measuring physical activity and location in real time

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


MEBI 591B Public Health Informatics Seminar

Slide 2 (of 45)

Confidentiality

• Unpublished data• Please do not distribute



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



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




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)



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)



No Data <10% 10% –14% 15%–19% 20%–24% ≥25%




No Data <10% 10% –14%




No Data <10% 10% –14%




No Data <10% 10% –14%




No Data <10% 10% –14%




No Data <10% 10% –14%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19%




No Data <10% 10% –14% 15%–19% ≥20




No Data <10% 10% –14% 15%–19% ≥20




No Data <10% 10% –14% 15%–19% ≥20




No Data <10% 10% –14% 15%–19% ≥20




No Data <10% 10% –14% 15%–19% 20%–24% ≥25%



(*BMI 30, or ~ 30 lbs overweight for 5’4” person)

No Data <10% 10% –14% 15%–19% 20%–24% ≥25%






No Data <10% 10% –14% 15%–19% 20%–24% ≥25%



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



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?



Slide 9 (of 45)

Overview





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



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



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



Slide 13 (of 45)

Introduction to GIS: Coordinate Framework



Slide 14 (of 45)

Introduction to GIS: Address Location

• GIS can match address records to spatial location



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



Slide 16 (of 45)

Introduction to GIS: Risk or Exposure Estimation

Miranda, M. L. and D. C. Dolinoy. 2005. Neurotoxicology. 26(2). 223-228



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



Slide 18 (of 45)


• Fast food restaurant KDE



Slide 19 (of 45)

Introduction to GIS: Risk Estimation

• Is there a relationship between fast food density and obesity?

p-value = 0.155



Slide 20 (of 45)


• 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



Slide 21 (of 45)

Overview





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



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



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



Slide 25 (of 45)


• IDEAA: recognizable activities



Slide 26 (of 45)


• IDEAA: categorized activities by time



Slide 27 (of 45)


• 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



Slide 28 (of 45)


• 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



Slide 29 (of 45)


• 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



Slide 30 (of 45)


• Multi-Sensor Board Classification of Activity90-minute interval



Slide 31 (of 45)

Overview





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?



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



Slide 34 (of 45)


!×

0 10.5Kilometers [

Land use

EDU

INST

MF

OFF

OTHER

RET-SER

SF



Slide 35 (of 45)


• GIS analysis results for each location

buffer(count)measures

proximitymeasures



Slide 36 (of 45)

Overview





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



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

!( !( !(!( !( !( !(!(

!( !(!(

!( !(!(!(!(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(

!( !( !( !( !( !( !( !( !( !( !( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !( !( !( !( !( !( !( !( !( !( !( !( !(!(!(!(!(!(!(!(!(!(!(!(!(!( !( !( !( !( !( !(!( !( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(

!(

!(

!(

!(!(

!(!(!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(!(

!(!(!(

!( !(

!(!(!(

!(!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(!(!(

!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!(!(

!( !( !( !(!( !(!(

!(!(!(!(!(

!(!(!(

!(!(!(

!(

!(

!(

!(

!(

!(

!( !(!(

!(!(!(!(

!(!(

!(!(!(!(

!(

!(

!(

!(

!( !( !(!(

!(!(!(

!(!(!(

!(

!(!(!(

!(

!(!(

!(!(!(

!(

!(!(!(

!(

!(

!(

!(!(!(

!(!(

!(!(

!(

!(

!(!( !(

!(!(!(

!(!(

!(!(!(!(

!(

!( !( !(

!(

!(!(

!(!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!( !(

!(!(!(!(

!(

!(

!(!(!(!(!(!(!(!(!(

!(

!(!(!(!(!(!(!(

!(

!(!(

!(!(!(!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!(!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(

!(

!( !( !( !(

!(!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!( !( !( !(!(!( !(!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(

!(!(!(!(!(!(

!(

!(!(!(!(!(!(!(

!(

!(!(

!(

!(

!( !( !( !(!(

!(

!(

!(!(

!(

!(!(

!(!(

!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(!(!(!(!(!(

!(

!(

!( !( !(!( !( !( !(!(

!(!(!(

!(

!(

!(

!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!( !( !(!(

!(!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(!(

!(

!(!(

!(

!(

!( !(

!(!(!(

!(!(

!(!(

!(

!(

!(!(!(

!(

!(!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(

!(!(!(!(!(!(!(!(!(!(!(!(!(

building

EDU

INST

MF

OFF

OTHER

RET-SER

SF

activity type

!( bike

!( drive

!( stand

!( walk0 100 200 300

Feet [

Data overload? 15 h * 60 min/h * 60 s/min * 7 d * 40 subjects = 15,120,000 data points



Slide 39 (of 45)


• Sampling strategy for data reduction without loss of variability

!( !( !(!( !( !( !(!(

!( !(!(

!( !(!(!(!(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(

!( !( !( !( !( !( !( !( !( !( !( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !( !( !( !( !( !( !( !( !( !( !( !( !(!(!(!(!(!(!(!(!(!(!(!(!(!( !( !( !( !( !( !(!( !( !(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(!(

!(!(!(

!(

!(

!(

!(!(

!(!(!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(!(

!(!(!(

!( !(

!(!(!(

!(!(

!(

!(

!(

!(

!(

!(

!(!( !(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(!(!(!(

!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(!(

!(!(

!( !( !( !(!( !(!(

!(!(!(!(!(

!(!(!(

!(!(!(

!(

!(

!(

!(

!(

!(

!( !(!(

!(!(!(!(

!(!(

!(!(!(!(

!(

!(

!(

!(

!( !( !(!(

!(!(!(

!(!(!(

!(

!(!(!(

!(

!(!(

!(!(!(

!(

!(!(!(

!(

!(

!(

!(!(!(

!(!(

!(!(

!(

!(

!(!( !(

!(!(!(

!(!(

!(!(!(!(

!(

!( !( !(

!(

!(!(

!(!(

!(

!(

!(

!(!(!(!(

!(

!(

!(

!( !(

!(!(!(!(

!(

!(

!(!(!(!(!(!(!(!(!(

!(

!(!(!(!(!(!(!(

!(

!(!(

!(!(!(!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(!(!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(

!(

!(

!(

!(!(

!(!(

!(

!(

!(

!(

!(!(

!(

!(

!(!(

!(!(

!(

!( !( !( !(

!(!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!( !( !( !(!(!( !(!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(!(

!(!(!(!(!(!(

!(

!(!(!(!(!(!(!(

!(

!(!(

!(

!(

!( !( !( !(!(

!(

!(

!(!(

!(

!(!(

!(!(

!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(!(!(!(!(!(

!(

!(

!( !( !(!( !( !( !(!(

!(!(!(

!(

!(

!(

!(!(!(!(!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(!( !( !(!(

!(!(!(!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(!(

!(

!(!(

!(

!(

!( !(

!(!(!(

!(!(

!(!(

!(

!(

!(!(!(

!(

!(!(

!(

!(!(!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

!(!(

!(!(!(!(

!(

!(

!(

!(!(

!(

!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(

!(!(!(!(!(!(!(!(!(!(!(!(!(

building

EDU

INST

MF

OFF

OTHER

RET-SER

SF

activity type

!( bike

!( drive

!( stand

!( walk0 100 200 300

Feet [

!( !(!( !( !(

!(!(!(

!( !(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!( !(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(!(!(

!(

building

EDU

INST

MF

OFF

OTHER

RET-SER

SF

activity type

!( bike

!( drive

!( stand

!( walk0 100 200 300

Feet [

10% sample → 1.5 million data points

(time or distance?)



Slide 40 (of 45)


• 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



Slide 41 (of 45)

RRF Funded Research: Results from Pilot & Feasibility Study

• Sample demographics



Slide 42 (of 45)


• MSB activity & location

Activity

bike

jog

walk

car

sit

stand

unclassed

0 500250meters



Slide 43 (of 45)


• 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



Slide 44 (of 45)

Overview


Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space

• Analysis Plan• Suggestions/Questions



Slide 45 (of 45)

Suggestions/Questions

Phil Hurvitz

[email protected]

gis.washington.edu/phurvitz

gis.washington.edu/phurvitz/msb

measuring physical activity and location in real time

Documents