Using GPS, Accelerometer and GIS

to Study Physical Activity Behavior

Lars Breum Christiansen Thomas Madsen, Charlotte Demant Klinker & Jasper Schipperijn

Research Unit for Active Living

Department of Sport Science and Clinical Biomechanics

University of Southern Denmark

Research Unit for Active Living

2

To evaluate and inform built environment changes to increase

physical activity in the general population?

When was your last bout of physical activity

and how did the built environment relate to it?

Relation between physcial activity

and built environment

100s of studies looking at environmental correlates of various

types of behavior

For many types of behavior, no consistent findings across

studies

If significant, typically only explaining a small percentage of the

variation

Problems studying physical activity

behavior

The context is often unclear: where and when does the

behavior take place?

Which environment or neighborhood is relevant for

which behavior? At what time?

The Modifiable Areal Unit Problem (Openshaw, 1983)

The Uncertain Geographic Context Problem (Kwan, 2012)

Different neighborhood definitions

Associations between the built environment

and transport cycling in Denmark

Thomas Madsen

Ph.D Thesis

#

#

1km network

buffer

Inner city resident

#

1 SD ellipse

(68 % of GPS

points)

G

City center

G

Outer city resident

G

1 SD ellipse

G

Directional

ellipse

Different neighborhood definitions

Problems studying physical activity

behavior 2

It is often unclear if an increase of physical activity in

one context is associated with a decrease in a different

context. For example:

- increased physical activity during transport might be

associated with a decrease during leisure time

- more cyclists on a new bikelane might be associated with

fewer cyclist on other nearby roads

The ActivityStat hypothesis (e.g. Gomersall et al. 2012)

Adding GPS and GIS data can help

solve these problems

Objectively measured physical activity can be

divided into activity domains by combining it with

GPS data

Transport detection of trips & tripmode

Home location-based

Work and/or school time or location-based

Leisure time or location-based

GPS provides an exact location for physical

activity within an activity domain

GPS reveals the true neighborhood in which

behavior takes place

Data collection in practice

Collect accelerometer & GPS data

Collect additional time-stamped data such as diaries, school

or work time-tables, surveys, etc.

Collect home, school and work addresses and geocode them

Collect relevant GIS layers (e.g. sport facilities, green space)

Merge accelerometer & GPS data

PALMS - Personal Activity Location Measurement System

Developed by the Center for Wireless & Population Health Systems,

University of California, San Diego

Merge and clean data (based on timestamp and extreme values)

Detect trips and assign trip-modes (based on GPS speed)

Assign physical activity levels (based on accelerometer data)

http://ucsd-palms-project.wikispaces.com

Identify domains using PostgreSQL

At home? Yes

No

At work ? Yes

No

In transport? Yes

No

In a leisure location?

Yes

Home domain

Work domain

Transport

domain

Active Passive

Leisure

domain

Clubs

Sports

facilities

Urban

Green Space

Shopping

centers

Input data

Commute Other trips

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

Results from the When Cities Move

Children study

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

The Centre is funded by TrygFonden and the Danish Cancer Society

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

1. To identify important domains and sub-

domains for physical activity among

children aged 11-16 yrs old based on

objective measurements

2. To investigate how week day physical

activity and movement patterns vary by

gender

Aim

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

• 523 youth (10-16) participated (623 invited),

509 returned accelerometer and gps data

• Inclusion criteria: 1 valid WEEK day of 9

hours combined accelerometer and GPS data

• Study population: n=368 (72.3%)

• Descriptives:

– Daily mean wear time 13.9 hours

– Mean of 2.5 days (range 1-4)

– Mean age 13.2

– 52.4% girls

Baseline data 2010-11

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

Data structure

Other

Home

School

Epoch: 15 sec

Leisure

Transport

Recess

PE

Walk/bike

Vehicle

School

Clubs

Sports facili.

Playgrounds

Urban G.S.

Shopping

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Outdoor

Domains Subdomains

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

MVPA

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

Pattern analyses – week days

Home

School

Epoch: 15 sec

Leisure

Transport

Domains Subdomains

MVPA

MVPA

MVPA

MVPA

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

Minutes of MVPA in domains

by gender (daily median, n=367)

21,8

24,9

12,7

4,8

11,7

18,8

12,3

6,5

0,0

5,0

10,0

15,0

20,0

25,0

30,0

Leisure School Transport Home

Boys(total

75.2***)

Girls(total

54.9)

*** p≤0.001 and * p<0.05 in multilevel model adjusting for age, BMI, number of valid days & time in domain. School included to account for clustering

***

***

*

Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

% time in MVPA in domains

by gender (daily median, n=367)

8,7 8,3

32,0

3,4

5,4 5,9

24,9

3,6

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

Leisure School Transport Home

Boys (total9.1%***)

Girls (total6.7%)

*** ***

***

*** p≤0.001 and * p<0.05 in multilevel model adjusting for age, BMI, number of valid days & time in domain. School included to account for clustering

Questions for the audience

What is the potential for context-specific measures

(GPS, GIS and accelerometer) to evaluate and inform

future built environment changes to increase physical

activity in the general population?

www.gps-hrn.org

Conclusion

Combination of methods provides great potential for

future studies where the context of physical activity

is of importance

The amount of data is large, but becoming more

manageable using ‘big data’ methods

Other sensors or data sources can easily be added

Use of GPS in health research is a growing field that

is developing fast

www.gps-hrn.org