How we try to make the signal fail

The first question is whether anything survives.

The formal component of this work establishes properties of a definition. Whether this definition accurately reflects a living body is an empirical question. This report outlines our approach to addressing this question with methodological rigor and self-critical evaluation.

The study employs a longitudinal design, linking each DRTT scan to the corresponding day's physiological signals from a wearable device, including standard health metrics such as sleep timing, walking speed, sleep score, blood oxygen, among others. The objective is to determine whether the packet-native measurements produced by our instrument covary with these physiological indicators. Currently, the study involves a single subject observed over several weeks, with approximately forty aligned scan-and-wearable days collected to date. This investigation remains in its early stages. The present findings do not constitute clinical validation or confirmed results. The following sections detail the methodology and provide a transparent account of the study's current status.

Why the first pass is deliberately strict

Single-subject daily longitudinal studies are particularly susceptible to detecting spurious signals. Several factors contribute to this risk: autocorrelation between days can cause yesterday's measurements to predict today's regardless of genuine effects; wearable signals are consumer-derived and may be smoothed; numerous packet metrics are tested; physiological metrics are reused across multiple comparisons; both series may share slow calendar trends; and weekly data may include regime shifts. Each of these factors can generate apparent structure from noise.

Therefore, the initial inquiry is whether any relationships persist after applying a screening process specifically designed to eliminate false structure. The initial screening was intentionally stringent. Only relationships that withstand such rigorous criteria are considered meaningful.

The survival gate

Before any relationship is allowed to count as a survivor, it has to clear five independent checks:

• Effective sample size. Because the days are autocorrelated, the true number of independent observations is smaller than the calendar count; a relationship has to hold on that reduced effective N.
• Circular-shift null. We slide the wearable signal in time relative to the scans and re-test. A real relationship weakens when its dates are pulled out of alignment; one that holds up under the shuffle was riding on the calendar all along.
• Trend robustness. We remove the shared slow drift from both series and require the relationship to keep its sign and a meaningful size afterward, so what remains is a real dependence with the calendar's common trend taken out.
• Differenced consistency. We require the day-to-day changes to move together as well, so what we are seeing is genuine movement in state, holding even once the standing levels are set aside.
• Sign and split-half stability. Two different correlation measures have to agree in sign, and the relationship has to keep its sign across the first and second halves of the data.

Only after all five does a relationship face a further, conservative correction for the sheer number of comparisons made.

The null hypothesis underlying the shift test is designed to accurately account for dependence. For each simulated replicate, each wearable metric is shifted once in time, and that same shift is applied wherever the metric appears. This approach preserves the metric's autocorrelation and its reuse across multiple comparisons while disrupting only the alignment between scan dates and physiological dates. The test thus evaluates how many relationships would survive if the physiological signals retained all inherent structure but were decoupled from the scan data. This constitutes a more stringent and relevant null hypothesis than random noise.

The preregistration boundary

There is one more discipline, and it is the one that separates honest tracking from wishful confirmation. The first stretch of days — the ones used to find the hypothesis — cannot also be used to confirm it. So the primary construct is preregistered with its exploratory window excluded, and confirmation requires a disjoint set of at least fifty aligned scan-days drawn only from later. Those discovery days did their job; they are not permitted to do it twice.

What survived, and what didn't

Here is the geography of the broad surface — the full set of relationships the screen was pointed at, grouped by scope:

Of those 5,487 relationships, 83 cleared all five rungs of the survival gate. After the correction for multiple comparisons, the number of broad individual relationships allowed to make a standalone claim was zero. A further 2,233 showed a tentative trend or a consistent sign and then failed a stricter rung or the correction — noticed, and not promoted.

The 83 surviving relationships fall within the range expected by the dependence-preserving null hypothesis; the global omnibus test does not reach statistical significance (p ≈ 0.17). Thus, the broad set of relationships is not globally associated with the wearable data. If the instrument and wearable merely shared calendar drift or smoothing effects, widespread correlations would be observed. The absence of such widespread associations underscores the significance of the subsequent findings.

Where signal does concentrate, it concentrates narrowly, and in physically coherent places. At the family level, the one grouping carrying a significant excess of survivors is pair-lag mismatch — the agreement of timing and lag between separated sites of the body. At the level of preregistered physical constructs, two survive the screen: site-timing / phase agreement — the organization of timing, lag, and amplitude across sites — tracks circadian and gait rails (mid-sleep timing, walking speed), across several related metrics and in day-to-day change rather than as one isolated correlation; and laminar sleep-recovery support — a route narrower than the broad laminar family, which is itself null and sits slightly below its null expectation — tracks sleep score, mid-sleep timing, and blood-oxygen minimum.

This distribution of results is critical. A broad set of relationships that largely fails to show significant associations, with signal persisting only in two narrow and physically interpretable domains, is a more robust outcome than widespread, indiscriminate correlations.

What we are not claiming

Given the potential for overinterpretation in studies of this nature, it is important to delineate the study's boundaries explicitly. This work does not constitute clinical validation. It does not demonstrate that DRTT provides diagnostic capability, nor does it establish that wearable data validate scan results. The preregistered confirmatory analysis, which requires a separate confirmatory window, is not yet complete. The broad laminar family does not correlate with the wearable data, and no such claim is made. The most accurate characterization is that of an interim, preregistered tracking signal that has survived a rigorous artifact-discrimination process at the construct level, with broad and individual claims appropriately constrained pending further confirmation.

The road to confirmation

The next objective is to continue collecting invariant-preserved daily scans until the disjoint confirmatory window achieves its fifty-day target, which is projected to occur by early August 2026. At that stage, the preregistered confirmatory analysis will be conducted as specified, using data that have not previously informed the hypothesis. Until then, the current findings are presented with the level of confidence warranted by the evidence: a stringent initial analysis that largely yields negative results, with a narrow and coherent signal persisting through rigorous testing.

The empirical companion to four notes on the formal side of the work: What is genuinely new, A boundary-observable certification algebra, The temperament of a bespoke algebra, and The making of an observable.