For two years, I worked in consulting. I spent long days at a laptop and got very good at ignoring what my body was doing while my attention was somewhere else.
I picked up a collection of back and neck aches and niggles. They were rarely bad enough to stop me working. That was the problem: I could always get through the day, so I did. They became a quiet tax on my quality of life that followed me from one week into the next.
Knowing what to do wasn’t the problem
I knew the usual advice. Sit up. Move more. Take breaks. The problem was remembering any of it when I was concentrating on the work in front of me.
During a workday, posture is not one position you fix in the morning. It is a set of small habits: how you settle into a chair, what happens when you focus, and how long you stay there without noticing.
I didn’t need another person telling me to sit up. I needed a way to notice the habit when I had stopped noticing it myself.
I wanted an app that could close that gap: notice the pattern, prompt me at a useful moment and give me enough accountability to follow through. It had to help without becoming another thing to manage.
Three things the app had to get right
Before building Forma, I looked closely at the apps already available. SitApp showed the value of personalised webcam feedback. BlinkAway took the lighter route of blink and break reminders. Zen Posture brought real-time analysis, notifications and progress into one product. Each handled part of the problem well. I could not find one that balanced all three in a way I would trust and keep using.
User friction. A habit app fails when setup feels like work or its reminders become automatic dismissals. Forma needed to sit quietly on the desktop, check in at useful moments and return me to work. The reminder is only useful if the loop is easy to repeat: notice, adjust, continue.
User privacy. Continuous camera tracking can create a denser record, but it also asks someone to leave a camera running through their workday. Forma uses short assessments and optional recurring check-ins instead. Camera frames are analysed transiently on the computer; ordinary use does not upload camera images, video or audio.
Medical rigour from day one. Forma is a general-wellbeing product today, but that should not be an excuse for vague measurements or loose claims. My longer-term aim is to build evidence strong enough for Forma to support—not replace—the work of physiotherapists. That means preserving how each observation was produced, defining what a laptop camera can and cannot measure, testing repeatability and accuracy, and withholding clinical interpretation until the research and regulatory work support it. That research is in progress.
Each decision affects the others. More camera time produces more continuity but asks more of the user’s trust. More notifications create more chances to act but also more reasons to mute the app. Simpler, stronger labels are easier to scan but can imply certainty the camera does not have.
Forma is built around one bounded loop: a brief check-in, a clear observation, one practical adjustment, then back to work. It trades a complete record for something I can understand, trust and keep using.
What Forma is learning
Forma is learning about the habit and privacy parts first: whether reminders are useful enough to act on, camera use is brief enough to trust, and the resulting observations help someone make a practical adjustment.
In parallel, the research is examining whether camera-derived measurements can become reliable enough to support future professional use. Forma does not currently diagnose, guide treatment or support clinical decisions.
For now, I want to know whether the loop helps a habit stick: notice a pattern, make one adjustment and return to work. If you try Forma, I would like to hear where that helps and where it creates friction.