Habitual lifestyle timing explains circadian timing, but daily lifestyle changes do not, in free-living humans across 2000 days

2026-06-26

AI Summary

This study investigates how lifestyle factors modulate the timing of individual circadian peaks under real-life conditions, disentangling the relative contributions of stable long-term habits (trait) and short-term fluctuations (state). Leveraging nearly 2,000 days of multimodal physiological data from wearable devices—including activity-corrected heart rate and continuous glucose monitoring—the authors employed linear mixed-effects models to decompose sleep, diet, and physical activity into their trait and state components. Results reveal that lifestyle traits account for 42.3% of circadian variation, with 86.5% of this attributable to inter-individual differences, whereas state-level factors explain only 0.9% (1.8% within individuals). This work provides the first quantification of these contributions in free-living settings, demonstrating that enduring lifestyle habits are a primary determinant of circadian phase and offering a foundation for precision chronotherapeutics.

Abstract

Background: Both between- and within-subject variations in circadian timing matter for health. If lifestyle changes could be used to regulate circadian timing, they would offer accessible and scalable routes to chronotherapy, but this link remains unclear under real-life conditions. Here, we explore how lifestyle 'traits' (such as typical wake time) and 'states' (day-to-day deviations from traits, such as waking up later than typical) explain between- and within-subject variation in acrophase (peak time) of the circadian rhythm of heart rate (CRHR). Methods: We collected free-living wearable data (smartwatch, continuous glucose monitor) from healthy volunteers for up to 4 weeks. The CRHR was derived from activity-adjusted heart rate, and acrophase was defined as time-of-day at daily CRHR peak. Sleep, food, and physical activity 'factors' were calculated and split into traits and states. Using a linear mixed-effects model, we tested how traits and states associate with between- and within-subject acrophase variance. Findings: Data from 105 healthy volunteers (66 female, age = 42.5 $\pm$ 15.7 years) spanning ~2000 days (18.8 $\pm$ 8.30 days each) were analysed. Traits were substantially more influential than states, explaining 42.3% versus 0.9% of total acrophase variance. Accordingly, traits explained 86.5% of between-subject variance, whereas states explained only 1.8% of within-subject variance. Sleep, food and physical activity factors contributed both jointly and uniquely, and lifestyle timing mattered most. Interpretation: Between-subject lifestyle traits explained acrophase better than within-subject lifestyle states. This asymmetry, alongside the considerable overlap between factors, supports sustained, holistic, timing-focused lifestyle adjustments as chronotherapy targets, testable through future interventional studies.

Problem

• circadian timing
• lifestyle traits
• lifestyle states
• acrophase
• chronotherapy

Innovation

• circadian rhythm
• lifestyle traits
• wearable data
• chronotherapy
• acrophase