© Phil Hurvitz, 2006Slide 1 (of 26) Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space Phil

© Phil Hurvitz, 2006 Slide 1 (of 26)

Validation of New Technologies and Methodologies for

Measuring Physical Activity and Location in Real Time-Space

Phil HurvitzUrbDP PhD Colloquium

2006.10.10

© Phil Hurvitz, 2006

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

Slide 2 (of 26)

Overview

• Background• The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions



Slide 3 (of 26)

Overview

• Background• The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions



Slide 4 (of 26)

Background

• Submitted for Royalty Research Funding, Fall 2006



Slide 5 (of 26)

Background

• Physical activity (PA) is important for health maintenance• Adequate PA decreases incidence of cancer, diabetes,

cardiovascular disease• US Surgeon General recommends 30 minutes of moderate

exercise most days of the week

• Physical activity is difficult to measure objectively in free-living individuals

• An accurate, reliable, valid, unobtrusive device for measuring PA would be valuable for:• research in health (obesity, rehab. medicine, etc.)• consumer level electronics



Slide 6 (of 26)

Background

Method Advantages Limitations

Direct observation Best recording of physical activity (PA) type

Information on PA context Applicable to children

Time consuming Potential reactivity of study participant Subjectivity of the observer Not appropriate for large-scale studies

Pedometers Lightweight, portable Simple, inexpensive Appropriate for free-living conditions

Only walking or running steps, no recording of horizontal or upper-body movements

No information of specific activity, only total (daily) PA No locational capability

Accelerometers Same advantages as pedometers Recording of accelerations in more than

one plane and for extended time period Measurement of intensity; possibility of

measuring a specific activity

No recording of horizontal or upper-body movements, carrying a load

Potential reactivity of study participant No locational capability

Questionnaires Applicable in epidemiological studies Valid for gross PA classification for a

population (e.g., low vs. high)

Limited validity; no detailed information of PA; dependent on subject’s memory and interpretation

Not suited for PA assessment at the individual level

IDEEA Accurate measure of type and dose of several activity patterns

Expensive Cumbersome; electrodes and wires may impede free

movement Validity limited to level walking and running; unknown if

device senses changes in elevation Not appropriate for large-scale studies No locational capability

adapted from Vanhees et al. 2005

• Comparison of physical activity measurement methods



Slide 7 (of 26)

Background

• Current consumer-level electronics

GPS with heart-rate monitor

accelerometer in shoelinked to iPod



Slide 8 (of 26)

Overview

• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions



Slide 9 (of 26)

The Multi-Sensor Board (MSB)

• Multi-modal, microprocessor based sensor of multiple environmental variables, developed by UW & Intel

• 3D acceleration• barometric pressure• humidity• temperature• compass bearing• light (daylight & fluorescent)• audio• location (from WiFi or GPS)

• 18 MHz for some measurements• Internal miniSD (2 GB) storage• Nokia cell phone for additional data storage/data transfer, auxiliary

data logger



Slide 10 (of 26)


• 1-second temporal resolution• In a pilot project, 92% accuracy (Lester et al., 2005)

• MSB classifies measurements• Hidden Markov models and Decision Stumps methods• Operationalized in Matlab

• Transforms raw sensor data into classified activities:

• walk (up/down stairs)• bicycle• sit• jog

• stand• car/bus• elevator (up/down)



Slide 11 (of 26)


from Lester et al. 2005



Slide 12 (of 26)


from Lester et al. 2005

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



Slide 13 (of 26)

Overview




Slide 14 (of 26)

Methods

• Subjects• 50 adults• 20-60 y• ~50% male, 50% female• ¿Twin registry?

• 7 day measurement period (to include 2 weekend days)• Self-report diary, hourly• PA questionnaire post-measurement (International

Physical Activity Questionnaire — IPAQ)



Slide 15 (of 26)

Methods

• Diary (self-reported measurements)• Hourly surveys on Nokia cell phone• ¿Same activity classes as MSB classification?



Slide 16 (of 26)

Methods

• IPAQ (Catalyst WebQ)



Slide 17 (of 26)

Overview




Slide 18 (of 26)

Expected data

• Diary report vs. MSB-classified (counts)

Self-Reported (Diary) Activities

Bicycling Sitting Walking

MSBClassifiedActivities

Bicycling 1 0 0

Sitting 3 21 3

Walking 1 0 1

Pilot data from 42 diary entries:

50 subjects * 7 d * 16 h/d * 3600 s/h = 20,160,000 measurements

50 subjects * 7 d * 16 h/d = 5,600 diary entries



Slide 19 (of 26)

Expected data

• IPAQ Results• ¿What to do with these?

• Compare self-reported (diary), MSB-classified, and IPAQ• Durations?

• Statistical tests comparing durations?



Slide 20 (of 26)

Expected data

• Map data (not specifically part of the RRF proposal, but essential for future research)

Activity

bike

jog

walk

car

sit

stand

unclassed

0 500250meters



Slide 21 (of 26)

Overview




Slide 22 (of 26)

Analyses for validation (PA)

• Compare counts or durations of self-reported vs. MSB-classified PA

• Cohen’s Kappa statistic?• Used to assess inter-rater reliability when observing or

otherwise coding qualitative/ categorical variables.• Kappa is considered to be an improvement over using %

agreement to evaluate this type of reliability.• Not inferential (no p-level)• κ > 0.7 considered satisfactory

• Chi-square (observed vs. expected) for inferential test?• Other statistics?



Slide 23 (of 26)

Analyses for validation (location)

• Obtain coordinates of location where diary was recorded• Define a buffer at the radius of instrument precision• Select buildings

or parcels withinbuffer

• If any features within buffer match self-reported location,consider this a match

• ¿What analyses?



Slide 24 (of 26)

Overview




Slide 25 (of 26)

Future Research Directions

• Size of spatial realm of activity, comparing low to high SES

• Patterns in locations of long dwell time• Stay tuned …• Ultimately: PA & Urban Form relationships?



Slide 26 (of 26)

I Want You!

• Pilot & Feasibility study is ongoing

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

Documents

© Phil Hurvitz, 2006Slide 1 (of 26) Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space Phil