The document discusses the exploration of sleep data using Fitbit, emphasizing the importance of understanding data and its limitations. It analyzes various statistics related to sleep quality and outlines correlations between sleep and factors like steps, heart rate, and alcohol consumption. The document concludes with suggestions for further experiments and improvements in data collection for better insights on sleep.

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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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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