Hypothesis

In non‑diabetic individuals, the standard deviation of nocturnal RR‑intervals (SDNN) measured during the first 90 minutes of sleep predicts the coefficient of variation (CV) of interstitial glucose the following day, beyond what is explained by average sleep duration or bedtime.

Rationale

• Population studies show low HRV predicts mortality (HR=2.27) and higher HRV predicts COVID‑19 survival (HR=0.53), indicating that autonomic balance is a potent biomarker of physiological resilience.
• Consumer sleep trackers give reliable total sleep time and circadian timing but misstage REM/deep sleep (macro F1 ≈ 0.69)3 and perform worse with age or pathology4; nevertheless, they capture stable epochs of low‑movement that correlate with parasympathetic dominance.
• Mechanistically, vagal output suppresses hepatic gluconeogenesis via the hepatic vagal afferent‑efferent loop; fluctuations in vagal tone during sleep therefore modulate overnight glucose production and set the hepatic ‘set‑point’ for morning insulin sensitivity.
• Because inter‑individual variability in HRV‑glucose coupling is high, population‑level thresholds fail to translate to personal predictions. A personalized calibration curve—derived from the first 7 days of concurrent HRV, sleep‑epoch, and CGM data—can capture each person’s unique autonomic‑metabolic gain.

Testable Prediction

For a given participant, a linear mixed‑effects model with glucose CV as the outcome, nocturnal SDNN as a fixed effect, and participant‑specific intercept and slope as random effects will show a significant negative slope (higher SDNN → lower glucose CV) after controlling for total sleep time and bedtime (p < 0.05). The model’s random‑slope variance should be > 0, confirming meaningful inter‑individual differences in the HRV‑glucose relationship.

Falsifiability

If, after ≥ 14 days of data collection, the fixed‑effect slope is not significantly different from zero (or is positive) and the random‑slope variance does not exceed the residual variance, the hypothesis is falsified. Additionally, shuffling the nightly SDNN values across days should abolish any predictive signal, confirming that the observed association is not an artifact of autocorrelation.

Implementation Sketch

1. Collect nightly HRV (SDNN) from a chest‑strap or validated PPG device during the first 90 min of sleep.
2. Derive total sleep time and midpoint from the consumer tracker (use only these robust metrics).
3. Download CGM data; compute glucose CV for the waking period (08:00–20:00).
4. Fit the mixed‑effects model using lme4 in R or statsmodels in Python.
5. Examine fixed‑effect estimate, p‑value, and variance components.

Expected Outcome

A personalized negative coupling would allow individuals to use their nightly HRV as a leading indicator of next‑day glycemic stability, enabling timely behavioral adjustments (e.g., carbohydrate modulation, light exercise) before glucose excursions become clinically relevant.