Methodologies for collectionand analysis of GPS datafor health researchYan KestensMontreal University, Social and Preventi...
ContextCHANGES• Recent push in health research along the ‘space-time’continuum• A consequence/correlate of society’s ‘spac...
ContextPOTENTIAL• Local trap / residential trap• Space-time geography• Potential path areas• Activity spaces• Network of u...
ContextTECHNOLOGICAL CHANGES• Wearable sensors• Ubiquity• Connectedness• 7 billion sensors• Quantified Self - mHealth
Context‘More data more often’vs.‘Less is more’
Spatial data collection for healthCAPTUREPROCESSINGUSAGE
Web serverAcquisition serverOutputs /ApplicationsEnd usersGISAlgorithmsGSM towerSensors
Issues with GPS data captureCAPTUREParticipation/adherence: privacy, participation burdenDevice usage: co-occurrence, lose...
GPS data captureCAPTUREMost current devicesGPS trackersLow battery lifePb of integration with additional sensorsLimited ca...
GPS data captureCAPTUREAttempts to address these issuesCollaborations with engineersValidation requirements
SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSAccelerometerAcquisition serverCentral UnitGPS GPRSAccelerometerANT ...
SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSMemoryAccelerometerANT ModuleAcquisition serverCentral UnitGPS GPRSM...
SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSMemoryAccelerometerANT ModuleAcquisition serverCentral UnitGPS GPRSM...
SenseDoc Multisensor DeviceCAPTUREAcquisition serverGPS – SIRF IVGPS performance validationSpatial accuracyTime to First F...
CAPTUREAverage of dist_moy Column LabelsRow Labels Etrex HTC MS Qstarz Grand TotalIndoorcold 13,6 9,0 7,7 16,7 12,3Brick b...
CAPTUREAverage of ttff Column LabelsRow Labels Etrex HTC MS Qstarz Grand TotalIndoorcold 136,3 255,0 33,2 86,3 102,3Brick ...
SenseDoc Multisensor DeviceCAPTUREAccelerometerMarie-Lyse Bélanger, M.Sc. Student in kinesiologyAccelerometer validation u...
SenseDoc Multisensor DeviceCAPTUREBattery lifeStrong battery (3200 maH)Axelle Chevallier, M.Sc. Student inelectrical engin...
SenseDoc Multisensor DeviceCAPTUREAcquisition serverBattery life
SenseDoc Multisensor DeviceCAPTUREData transmissionGPS Data sent over the air (cellphone network) every 30 minutesPossible...
SenseDoc Multisensor DeviceCAPTUREChallenges in developping new hardwareHardware / Software / User InterfaceMiniaturisatio...
Web serverAcquisition serverOutputs /ApplicationsEnd usersGISAlgorithmsGSM towerSensorsCAPTUREPROCESSINGUSAGE
Spatial data collection for healthCAPTUREPROCESSINGUSAGE
Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
Issues in data processingPROCESSINGTransforming raw GPS data into meaningful and usefulinformation- ‘Putting things into c...
Activity location detectionPROCESSINGDevelopment of kernel-density based algorithm totransform raw data into history of ac...
Issues in data processingPROCESSINGActivity location algorithm validation method- Artificial track generation with control...
Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)M...
Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)M...
Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)M...
Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)M...
Issues in data processingPROCESSINGLinkage of GPS locations with additional information- Temporal linkage:- Additional wea...
Issues in data processingPROCESSINGMapping – visualisationTool for communication / counseling, etc.Issue of privacy – arti...
UsageUsing GPS to locate behaviour and assess exposureImproving the understanding of mechanisms linkingenvironments to hea...
Usage: Prompted recallGPS / accelerometer data provides limited informationon:- What people are actually doing- Decision p...
Usage: Prompted recallExample 1: Bike share study (PI: Gauvin)Pilot study (n=25) on combined use of cellphones andaccelero...
Usage: Prompted recallBike share studyN=25, study period=7 daysAccelerometer:- PA assessmentCellphone:- GPS data – sent to...
Usage: Prompted recallExample 2: RECORD GPS Study (Chaix & Kestens)GPS + AccelerometerMWM prompted recall survey (Mobility...
Usage: Prompted recallMWM prompted data a useful tool to improve activitylocation algorithm / data collection- Match/misma...
Usage: Prompted recallAnalyses comparing activity locations, trips andcorresponding timetables obtained through:- Spherela...
Usage: Prompted recallUSAGEMWM AlgorithmA A <50mA A >50mA TT AT TAA >50m Misplaced activityAA-AT-TT Early departureAA-AT-A...
Usage: Prompted recallUSAGE96.0%Correctly classifiedMisplaced activity locationEarly departureFalse tripMissing tripLate d...
USAGE+ +Trimble JunoSC GPS +ArcpadActigraphGT3XPolar HRmonitor7-day data collectionUsage: Support for clinical interventio...
USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical in...
USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical in...
USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical in...
How does GPS compare to regulardestinations?Comparing spatial distribution of- 7-day GPS data- Regular destinations collec...
How does GPS compare to regulardestinations?89 participants of the RECORD GPS StudyVERITAS activity locations• Total of 1,...
How does GPS compare to regulardestinations?89 participants of the RECORD GPS Study7-day continuous GPS monitoring01020304...
1000250500100
0.000.100.200.300.400.500.600.700.800.901.00Within 100 m Within 250 m Within 500 m Within 1000mProportion of total survey ...
87%85%78%66%1000250500100
020406080100120140Shortest distance between a GPSdetected location (unspecified category)and a VERITAS location (specified...
CONCLUSIONSGPS opens great possibilitiesCapture – Processing – UsageMultidisciplinarity – Health – Transportation - Geogra...
Thank you!SPHERE Lab .orgBenoit Thierry from SPHERELAB Claire Merrien from RECORDParticipants of all the studies!
Yan Kestens - Methodologies for collection and analysis of GPS data  for health research
Yan Kestens - Methodologies for collection and analysis of GPS data  for health research
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Yan Kestens - Methodologies for collection and analysis of GPS data for health research

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Keynote address of the 'Spatial Analysis of GPS Data Workshop' held at Exeter, UK, the 16-17 May 2013.

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Yan Kestens - Methodologies for collection and analysis of GPS data for health research

  1. 1. Methodologies for collectionand analysis of GPS datafor health researchYan KestensMontreal University, Social and Preventive MedicineMontreal Hospital University Research Center (CRCHUM)SPHERE Lab .orgSpatial analysis of GPS data, Exeter University, UK16th May 2013
  2. 2. ContextCHANGES• Recent push in health research along the ‘space-time’continuum• A consequence/correlate of society’s ‘space-timeconvergence’ ?• Space: From ‘place based’ to ‘people based’• Time: From snapshots to continuous measures, from delaybetween collection and results to ‘real-time’• Convergence of space and time, convergence of fields• New methods, new developments, new continuums
  3. 3. ContextPOTENTIAL• Local trap / residential trap• Space-time geography• Potential path areas• Activity spaces• Network of usual places• Multiple exposures
  4. 4. ContextTECHNOLOGICAL CHANGES• Wearable sensors• Ubiquity• Connectedness• 7 billion sensors• Quantified Self - mHealth
  5. 5. Context‘More data more often’vs.‘Less is more’
  6. 6. Spatial data collection for healthCAPTUREPROCESSINGUSAGE
  7. 7. Web serverAcquisition serverOutputs /ApplicationsEnd usersGISAlgorithmsGSM towerSensors
  8. 8. Issues with GPS data captureCAPTUREParticipation/adherence: privacy, participation burdenDevice usage: co-occurrence, lose vs. on body, devicemanipulationDevice performance: Battery life, data storage space,precision/validity of data points (TTFF, drift,indoor/outdoor)Temporal aspects: Epoch, survey duration, linkage withother sensors and GIS data
  9. 9. GPS data captureCAPTUREMost current devicesGPS trackersLow battery lifePb of integration with additional sensorsLimited capacity of data transmissionNot designed for health researchGPS in cellphonesBattery life major hinderSimultaneous usage with other applications not alwayspossible
  10. 10. GPS data captureCAPTUREAttempts to address these issuesCollaborations with engineersValidation requirements
  11. 11. SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSAccelerometerAcquisition serverCentral UnitGPS GPRSAccelerometerANT ModuleMemorySPHERE Lab .org125 g137 g96 * 80 mm115 * 59 mm
  12. 12. SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSMemoryAccelerometerANT ModuleAcquisition serverCentral UnitGPS GPRSMemoryAccelerometerANT Module
  13. 13. SenseDoc Multisensor DeviceCAPTURECentral UnitGPS GPRSMemoryAccelerometerANT ModuleAcquisition serverCentral UnitGPS GPRSMemoryAccelerometerANT ModuleGlucosemonitorGalvanic skinresponseAccele-rometerHRmonitorBloodpressureOther
  14. 14. SenseDoc Multisensor DeviceCAPTUREAcquisition serverGPS – SIRF IVGPS performance validationSpatial accuracyTime to First Fix (TTFF)Indoor – OutdoorFixed - Moving
  15. 15. CAPTUREAverage of dist_moy Column LabelsRow Labels Etrex HTC MS Qstarz Grand TotalIndoorcold 13,6 9,0 7,7 16,7 12,3Brick building, hallway 14,1 5,7 4,1 15,4 10,4Brick building, window 14,4 12,4 7,6 15,5 12,5Concrete building, window 12,3 11,6 19,4 14,4hot 12,9 11,3 10,0 15,5 12,6Brick building, hallway 11,2 7,2 6,3 15,8 10,5Brick building, window 7,6 5,0 6,9 12,8 8,5Concrete building, window 19,9 21,8 16,9 17,9 18,7warm 14,0 13,5 20,3 15,8 16,1Brick building, hallway 10,4 15,6 22,1 11,1 14,8Brick building, window 7,8 10,4 21,7 13,2 13,7Concrete building, window 23,8 12,2 17,0 23,0 20,0Outdoorcold 7,8 16,6 11,0 17,6 13,0Narrow streets 21,4 20,0 16,2 35,3 23,2Open surroundings 2,8 12,0 1,4 1,1 4,3Residential areas 2,4 4,1 0,9 2,5Sky scrapers 4,7 17,8 22,2 33,0 19,4hot 5,5 10,6 3,4 4,8 6,1Narrow streets 12,9 18,6 4,9 9,5 11,5Open surroundings 1,5 1,8 2,2 1,8 1,8Residential areas 3,2 3,4 1,9 3,1 2,9Sky scrapers 4,4 18,4 4,6 4,8 8,5warm 8,6 9,1 6,5 10,0 8,5Narrow streets 26,7 21,9 16,2 20,5 21,3Open surroundings 3,0 5,4 3,3 4,1 3,9Residential areas 4,1 4,6 2,8 5,0 4,1Sky scrapers 5,0 8,9 7,4 15,2 9,1Grand Total 10,3 11,4 9,5 13,0 11,0
  16. 16. CAPTUREAverage of ttff Column LabelsRow Labels Etrex HTC MS Qstarz Grand TotalIndoorcold 136,3 255,0 33,2 86,3 102,3Brick building, hallway 68,0 104,0 12,5 23,0 44,4Brick building, window 252,0 406,0 9,5 193,0 187,9Concrete building, window 89,0 77,5 43,0 69,8hot 18,5 181,3 5,5 13,5 36,6Brick building, hallway 6,5 82,0 6,0 2,5 16,0Brick building, window 41,0 143,0 4,0 35,0 43,3Concrete building, window 8,0 319,0 6,5 3,0 50,6warm 101,7 293,3 46,5 204,7 149,5Brick building, hallway 27,0 563,5 0,0 69,0 164,9Brick building, window 107,0 26,0 84,5 191,0 113,0Concrete building, window 171,0 20,0 55,0 354,0 168,6Outdoorcold 37,8 171,7 26,0 40,5 62,1Narrow streets 44,0 247,0 36,0 40,0 91,8Open surroundings 39,0 104,0 37,0 57,0 59,3Residential areas 26,0 20,0 26,0 24,0Sky scrapers 42,0 164,0 11,0 39,0 64,0hot 16,5 36,1 21,9 10,1 21,5Narrow streets 11,5 110,0 29,0 12,5 40,8Open surroundings 10,5 15,0 4,5 1,0 7,8Residential areas 8,5 10,0 7,5 3,0 7,3Sky scrapers 35,5 9,5 46,5 38,0 31,6warm 26,4 46,8 39,4 31,6 36,1Narrow streets 40,0 45,0 45,0 40,0 42,5Open surroundings 21,0 36,0 45,0 35,0 34,3Residential areas 30,0 68,5 44,5 29,5 43,1Sky scrapers 11,0 16,0 18,0 24,0 17,3Grand Total 55,8 130,6 28,2 65,2 65,3
  17. 17. SenseDoc Multisensor DeviceCAPTUREAccelerometerMarie-Lyse Bélanger, M.Sc. Student in kinesiologyAccelerometer validation using indirect calorimetryLab – 14 controlled exercises from sedentary to vigouros PAEleven adult subjectsCalculation of Vertical Magnitude Acceleration (VMAG)Testing of various bandpass filtersComparison with Actigraph GT3X performenceBest results obtained with Bandpass filter 0.1 Hz – 3.5 HzModelling of Energy Expenditure: Adj. R-square of .79Use of Vector Body Dynamic Acceleration (VEDBA)
  18. 18. SenseDoc Multisensor DeviceCAPTUREBattery lifeStrong battery (3200 maH)Axelle Chevallier, M.Sc. Student inelectrical engineeringMohamad Sawan,Battery optimisation algorithm- Movement- Location and movement
  19. 19. SenseDoc Multisensor DeviceCAPTUREAcquisition serverBattery life
  20. 20. SenseDoc Multisensor DeviceCAPTUREData transmissionGPS Data sent over the air (cellphone network) every 30 minutesPossible alerts depending on- Location- Activity- TimeConnection to other sensors (2.4 GHz ANT+) Heart rate monitor,footpod, RFID tags, etc.
  21. 21. SenseDoc Multisensor DeviceCAPTUREChallenges in developping new hardwareHardware / Software / User InterfaceMiniaturisationFrom prototype to marketChallenges
  22. 22. Web serverAcquisition serverOutputs /ApplicationsEnd usersGISAlgorithmsGSM towerSensorsCAPTUREPROCESSINGUSAGE
  23. 23. Spatial data collection for healthCAPTUREPROCESSINGUSAGE
  24. 24. Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
  25. 25. Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
  26. 26. Issues in data processingPROCESSINGContinuous monitoring = Huge pile of data!!!
  27. 27. Issues in data processingPROCESSINGTransforming raw GPS data into meaningful and usefulinformation- ‘Putting things into context’- Activity locations- Trips between locations
  28. 28. Activity location detectionPROCESSINGDevelopment of kernel-density based algorithm totransform raw data into history of activities and tripsArcGIS ArcToolBox (see www.spherelab.org toolssection)- Input: raw GPS data- Output:- Location of activity places- Activity places timetable- Trip timetable with origins and destinationsThierry et al. (2013) IJHG
  29. 29. Issues in data processingPROCESSINGActivity location algorithm validation method- Artificial track generation with controlledparameters (noise, stop time)- Testing of algorithm performance in relation totrack characteristics and algorithm parametersThierry et al. (2013) IJHG
  30. 30. Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)Meannumberofstopsfoundp*10m radius missing
  31. 31. Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)Meannumberofstopsfoundp*10m radius missing(a)Meannumberofstopsfou(b)Meandistancetotruestopo*10m radius missing*10m radius missing
  32. 32. Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)Meannumberofstopsfoundstop*10m radius missing(b)Meandistancetotru(c)Averagetimediff.relativetotruestopduration*10m radius missingNoise categories Noise categories
  33. 33. Issues in data processingPROCESSINGActivity location detection kernel-based algorithmThierry et al. (2013) IJHGAft Akd(a)Meannumberofstopsfoundp*10m radius missing
  34. 34. Issues in data processingPROCESSINGLinkage of GPS locations with additional information- Temporal linkage:- Additional wearable sensors (Accelerometer,Heart rate monitor, continuous glucosemonitor)- Spatial linkage: GIS data – exposure at any givenlocation/time – descriptive vs. causalunderstanding- Spatio-temporal linkage – spatio-temporal GIS
  35. 35. Issues in data processingPROCESSINGMapping – visualisationTool for communication / counseling, etc.Issue of privacy – artificial blurring
  36. 36. UsageUsing GPS to locate behaviour and assess exposureImproving the understanding of mechanisms linkingenvironments to health behaviours and profilesUsing GPS to prompt recall and gain additional insightUsing GPS to support qualitative studies (go-along, geo-ethnography, geo-tagged photos, environmentalperception, etc.)Using GPS data to assist clinical practice (mHealth)USAGE
  37. 37. Usage: Prompted recallGPS / accelerometer data provides limited informationon:- What people are actually doing- Decision processes- How they feelGPS-prompted recall can help gather additionalinformationUSAGE
  38. 38. Usage: Prompted recallExample 1: Bike share study (PI: Gauvin)Pilot study (n=25) on combined use of cellphones andaccelerometers for gathering of:- GPS data- Nature of activities- Transportation modes- Accelerometry (PA)- Momentary Impact Assessment (feelings)USAGE
  39. 39. Usage: Prompted recallBike share studyN=25, study period=7 daysAccelerometer:- PA assessmentCellphone:- GPS data – sent to server every hour- Feelings (real-time questionnaires)Daily online prompted recall data collection using theMWM (Mobility Web Mapping) application:- History of mobility- Nature of activities- Transportation modesUSAGE
  40. 40. Usage: Prompted recallExample 2: RECORD GPS Study (Chaix & Kestens)GPS + AccelerometerMWM prompted recall survey (Mobility Web Mapping)after reception and processing of GPS data:- Validation of activity places and trips- Nature of activities- Transportation modesUSAGE
  41. 41. Usage: Prompted recallMWM prompted data a useful tool to improve activitylocation algorithm / data collection- Match/mismatch between algorithm detection andreported timetable (locations/times)- Preliminary analyses: N=80USAGE88.5%11.5%GPS raw dataGPS dataMissing data
  42. 42. Usage: Prompted recallAnalyses comparing activity locations, trips andcorresponding timetables obtained through:- Spherelab GPS algorithm- MWM GPS-prompted recallN=80Median of 88.5% of survey period with usable (raw andinterpolated) GPS data (11.5% of period with missing data)USAGE88.5%11.5%Proportion of survey time with GPSdataGPS dataMissing data
  43. 43. Usage: Prompted recallUSAGEMWM AlgorithmA A <50mA A >50mA TT AT TAA >50m Misplaced activityAA-AT-TT Early departureAA-AT-AA False tripAA-TA-AA Missing tripAA-TA-TT Late departureTT-AT-TT Late arrivalTT-TA-TT False positiveTT-AT-TT False negativeTT-TA-AA Early arrival
  44. 44. Usage: Prompted recallUSAGE96.0%Correctly classifiedMisplaced activity locationEarly departureFalse tripMissing tripLate departureLate arrivalFalse positiveFalse negativeEarly arrivalOther4%Proportion of valid GPS time with match / mismatch withprompted recall data
  45. 45. USAGE+ +Trimble JunoSC GPS +ArcpadActigraphGT3XPolar HRmonitor7-day data collectionUsage: Support for clinical interventionsDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: MH Henderson)Spatio-behaviouralindicators -ArcToolBoxInteractive map-based webapplicationApplication supportslifestyle counseling
  46. 46. USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical interventions
  47. 47. USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical interventions
  48. 48. USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-JustinePediatric Hospital (PI: M. Henderson)Usage: Support for clinical interventions
  49. 49. How does GPS compare to regulardestinations?Comparing spatial distribution of- 7-day GPS data- Regular destinations collected through an online interactivemapping questionnaire (VERITAS)
  50. 50. How does GPS compare to regulardestinations?89 participants of the RECORD GPS StudyVERITAS activity locations• Total of 1,314 locations• Median of 14 loc./ind.
  51. 51. How does GPS compare to regulardestinations?89 participants of the RECORD GPS Study7-day continuous GPS monitoring0102030405060708090100PercentageofsurveydurationwithGPSfixesProportion of GPS survey duration withvalid GPS data5 Days & 07:07:153 Days & 10:25:256 Days & 04:45:205 Days & 07:07:15
  52. 52. 1000250500100
  53. 53. 0.000.100.200.300.400.500.600.700.800.901.00Within 100 m Within 250 m Within 500 m Within 1000mProportion of total survey time spent withinrange of VERITAS locations
  54. 54. 87%85%78%66%1000250500100
  55. 55. 020406080100120140Shortest distance between a GPSdetected location (unspecified category)and a VERITAS location (specifiedcategory)(median value; n=1,314)VERITAS CATEGORIESDistanceinmeters
  56. 56. CONCLUSIONSGPS opens great possibilitiesCapture – Processing – UsageMultidisciplinarity – Health – Transportation - Geography –EngineeringApplications very diverseFor epidemiology – validity is key
  57. 57. Thank you!SPHERE Lab .orgBenoit Thierry from SPHERELAB Claire Merrien from RECORDParticipants of all the studies!

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