PHPMem v2.0.1

Version
1.6.45
Uptime
18 days 10 hours 54 minutes 29 seconds

Memory

Total
512MB
Used
12,33MB (2.41%)
Free
499,67MB

Keys

Current
13 424
Total (since start)
40 994
Evictions
0
Reclaimed
762
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
2 / 1 024 max
Total
246 116
Rejected
0
llm:12c528c9a705580b88f3ef638a148054852c1bd13e8531528ad32f6a0d01748e
TTL 4 days 56 minutes 5 seconds Size 1,92KB Export
Edit
**The most counterintuitive finding is that late caffeine barely changes how long people sleep, but it strongly cuts deep sleep.** Across all 8,500 users, post-5pm caffeine has these correlations (step-7 correlation query): | Outcome | Correlation with caffeine after 5pm | |---|---| | Total sleep hours | -0.04 | | Deep sleep % | -0.34 | | Sleep latency | +0.20 | | Next-day fatigue | +0.18 | - **Duration versus quality:** The usual advice ("caffeine cuts your sleep") would predict a clear drop in hours. Instead, duration is essentially unaffected, and the damage shows up in sleep quality. Deep sleep is the most strongly caffeine-linked sleep-architecture measure here. - **Phone use dwarfs it:** Bedtime phone minutes correlate +0.86 with sleep latency, +0.71 with fatigue and -0.55 with sleep hours. Caffeine is a minor factor next to that. - **Fatigue tracks duration:** Fatigue is tied to short sleep (-0.88 with sleep hours) and to alarm snoozes (+0.88). Snoozing looks like a symptom of short sleep, not a cause. **Two other findings that go against expectation:** - **Exercise shows no link to sleep:** Physical activity has near-zero correlation with sleep hours (+0.004) and with fatigue (-0.03). - **Blue-light filters do little:** Filter users and non-users differ only slightly (step-3 grouped averages). | | Filter on (3,976 users) | Filter off (4,524 users) | |---|---|---| | Average sleep latency | 39.6 min | 41.6 min | | Average sleep | 6.33 h | 6.21 h | The correlations with latency (-0.06) and sleep hours (+0.04) are tiny. Screen brightness is also only weakly related to latency (+0.09). Phone time is what matters, not how the screen is configured. **Caveat:** These are observational correlations. They don't show causation, and I did not control for confounders such as occupation, chronotype or age.