yan kestens - methodologies for collection and analysis of gps data for health research

Methodologies for collection and analysis of GPS data

for health research

Yan KestensMontreal University, Social and Preventive Medicine

Montreal Hospital University Research Center (CRCHUM)

SPHERE Lab .org

Spatial analysis of GPS data, Exeter University, UK16th May 2013

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

Context

POTENTIAL

• Local trap / residential trap

• Space-time geography

• Potential path areas

• Activity spaces

• Network of usual places

• Multiple exposures

Context

TECHNOLOGICAL CHANGES

• Wearable sensors

• Ubiquity

• Connectedness

• 7 billion sensors

• Quantified Self - mHealth

Context

‘More data more often’

vs.

‘Less is more’

Spatial data collection for health

CAPTURE

PROCESSINGUSAGE

Web server

Acquisition server

Outputs /Applications

End users

GISAlgorithms

GSM towerSensors

Issues with GPS data capture

CAPTURE

Participation/adherence: privacy, participation burden

Device usage: co-occurrence, lose vs. on body, devicemanipulation

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

GPS data capture

CAPTURE

Most current devicesGPS trackersLow battery lifePb of integration with additional sensorsLimited capacity of data transmissionNot designed for health research

GPS in cellphonesBattery life major hinderSimultaneous usage with other applications not alwayspossible

GPS data capture

CAPTURE

Attempts to address these issues

Collaborations with engineers

Validation requirements

SenseDoc Multisensor Device

CAPTURE

Central Unit

GPS GPRSAccelerometer

Acquisition server

Central Unit

GPS GPRSAccelerometer

ANT ModuleMemory

SPHERE Lab .org

125 g

137 g

96 * 80 mm

115 * 59 mm


CAPTURE

Central Unit

GPS GPRS

Memory

Accelerometer

ANT Module

Acquisition server

Central Unit

GPS GPRS

Memory

Accelerometer

ANT Module


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


CAPTURE

Acquisition server

GPS – SIRF IV

GPS performance validation

Spatial accuracy

Time to First Fix (TTFF)

Indoor – Outdoor

Fixed - Moving

CAPTURE

Average of dist_moy Column Labels

Row Labels Etrex HTC MS Qstarz Grand Total

Indoor

cold 13,6 9,0 7,7 16,7 12,3

Brick building, hallway 14,1 5,7 4,1 15,4 10,4

Brick building, window 14,4 12,4 7,6 15,5 12,5

Concrete building, window 12,3 11,6 19,4 14,4

hot 12,9 11,3 10,0 15,5 12,6



Concrete building, window 19,9 21,8 16,9 17,9 18,7

warm 14,0 13,5 20,3 15,8 16,1




Outdoor

cold 7,8 16,6 11,0 17,6 13,0

Narrow streets 21,4 20,0 16,2 35,3 23,2

Open surroundings 2,8 12,0 1,4 1,1 4,3

Residential areas 2,4 4,1 0,9 2,5

Sky scrapers 4,7 17,8 22,2 33,0 19,4

hot 5,5 10,6 3,4 4,8 6,1

Narrow streets 12,9 18,6 4,9 9,5 11,5


Residential areas 3,2 3,4 1,9 3,1 2,9

Sky scrapers 4,4 18,4 4,6 4,8 8,5

warm 8,6 9,1 6,5 10,0 8,5

Narrow streets 26,7 21,9 16,2 20,5 21,3



Sky scrapers 5,0 8,9 7,4 15,2 9,1

Grand Total 10,3 11,4 9,5 13,0 11,0

CAPTURE

Average of ttff Column Labels

Row Labels Etrex HTC MS Qstarz Grand Total

Indoor

cold 136,3 255,0 33,2 86,3 102,3



Concrete building, window 89,0 77,5 43,0 69,8

hot 18,5 181,3 5,5 13,5 36,6




warm 101,7 293,3 46,5 204,7 149,5




Outdoor

cold 37,8 171,7 26,0 40,5 62,1

Narrow streets 44,0 247,0 36,0 40,0 91,8


Residential areas 26,0 20,0 26,0 24,0

Sky scrapers 42,0 164,0 11,0 39,0 64,0

hot 16,5 36,1 21,9 10,1 21,5

Narrow streets 11,5 110,0 29,0 12,5 40,8



Sky scrapers 35,5 9,5 46,5 38,0 31,6

warm 26,4 46,8 39,4 31,6 36,1

Narrow streets 40,0 45,0 45,0 40,0 42,5



Sky scrapers 11,0 16,0 18,0 24,0 17,3

Grand Total 55,8 130,6 28,2 65,2 65,3


CAPTURE

Accelerometer

Marie-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 performence

Best 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)


CAPTURE

Battery life

Strong battery (3200 maH)

Axelle Chevallier, M.Sc. Student in electrical engineeringMohamad Sawan,

Battery optimisation algorithm- Movement- Location and movement


CAPTURE

Acquisition server

Battery life


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.


CAPTURE

Challenges in developping new hardware

Hardware / Software / User Interface

Miniaturisation

From prototype to market

Challenges

Web server

Acquisition server

Outputs /Applications

End users

GISAlgorithms

GSM towerSensors

CAPTURE

PROCESSING

USAGE

Spatial data collection for health

CAPTURE

PROCESSINGUSAGE

Issues in data processing

PROCESSING

Continuous monitoring = Huge pile of data!!!


PROCESSING

Transforming raw GPS data into meaningful and usefulinformation

- ‘Putting things into context’- Activity locations- Trips between locations

Activity location detection

PROCESSING

Development of kernel-density based algorithm to transform raw data into history of activities and trips

ArcGIS ArcToolBox (see www.spherelab.org toolssection)

- Input: raw GPS data- Output:

- Location of activity places- Activity places timetable- Trip timetable with origins and destinations

Thierry et al. (2013) IJHG

http://www.spherelab.org/


PROCESSING

Activity location algorithm validation method

- Artificial track generation with controlledparameters (noise, stop time)

- Testing of algorithm performance in relation to track characteristics and algorithm parameters



PROCESSING

Activity location detection kernel-based algorithm


Aft Akd

(a) M

ean

num

bero

f sto

ps fo

und

(b) M

ean

dist

ance

to tr

uest

op(c

) Ave

rage

time

diff.

rela

tive

totr

uest

op d

urat

ion

*10m radius missing

*10m radius missing

Noise categories Noise categories


PROCESSING



Aft Akd

(a) M

ean

num

bero

f sto

ps fo

und

(b) M

ean

dist

ance

to tr

uest

op(c

) Ave

rage

time

diff.

rela

tive

totr

uest

op d

urat

ion

*10m radius missing

*10m radius missing


Aft Akd

(a) M

ean

num

bero

f sto

ps fo

und

(b) M

ean

dist

ance

to tr

uest

op(c

) Ave

rage

time

diff.

rela

tive

totr

uest

op d

urat

ion

*10m radius missing

*10m radius missing



PROCESSING



Aft Akd

(a) M

ean

num

bero

f sto

ps fo

und

(b) M

ean

dist

ance

to tr

uest

op(c

) Ave

rage

time

diff.

rela

tive

totr

uest

op d

urat

ion

*10m radius missing

*10m radius missing



PROCESSING

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 givenlocation/time – descriptive vs. causal understanding

- Spatio-temporal linkage – spatio-temporal GIS


PROCESSING

Mapping – visualisation

Tool for communication / counseling, etc.

Issue of privacy – artificial blurring

Usage

Using GPS to locate behaviour and assess exposure

Improving the understanding of mechanisms linkingenvironments 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, environmentalperception, etc.)

Using GPS data to assist clinical practice (mHealth)

USAGE

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 additionalinformation

USAGE


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


Bike share studyN=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


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


MWM prompted data a useful tool to improve activitylocation 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


Analyses comparing activity locations, trips and corresponding timetables obtained through:

- Spherelab GPS algorithm- MWM GPS-prompted recall

N=80Median 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

Usage: Prompted recallUSAGE

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

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 withprompted recall data

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-behaviouralindicators -ArcToolBox

Interactive map-based web application

Application supports lifestyle counseling

USAGEDyn@mo Intervention – CIRCUIT Clinic – Ste-Justine Pediatric Hospital (PI: M. Henderson)

Usage: Support for clinical interventions

How does GPS compare to regulardestinations?

Comparing spatial distribution of- 7-day GPS data- Regular destinations collected through an online interactive

mapping questionnaire (VERITAS)


89 participants of the RECORD GPS Study

VERITAS activity locations • Total of 1,314 locations• Median of 14 loc./ind.


89 participants of the RECORD GPS Study

7-day continuous GPS monitoring

0

10

20

30

40

50

60

70

80

90

100

Pe

rce

nta

ge o

f su

rvey

du

rati

on

w

ith

GP

S fi

xes

Proportion of GPS survey duration with valid GPS data

5 Days & 07:07:153 Days & 10:25:25

6 Days & 04:45:20

5 Days & 07:07:15

1000

250

500

100

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Within 100 m Within 250 m Within 500 m Within 1000m

Proportion of total survey time spent within range of VERITAS locations

87%

85%

78%

66%

1000

250

500

1000

100

0

20

40

60

80

100

120

140

Shortest distance between a GPS detected location (unspecified category)

and a VERITAS location (specified category)

(median value; n=1,314)

VERITAS CATEGORIES

Dis

tan

ce in

met

ers

CONCLUSIONS

GPS opens great possibilities

Capture – Processing – Usage

Multidisciplinarity – Health – Transportation - Geography –Engineering

Applications very diverse

For epidemiology – validity is key

Thank you!

SPHERE Lab .org

Benoit Thierry from SPHERELAB Claire Merrien from RECORD

Participants of all the studies!

yan kestens - methodologies for collection and analysis of gps data for health research

Health & Medicine