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Cure Sleep Problem through Online Classes

Hacker News

Cure Sleep Problem through Online Classes

Dr. Jocelyn Sze is teaching a free online sleep class. Dr. Sze, has a PhD from UC Berkeley in Clinical Psychology, and specializes in sleep problems. While working at the VA she created programs that helped veterans with decades of sleep difficulties recover. You can sign-up for the class here: www.dobetter.com/sleep Here's how the class works. Most people who have sleeping problems have lots of habits that cause them to associate bed and nighttime with wakefulness. These include: Watching TV right before bed Spending hours in bed trying to fall asleep unsuccessfully Taking a nap late in the day making it harder to fall asleep or stay asleep at night This class helps you break those habits and create better ones. Through weekly 20 minute video lectures, Jocelyn will explain what those habits are, how they cause your sleeping problems and help you set better habits around sleep by tracking your sleep schedule and making changes to it so that you can fall asleep when you go to bed. These methods have been validated in a variety of studies going back 20 years. More recently a study of an online program like ours done at the University of Virginia showed that 73% of people with moderate to severe insomnia no longer had any insomnia at the end of program.[1] (This approach is known as cognitive behavioral therapy for insomnia in the research literature) The class starts on March 25, this Monday. We are only taking 100 more students so sign-up soon if you are interested. Curious to get comments as well as signups, we're working on some other courses (with professors from Harvard Medical School) and are eager to hear about people's thoughts about the class model in health. [1] http://archpsyc.jamanetwork.com/article.aspx?articleid=483124

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
92%92% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, plain · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
35%35% 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
20%20% 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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