This document summarizes the Health eHeart Study, which links Fitbit step data to health measures. It notes that health warning signs are often missed, and that real-world walking data is hard to obtain, messy to analyze, and usually not linked to health metrics. The study analyzes daylong and yearly step time-series with features like maximum pace and steps. It finds links between walking patterns and cholesterol levels and decodes daily breathing scores from step data with 61-64% accuracy. Challenges include small clinical groups and correlated features.

1. Health eHeart Study
fitbit data ← → health measures

2. Problem

3. Problem
[1] Health warning signs often go
unnoticed (or ignored).

4. Problem
[1] Health warning signs often go
unnoticed (or ignored).
[2] “Real life” walking data is:

5. Problem
[1] Health warning signs often go
unnoticed (or ignored).
[2] “Real life” walking data is:
(a) hard to obtain

6. Problem
[1] Health warning signs often go
unnoticed (or ignored).
[2] “Real life” walking data is:
(a) hard to obtain
(b) messy to analyze

7. Problem
[1] Health warning signs often go
unnoticed (or ignored).
[2] “Real life” walking data is:
(a) hard to obtain
(b) messy to analyze
(c) often unlinked to health measures

8. Problem
[1] Health warning signs often go
unnoticed (or ignored).
[2] “Real life” walking data is:
(a) hard to obtain
(b) messy to analyze
(c) often unlinked to health measures

9. Data and features
Daylong time-series (raw)

10. Data and features
Daylong time-series (raw) Daylong time-series (smoothed)

11. Data and features
User Date Steps
(total)
Max1 Mins > 5
steps
Max10 Max60 Mean steps/
minute (active)
Stdev steps/
minute (active)
1234 12/25/14 17953 116 352 107.7 45.1 59.91 36.28
Daylong time-series (raw) Daylong time-series (smoothed)

12. Data and features
User Date Steps
(total)
Max1 Mins > 5
steps
Max10 Max60 Mean steps/
minute (active)
Stdev steps/
minute (active)
1234 12/25/14 17953 116 352 107.7 45.1 59.91 36.28
Daylong time-series (raw) Daylong time-series (smoothed)

13. Data and features
User Date Steps
(total)
Max1 Mins > 5
steps
Max10 Max60 Mean steps/
minute (active)
Stdev steps/
minute (active)
1234 12/25/14 17953 116 352 107.7 45.1 59.91 36.28
Daylong time-series (raw) Daylong time-series (smoothed) Year time-series (max10)

14. Data and features
User Date Steps
(total)
Max1 Mins > 5
steps
Max10 Max60 Mean steps/
minute (active)
Stdev steps/
minute (active)
1234 12/25/14 17953 116 352 107.7 45.1 59.91 36.28
Daylong time-series (raw) Daylong time-series (smoothed)

15. Data and features
User Date Steps
(total)
Max1 Mins > 5
steps
Max10 Max60 Mean steps/
minute (active)
Stdev steps/
minute (active)
1234 12/25/14 17953 116 352 107.7 45.1 59.91 36.28
Daylong time-series (raw) Daylong time-series (smoothed)

16. Features (descriptive)
Maximum hourly pace à
Totaldailystepsà

17. Features (descriptive)
Maximum hourly pace à
Totaldailystepsà
Total daily steps à
Maxsteps/minuteà

18. Features (descriptive)
Maximum hourly pace à
Totaldailystepsà
Total daily steps à
Maxsteps/minuteà

19. Features (descriptive)
Maximum hourly pace à
Totaldailystepsà
Total daily steps à
Maxsteps/minuteà

20. Features (descriptive)
Running after
FedEx guy?
Maximum hourly pace à
Totaldailystepsà
Total daily steps à
Maxsteps/minuteà

21. Clinical-linked results (descriptive)
Weekday max steps/minute —
high vs. regular cholesterol levels

22. Clinical-linked results (descriptive)
Daily self-reported “shortness of breath” score Weekday max steps/minute —
high vs. regular cholesterol levels

23. Model (decoding)
Daily self-reported “shortness of
breath” score

24. Model (decoding)
Daily self-reported “shortness of
breath” score

25. Model (decoding)
Random forest classifiers
Daily self-reported “shortness of
breath” score

26. Model (decoding)
61-64% cross-validated
binary decoding accuracy
Random forest classifiers
Daily self-reported “shortness of
breath” score

27. Validation
Monte Carlo hypothesis testing…

28. Validation
Monte Carlo hypothesis testing…
p < 0.01

29. Validation
Monte Carlo hypothesis testing…
p < 0.01
Conclusion:
!
Information about
health does “live” in an
individual’s very noisy
step data.

30. Challenges

31. Challenges
[1] Dealing with dramatically unbalanced groups (i.e. the
most interesting clinical populations are tiny).

32. Challenges
[1] Dealing with dramatically unbalanced groups (i.e. the
most interesting clinical populations are tiny).
[2] Engineered features are highly correlated (despite my
best creative efforts).

33. Challenges
[1] Dealing with dramatically unbalanced groups (i.e. the
most interesting clinical populations are tiny).
[2] Engineered features are highly correlated (despite my
best creative efforts).
With more time:

34. Challenges
[1] Dealing with dramatically unbalanced groups (i.e. the
most interesting clinical populations are tiny).
[2] Engineered features are highly correlated (despite my
best creative efforts).
With more time:
— Create more and better features

35. Challenges
[1] Dealing with dramatically unbalanced groups (i.e. the
most interesting clinical populations are tiny).
[2] Engineered features are highly correlated (despite my
best creative efforts).
With more time:
— Create more and better features
— Link step data with additional clinical/demographic info

36. About me

37. About me
Ph.D. in neuropsychology from
McGill University (music cognition)