A Harvard team just ran a clinical trial on using wearables + smart thermostats to fix sleep. I built the consumer version

A research group at Hebrew SeniorLife (Harvard Medical School's geriatrics arm) put Garmin watches and ecobee thermostats in people's bedrooms and ran a structured trial to see if matching room temperature to each person's physiology changes sleep outcomes. [NCT06770400](https://clinicaltrials.gov/study/NCT06770400). They finished collecting data last year, results aren't published yet.

I didn't know about the study when I started building. I'd been working on the same idea independently, an app that connects your wearable to your thermostat and runs one-variable-at-a-time temperature experiments while you sleep. Found the trial listing after launch. That was a weird day.

My app (CircadiaOS) has been live for about six weeks. The approach is similar to what the Harvard team did: set 4 temperature points across the night, change one at a time, hold for 7–14 nights, check wearable scores, keep or revert.

What I keep seeing in the data:

Almost everyone's thermostat is too warm in the first half of the night. The 11pm–2am deep sleep window — people are sitting at 70°F through it. Pre-sleep cooling (dropping 2–3°F about an hour before bed) is the most consistent lever across the whole user base. Pre-wake warming is the second biggest. And individual variation is way larger than I expected — I've got users dialing in at 66 and users at 69. No magic number.

The noisy part: you need 7+ nights at any setting before the wearable data means anything. One night is pure noise. That's why I built this as an automated experiment loop instead of a static schedule.

Works with Oura, Apple Watch, Whoop, Garmin, Fitbit → Nest, ecobee, Honeywell, +20 more.

Happy to talk methodology or data pipeline.