© phil hurvitz, 2006slide 1 (of 26) validation of new technologies and methodologies for measuring...
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© 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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 3 (of 26)
Overview
• Background• The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 4 (of 26)
Background
• Submitted for Royalty Research Funding, Fall 2006
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 7 (of 26)
Background
• Current consumer-level electronics
GPS with heart-rate monitor
accelerometer in shoelinked to iPod
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 8 (of 26)
Overview
• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 10 (of 26)
The Multi-Sensor Board (MSB)
• 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)
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 11 (of 26)
The Multi-Sensor Board (MSB)
from Lester et al. 2005
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 12 (of 26)
The Multi-Sensor Board (MSB)
from Lester et al. 2005
precision = true positive / (true positive + false positive) recall = true positive / (true positive + false negative)
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 13 (of 26)
Overview
• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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)
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 15 (of 26)
Methods
• Diary (self-reported measurements)• Hourly surveys on Nokia cell phone• ¿Same activity classes as MSB classification?
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 16 (of 26)
Methods
• IPAQ (Catalyst WebQ)
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 17 (of 26)
Overview
• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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?
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 21 (of 26)
Overview
• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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?
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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?
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
Slide 24 (of 26)
Overview
• Background • The Multi-Sensor Board (MSB)• Methods• Expected data• Analyses for validation• Future research directions
© Phil Hurvitz, 2006
Validation of New Technologies and Methodologies for Measuring Physical Activity and Location in Real Time-Space
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?
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