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
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