Effects of Chronic and State Loneliness on Heart Rate Variability in Women
Bibliographic record
Abstract
BACKGROUND: Loneliness, the subjective experience of social isolation, represents one of the largest risk factors for physical illness and early death in humans. However, the mechanisms by which loneliness leads to adverse health outcomes are not well understood. PURPOSE: In this study, we examined altered parasympathetic nervous system function as a potential pathway by which chronic loneliness and state loneliness may "get under the skin" to impact cardiovascular physiology. METHODS: In a controlled laboratory setting, vagally mediated resting heart rate variability (HRV), HRV reactivity to an induction of state loneliness, and HRV reactivity to a cognitive challenge task were assessed in a sample of 316 healthy women (18-28 years). RESULTS: Greater chronic loneliness in women predicted lower resting HRV, an independent risk factor for cardiovascular disease and all-cause mortality, after controlling for demographic, psychosocial, and health behavior covariates. Furthermore, women higher in chronic loneliness experienced significantly larger increases in HRV to state loneliness and reported significantly higher levels of negative affect immediately following state loneliness, compared with their less chronically lonely counterparts. Chronic loneliness also predicted blunted HRV reactivity-a maladaptive physiological response-to cognitive challenge. CONCLUSIONS: The current findings provide evidence that chronic loneliness is associated with altered parasympathetic function (both resting HRV and HRV reactivity) in women, and that the immediate experience of state loneliness is linked to a proximate increase in HRV among chronically lonely women. Results are discussed in terms of implications for cardiovascular health and the evolutionary functions of loneliness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".