Long-Term Changes in Daily Stress Reactivity as a Mediator Between Changes in Health and Well-Being Across 20 Years
Bibliographic record
Abstract
Abstract The daily within-person association between stress exposure and negative affect (i.e., stress reactivity) has been shown to be predictive of adverse health and well-being outcomes. These findings typically rely on a single burst of daily assessments. Recent findings have shown that long-term changes in daily stress reactivity across adulthood are associated with changes in health outcomes. The present study extends this work by examining whether long-term changes in daily stress reactivity mediates the association between longitudinal changes in functional health and life satisfaction across 20 years. We used measurement burst data from the National Study of Daily Experiences (N = 2,880) embedded within the MIDUS longitudinal study. Three measurement bursts were separated by ten years, with each containing daily measures of stress and affect across eight consecutive days, yielding 33,942 days of data across 20 years of adulthood. Functional health and life satisfaction were also measured every 10 years. Multilevel SEM were fit to simultaneously model daily within-person associations between stress and affect (i.e., stress reactivity) at Level 1; long-term changes in stress reactivity at Level 2; and the mediational effect of changes in stress reactivity on the pathway of changes in functional health predicting changes in life satisfaction at Level 3. Declines in functional health predicted declines in life satisfaction across 20 years (estimate=1.12, SE = 0.37, p<.001). Importantly, changes in stress reactivity mediated this effect (indirect effect=17.51, SE = 8.40, p=.02). Results suggest that the link between declines in functional health and life satisfaction may be accounted for by increases in daily stress reactivity.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".