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I made an app to help insomniacs learn how to sleep again

Hacker News

I made an app to help insomniacs learn how to sleep again

Hi HN! I suffered from chronic insomnia for over a year and tried everything from cutting coffee, blocking blue light, to taking melatonin and antihistamine, but couldn’t find anything that worked. I even bought a $500 research-grade EEG device to track my sleep, which was honestly kind of depressing because it showed that I was sleeping less than 4 hours per night for weeks straight. In the day, it took an immense amount of energy for me to perform even the most mundane of tasks, such as doing my laundry or ordering groceries. At night, I felt an overwhelming sense of loneliness and resentment as I lay in bed wide-awake, reading and re-reading Sleep by Murakami or mindlessly scrolling through reddit/ HN. My performance at work suffered, my personal relationships suffered, and my happiness suffered. When I finally decided to see a sleep specialist, I was put on a 3-month long waiting list. Eventually, I was able to get my insomnia treated, but I realized that there is no reason why anyone should wait 3 months to get treatment when the same therapy that I received can be delivered online. My co-founder and I both have experience in digital health, so we decided to partner with sleep experts to create a mobile app to help people with insomnia get better sleep using psychology. We launched in February this year, and have already helped over 500 patients improve their sleep permanently. Our data shows that our program is just as effective as group, in-person sleep therapy, and we’re doing a clinical study with Brigham and Women’s hospital and Harvard Medical School to prove the efficacy of our product. On average, our users sleep 74 minutes longer than before and spend 52% less time awake in the middle of the night. If you have trouble with sleep, try our app and let us know what you think!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks, using · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, users · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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