measuring physical activity and location in real time
DESCRIPTION
Measuring Physical Activity and Location in Real Time. Phil Hurvitz University of Washington College of Architecture and Urban Planning Urban Form Lab gis.washington.edu/phurvitz MEBI 591B Public Health Informatics Seminar 2007.05.04. Confidentiality. Unpublished data - PowerPoint PPT PresentationTRANSCRIPT
MEBI 591B Public Health Informatics Colloquium
© Phil Hurvitz, 2006
Measuring Physical Activityand Location in Real Time
Phil HurvitzUniversity of Washington
College of Architecture and Urban PlanningUrban Form Lab
gis.washington.edu/phurvitz
MEBI 591B Public Health Informatics Seminar2007.05.04
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 2 (of 45)
Confidentiality
• Unpublished data• Please do not distribute
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 3 (of 45)
Overview
• Introduction/Background/Relevance• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 4 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 5 (of 45)
Introduction/Background: Obesity
• Obesity threatens personal health and may bankrupt the US health care system
• Obesity incidence has increased dramatically over the last 20 years
1990 1992 1994 1996 1998 2000 2002
10
15
20
25
Median BMI, 1990-2002, USA
year
me
dia
n %
BM
I
Source: CDC BRFSS (http://apps.nccd.cdc.gov/brfss/Trends/trendchart.asp)
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 6 (of 45)
Introduction/Background: Obesity Trends
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1985
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2004
No Data <10% 10% –14% 15%–19% 20%–24% ≥25%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1986
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1987
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1988
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1989
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1990
No Data <10% 10% –14%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1991
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1992
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1993
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1994
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1995
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1996
No Data <10% 10% –14% 15%–19%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1997
No Data <10% 10% –14% 15%–19% ≥20
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1998
No Data <10% 10% –14% 15%–19% ≥20
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 1999
No Data <10% 10% –14% 15%–19% ≥20
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2000
No Data <10% 10% –14% 15%–19% ≥20
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2001
No Data <10% 10% –14% 15%–19% 20%–24% ≥25%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Source: Behavioral Risk Factor Surveillance System, CDC.
(*BMI 30, or ~ 30 lbs overweight for 5’4” person)
No Data <10% 10% –14% 15%–19% 20%–24% ≥25%
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Obesity Trends* Among U.S. AdultsBRFSS, 2002
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. AdultsBRFSS, 2003
(* BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
No Data <10% 10% –14% 15%–19% 20%–24% ≥25%
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 7 (of 45)
Introduction/Background: Diet & Exercise
• Nutrition guidelines: “Eat more grains, fruits, vegetables…”
• Health care system says, “Eat less, exercise more.”• Technology and food provides choices that are not
conducive to healthy lifestyles
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 8 (of 45)
Introduction/Background: Physical Activity
• Increasing physical activity is important for maintaining or decreasing weight, and for general health
• The built environment can either promote or hinder physical activity, e.g., • Presence/absence of sidewalks• Presence/absence of utilitarian destinations (e.g.,
restaurants, retail stores, restaurants, banks)
• Research Question: How does physical activity vary with different compositions and configurations of environment?
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 9 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 10 (of 45)
Introduction to GIS: What is GIS?
• A computer-based method for • Capture,• Storage,• Manipulation,• Analysis, and• Display
of spatially referenced data
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 11 (of 45)
Introduction to GIS: What is GIS?
• Any object or phenomenon that is or can be placed on a map can be stored, managed, and analyzed in a GIS.• Built environment features (streets, buildings, bus routes,
restaurants, schools)• Households (address points, tax-lot polygons)• Individuals (points or travel lines/polygons)• Ground surface elevation or slope• Movement of objects through time and/or space• Demographics, socioeconomics• Patient residence, work, and school locations• Exposure or risk estimation• Disease occurrence
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 12 (of 45)
Quantiles of Med H Inc
0 - 21161
21162 - 27212
27213 - 33333
33334 - 41250
41251 - 150001
0 1 2 3 40.5Miles
[
Introduction to GIS: Data Framework
Quantiles of Med H Inc
0 - 21161
21162 - 27212
27213 - 33333
33334 - 41250
41251 - 150001
0 1 2 3 40.5Miles
[
GIS combines coordinate (map) and attribute (tabular/statistical) data
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 13 (of 45)
Introduction to GIS: Coordinate Framework
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 14 (of 45)
Introduction to GIS: Address Location
• GIS can match address records to spatial location
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 15 (of 45)
Introduction to GIS: Analysis
• Analytical techniques (a very simple list)• Spatial aggregation
• Disease rates per census or zip code area• Buffering
• How many pedestrian-auto collisions within 1 mile of schools?
• Overlay/Proximity analysis • How much of each census block group is affected by a toxic
aerosol plume?• How many parcels of each type of land use are within ½
mile of all walking locations visited within a day?• Surface generation, interpolation
• Trend or density surfaces• Kriging
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 16 (of 45)
Introduction to GIS: Risk or Exposure Estimation
Miranda, M. L. and D. C. Dolinoy. 2005. Neurotoxicology. 26(2). 223-228
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 17 (of 45)
Introduction to GIS: Risk Surface Estimation
• Kernel density estimator (KDE)
creates a Gaussian surface for each individual point location and sums each individual surface across XY space
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 18 (of 45)
Introduction to GIS: Risk Surface Estimation
• Fast food restaurant KDE
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 19 (of 45)
Introduction to GIS: Risk Estimation
• Is there a relationship between fast food density and obesity?
p-value = 0.155
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 20 (of 45)
Introduction to GIS: Risk Surface Estimation
• Kriging (geostatistical analysis)
Hellstrom, L., L. Jarup, B. Persson and O. Axelson. 2004. J Expo Anal Environ Epidemiol. 14(5). 416-23.
sig. relationshipbetween Pb insoil and blood♀ eating homegrownvegetables
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 21 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 22 (of 45)
Measuring Physical Activity: How?
• Subjective• Observation• Self-Report
• Stanford 7-Day Activity Survey
• International Physical Activity Questionnaire (IPAQ)
• Travel Diaries
• Objective • Pedometers• Accelerometers• New Generation Devices
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 23 (of 45)
Measuring Physical Activity: Benefits & Drawbacks
Type Benefits Drawbacks
Subjective
Observation does not require effort on part of subject
accuracy varies by observer & instance
high cost
Self-Report does not require observer
low cost
over-reporting common
recall bias
Objective
Pedometer low cost
easy to use
acceptable for free-living subjects
not suitable for all populations
no activity discrimination
no location
no temporal resolution
Accelerometer no activity discrimination
no location
New Generation Devices
varies varies
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 24 (of 45)
Measuring Physical Activity: New Generation Devices
• Intelligent Device for Energy Expenditure and Activity (IDEAA)• sensors attached to skin (cumbersome)• relative accelerometry of different body parts• no locational capability• no external environmental cues• $4,000 per unit
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 25 (of 45)
Measuring Physical Activity: New Generation Devices
• IDEAA: recognizable activities
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 26 (of 45)
Measuring Physical Activity: New Generation Devices
• IDEAA: categorized activities by time
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 27 (of 45)
Measuring Physical Activity: New Generation Devices
• Multi-Sensor Board• UW/Intel invention, recent development• single sensing unit with data logger (smart phone)• easily worn• measures multiple environmental data streams• obtains XY locational data• estimated $100 per unit cost
in large manufacturing run
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 28 (of 45)
Measuring Physical Activity: New Generation Devices
• Multi-Sensor Board• On-board sensors:
• accelerometry• audio• IR / visible light• high-frequency light• barometric pressure• humidity, temperature• geophysical location (from GPS)
• Multivariate data stream can be interpreted as a number of common activities using Hidden Markov Model with Decision Stumps classifiers
• Used in ECOR Pilot & Feasibility Study
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 29 (of 45)
Measuring Physical Activity: New Generation Devices
• Multi-Sensor Board Activity Classifier (overall accuracy > 95%)• Validated against videography
Sitting Standing Walking JoggingWalking up stairs
Walking down stairs
Riding a bicycle
Driving car
Riding elevator down
Riding elevator up
Sitting 89.8% 38.5% 0.5% 0.4% 33.4%Standing 10.1% 50.8% 1.4%Walking 0.1% 7.4% 97.7% 5.2% 2.5%Jogging 100.0%Walking up stairs 94.8%Walking down stairs 0.5% 97.5%Riding a bicycle 3.3% 99.6%Driving car 66.6%Riding elevator down 100.0%Riding elevator up 100.0%
Classified Activity (by HMM)
Pre
cis
ion
Lab
eled
Act
ivit
ies
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 30 (of 45)
Measuring Physical Activity: New Generation Devices
• Multi-Sensor Board Classification of Activity90-minute interval
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 31 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 32 (of 45)
Measuring the Built Environment: What and Where?
• What to Measure?• Based on Research Question(s)
• GIS Data Sources
• Point Locations
• Buffer Measures
• Proximity Measures
• Where to Measure?• Home-centered
• Frank et al. 2005
• Moudon et al. 2005
• Where does activity take place in real time?
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 33 (of 45)
Measuring the Built Environment: A GIS Based Approach
• Point-centered Analysis of Location• Any number of different data sets can be quantified
• Enumeration & relative proportion of different land uses• Parcel density• Street-block size• Total length of sidewalk • Number of intersections, lighted crosswalks• Area and count of parks• Distance to different built environment features
• We should quantify & analyze all locations that are experienced during the day, not only the home location
• Work & school environments may be key determinants of physical activity
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 34 (of 45)
Measuring the Built Environment: A GIS Based Approach
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© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 35 (of 45)
Measuring the Built Environment: A GIS Based Approach
• GIS analysis results for each location
buffer(count)measures
proximitymeasures
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 36 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 37 (of 45)
RRF Funded Research
• Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
• MSB to capture• Activity type• Location
• Walkable-Bikeable Communities GIS Software• Quantifying & analyzing the Built Environment
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 38 (of 45)
RRF Funded Research: Analysis Plan
• MSB activity & location• Validity tests against diary (real-time location &
activity), IPAQ (self-reported physical activity summary)• WBC location analysis of Built Environment
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Data overload? 15 h * 60 min/h * 60 s/min * 7 d * 40 subjects = 15,120,000 data points
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 39 (of 45)
RRF Funded Research: Analysis Plan
• Sampling strategy for data reduction without loss of variability
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10% sample → 1.5 million data points
(time or distance?)
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 40 (of 45)
RRF Funded Research: Analysis Plan
• This will be the first study to measure objectively• both physical activity types and Built Environment in a
real-time, real-world setting with free-roaming individuals
• Statistical associations?• Activity types/intensities & Built Environment types?
• What do we gain if a pattern is discovered?• Policy recommendations• Quantitative urban design guidelines• A new “gold standard” for measurement of physical
activity in real-time
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 41 (of 45)
RRF Funded Research: Results from Pilot & Feasibility Study
• Sample demographics
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 42 (of 45)
RRF Funded Research: Results from Pilot & Feasibility Study
• MSB activity & location
Activity
bike
jog
walk
car
sit
stand
unclassed
0 500250meters
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 43 (of 45)
RRF Funded Research: Results from Pilot & Feasibility Study
• Automatic classification vs. self-report (42 diary entries)
p=0.05, Fisher’s exact test
* “None” indicates the classifier was not able to classify a given activity† “Shopping” was a user-added activity type that had no match in the automatic classification scheme
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 44 (of 45)
Overview
• Introduction/Background• What is GIS, and what is its role in Public Health?• Measuring Physical Activity• Measuring the Built Environment• UW-RRF Funded Research: Validation of New
Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
• Analysis Plan• Suggestions/Questions
© Phil Hurvitz, 2007
MEBI 591B Public Health Informatics Seminar
Slide 45 (of 45)
Suggestions/Questions
Phil Hurvitz
gis.washington.edu/phurvitz
gis.washington.edu/phurvitz/msb