PHPMem v2.0.1
Version
1.6.45
Uptime
17 days 15 hours 1 minute 56 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
14 / 1 024 max
Total
239 725
Rejected
0
llm:c83d2f09e990579e5c5ce4f844bd516ddae6dabe86ccad622468844973ee83c2
Edit
**The most counterintuitive finding is that the usual sleep-hygiene fixes barely register in this data. Blue-light filters, screen brightness, late caffeine and exercise show almost no link to sleep duration, while the sheer minutes of bedtime phone use dominate.**
These are all linear correlations across 8,500 users from the correlation queries I ran (steps 0 and 1). The data is observational, so none of this shows cause and effect.
**What the data says about phone time:**
- Bedtime phone minutes correlate very strongly with sleep latency (r = 0.86) and next-day fatigue (r = 0.71).
- They correlate negatively with total sleep hours (r = -0.55).
**What the common remedies show:**
- **Blue-light filter:** The filter is essentially uncorrelated with sleep latency (r = -0.057) and total sleep hours (r = +0.039). It is the most widely recommended fix, yet users with it active look almost the same as users without it.
- **Screen brightness:** It has a very weak link to sleep latency (r = 0.09). Dimming the screen looks far less important than simply putting the phone down.
- **Caffeine after 5pm:** It has no linear relationship with total sleep hours (r = -0.038) and only a modest one with latency (r = 0.20). Its clearest footprint is on deep sleep (r = -0.34), so it seems to hurt sleep quality rather than quantity.
- **Physical activity:** It is essentially unrelated to sleep hours (r = 0.004) and fatigue (r = -0.029), despite being a staple of health advice.
**Why it matters:** The pattern points to one dominant driver, which is how long people spend on the phone in bed. If so, a product that adds a blue-light filter or a caffeine cutoff may do little unless it also reduces phone time. Fatigue also tracks sleep hours almost one-to-one (r = -0.88) and alarm snoozes (r = 0.88), so it looks like a downstream symptom rather than an independent outcome.
**Caveat:** Correlations only capture linear effects. A filter or caffeine effect that applies only to some subgroups, such as certain chronotypes or heavy phone users, could be hidden. The pre-aggregated chronotype and filter breakdowns would be the next place to check.