Every morning: open spreadsheet, log yesterday’s count, reset counter. A small ritual that turned into a surprisingly clear window into habit, nicotine dependency, and what it actually takes to stop.
Days logged
963
of 996 calendar days
Total puffs
476k
all time
Peak nicotine
60mg
per day · Sep 2023
Day 996
0
puffs · braided hair · fine
01
How did my usage change over 1,000 days?
963 logged days · each dot = one day · purple line = 30-day rolling average
Daily puffs · Aug 2023 → May 2026
02
How is that usage distributed?
Before (Aug 23–Apr 25, n=581, μ=556, σ=149) vs After (Oct 25–May 26, n=215, μ=417, σ=103)
BeforeAfter
The After distribution is not just lower — it’s tighter. Standard deviation dropped from 149 to 103. More predictable behaviour, less day-to-day chaos. To reliably detect a 50-puff difference in this data requires ~164 days per group — which is why the post-quit dataset (n=215) is only just becoming powerful enough to detect moderate effects.
03
Does the day of the week matter?
Before (Aug 23–Apr 25) vs After (Oct 25–May 26) · Sat & Sun combined · no statistical difference between Sat and Sun (p=0.22 before, p=0.31 after)
BeforeAfterMean
Before the quit attempt, weekends were the heaviest days. After, they’re the lightest — by a large margin (p<0.001, d=0.94). The pattern completely reversed. Wednesday is the only day where the Before→After drop isn’t statistically significant (p=0.17).
04
Is it about where I am or what I’m doing?
WFH · Office · Weekend · London · Before vs After
BeforeAfterMean
WFH days are consistently higher than office days in both eras. London is reliably the lowest — busier days, more distraction, less desk time. The WFH–Office gap is real but modest; location explains about 13% of variance in the post-quit model.
05
Does biology play a role?
36 cycles tracked · phases calculated from period start dates · Before vs After
BeforeAfterMean
Ovulation is consistently the lowest phase — and was statistically significant in the Before era (p=0.025 vs Luteal). After the quit attempt the signal fades: lower overall usage means less variance to detect. More data needed to be certain, but the pattern is consistent across all 36 cycles.
06
What actually impacts how much I vape?
Decision tree · four candidate variables · Before R²=0.08 · After R²=0.21 · 95% of variance unexplained
Before quit attempt
After quit attempt
The drivers completely flipped between eras. Before: location explained 81% of what the model could find. After: day of week took over at 71%. The model also explains more variance post-quit (R²=0.21 vs 0.08) — lower, more settled usage is simply more predictable. 95% of the variance in any given day is still unexplained: mood, sleep, stress, and a hundred things not tracked.
07
What am I actually addicted to?
Mean puffs per day by nicotine concentration — does reducing nicotine change behaviour?
Mean daily puffs by liquid strength
* 0mg/ml: 3 active days shown (one zero-puff day excluded — that was the last day of vaping entirely)
Each time nicotine concentration was halved, puff count barely moved. The behaviour was essentially unchanged from 10mg/ml all the way to 0mg/ml. The physical nicotine dependency had already faded long before the habit was broken — which made the final step surprisingly easy.
Total nicotine consumed over 996 days: approximately 29 grams. By the end, daily intake was under 2mg — less than a single cigarette’s worth, delivered slowly across the whole day. The stepdown strategy worked because each reduction was small enough that the body barely noticed.
What actually worked
Aug 2023
Started logging. Running at ~600 puffs/day on 10mg/ml — roughly 60mg nicotine per day.
Apr 2024
Switched to 5mg/ml. Puff count: unchanged. Nicotine intake: halved overnight.
May 2025
Attempted to quit through willpower. Count crashed from 527 → 200, then bounced back to 440. The data does not lie.
Apr 2026
Started blending own liquid. 0.83mg/ml → 0.45mg/ml. Each time: puff count unchanged, nicotine halved.
16 May 2026
Switched to 0mg liquid. ~1.8mg nicotine/day — less than a single cigarette. Puff count: unchanged.
19 May 2026
Didn’t pick up until 9am. Put it in a drawer. Braided hair. Zero puffs. Zero withdrawal.
Liquid strength
Days
Mean puffs
Mean nicotine/day
vs peak
10mg/ml
223
561
56.1mg
baseline
5mg/ml
683
482
24.1mg
−57%
0.83mg/ml
20
391
3.2mg
−94%
0.45mg/ml
13
349
1.6mg
−97%
0mg/ml
3+
379 → 0
0mg
done
Three things the data taught me
1
Recording data doesn’t on its own drive action. I logged 600 puffs a day for a year before doing anything about it.
2
But data does enable informed decisions. When I halved my nicotine concentration, everyone said “you’ll just puff twice as much.” The data proved that wasn’t how my behaviour worked — and that proof made each subsequent step-down feel safe.
3
95% of the variance is unexplained. Location, day of week, cycle phase, and kids together explain about 5% of why I vaped more or less on any given day. The rest is mood, stress, sleep, and a hundred things I didn’t track. Measurement is powerful; it is not omniscient.