2.2 - good self-report measures of physical activity vs. sensors: let’s not throw the baby out...
DESCRIPTION
Tuesday, October 23, 2012 Technical Session #2: Energy Expenditure Donna Spruijt-Metz (University of Southern California, US), Britni Belcher (National Cancer Institute, US), Ya-Wen Hsu (Chia Nan University of Pharmacy & Science, US)TRANSCRIPT
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'Good' Self-Report Measures of Physical Activity vs. Sensors: Let's not throw the baby out
with the bathwater
Donna Spruijt-Metz (USC)Britni Belcher (NCI)Peisheng Shi (USC)
Janice Ya-Wen Hsu (Chia Nan University)
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Objective, Ubiquitous MeasurementThe Holy Grail?
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Measuring Physical Activity: A Brief History
Self report (trials and tribulations) Rise of objective measures
(1981 ~ 10 articles, 2011 ~ 800 articles) Special issue MSSE 2005 Troiano + 2007 NHANES pub objective PA in
large samples = feasible & informative Self-report falling out of favor (Sirard + 2001,
Spruijt-Metz + 2009)
Special issue MSSE 2012: Intille+ recognizes value of self-report.
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Proliferation of sensors – nothing does everything
Bypass report bias Raw data vs. black box Include other measures
(heart rate, Galvanic Skin Response)
Syncs to apps & has cool user interfaces
Gamefied Will give you
participant data Will give you data in
real time
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Activity Monitor Trade-offs
PROS Some research-grade
monitors are validated Paired with other
sensors (like GPS) = valuable synced levels of data
Ubiquitous If wirelessly enabled,
can give you real time data
If comfy, low participant burden
CONS Many not (well)
validated Might be expensive
or user-unfriendly Miss swimming,
biking, weight lifting, other activities
No contextual information (what are you doing, who are you with?)
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Activity: Self-report versus Sensor
Different measures are related to or explain different phenomena, and…
Give you types of different information
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Example 1: Activity & Metabolic Syndrome
Sample:› Combined baseline data from 3 related studies › N=105 (complete data)› 74% Latino, 75% female, mean age=13±3 yrs (range: 8-
18)
Protocols: Overnight admission to GCRC› Body composition: air plethysmography (BodPodTM )› Blood pressure: in triplicate› Waist circumference: measured at the umbilicus› Fasting glucose, plasma lipids: collected after an
overnight fast
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Methods - Activity Measures
Accelerometer (Actigraph GT1M)› Wear for 7 days (Inclusion criteria: 4 days 10 hours)
3-day Physical Activity Recall (3DPAR)› 7.00 am-12.00 am/day› Identify main activities in half-hour intervals and rate the
relative intensity level for each activity
8
Moderate-to-Vigorous Physical Activity (MVPA) 1
Sedentary Behavior 2
Threshold ≥4 MET <100 counts/min
MVPA 1 Sedentary Behavior 3
Threshold ≥4 MET playing video game, talking on phone, watching TV/movie
1 Freedson et al 1998 2 Matthews et al 2008 3 Pate et al 2003
METs= Metabolic equivalent tasks, or the ratio of working to resting metabolic rate
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Pediatric Definition of MetS
3 or more of the following 5 features:
Feature Criteria
Elevated Triglycerides ≥ 90th % for Age and Sex, and Ethnicity (NHANES Ⅲ)1
Low HDL-cholesterol ≤ 10th % for Age and Sex, and Ethnicity (NHANES Ⅲ) 1
Abdominal adiposity ≥ 90th % for Age Gender, and Ethnicity (NHANES Ⅲ) 1
Elevated Blood Pressure ≥90th % for Age, Sex, and Height (NHBPEP)* 1
Hyperglycemia Impaired Fasting Glucose ≥100 mg/dl (ADA guideline) 2
* NHBPEP: National High Blood Pressure Education Program1 Cruz et al, 2004 2 Cook et al , 2003 9
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MetS (N=17/16%)
Non-MetS (N=88/84%) p-value
Gender Female 9 (11%) 70 (89%)
0.02 * Male 8 (31%) 18 (69%)Ethnicity Latino 15 (19%) 63 (81%)
0.22 African American 2 (7%) 25 (93%)BMI Percentile 98.5 (±1.6) 89.0 (±18.1) <0.001 ‡Fat mass (kg) 41.8 (±19.1) 25.2 (±15.8) 0.001 **Lean mass (kg) 56.0 (±14.0) 46.0 (±16.3) 0.007 **
p <.05, ** p <.01, ‡ p <.001
x2 tests were used for categorical variables, independent t tests/ Wilcoxon rank sum test for continuous variables 10
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11
0
100
200
300
400
500
600
26.5
462.2
102.9
291.8
31.6
461.8
147.2
210.1
MetS
Non-MetS
Accelerometer 3DPAR
Acti
vit
y levle
s in
min
ute
Activity Levels by MetS Status
*
*
* p<0.01
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Subjective(3DPAR)
Objective(Accelerometry)
Dominant activity for each half-hour block Detectable time spent in specific intensity levels of physical activity
May explain the relatively higher estimates of MVPA obtained by 3DPAR
Influenced by respondent’s perceived of fitness levels, social desirability, memory
Determined by continuous wearable monitoring (take human reporting errors out of the equation)
May explain the relatively larger variations in MVPA obtained by 3DPAR
Within Time Dimension for MVPA:
Within Time Dimension for Sedentary Behavior:
Subjective(3DPAR)
Objective(Accelerometry)
Sedentary activities selected based on empirical evidence
Captures all detected activities under a certain cut-off
May explain greater differences between MetS groups
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Adjusted Odds Ratio for MetS (3+)
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Adjusted OR for MetS a
Accelerometer (min)b
MVPA 0.49 (95% CI: 0.25-0.95)
Sedentary Behavior
2.56 (95% CI: 0.06-119.63)
3DPAR (min)b
MVPA 0.86 (95% CI: 0.58-1.26)
Sedentary Behavior
4.44 (95% CI: 1.41-14.02)
a. Adjusted for age, gender, ethnicity, Tanner stage, fat mass, lean mass b. Log-transformed values were used
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Adjusted Odds Ratio for MetS (3+)
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Adjusted OR for MetS a
Adjusted OR for MetS a
(keep both MVPA & SB in )
Accelerometer (min)b
MVPA 0.49 (95% CI: 0.25-0.95) 0.49 (95% CI: 0.25-0.98)
Sedentary Behavior
2.56 (95% CI: 0.06-119.63)
1.01 (95% CI: 0.02-61.70)
3DPAR (min)b
MVPA 0.86 (95% CI: 0.58-1.26) 0.99 (95% CI: 0.64-1.56)
Sedentary Behavior
4.44 (95% CI: 1.41-14.02)
4.44 (95% CI: 1.33-14.79)a. Adjusted for age, gender, ethnicity, Tanner stage, fat mass, lean mass b. Log-transformed values were used
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Pearson/Partial Correlations between Activity Levels and Features of MetS
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AccelerometerUnadjusted correlation
MVPA SB
Triglycerides -0.21* ̶U
HDL-cholesterol ̶U ̶U
Waist circumference -0.42 ‡ 0.32 **
Fasting glucose ̶U ̶U
Systolic Blood Pressure -0.33 ** 0.21 *
Diastolic Blood Pressure -0.27** ̶U3DPAR
Unadjusted correlation
MVPA SB
Triglycerides ̶U ̶U
HDL-cholesterol ̶U -0.22 *
Waist circumference ̶U ̶U
Fasting glucose ̶U ̶U
Systolic Blood Pressure ̶U 0.31**
Diastolic Blood Pressure ̶U ̶USB=sedentary behavior - : not significant * p <0.05, ** p <.01, ‡ p <.001
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AccelerometerUnadjusted correlation Partial correlation
MVPA SB MVPA SB
Triglycerides -0.21* ̶U ̶U ̶U
HDL-cholesterol ̶U ̶U ̶U ̶U
Waist circumference -0.42 ‡ 0.32 ** ̶U ̶U
Fasting glucose ̶U ̶U -0.21* ̶U
Systolic Blood Pressure -0.33 ** 0.21 * -0.25* ̶U
Diastolic Blood Pressure -0.27** ̶U ̶U ̶U3DPAR
Unadjusted correlation Partial correlation
MVPA SB MVPA SB
Triglycerides ̶U ̶U ̶U ̶U
HDL-cholesterol ̶U -0.22 * ̶U -0.21*
Waist circumference ̶U ̶U ̶U ̶U
Fasting glucose ̶U ̶U ̶U ̶U
Systolic Blood Pressure ̶U 0.31** ̶U 0.26 **
Diastolic Blood Pressure ̶U ̶U ̶U ̶UAdjusted for age, gender, ethnicity, tanner stage, fat mass, lean mass * p <0.05, ** p <.01, ‡ p <.001
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SummaryAccelerometer 3DPAR
Physical Activity↑
• Triglycerides↓• Waist circumference↓• Fasting glucose↓• Systolic blood pressure↓• Diastolic blood pressure↓• Odds of MetS↓
Sedentary Behavior↑
• Waist circumference ↑• Systolic blood pressure ↑
• HDL-cholesterol↓• Systolic blood pressure↑• Odds of MetS ↑Only significant findings were reported, those with underline were adjusted for
covariates
Findings regarding MVPA and MetS were based on the accelerometer data
The adjusted associations between sedentary behavior and MetS were based on the data by 3DPAR
Hsu et al, MSSE 2011
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Example 2: Activity & Acculturation
Assimilation (United States Orientation),
Separation (Home Country Orientation),
Integration (Both Countries Orientation),
Marginalization (Neither Country Orientation)1.
Physical activity measured by accelerometry and 3-Day PAR
1Unger et al 2002
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95 Latina girls
Mean (SD)
Age 12.1 ± 3.1
BMI %tile 87.0 ± 20.0
% body fat 32.5 ± 11.2
Mean mins. MVPA per day (accelerometer) 31.4 ± 21.1
Mean mins. MVPA per day (self-report) 96.9 ± 74.2
Mean mins. Sedentary per day (accelerometer)
467.0 ± 96.0
Mean mins Sedentary per day (self-report) 187.7 ± 107.7
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Multivariate linear regressions for activity levels
MVPA
SED
3DPAR-MVPA
3DPAR-SED
β p β p β p β p
AGE -3.3<0.000
1 11.0 0.02 -7.2 0.03 -3.9 0.50
% fat -0.4 0.05 0.9 0.49 -0.1 0.90 0.2 0.91Margin-ization 12.6
<0.0001 -31.4 0.10 -12.8 0.08 29.1 0.02
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Top 10 activities
Not Marginalized MarginalizedSleeping Sleeping
Sitting in class Sitting in class
Watching TV or movie Watching TV or movie
Hanging around Eating a meal
Eating a meal Homework
Riding in a car/bus Hanging aroundPlaying video games/ surfing Internet while sitting Lunch/free time/study hall
Doing house chores Riding in a car/bus
Travel by walking Doing house chores
Homework Working (e.g., part-time job)
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Caution: Not all self-report is created equal
Valid? Appropriate for targeted population? Train (either participant or staff) to
administer? HUMAN FACTOR2: even a great self-
report measure can’t protect you from poor data collection.
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Conclusion & Suggestion Both *GOOD* objective and *GOOD*
subjective measures of activity contribute to understanding activity and health
Powerful methodologies will combine objective measures, a mix of sensors, and prompted real-time self-report
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Acknowledgements
NCI USC Center for Transdisciplinary Research on Energetics and Cancer (U54 CA 116848)