Digital Isolation and Depression Risk in Older Adults Using the National Health and Aging Trends Study Database: 8-Year Longitudinal Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".