Implicit Affect, Heart Rate Variability, and the Metabolic Syndrome
Bibliographic record
Abstract
OBJECTIVE: Greater negative affect has been associated with an increased risk of the metabolic syndrome (METs). However, all studies to date have examined this association using explicit affect measures based on subjective ratings of emotional experiences. Prior studies suggest that implicit affect, representing the automatic, prereflective appraisal process involved in conscious emotional experiences, is associated with physiological stress responses independent of explicit affect. Furthermore, low resting heart rate variability (HRV) may increase the risk of stress-related diseases. The goals of this study were to evaluate the associations between implicit and explicit affect and METs and to assess whether these associations were amplified by lower HRV. METHODS: This secondary analysis of a larger study included 217 middle-aged women who completed measures of implicit affect, explicit affect, high-frequency HRV, and the different components of METs. RESULTS: There was a significant interaction between implicit negative affect and HRV predicting METs (odds ratio = 0.57, 95% confidence interval = 0.35-0.92), such that the combination of higher implicit affect and lower HRV was associated with a greater likelihood of METs. Similarly, there was a main effect of implicit negative affect as well as an interaction between implicit negative affect and HRV on the lipid accumulation product (b (standard error) = -0.06 (0.02), 95% confidence interval = -0.11 to -0.02), a combination of waist circumference and triglycerides. CONCLUSIONS: Higher implicit negative affect in the context of lower HRV may be related to a greater risk of METs. The present findings highlight the relevance of including implicit affect measures in psychosomatic medicine research.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".