Long term monitoring shows phase lagged behaviorally driven locomotor autonomic coupling
Bibliographic record
Abstract
The temporal coordination between behavioral and autonomic rhythms is a critical feature of circadian physiology, yet the precise alignment and causal structure of this coupling remain poorly characterized in free-living humans. Using long-term wearable data spanning 30 days from 52 individuals, we analyzed accelerometry (ACC) and heart rate (BPM, via IBI) to quantify circadian phase alignment, inter-day stability, and temporal directionality between locomotor and autonomic systems. Across individuals, behavioral activity rhythms consistently peaked earlier than autonomic rhythms (mean lag: -1.8h, p < 0.001), with the lag largely attributable to greater variability in locomotor phase. Despite this temporal dissociation, both signals exhibited coherent 24-h patterns and relatively stable inter-day acrophases. Lag magnitude was negatively correlated with nighttime BPM (r = -0.55, p < 0.001), suggesting a link between autonomic hyperactivation and desynchrony. Crucially, behavioral acrophase more strongly predicted daily lag fluctuations than BPM acrophase, and causal analyses revealed asymmetric dependencies: same-day activity levels were significantly predictive of nighttime heart rate, whereas the reverse relationship was weaker and less consistent. Granger causality confirmed: a predominant flow from ACC to BPM across subjects (p = 0.0045). These findings establish that autonomic rhythms lag behind and are shaped by preceding behavioral activation, supporting a behavior-first model of internal circadian organization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".