Buffering Effects of Internet Use on Caregiving-Related Health Impacts and Loneliness Among Older Informal Caregivers in California: Cross-Sectional Study
Bibliographic record
Abstract
Background: Loneliness has emerged as a global public health issue, with recent data indicating that 27.6% of adults aged 65 to 80 report feelings of loneliness despite the postpandemic resumption of social activities. Older caregivers face unique challenges that may exacerbate feelings of loneliness due to the demanding nature of caregiving responsibilities. While internet use has been suggested as a potential intervention to reduce loneliness, its moderating effect on the relationship between caregiving-related health effects and loneliness remains understudied. Objective: This study aims to investigate: (1) the association between caregiving-related health effects and loneliness among older informal caregivers; (2) the relationship between internet use frequency and loneliness; and (3) whether internet use moderates the association between caregiving-related health effects and loneliness. Methods: We analyzed cross-sectional data from the 2019-2020 California Health Interview Survey, focusing on 3957 informal caregivers aged 65 and older. Loneliness was measured using a modified 3-item UCLA Loneliness Scale. Health effects of caregiving were assessed by self-reported physical or mental health problems due to caregiving responsibilities. Internet use frequency was measured on a 4-point scale. Multivariable linear regressions were used to test the study aims, adjusting for sociodemographic factors, health status, and caregiving-context characteristics. Results: Among participants, 475 (12.0%) reported experiencing physical or mental health problems due to caregiving responsibilities. After adjusting for covariates, caregivers who experienced health problems related to caregiving reported higher levels of loneliness compared to those who did not (β=0.76, SE .07, P<.001). More frequent internet use was associated with a lower level of loneliness (β=-0.11, SE 0.03, P<.001). Additionally, internet use significantly moderated the relationship between caregiving-related health effects and loneliness (β=-.16, SE 0.07, P=.02), suggesting that the negative impact of caregiving-related health effects on loneliness was attenuated among caregivers who used the internet more frequently. Conclusions: Caregiving-related health effects are associated with increased loneliness among older informal caregivers, but more frequent internet use may both directly reduce loneliness and buffer against the adverse impact of caregiving on loneliness. These findings align with recent research highlighting the potential of technology-based interventions to combat social disconnection among older adults. Health care providers and policy makers should consider implementing programs that enhance internet access among older caregivers as part of comprehensive strategies to address loneliness in this vulnerable population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".