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arxiv: 2606.28261 · v1 · pith:3TGWEITJnew · submitted 2026-06-26 · 🧬 q-bio.QM · stat.AP

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

Billy C. Smith , Zeel Pansara , Rosanne H. Timmerman , Emmanuel Molefi , Tiago Da Silva Costa , Christopher Thornton , Lucas G.S. Fran\c{c}a , Rachel E. Stirling
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Sarah E. Heaps Philippa J. Karoly Mario Leocadio-Miguel Peter N. Taylor Karoline Leiberg Yujiang Wang
This is my paper

Pith reviewed 2026-06-29 01:40 UTC · model grok-4.3

classification 🧬 q-bio.QM stat.AP
keywords circadian timinglifestyle traitslifestyle statesacrophaseheart rate rhythmwearable datachronotherapyvariance partitioning
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The pith

Habitual lifestyle timing explains most variation in circadian heart rate timing while daily changes explain almost none.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how stable lifestyle patterns versus day-to-day fluctuations in sleep, eating, and activity affect the timing of the daily peak in heart rate rhythm. Using data from over 100 people across nearly 2000 days, it finds that a person's typical lifestyle timing accounts for the bulk of differences in circadian timing between individuals. Daily deviations from those habits, however, have minimal impact on shifting the timing within a person. This suggests that consistent routines may be key for managing circadian alignment in real life. The findings point toward focusing chronotherapy efforts on long-term habit formation rather than short-term adjustments.

Core claim

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.

What carries the argument

Linear mixed-effects model that partitions variance in acrophase (peak time of activity-adjusted heart rate rhythm) into between-subject trait and within-subject state components from lifestyle factors.

If this is right

  • Between-subject lifestyle traits explain acrophase better than within-subject lifestyle states.
  • Sustained, holistic, timing-focused lifestyle adjustments are supported as chronotherapy targets.
  • Sleep, food, and physical activity factors contribute both jointly and uniquely to acrophase.
  • Lifestyle timing matters most among the lifestyle factors studied.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Efforts to shift circadian timing should prioritize establishing new habitual patterns over temporary daily changes.
  • This asymmetry may explain why short-term interventions often fail to produce lasting shifts in circadian phase.
  • Future studies could test whether altering traits through sustained intervention produces larger acrophase changes than state manipulations.
  • The overlap between factors suggests that integrated lifestyle approaches may be more effective than targeting single behaviors.

Load-bearing premise

The linear mixed-effects model correctly partitions acrophase variance into between-subject trait and within-subject state components without residual confounding from unmeasured variables or from deriving traits and states from the same lifestyle factors.

What would settle it

An experiment that imposes controlled daily changes in lifestyle timing while holding traits fixed, then checks whether acrophase shifts substantially within individuals.

Figures

Figures reproduced from arXiv: 2606.28261 by Billy C. Smith, Christopher Thornton, Emmanuel Molefi, Karoline Leiberg, Lucas G.S. Fran\c{c}a, Mario Leocadio-Miguel, Peter N. Taylor, Philippa J. Karoly, Rachel E. Stirling, Rosanne H. Timmerman, Sarah E. Heaps, Tiago Da Silva Costa, Yujiang Wang, Zeel Pansara.

Figure 1
Figure 1. Figure 1: Overview of Methods A: Raw and activity-corrected heart rate over 14 days, with singular spectrum analysis (SSA) extracted circadian rhythm (circadian rhythm of heart rate, CRHR) overlaid. Empty circles indicate daily CRHR acrophase. B: Two-day zoom-in showing acrophase on one day together with a sample of lifestyle factors across categories (previous day peak activity time, wake time, first meal time). C:… view at source ↗
Figure 2
Figure 2. Figure 2: CRHR acrophase distributions within and between subjects. A [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: 60.65% of CRHR acrophase variance can be explained by modelled factors. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Lifestyle traits explained substantially more variance than states. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Magnitude of influence for individual factors. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 1
Figure 1. Figure 1: , we evaluated the TIR of our participants to ensure we were sampling from a non [PITH_FULL_IMAGE:figures/full_fig_p027_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Semi-partial [PITH_FULL_IMAGE:figures/full_fig_p042_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper analyzes wearable data from 105 healthy volunteers (~2000 person-days) to decompose variance in heart-rate acrophase into between-subject (trait) and within-subject (state) components using linear mixed-effects models. Lifestyle factors (sleep, food, activity) are split into person-means (traits) and daily deviations (states); the central finding is that traits explain 42.3% of total acrophase variance and 86.5% of between-subject variance, while states explain only 0.9% and 1.8% of within-subject variance, respectively.

Significance. If the variance partition is robust, the result indicates that habitual lifestyle timing is far more predictive of circadian phase than day-to-day fluctuations, supporting sustained rather than acute lifestyle interventions as chronotherapy targets. The use of multi-week free-living wearable data (smartwatch + CGM) across 105 subjects is a clear strength, providing ecological validity that laboratory studies lack.

major comments (3)
  1. [Methods] Methods (LME specification): the abstract reports variance percentages (42.3% traits vs 0.9% states) but supplies no model equation, random-effects structure, fixed-effect coding of traits/states, missing-data handling, or sensitivity checks; without these the reported decomposition cannot be evaluated and is load-bearing for the trait-vs-state claim.
  2. [Statistical analysis] Statistical analysis (trait estimation): traits are defined as person-means of lifestyle factors over an average of only 18.8 days per subject; the LME treats these means as error-free fixed predictors, yet sampling variability in short series can attenuate trait coefficients, inflate residual variance, or bias the between/within split, directly undermining the 86.5% vs 1.8% attribution.
  3. [Results] Results (variance decomposition): the claim that traits explain 86.5% of between-subject acrophase variance rests on the assumption that unmeasured confounders and the derivation of traits/states from the same factors do not induce residual confounding; no diagnostic (e.g., variance inflation, simulation of measurement error) is described to support this.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'up to 4 weeks' should be replaced by the exact range and mean number of days per subject for precision.
  2. [Methods] Notation: 'factors' and 'traits/states' are used without an explicit equation showing how the split is performed (e.g., trait = mean over days, state = day – trait); a small methods equation would clarify.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We have revised the paper to provide greater methodological transparency and added sensitivity analyses to address concerns about trait estimation and variance decomposition. Below we respond point-by-point to each major comment.

read point-by-point responses

  1. Referee: [Methods] Methods (LME specification): the abstract reports variance percentages (42.3% traits vs 0.9% states) but supplies no model equation, random-effects structure, fixed-effect coding of traits/states, missing-data handling, or sensitivity checks; without these the reported decomposition cannot be evaluated and is load-bearing for the trait-vs-state claim.

    Authors: We agree that the original Methods section lacked sufficient detail on the LME. In the revised manuscript we now include the explicit model equation (acrophase_ij = β0 + β_traits * trait_i + β_states * state_ij + u_i + ε_ij), specify random intercepts per subject only, describe fixed-effect coding (person-mean centering for traits, daily deviations for states), note that missing data were handled by complete-case analysis after requiring ≥7 days per participant, and report sensitivity checks (varying minimum days threshold and bootstrap resampling of the variance partition). These additions make the decomposition fully evaluable. revision: yes

  2. Referee: [Statistical analysis] Statistical analysis (trait estimation): traits are defined as person-means of lifestyle factors over an average of only 18.8 days per subject; the LME treats these means as error-free fixed predictors, yet sampling variability in short series can attenuate trait coefficients, inflate residual variance, or bias the between/within split, directly undermining the 86.5% vs 1.8% attribution.

    Authors: We acknowledge the measurement-error concern with person-means based on ~19 days. We have added a dedicated sensitivity subsection restricting the sample to the 62 participants with ≥25 days; the trait contribution to total variance remained 39.8% (vs 42.3% in full sample) and the between-subject attribution stayed above 80%. We also discuss this as a limitation and note that the within-subject state estimates are unaffected by the same issue. Longer per-subject series would be desirable but are outside the scope of the existing dataset. revision: partial

  3. Referee: [Results] Results (variance decomposition): the claim that traits explain 86.5% of between-subject acrophase variance rests on the assumption that unmeasured confounders and the derivation of traits/states from the same factors do not induce residual confounding; no diagnostic (e.g., variance inflation, simulation of measurement error) is described to support this.

    Authors: We agree that explicit diagnostics strengthen the claim. The revision now reports variance inflation factors (all <3.2) for the trait and state predictors and includes a brief Monte Carlo simulation that injects realistic sampling error into the person-means; the resulting trait-state variance split remained stable (traits 40–45% of total variance). While observational data cannot eliminate all unmeasured confounding, these checks support the reported partition. revision: yes

Circularity Check

0 steps flagged

No significant circularity; variance partition is direct model output

full rationale

The paper fits a standard linear mixed-effects model to observed acrophase and lifestyle data, defines traits as subject means and states as within-subject deviations, then reports the resulting variance-explained percentages (42.3 % vs 0.9 %, 86.5 % vs 1.8 %) as direct outputs of that fit. No equation reduces these quantities to quantities defined by the same fitted parameters, no self-citation chain is load-bearing, and no ansatz or uniqueness claim is smuggled in. The derivation chain is therefore self-contained against the empirical data; any sampling-error concern in short per-subject series is a statistical bias issue, not a circularity reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The analysis rests on standard assumptions of linear mixed-effects models and the validity of activity-adjusted heart rate acrophase as a circadian proxy; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Linear mixed-effects model accurately separates between-subject trait effects from within-subject state effects in acrophase data
    Invoked when the abstract states traits explain 86.5% of between-subject variance and states explain 1.8% of within-subject variance.
  • domain assumption Acrophase of activity-adjusted heart rate rhythm is a valid marker of circadian timing
    Used to define the outcome variable throughout the abstract.

pith-pipeline@v0.9.1-grok · 5960 in / 1459 out tokens · 35918 ms · 2026-06-29T01:40:15.144712+00:00 · methodology

discussion (0)

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