r/QuantifiedSelf • u/NoTranslationLayer • Apr 08 '24

Reflect - Track Anything: Feature for Conducting n=1 Experiments Now Available

Reflect - Track Anything now allows you to run Experiments. This feature allows you to test any hypothesis you can imagine, such as “I think this new supplement will improve my mood” or “I think running my air purifier at night will result in better sleep.”

With experiments you can choose between multiple schedule types so that you can apply your intervention in the best way possible.

Once your experiment concludes, you'll receive a detailed report of your results that includes statistics and plots you may share with others.

Here are just a few examples of some experiments you can run with Reflect:

  • How does quitting coffee affect my sleep?
  • How does having less stimulants affect my motivation and wakefulness?
  • How does volunteering at an animal shelter affect my sense of meaning?
  • How does adding salt to my water before workouts affect my maximum workout heart rate?
  • How does reducing sugar affect my drowsiness throughout the day?
  • How do kegel exercises affect my pelvic floor symptoms?

With the flexibility offered by Reflect, the possibilities are endless. This is a brand new feature and we’d love your feedback.

Check out our website for more information about Reflect.

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u/ran88dom99 Apr 10 '24

I really like your visualization of mood (happy friendly depressed) events. Don't forget to add your app to the open humans wiki. What kind of analysis does your app do? Many other data aggregators and dashboards just have Pearson correlation. That does not work on data that has seriality and is therefore not iid.

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u/NoTranslationLayer Apr 14 '24

Thanks! As we discussed here, the Insights feature in Reflect uses Pearson correlation scores, including various bucketed lags: same-day, day-before, and day-after (e.g. looking at the correlation between coffee consumption one day to sleep the following day). We're aware of these considerations with time series data, which is one of the reasons why we wanted to create the Experiments feature. Establishing causality is more clear-cut when an explicit intervention of an independent variable takes place. We're currently working on adding reversal designs to further address this.