How to Tell If a Medication Is Working (and What Else It's Doing)

8 min read · Updated 2026-07-18

Two questions come up with any new medication: is it working, and is it doing something it shouldn't? Both have the same answer, and it isn't how you feel today. Log a baseline before the first dose, mark the start date, then compare window averages once your body has settled in.

Some medications announce themselves in an hour. Plenty of others - many antidepressants, mood stabilizers, some blood pressure medications - take four to eight weeks to reach full effect. That gap creates a genuinely hard question: is this doing anything?

There's a second question running alongside it. Search whether any given medication causes weight gain and you'll get the same unsatisfying answer every time: sometimes, for some people, to some degree. That's not evasion, it's how the data works. A side effect listed at 8% incidence means most people never get it and a few get it strongly.

Both questions have the same shape. Neither one is about the population, and neither one can be answered from memory. Six weeks in, "how do you feel compared to before?" is really asking you to compare today against a memory filtered through everything that's happened since - a rough patch at work, better sleep, worse sleep, hope, doubt. People abandon medications that were quietly working and stick with ones that never did, based on a comparison nobody could make in their head.

Infographic titled "Data-Driven Recovery: A Guide to Tracking Your Medication Results," laid out in two phases. Phase 1, preparation and the onset window, covers three steps: log a pre-medication baseline by recording daily ratings before your first dose to create a fair test; respect the four-to-eight-week onset window, because slow-acting medications need time to settle before you can judge them; and record every dose and dosage change with exact dates, so you can see whether trends bend at those points. A center column headed "Look for a trend that survives averaging" contrasts a jagged daily-noise line against a smooth seven-day average trend, with a note to use seven-day averages for weight to filter out daily water-weight noise. Phase 2, what to track and how to evaluate, shows a 1-10 rating scale for mood and pain and explains that a simple daily number is more sustainable than long-form journaling. Below it, a "Compare averages, not days" panel weighs an average before day one against an average after week seven on a balance scale. A closing table lists three metrics to log daily: target symptom as a 1-10 rating, sleep as hours rather than adjectives, and weight as a seven-day average. — Two phases: build the baseline and wait out the onset window, then compare averages instead of days.

The onset window changes what "before and after" means

For a slow-onset medication, comparing week one against week two tells you almost nothing - you're comparing two points inside the ramp-up. The comparison that matters is everything before you started against everything after the expected onset window closed. If the ramp-up is expected to take six weeks, the fair test is your average from before day one versus your average from week seven onward.

That framing only works if you have numbers on both sides of the window. Which means the single most useful thing you can do starts before the first dose. The boring stretch you log before anything changes turns out to be the most valuable data you'll ever have, and it's the one people skip.

What to log

Two categories: the thing the medication is supposed to move, and the three numbers most likely to move when it isn't supposed to.

A daily 1-10 rating of whatever the medication should help - mood, energy, pain, focus. One honest number a day beats a paragraph you'll stop writing by week two.
Sleep duration. It is the most commonly disturbed and the easiest to log honestly - hours, not adjectives. A lot of off-target effects show up here first, and it helps separate "the medication helped" from "I finally slept."
Weight, but as a 7-day average, never a single morning. Day-to-day weight swings a couple of pounds on water alone. A real medication effect is a trend that survives averaging, not a scary Tuesday.
Every dose taken. Adherence is the first thing a doctor will ask about, and gaps explain a lot of "it stopped working."
Any dosage change, on the day it happened.

Compare the averages, not the days

Once the window closes, the question becomes answerable with arithmetic: your average before starting versus your average since the window ended. "Mood averaged 5.3 before, 7.2 after" is a sentence a doctor can act on in a way that "I think maybe it's helping?" is not. Sample size matters too - a 1.9-point jump across nine days of data means more than the same jump across two.

Which features actually matter in a medication tracker comes down to this kind of thing. Dosage changes are part of the record. If your weight trend was flat for six weeks and bends in the week a dose doubled, that's a very different conversation than a drift that started before the prescription did. A date-stamped dosage change turns "I think it started around spring?" into a vertical line on a chart.

Signal versus noise

A real effect persists across weeks of averages. Noise lives in single days.
A real effect usually tracks the medication timeline - it starts after the start date, or it bends at a dosage change.
Check the confound first. A weight change that lines up perfectly with a sleep change may be about the sleep. This is what correlation views across all your metrics are for.
Absence of evidence counts. Three months of flat 7-day weight averages after starting is a genuinely reassuring answer to the question you were googling.

One important boundary

A log tells you what happened. It doesn't tell you what to do about it, and that includes a chart that scares you. Never start, stop, or change a dose on your own - bring the record to the person who prescribed it. Off-target effects are a trade-off conversation, and sometimes the answer is switching, sometimes it's dose timing, and sometimes it's that the thing you noticed is unrelated and here's why. All three go better with numbers.

HabitSync is built for this exact question. Log your doses and daily check-ins, tell it the expected onset window, and it plots your numbers from before you started against after the window closed. Weight is shown as a 7-day average with the daily noise de-emphasized, dosage changes appear as marked steps in your medication history, and your full record is preserved even if you stop taking something - ready for the next appointment.

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If you want to see this in the app, here is how HabitSync tracks medications.