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A Future “View” of Digital Data from Wearable Devices
Dan Schramek, GSK2019 PhUSE US Connect,Baltimore, MD. February 2019
Agenda
Background
Time-Series Plots
Bar Charts
Summary Figures – Heat Maps
Data Visualization Concepts for Digital Wearable Devices
Background
3
BackgroundTraditional Trial Study Designs
4
TYPICAL CLINICAL TRIAL TIMELINE
Start End of the trial
BackgroundTraditional Trial Study Designs
5
TYPICAL CLINICAL TRIAL TIMELINE
Start End of the trial
Screening
RandomisationVisit 2
Visit 3 Visit 4 Visit N
Follow-upVisit 1
BackgroundTraditional Trial Study Designs
6
TYPICAL CLINICAL TRIAL TIMELINE
Start End of the trial
Screening
RandomisationVisit 2
Visit 3 Visit 4 Visit N
Follow-up..........Questionnaires
Questionnaires
Questionnaires
Questionnaires
Questionnaires
Visit 1
BackgroundTraditional Trial Study Designs
7
TYPICAL CLINICAL TRIAL TIMELINE
Start End of the trial
Screening
RandomisationVisit 2
Visit 3 Visit 4 Visit N
Follow-up..........Questionnaires
Questionnaires
Questionnaires
Questionnaires
Questionnaires
Visit 1
Wearables
Background
Benefits Challenges
Initial operating costs and increased trial complexity
Data transmission, handling and Analytics
Clinical acceptability and subject burden
Regulatory requirements –devices & endpoints
‘Real-World’ data
Potential for real-time data capture
Potential to discover novel endpoints
Continuous, Objective measurement
8
Background
9
How?
Uni
tsW
alk
speed
Cadence
Asym
metry
Ste
p length
Strid
e length
Ste
p tim
e
Time
Accele
ration
Raw data
Vertical AxisLateral AxisForward Axis
Minute-by-minute data include:
• METs: rate of energy
expenditure
(1 MET= 1 #$%& #'() ℎ())• Total energy expenditure
(Kcal)
• Number of steps
• Type of activity: lying,
standing, sitting, walking,
running
• Intensity of activity: sedentary,
moderate, vigorous, very
vigorous
• Skin temperature
• Speed, distance, sleep…
Time-Series Plots
10
Time-Series Plots
– 1 week Data Collection - 300 data points per second × 3600 seconds per hour ×24 hours per day × 7 days per week = 181.44 million data points
– Minute-by-minute means below:
Digital Data from Wearable Devices is Complex!
Day 1
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Day 1 Day 2
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Censor off
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Missing Data
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Missing Data Night Day
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Time-Series PlotsYou can learn much about the quality and quantity of the data collection
Time-Series PlotsSpiral Plots
Time-Series PlotsSpiral Plots
Time-Series PlotsSpiral Plot
Credit – V Ashwin GSK
All Data
Sleeping Removed
Time-Series PlotsTime Spent Sleeping
All Data
Sleeping Removed
Time-Series PlotsTime Spent Sleeping
Bar Charts
24
Time-Series Plots
Sedentary Moderate Vigorous Very Vigorous
Activity Categories
25
Bar Charts100% Stacked Bar Chart
Bar ChartsTime Spent Sleeping
Summary Figures – Heat Maps
28
T
T
T
Treatment 1
Treatment 2
Treatment 3
Summary Figures – Heat Maps
Treatment 1
Treatment 2
Treatment 3
Summary Figures – Heat MapsTime Spent Sleeping Data Removed
Treatment 3
Treatment 2
Treatment 1
Treatment 3
Treatment 2
Treatment 1
Summary Figures – Heat MapsSummary
Summary
32
Summary
– Digital data from wearable devices may be utilized on clinical trials in the future
– Benefits– Objective and patient focused– Yields more complete information than discrete data collection– Better ‘real-world’ evidence
– Challenges– Meaningful and validated endpoints need to be identified– Investigator and regulatory agency buy-in– Complexity, size and cost of the data and transfer issues
– Time-Series, Bar charts and Heat Maps are key visualizations– Innovation opportunity for Clinical statisticians, quantitative data
scientists and programmers
Acknowledgement
– Special thanks to the members of the GSK Advanced Analytics for Digital Data group and especially the Analysis and Visualization sub-team for the tremendous amount of work done to date and for the input into this presentation.
– Kirsty Hicks, Juan Abellan, Valentin Hamy, Sandra Joksaite, Peter Lau, Edoardo Lisi, Min Sun, V Ashwin and Sarah Watts.
– Finally, special thanks to V Ashwin for the development and delivery of the spiral graph shown in this presentation.
GSK AADD Team and Analysis and Visualization Sub-team
Thank you!
Any Questions?
36
