How to track your vape puffs (and why counting helps)

Count before you change anything#

Counting is a measurement, and a measurement comes first. Most people who vape have a rough idea of how often they do it, and a rough idea is hard to act on. You can't see which habit to change until you have measured it, and you can't tell whether a change worked without a before.

Self-monitoring is a named technique in the research. The behaviour change taxonomy describes 93 distinct techniques.¹ Goal setting, self-monitoring of behaviour and action planning are among the examples given in a study of how coders apply that taxonomy.² What we didn't find is a trial that tests counting on its own for vaping and measures how much it cuts use. So treat counting as a measurement first and a change tool second.

Compare yourself with yourself#

A puff isn't the same thing from one device to the next. In one study of tobacco and nicotine products, the same group of users took significantly more puffs on one product than on two others.³ Puff counts also differ from person to person, so a number from a friend, a forum or a product page doesn't tell you much about yours.

Use your own numbers as the yardstick. Log the same way every day, on the same device if you can, and compare this week with your own earlier weeks. A change from your own baseline is the signal you can act on. A number that looks high next to someone else's doesn't tell you what to do.

Five ways to count, and the catch with each#

• A tally on paper or a sticky note. Costs nothing, needs no phone, and the marks are honest. The catch: it only works if you make the mark straight after each puff, and a note in a pocket gets forgotten fast.
• Notes on your phone or a spreadsheet. You can record the time, the place and the trigger for each entry, and you can sort the week later. The catch: you add up the totals yourself, and typing in the moment is a hassle.
• A tracking app: ExHale, our pick. We built ExHale for exactly this count. One tap logs a puff or a craving, the Stats and Health screens turn those taps into trends, and a calibration week records your baseline before the step-down plan starts from it. The catch: a tap log is only as good as the taps, and that holds for the apps we compared in our guide to quit vaping apps too.
• Watch-based detection. Vape Sense (BETA), ExHale's feature for Apple Watch, looks for the hand-to-mouth motion and suggests sessions. It asks before it logs anything, and a suggestion can arrive up to about 15 minutes late. The catch: it only sees the arm the watch is on, and as a beta it has no real-world accuracy figures yet. Read how we built it and what it detects.
• A device that counts. ExCap is the puff-counting cap we are building in our lab. It sits on the mouthpiece and logs each puff to the ExHale app. The catch: it is a prototype, not for sale, with no release date or price. Follow the build in the ExCap dev log.

Pick the one you will keep using for a week. A rough count you finish beats a perfect one you abandon on day two.

What to write down for each puff#

Four fields are enough. The time tells you when the habit runs. The place tells you where. The trigger is the most useful one: what happened just before, such as coffee, a work break, a meal, or a friend lighting up. The fourth is optional: whether the puff was planned or you didn't notice taking it.

Don't try to cut anything in the baseline week. If you change the habit while you measure it, you measure the change, not the habit. Note any puff you take, including the ones you feel bad about.

A one-week baseline, and how to read it#

The table below is a made-up sample week, so you can see the shape of the result. It is not anyone's data. Your own log will look different, and that is the point.

Reading the week

• Puffs per day. Add the week and divide by seven. In the sample, 298 puffs over seven days is about 42.6 a day. That is the baseline average you compare future weeks against.
• Peak hours. Count which hour block comes up most. The sample peaks at 3 to 4pm and 9 to 10pm, with a smaller morning peak on Monday. A peak tells you where a plan needs a backup.
• Top triggers. Count the trigger column. Work breaks show up on three days, so they are the first link to work on. A trigger that appears on one day only is a note, not a pattern.
• The spread. Look at the highest and lowest days, not just the average. The sample swings from 28 to 61, and the weekend is the heavy part. An average hides that.

How the number feeds a taper#

A taper starts from the number you actually have, not the one you wish you had. The baseline gives you the starting figure, the peak hours show you where to begin cutting, and the top triggers tell you which moments need a different response. A step-down plan then sets a small, specific change for the next week and checks it against the same log.

The mistake to avoid is cutting everywhere at once. In the sample, the work breaks and the late evenings are where the most puffs cluster, so they are the places a plan can start. For the full approach, read how to taper off vaping with a step-down plan, and for the wider picture of quitting, the practical guide to quitting vaping.

Run your baseline week in ExHale#

We built ExHale to run this guide for you. One tap logs a puff or a craving, and the Stats and Health screens turn those taps into trends you can compare week to week. Your first week is a calibration week, so ExHale measures your baseline before it asks you to change anything, then the step-down plan sets each weekly step from what you actually logged. On Apple Watch, Vape Sense (BETA) suggests sessions from wrist motion and asks before it logs anything.

Questions about tracking vape puffs#

References#

1. Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 2013. pubmed.ncbi.nlm.nih.gov/23512568/
2. Applying the behaviour change technique (BCT) taxonomy v1: a study of coder training. PMC. pmc.ncbi.nlm.nih.gov/articles/PMC4444702/
3. A cross-category puffing topography, mouth level exposure and consumption study among Italian users of tobacco and nicotine products. Scientific Reports, 2020. www.nature.com/articles/s41598-019-55410-5