Social network characteristics and levels of fluctuations in momentary depressive symptomatology among older adults
Bibliographic record
Abstract
BACKGROUND: Social networks are known to protect against depressive symptoms in older adults. However, most research relies on retrospective self-reported depression measures and cross-sectional data, which may introduce bias. Ecological momentary assessment with longitudinal data overcomes these limitations by repeatedly measuring the subject's experience in the present moment. This study examined how social network characteristics relate to momentary depressive symptoms and their daily fluctuations in older adults. METHODS: We analysed data from 216 older adults in Paris, France, using the Healthy Aging and Networks in Cities and Promoting Mental Well-Being and Healthy Aging in Cities studies. Social network characteristics included network size and frequency of in-person and digital interactions per week. Depressive symptomatology was assessed using a daily smartphone survey of the Center for Epidemiological Studies-Depression over a week. Linear mixed-effect models estimated associations between social network characteristics and momentary depressive symptoms, while multivariable linear models examined relationships with daily symptom fluctuations. RESULTS: Network size and frequency of contact from digital communications per week were not associated with fewer depressive symptoms; however, there was suggestion that having more in-person contact was related to fewer depressive symptoms (exp(β) = 0.90, 95% CI 0.82 to 1.00). Moreover, having a larger social network (exp(β) = 0.91, 95% CI 0.85 to 0.98) and more in-person contacts (exp(β) = 0.96, 95% CI 0.93 to 0.98) were associated with less fluctuations in daily depressive symptoms, but no association for the frequency of contact from digital communications was observed. CONCLUSION: Findings from this study suggest that larger social networks and more in-person contact may promote more stable and better mental health among older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".