Social Network Negativity and Physical Activity: New Longitudinal Evidence for Young and Older Adults 2015-2018
Bibliographic record
Abstract
Purpose: Physical activity (PA) has considerable public health benefits. Positive aspects of the interpersonal environment are known to affect PA, yet few studies have investigated whether negative dimensions also influence PA. This study examines the link between changing social network negativity and PA, net of stable confounding characteristics of persons and their environments.Method: Polling respondents in the San Francisco Bay Area over three waves (2015-2018), the UCNets project provides a panel study of social networks and health for two cohorts of adults. Respondents were recruited through stratified random address sampling, and supplemental sampling was conducted through Facebook advertising and referral. With weights, the sample is approximately representative of Californians aged 21-30 and 50-70. Personal social networks were measured using multiple name-generating questions. Fixed effects ordered logistic regression models provide parameter estimates. Results: Younger adults experience significant decreases in PA when network negativity increases, while changes in other network characteristics (e.g., support, size) did not significantly predict changes in PA. No corresponding association was found for older adults. Results are net of baseline covariate levels, stable social and individual differences, and select time-varying characteristics of persons and their environments.Conclusion: Leveraging longitudinal data from two cohorts of adults, this study extends understanding on interpersonal environments and PA by considering the social costs embedded in social networks. This is the first study to investigate how changes in network negativity pattern PA change. Interventions which help young adults resolve or manage interpersonal conflicts may have the benefit of helping to promote healthy lifestyle choices.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".