Hypothesis

In healthy, non‑diabetic adults, a decision rule that flags days when sleep efficiency <85%, HRV RMSSD drops >15% from baseline, and post‑prandial glucose variability (CV) >20% predicts a measurable decline in next‑day sustained attention (≥10% increase in reaction time variability on the Psychomotor Vigilance Test). Providing this alert and prompting a simple behavioral adjustment (e.g., advancing bedtime by 30 min, avoiding high‑glycemic carbs after 18 h, or performing 5 min of paced breathing) will reduce the flag’s occurrence and improve PVT performance compared to passive tracking alone.

Mechanistic Rationale

• Sleep → Insulin Sensitivity: Even one night of fragmented sleep reduces hepatic insulin sensitivity via increased sympathetic tone and altered circadian cortisol, raising post‑meal glucose excursions【https://mhealth.jmir.org/2023/1/e50983】.
• Glucose Variability → Autonomic Balance: Acute glucose spikes trigger oxidative stress in vagal afferents, decreasing HRV (RMSSD) through reduced parasympathetic outflow【https://pmc.ncbi.nlm.nih.gov/articles/PMC12007415】.
• HRV → Sleep Architecture: Low parasympathetic activity impairs the transition to deep NREM sleep, creating a vicious loop where poor sleep further lowers HRV【https://pmc.ncbi.nlm.nih.gov/articles/PMC9666953】. Thus, the trio forms a closed‑loop feedback system; breaking any link (e.g., by stabilizing glucose) should improve the others.

Testable Protocol (n=1 crossover)

1. Baseline (14 days): Wear a validated sleep tracker (e.g., Fitbit Sense), CGM (Dexcom G6), and HRV chest strap; collect sleep efficiency, HRV RMSSD, and glucose CV each night.
2. Rule‑building: Compute individualized thresholds (baseline‑minus‑1 SD) for each metric.
3. Intervention phases (2 × 21 days) in random order:
   • Alert+Action: When all three thresholds are breached, receive a smartphone notification prompting a pre‑planned countermeasure (earlier bedtime, low‑GI snack, 5 min paced breathing). Log adherence.
   • Passive: Receive only a daily summary dashboard; no prompts.
4. Outcome measurement: Each morning after the night’s data, complete a 5‑min PVT; primary metric = reaction time variability (RTV).
5. Analysis: Compare mean RTV between Alert+Action and Passive phases using paired t‑test; also test whether the composite alert predicts ≥10% RTV increase in the subsequent Passive days (logistic regression).

Falsifiability

If the Alert+Action condition does not produce a statistically significant reduction in RTV (p>0.05) and the composite alert fails to predict next‑day RTV changes, the hypothesis is falsified. Conversely, a significant improvement supports the claim that multimodal, rule‑based feedback can translate self‑tracked data into tangible cognitive benefits in healthy individuals.

Broader Implication

Demonstrating a causal chain would justify investing in personalized calibration and closed‑loop interfaces for consumer wearables, shifting the field from passive monitoring to active, physiology‑driven optimization.