3,923 continuous-glucose readings over 14 days in August 2026, asked what they do as a function of time of day. They are strongly clock-aligned: about 107 mg/dL between the 05:00 trough and the 11:00 peak. He falls through the night, climbs steeply after 08:00, and shows no early-morning rise at all. Every result here is a lead rather than a finding, and the last section says what would change that.
Five sections: the shape of his day, three ways of measuring it, what was done to try to break the result, the overall level for context, and one thin lead reported with its fragility.
Is glucose aligned to the clock at all? Yes.
His glucose is lowest around 05:00 and highest around 11:00 — a swing of about 107 mg/dL across the day.
Each point is the mean across the 14 days of that clock hour's readings. The band is a 95% bootstrap interval <b>resampling days</b> — how much this shape would move had he worn the sensor on a different fortnight. Dexcom's own report shades the spread of readings instead, which looks similar and answers a different question. 107 mg/dL against a null that reaches 63 only one time in twenty (p<0.0001, 13 fully covered days).
| hour | glucose | interval | |
|---|---|---|---|
| trough | 05:00 | 189 mg/dL | 175 – 214 |
| peak | 11:00 | 296 mg/dL | 271 – 315 |
| swing | — | 107 mg/dL | null median 43, 95th pct 63 |
<b>The shape has two parts.</b> Overnight he <i>falls</i> — from 244 at midnight to 194 by 05:00, steadily, with no early-morning rise. Then from 08:00 he climbs hard, gaining 95 mg/dL in four hours to the 11:00 peak. A second, smaller rise arrives around 21:00.
<b>This shows <i>when</i>, not <i>why</i>.</b> The late-morning peak is almost certainly breakfast and the evening bump almost certainly dinner, but nothing in this export records a meal, so both are guesses. Logging meal times for two weeks would settle it, and is the single most valuable change available.
How big is the day–night difference, and is there a dawn effect? Big, and no.
His waking day averages 49 mg/dL above his night. But there is no early-morning rise: between 03:00 and 08:00 he is still drifting <i>down</i>.
Each row is one within-day contrast, tested across days with a sign-flip permutation. Intervals are normal approximations on the day-level standard deviation; the p-values are the exact permutation values. The day–night gap is large and reliable. The dawn slope is -1.4 mg/dL per hour with p=0.24 — reported as a null, because a tested absence is worth more than a silence.
| contrast | effect | days | p |
|---|---|---|---|
| Waking day (08–22) − night (00–06) | +49.1 | 13 | 0.0010effect found |
| Dawn rise (06–08 mean − 00–05 trough) | +7.4 | 14 | 0.0015effect found |
| Dawn slope, 03:00–08:00 (per hour) | -1.4 | 14 | 0.2437no effect found |
<b>The absent dawn effect is the interesting one.</b> A classic “dawn phenomenon” is a rise in the hours before waking, and it is what the first subject in this project shows. Alan does the opposite: he keeps falling until roughly 08:00, and his rise begins after that. Whatever drives his mornings starts when the day does, not before it.
Could the sensor itself have produced this? No.
The shape survives dropping either sensor, and survives discarding the first 24 hours of each one.
The average day computed separately on each of the two transmitters. The transmitter changed once, mid-window, so these are two separate pieces of hardware measuring the same person — a stronger split than a calendar one, because it varies the instrument as well as the days. The swing ranges 97–116 mg/dL across the two leave-one-out fits and the peak does not move from 11:00.
| attack | result | p |
|---|---|---|
| drop sensor 1 | swing 97 mg/dL, peak 11:00 | 0.00625effect found |
| drop sensor 2 | swing 116 mg/dL, peak 11:00 | 0.00050effect found |
| drop 24 h after each sensor start | day−night +47 mg/dL (12 days left) | 0.0042effect found |
| is it just his level, not a shape? | r=-0.81 against his overnight mean | — |
<b>Dexcom records the hardware change that Libre's export does not.</b> The transmitter id changes when the sensor does, so these wear periods are known rather than inferred from gaps — which is why this check is stronger here than the equivalent one on the first subject, where sensor boundaries had to be guessed. The day-night gap is larger on days he runs lower overnight (r=-0.81), so it is not a restatement of his level.
Two sensors is the smallest number that permits this test at all. It rules out one sensor having invented the pattern; it cannot rule out a fortnight that was unusual for other reasons.
The level, stated plainly and not interpreted
Average 242 mg/dL over the 14 days, with 12% of readings between 70 and 180.
The same average day, with his own fortnight mean marked instead of the 180 line. Every figure reproduces Dexcom's own report to within 0.4 — which is what says the file is being read correctly.
| figure | this analysis | Dexcom Clarity |
|---|---|---|
| mean mg/dL | 241.9 | 242.0 |
| gmi % | 9.1 | 9.1 |
| sd mg/dL | 51.0 | 51.0 |
| cv % | 21.1 | 21.1 |
| in range 70 180 % | 12.4 | 12.0 |
| low 54 69 % | 0.0 | 0.0 |
| very low % | 0.0 | 0.0 |
| very high % | 43.6 | 44.0 |
| high 181 250 % | 43.9 | 44.0 |
<b>These numbers are reported, not interpreted.</b> This apparatus reports associations; what a glucose level means for a person is a clinical question and belongs to his doctor, not to this page. The figures are here because they are his, because they are the context for everything above, and because reproducing the vendor's own arithmetic is how a reader knows the parsing is right.
Does the day of the week matter? Possibly — but do not act on it yet.
Weekend days averaged 29 mg/dL lower than weekdays (p=0.02) — on 4 weekend days.
One dot per day. Bars are 95% bootstrap intervals on each group's mean. Statistically this clears the usual threshold. Practically it rests on 4 weekend days inside a single fortnight, which is not enough to separate “weekends are different” from “those two weekends were different”.
| days | mean glucose | |
|---|---|---|
| weekday | 10 | 250 mg/dL |
| weekend | 4 | 222 mg/dL |
<b>Reported because it was tested, flagged because it is thin.</b> This was one of several exploratory comparisons and it has not been corrected for that. The honest status is a lead worth re-testing on the next sensor, not a finding — and it is exactly the kind of result a longer wear would settle cheaply.
When each stream of data actually exists. A stream that stops before the analysis window opens is greyed out: it is in the files, but it cannot speak to this question.
One continuous fortnight of wear across two transmitters. The final day ends mid-afternoon, where the export was taken.
What each source contributes to this question — and, where it matters more, what it cannot contribute. Every item links back to the audit page for the file it comes from.
A Clarity CSV export covering 2026-08-15 to 2026-08-28. Names and device serials were removed before analysis; timestamps and glucose values are untouched. See the data audit →
Validated against Dexcom's own Overview report: mean 242 vs 242, GMI 9.1 vs 9.1, CV 21.1 vs 21.1.
Pseudonymised to T1/T2 rather than deleted, which preserves the sensor boundary. That is what makes the leave-one-sensor-out check possible.
His alarm thresholds and device models. Not measurements — a glucose column on an Alert row is a setting, and reading the file without the EGV filter would treat it as a reading.