Digital Isolation and Depression Risk in Older Adults Using the National Health and Aging Trends Study Database: 8-Year Longitudinal Study
Notice bibliographique
Résumé
Background: The rapid advancement of digital technologies has profoundly transformed communication practices. However, this technological revolution has also led to "digital isolation," a form of social disconnection caused by limited or absent engagement with digital communication tools, including smartphones, computers, email, and the internet. This issue is particularly concerning for older adults, as it may increase their likelihood of developing mental health disorders, with depression being a primary concern. Although digital isolation has been studied less frequently than traditional social isolation, it may be a significant contributor to both the initiation and progression of depression in this population. Objective: This investigation seeks to assess longitudinal relationships between multidimensional digital disengagement (encompassing 4 dimensions: mobile device use, computer interaction, electronic correspondence, and web-based engagement) and incident depression among older adults, using longitudinal data from the nationally representative National Health and Aging Trends Study (NHATS). Methods: The analysis was conducted based on the NHATS dataset, a nationally representative longitudinal survey using multistage sampling to represent community-dwelling Medicare beneficiaries aged 65 years and older in the United States. We analyzed data from 2011 (Round 1) to 2018 (Round 8), including 8199 participants in the discovery and validation cohorts. Digital isolation was measured using a 4-item index based on self-reported nonuse of mobile phones, computers, email, and the internet. Participants were categorized into high (aggregate score ≥3) or low (aggregate score ≤2) digital isolation groups. Weighted Cox regression models with proportional hazards assumptions were used to quantify longitudinal associations between the digital isolation index (and its individual components) and incident depression, incorporating multivariable adjustment for sociodemographic characteristics (age, sex, and race or ethnicity), socioeconomic indicators (education level, family income, and marital status), and clinical profiles (tobacco use history and multimorbidity burden). Time-to-event analyses were visualized through Kaplan-Meier estimators, complemented by prespecified subgroup analyses evaluating effect modification patterns through interaction term testing. Results: A high level of digital isolation, as measured by the composite index, was associated with a significantly greater risk of incident depression (fully adjusted model: hazard ratio 1.35, 95% CI 1.18-1.55; P<.001). Furthermore, analysis of the individual components showed that nonuse of computers, email, and the internet was each significantly associated with a higher depression risk, whereas mobile phone isolation had a weaker, nonsignificant association. Conclusions: The study revealed a robust association between increased digital isolation and a higher likelihood of depression in the older population. These results underscore the importance of implementing tailored public health strategies to address digital isolation, especially for older adults. To minimize its detrimental effects on mental health, policymakers should encourage digital literacy programs and strengthen mental health services.
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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 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».