regarding the patterns of older adults’ ICT utilization, investigate different attributes across various ICT utilization patterns, and further examine how these patterns influence self-rated health. Methods: Data came from the 2012 and 2014 Health and Retirement Study, a nationally representative sample of Americans aged 51 and older. Our sample was restricted to individuals who responded to a special survey about technology use asked only to a subsample of 2012 interviews (N=1504). Latent class analysis was used to identify ICT utilization patterns based on ICT utilization variables: (1) communication-related utilization, including use of email, social networking sites, online video call, instant messenger, and smartphones; (2) finance-related utilization, such as online bill payment and online banking; (3) health-related utilization, including exercise equipment, exercise videos, online wellness programs, online health information, health monitoring devices, and Wii Fit; and (4) entertainment-related utilization, including e-readers/tablets, mp3 players, online streaming media, and video game. Ordinary least squares regressions were used to examine the effects of ICT utilization patterns on self-rated health at follow-up as compared to baseline. Results: Four ICT utilization patterns were identified: multifarious (n=90: high level of ICT utilization across most variables), e-commerce-oriented (n=147: high level of finance-related utilization), fundamental (n=280: email and online search focused utilization), and minimal users (n=552: low level of ICT utilization across most variables). We found that multifarious users were younger, more often female, married, and had higher education and income levels and better physical and mental health than other groups. Minimal users were more likely to be older, non-white, and single, and more likely to have lower level of education and income and poor physical and mental health. Regression models showed multifarious users were most likely to have better self-rated health, and minimal users tended to have the worst self-rated health over time, even after controlling for sociodemographic attributes and health conditions. E-commerce-oriented users were more likely to have better self-rated health than fundamental users. Conclusions: This study identified clearly different ICT utilization patterns among older adults and demonstrated positive effects of ICT utilization on health among older adults. Improving access to ICTs and ICT education programs will help to improve health outcomes of older adults, but the effects of different ICT utilization patterns need to be highlighted in future studies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| 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 ».