Validating objective measures of physical activity with a novel device
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Validating Objective Measures of Physical Activity with a Novel Device. Phil Hurvitz UrbDP 598: Built Environment & Health January 22, 2008. Overview. Background Currently used devices: benefits and drawbacks A new device: the Multi-Sensor Board (MSB) Preliminary data. Conceptual Model.

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Validating objective measures of physical activity with a novel device

Validating Objective Measures ofPhysical Activity with a Novel Device

Phil Hurvitz

UrbDP 598: Built Environment & Health

January 22, 2008


Overview

Overview

  • Background

  • Currently used devices: benefits and drawbacks

  • A new device: the Multi-Sensor Board (MSB)

  • Preliminary data

Slide 2 (of 35)


Conceptual model

Conceptual Model

  • Behav, Env > Health Outcome


High prevalence of no physical activity

High prevalence of no physical activity

BRFSS: http://www.cdc.gov/brfss/

Slide 3 (of 35)


Increasing prevalence of obesity

Increasing prevalence of obesity

BRFSS: http://www.cdc.gov/brfss/

Slide 4 (of 35)


How did this happen

How did this happen?

  • Natural history of the human animal: we need physical activity (PA) to maintain a healthy body & mind

  • Modern conveniences have engineered PA out of our daily lives

  • Better to be fit & fat than thin & sedentary

Slide 5 (of 35)


Modern conveniences

Modern conveniences

“... the life of man, solitary, poor, nasty, brutish, and short. ”

--Thomas Hobbes

Slide 6 (of 35)


Modern conveniences1

Modern conveniences

Slide 7 (of 35)


A worldwide crisis

A worldwide crisis?

Slide 8 (of 35)


A worldwide crisis1

A worldwide crisis?

Slide 9 (of 35)


Validating objective measures of physical activity with a novel device

Prevalence of Obesity

Females age 15-100, 2005

WHO: http://www.who.int/en/

Slide 10 (of 35)


Validating objective measures of physical activity with a novel device

Prevalence of Obesity

Males age 15-100, 2005

WHO: http://www.who.int/en/

Slide 11 (of 35)


Better to be fit fat than thin sedentary

Better to be fit & fat than thin & sedentary

Sui X, LaMonte MJ, Laditka JN, Hardin JW, Chase N, Hooker SP, Blair SN.

Cardiorespiratory fitness and adiposity as mortality predictors in older adults.

JAMA. 2007 Dec 5;298(21):2507-16.

Slide 12 (of 35)


Goal increase physical activity

Goal: increase physical activity

  • No apparent decrease in development and use of labor-saving devices

  • Will health agency guidelines “work” in the face of technological advances?

  • How will we know if there are changes in physical activity rates?

  • Current methods of measuring physical activity are not adequate!

Slide 13 (of 35)


Current methods

Current methods

Slide 14 (of 35)


State of the art ideea

State-of-the-art: IDEEA

  • 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 15 (of 35)


State of the art msb

State-of-the-art: MSB

  • 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 location data

    • estimated $100 per unit costin large manufacturing run

Slide 16 (of 35)


Environmental data

Environmental data

  • Raw sensor data

  • Measurement frequency >200 Hzfor some variables

Slide 17 (of 35)


Locational data

Locational data

  • Real-time differentially corrected GPS

  • Ground accuracy: 3-7 m under typically good conditions

  • GPS data combined with MSB data

  • 1 s temporal resolution

Slide 18 (of 35)


Data classification

Data classification

  • Hidden Markov Model with Decision Stumps(a probabilistic data classification model based on multiple variables)

Lester, J., Choudhury, T., Kern, N., Borriello, G., & Hannaford, B. (2005).

A hybrid discriminative/generative approach for modeling human activities.

Paper presented at the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI).

Slide 19 (of 35)


Data classification pilot study

Data classification: pilot study

precision = (true positive/(true positive + false positive))

recall = (true positive/(true positive + false negative))

Lester, J., Choudhury, T., Kern, N., Borriello, G., & Hannaford, B. (2005).

A hybrid discriminative/generative approach for modeling human activities.

Paper presented at the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI).

Slide 20 (of 35)


Data collection process

Data collection process

  • Environment measures

    • Subjects wear MSB on belt

    • MSB measures (8 variables)

    • GPS location

    • Continuous data sampling, 1Hz

  • Activity self-report

    • Subjects carry Windows Mobile phone

    • Hourly data sampling

Slide 21 (of 35)


Activity self report

Activity self-report

  • Questionnaire administered on Smart Phone

Slide 22 (of 35)


Activity self report1

Activity self-report

  • Text message sent after questionnaire completed

Slide 23 (of 35)


Activity self report2

Activity self-report

Slide 24 (of 35)


Preliminary data location

Preliminary data: location

  • 1 subject, 7 d, ~146,000 points

Slide 25 (of 35)


Preliminary data classification

Preliminary data: classification

Slide 26 (of 35)


Research question s

Research Question(s)

  • What build environment (BE) characteristics are associated with different activities?

    • Walking

    • Running

    • Pedal-cycling

    • Sitting

    • Driving

Slide 27 (of 35)


Research question s1

Research Question(s)

  • What BE characteristics are associated with:

    • Walking trips < 1 mi

    • Driving trips < 1 mi

  • What is the size of an individual’s spatial extent of activity

    • All activity

    • Active modes of transport vs. Inactive modes

  • How does spatial extent vary across age, gender, wealth, BMI, BE of home &/or work location?

Slide 28 (of 35)


Measuring built environment

Measuring built environment

  • 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 29 (of 35)


Measuring built environment1

Measuring built environment

  • 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 30 (of 35)


Measuring built environment land use

Measuring built environment: land use

Slide 31 (of 35)


Data reduction sampling strategy

Data reduction sampling strategy

  • Sampling strategy for data reduction without loss of variability

10% sample → 1.5 million data points (time or distance?)

Slide 32 (of 35)


Challenges

Challenges

  • Administrative challenges

    • Bugs

      • “Cutting edge” may sound super-cool, but it leads to “bleeding”

    • Validating a new method requires a big commitment

    • Interdisciplinarity has its ups & downs

      • The future of all complex research

      • Relying on others’ time & expertise

      • Working around another project’s timeline

Slide 33 (of 35)


Challenges1

Challenges

  • Operational challenges

    • Lots of data to deal with

      50 subjects * 7 d * 8 h/d * 60 m/h * 60 s/m = 10,080,000 observations

      50 subjects * 8 h/d * 7 d * 1 survey/h =

      2800 surveys

Slide 34 (of 35)


Conclusion

Conclusion

  • Questions?

  • Interested in volunteering?

    http://tinyurl.com/3x2rkv

    http://gis.washington.edu/phurvitz/msb/

    [email protected]

Slide 35 (of 35)


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