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Yan Kestens - Methodologies for collection and analysis of GPS data for health research
1. Methodologies for collection
and analysis of GPS data
for health research
Yan Kestens
Montreal University, Social and Preventive Medicine
Montreal Hospital University Research Center (CRCHUM)
SPHERE Lab .org
Spatial analysis of GPS data, Exeter University, UK
16th May 2013
2. Context
CHANGES
• Recent push in health research along the ‘space-time’
continuum
• A consequence/correlate of society’s ‘space-time
convergence’ ?
• Space: From ‘place based’ to ‘people based’
• Time: From snapshots to continuous measures, from delay
between collection and results to ‘real-time’
• Convergence of space and time, convergence of fields
• New methods, new developments, new continuums
3. Context
POTENTIAL
• Local trap / residential trap
• Space-time geography
• Potential path areas
• Activity spaces
• Network of usual places
• Multiple exposures
8. Issues with GPS data capture
CAPTURE
Participation/adherence: privacy, participation burden
Device usage: co-occurrence, lose vs. on body, device
manipulation
Device performance: Battery life, data storage space,
precision/validity of data points (TTFF, drift,
indoor/outdoor)
Temporal aspects: Epoch, survey duration, linkage with
other sensors and GIS data
9. GPS data capture
CAPTURE
Most current devices
GPS trackers
Low battery life
Pb of integration with additional sensors
Limited capacity of data transmission
Not designed for health research
GPS in cellphones
Battery life major hinder
Simultaneous usage with other applications not always
possible
11. SenseDoc Multisensor Device
CAPTURE
Central Unit
GPS GPRS
Accelerometer
Acquisition server
Central Unit
GPS GPRS
Accelerometer
ANT Module
Memory
SPHERE Lab .org
125 g
137 g
96 * 80 mm
115 * 59 mm
13. SenseDoc Multisensor Device
CAPTURE
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
Acquisition server
Central Unit
GPS GPRS
Memory
Accelerometer
ANT Module
Glucose
monitor
Galvanic skin
response
Accele-
rometer
HR
monitorBlood
pressure
Other
17. SenseDoc Multisensor Device
CAPTURE
Accelerometer
Marie-Lyse Bélanger, M.Sc. Student in kinesiology
Accelerometer validation using indirect calorimetry
Lab – 14 controlled exercises from sedentary to vigouros PA
Eleven adult subjects
Calculation of Vertical Magnitude Acceleration (VMAG)
Testing of various bandpass filters
Comparison with Actigraph GT3X performence
Best results obtained with Bandpass filter 0.1 Hz – 3.5 Hz
Modelling of Energy Expenditure: Adj. R-square of .79
Use of Vector Body Dynamic Acceleration (VEDBA)
18. SenseDoc Multisensor Device
CAPTURE
Battery life
Strong battery (3200 maH)
Axelle Chevallier, M.Sc. Student in
electrical engineering
Mohamad Sawan,
Battery optimisation algorithm
- Movement
- Location and movement
20. SenseDoc Multisensor Device
CAPTURE
Data transmission
GPS Data sent over the air (cellphone network) every 30 minutes
Possible alerts depending on
- Location
- Activity
- Time
Connection to other sensors (2.4 GHz ANT+) Heart rate monitor,
footpod, RFID tags, etc.
24. Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
25. Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
26. Issues in data processing
PROCES
SING
Continuous monitoring = Huge pile of data!!!
27. Issues in data processing
PROCES
SING
Transforming raw GPS data into meaningful and useful
information
- ‘Putting things into context’
- Activity locations
- Trips between locations
28. Activity location detection
PROCES
SING
Development of kernel-density based algorithm to
transform raw data into history of activities and trips
ArcGIS ArcToolBox (see www.spherelab.org tools
section)
- Input: raw GPS data
- Output:
- Location of activity places
- Activity places timetable
- Trip timetable with origins and destinations
Thierry et al. (2013) IJHG
29. Issues in data processing
PROCES
SING
Activity location algorithm validation method
- Artificial track generation with controlled
parameters (noise, stop time)
- Testing of algorithm performance in relation to
track characteristics and algorithm parameters
Thierry et al. (2013) IJHG
30. Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
31. Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
(a)Meannumberofstopsfou(b)Meandistancetotruestop
o
*10m radius missing
*10m radius missing
32. Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundstop
*10m radius missing
(b)Meandistancetotru
(c)Averagetimediff.relativeto
truestopduration
*10m radius missing
Noise categories Noise categories
33. Issues in data processing
PROCES
SING
Activity location detection kernel-based algorithm
Thierry et al. (2013) IJHG
Aft Akd
(a)Meannumberofstopsfoundp
*10m radius missing
34. Issues in data processing
PROCES
SING
Linkage of GPS locations with additional information
- Temporal linkage:
- Additional wearable sensors (Accelerometer,
Heart rate monitor, continuous glucose
monitor)
- Spatial linkage: GIS data – exposure at any given
location/time – descriptive vs. causal
understanding
- Spatio-temporal linkage – spatio-temporal GIS
35. Issues in data processing
PROCES
SING
Mapping – visualisation
Tool for communication / counseling, etc.
Issue of privacy – artificial blurring
36. Usage
Using GPS to locate behaviour and assess exposure
Improving the understanding of mechanisms linking
environments to health behaviours and profiles
Using GPS to prompt recall and gain additional insight
Using GPS to support qualitative studies (go-along, geo-
ethnography, geo-tagged photos, environmental
perception, etc.)
Using GPS data to assist clinical practice (mHealth)
USAGE
37. Usage: Prompted recall
GPS / accelerometer data provides limited information
on:
- What people are actually doing
- Decision processes
- How they feel
GPS-prompted recall can help gather additional
information
USAGE
38. Usage: Prompted recall
Example 1: Bike share study (PI: Gauvin)
Pilot study (n=25) on combined use of cellphones and
accelerometers for gathering of:
- GPS data
- Nature of activities
- Transportation modes
- Accelerometry (PA)
- Momentary Impact Assessment (feelings)
USAGE
39. Usage: Prompted recall
Bike share study
N=25, study period=7 days
Accelerometer:
- PA assessment
Cellphone:
- GPS data – sent to server every hour
- Feelings (real-time questionnaires)
Daily online prompted recall data collection using the
MWM (Mobility Web Mapping) application:
- History of mobility
- Nature of activities
- Transportation modes
USAGE
40. Usage: Prompted recall
Example 2: RECORD GPS Study (Chaix & Kestens)
GPS + Accelerometer
MWM prompted recall survey (Mobility Web Mapping)
after reception and processing of GPS data:
- Validation of activity places and trips
- Nature of activities
- Transportation modes
USAGE
41. Usage: Prompted recall
MWM prompted data a useful tool to improve activity
location algorithm / data collection
- Match/mismatch between algorithm detection and
reported timetable (locations/times)
- Preliminary analyses: N=80
USAGE
88.5%
11.5%
GPS raw data
GPS data
Missing data
42. Usage: Prompted recall
Analyses comparing activity locations, trips and
corresponding timetables obtained through:
- Spherelab GPS algorithm
- MWM GPS-prompted recall
N=80
Median of 88.5% of survey period with usable (raw and
interpolated) GPS data (11.5% of period with missing data)
USAGE
88.5%
11.5%
Proportion of survey time with GPS
data
GPS data
Missing data
43. Usage: Prompted recall
USAGE
MWM Algorithm
A A <50m
A A >50m
A T
T A
T T
AA >50m Misplaced activity
AA-AT-TT Early departure
AA-AT-AA False trip
AA-TA-AA Missing trip
AA-TA-TT Late departure
TT-AT-TT Late arrival
TT-TA-TT False positive
TT-AT-TT False negative
TT-TA-AA Early arrival
44. Usage: Prompted recallUSAGE
96.0%
Correctly classified
Misplaced activity location
Early departure
False trip
Missing trip
Late departure
Late arrival
False positive
False negative
Early arrival
Other
4%
Proportion of valid GPS time with match / mismatch with
prompted recall data
45. USAGE
+ +
Trimble Juno
SC GPS +
Arcpad
Actigraph
GT3X
Polar HR
monitor
7-day data collection
Usage: Support for clinical interventions
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: MH Henderson)
Spatio-behavioural
indicators -
ArcToolBox
Interactive map-
based web
application
Application supports
lifestyle counseling
46. USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
47. USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
48. USAGE
Dyn@mo Intervention – CIRCUIT Clinic – Ste-Justine
Pediatric Hospital (PI: M. Henderson)
Usage: Support for clinical interventions
49. How does GPS compare to regular
destinations?
Comparing spatial distribution of
- 7-day GPS data
- Regular destinations collected through an online interactive
mapping questionnaire (VERITAS)
50. How does GPS compare to regular
destinations?
89 participants of the RECORD GPS Study
VERITAS activity locations
• Total of 1,314 locations
• Median of 14 loc./ind.
51. How does GPS compare to regular
destinations?
89 participants of the RECORD GPS Study
7-day continuous GPS monitoring
0
10
20
30
40
50
60
70
80
90
100
Percentageofsurveyduration
withGPSfixes
Proportion of GPS survey duration with
valid GPS data
5 Days & 07:07:15
3 Days & 10:25:25
6 Days & 04:45:20
5 Days & 07:07:15
58. CONCLUSIONS
GPS opens great possibilities
Capture – Processing – Usage
Multidisciplinarity – Health – Transportation - Geography –
Engineering
Applications very diverse
For epidemiology – validity is key
59. Thank you!
SPHERE Lab .org
Benoit Thierry from SPHERELAB Claire Merrien from RECORD
Participants of all the studies!