Introduction

A wrist-worn device gives you a nearly continuous record of physiology over months, far denser than any biospecimen schedule. The biology (a metabolomics panel, a biomarker, a drug concentration) is measured a handful of times. A lot of the work in linking the two is deciding what the continuous stream means as a variable before any model sees it.

These notes cover three decisions: how to aggregate a stream into an exposure, how to line that exposure up with a sparse sample, and what within-day structure does to an association if you ignore it.

What a wearable measures

Very little coming off a consumer or research-grade wearable is a direct measurement of what it’s named after.

• Steps are an accelerometer signal run through a proprietary step-detection algorithm. Two devices disagree with each other, and one device can disagree with itself after a firmware update.
• Sleep stages are inferred from movement and heart rate; this isn’t polysomnography. Total sleep time is estimated reasonably well, but stage-level output is much weaker, and I’d treat REM/deep splits as device-specific constructs rather than physiology.
• Heart rate variability comes from an optical pulse signal, so strictly it’s pulse rate variability, and it’s sensitive to motion and perfusion. The reported metric is often RMSSD over a fixed nocturnal window, which is one choice among several and not a standard.
• Resting heart rate is a daily summary from a vendor algorithm whose definition of “resting” is undocumented and can change.

So a wearable variable is much safer to compare within a device and firmware version than across them. Either put device model in the model as a covariate or restrict the cohort to one device. It also helps to think of these as digital phenotypes: reproducible constructs with some biological meaning, but not gold-standard measurements. That’s enough for association work but not for claims about mechanism.

Missing data

A wearable records nothing when it’s off the wrist, and people take it off for reasons: charging, showering, discomfort, illness, travel, losing interest in the study. That is missing-not-at-random in the most direct way, because non-wear is correlated with the states you’re studying.

At minimum, set a wear-time rule in advance. A day counts only if wear time is above some threshold (10 or 16 hours are common; nocturnal metrics need the night itself covered), and a participant-period counts only if it has enough valid days. These thresholds matter, so report how many participant-days, and which participants, each rule removed. If the cohort goes from 60 participants to 34 by the time you fit the model without anyone saying so, the analysis is no longer about the cohort in the methods section.

Check whether valid-day counts differ by group. If they do, the missingness is informative and the complete-case analysis is biased. State the likely direction of the bias even if you can’t correct it.

From stream to exposure

The model needs one number per sample and the sensor gives thousands. Most of the analytic freedom is in this aggregation step, so fix it before looking at outcomes. There are four choices:

1. Window: how much time before (or around) the biological sample counts. The preceding night, 24 hours, 7 days. Match it to the biology’s timescale; a metabolite with a half-life of hours shouldn’t be regressed on a 7-day activity average.
2. Summary: mean, median, total, time above a threshold, or a variability measure like the standard deviation across days. This is the same idea as exposure metrics in PK, where $AUC$, $C_{max}$ and time above target are three summaries of one curve that answer different questions.
3. Reference: the absolute value, or the deviation from the participant’s own baseline. The deviation is usually better, because between-person differences in resting heart rate are much bigger than the within-person changes you care about. That’s what person-mean centring gives you:

Putting both terms in separates the within-person effect from the between-person one. Using only the raw value estimates a mix of the two and reports it as one effect.

1. Lag: whether the exposure comes before the sample, after it, or around it. Only “before” supports a directional reading, and only weakly.

The risk here is multiplicity that nobody counts. Three windows, four summaries and two references make 24 versions of the same association, and reporting whichever one reached significance is the garden of forking paths again. Pre-specify one primary aggregation. If you run the others, call them a sensitivity analysis and report where they disagree.

Diurnal structure

Both sides of the association vary over the day. Many plasma metabolites (cortisol-associated species, bile acids, amino acids after a meal) have systematic diurnal variation, and so does every wearable metric. If sample collection time differs between groups, time of day is a confounder that looks just like an effect.

The cheap fix is to record the clock time of sampling and adjust for it. Two harmonic terms handle a daily rhythm without using many degrees of freedom:

This is a cosine with amplitude $\sqrt{\beta_c^2 + \beta_s^2}$ and phase $\arctan(\beta_s / \beta_c)$. It’s linear in its parameters, so any mixed model can estimate it. A second harmonic ($2\pi t / 12$) picks up a bimodal shape, such as a second peak after a meal.

Describing diurnal variability is also a result in itself, and often a more reliable one than the cross-sectional association: which analytes have a detectable within-day rhythm, how large it is, and its phase relative to sleep offset. That question actually uses the dense side of the data. A single-timepoint association mostly doesn’t.

The model

With repeated visits per participant, start with a random intercept per participant:

Here $y_{ij}$ is the log-transformed analyte, $x$ is the aggregated wearable exposure split into within- and between-person parts, $f(t_{ij})$ are the diurnal terms, and $\mathbf{z}$ holds covariates (age, sex, BMI, device model, and season if the study runs long enough).

On interpretation: $\beta_1$ is a within-person effect. It describes what happens to the analyte when this participant’s activity or sleep moves away from their own usual level. That’s closer to a causal reading, and stable between-person traits don’t confound it. With a log-transformed analyte, $\beta_1$ is multiplicative (a unit change in exposure multiplies the analyte by $e^{\beta_1}$), so report it that way rather than as a raw coefficient on an arbitrary intensity scale.

Multiple testing

Thousands of analytes crossed with a few wearable metrics is a lot of tests. Run Benjamini–Hochberg within each wearable metric, not pooled across metrics or with unrelated contrasts, so the threshold stays interpretable.

Expect small effects. A within-person association between last night’s sleep duration and a plasma metabolite is a modest multiplicative shift, and a study powered for a large effect will find nothing. The analytes are also correlated, often because they sit in the same pathway or are adducts of one compound, so 50 significant features may be far fewer distinct findings. Collapse to compounds or pathways before counting.

The findings that replicate tend to be the ones that hold across the sensitivity aggregations, which aren’t necessarily the ones with the smallest $q$-value in the primary analysis.

Summary

Linking wearables to molecular data is mostly a problem of defining the measurement. Work out what the device is reporting, set a wear-time rule and report what it removes, fix one aggregation window and summary before looking at outcomes, split within- from between-person variation, adjust for time of day on both sides, and control error within each metric. The sensor stream is where most of the information is, and a single cross-sectional correlation wastes most of it; looking at within-day and within-person structure makes better use of it. The note on untargeted metabolomics covers the other side of the join.