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A quest for better sleep

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Slides for my talk at the Quantified Self Dublin meetup (https://www.meetup.com/Quantified-Self-Dublin/). Overview of my findings from a descriptive analysis of personal Fitbit sleep data.

Published in: Data & Analytics
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A quest for better sleep

  1. 1. A Quest For Better Sleep (with Fitbit data analysis) Alex Martinelli | @5agado
  2. 2. Index ● Why? ● The Data ● Exploring Sleep Data ● The Heatmap Case ● Correlation ● What’s Next? 2/15
  3. 3. Why? Be your own data scientist! Learn.. How data “works”: play with it, learn about tools, statistics and biases. Learn to give a meaning to data < learn to give a proper meaning to data. ..and Learn How you “work”: an app dashboard is not enough. Investigation based on your needs and knowledge: insight, diagnosis, experiments and improvements. 3/15
  4. 4. The Data Premise: sleep trackers and their inherent inaccuracy The Fitbit case Getting your data is not as easy as expected, considering that is YOUR data. Options: premium plan, scraping or APIs (again with limitations) Data format (cleaned) For each minute: 0=None (no measure taken), 1=Sleeping, 2=Restless, 3=Awake Sleeping periods can be manually recorded, or are otherwise recognized automatically (based on amount of time you didn’t move, so there are limitations). 4/15
  5. 5. Data image (table)
  6. 6. Exploring Sleep Data Basic Stats - sleep efficiency, hours of sleep... Timing Stats - to bed time, wake up time - sleep intervals Intraday Stats (minute to minute analysis) Aggregation (hour, weekday, month, year) Correlation 6/15
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  9. 9. “Looks cool, but what does it mean?” [cit. everyone] 9/15
  10. 10. Minute Sleep Quality For each minute, what percentage of recorded “times in bed” I was actually asleep 10/15
  11. 11. Correlation Premise: just observations. We need more formal experiments to show causal connections. ● No correlation between steps and sleep quality (see next slide image) ● Daily heart resting rate negatively correlates with sleep efficiency ● Alcohol: asleep instantly, less restless, but more awakenings ● Supplements Melatonin: decrease in sleep efficiency, while minor increase with 5HTP Not enough data for vitamin B complex 11/15
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  13. 13. What’s Next? ● More data for correlation (drinking, eating, activity, cognitive performances, habits and routines) ● Self experimentation to support causal relationship hypotheses ● Demographics ● Predictive models? ● Real quality data: EEG integration ● A personal quirky case: lucid dreaming 13/15
  14. 14. Q&A
  15. 15. Useful Links 1. Introductory article for this project (https://medium.com/@5agado/a-quest-for-better-sleep-with-fitbit-data-analysis-5f10b3f548a#.925f35k2f) 2. Github repository with project code (https://github.com/5agado/fitbit-analyzer) 3. Intraday data via personal apps - Fitbit announcement post (https://community.fitbit.com/t5/Web-API/Intraday-data-now-immediately-available-to-personal-apps/td-p/1014524) 4. Study on Fitbit accuracy on sleep measurements (https://www.ncbi.nlm.nih.gov/pubmed/21971963) 5. Cross-sectional study on the validity of consumer-level wearables (https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-015-0201-9) 15/15

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