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.

1. A Quest For Better Sleep
(with Fitbit data analysis)
Alex Martinelli | @5agado

2. Index
● Why?
● The Data
● Exploring Sleep Data
● The Heatmap Case
● Correlation
● What’s Next?
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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.
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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).
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5. Data image (table)

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
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7. 7/15

8. 8/15

9. “Looks cool, but what does it mean?”
[cit. everyone]
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10. Minute Sleep Quality
For each minute, what percentage of recorded “times in bed” I was actually asleep
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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
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12. 12/15

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
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14. Q&A

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)
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