# Presentation: 758896

## Triangulation of Physical Activity Measures

**Charles E. Matthews, M.S., Ph.D.**
** **
**Research Assistant Professor**
**Department of Epidemiology and Biostatistics**
**Norman J. Arnold School of Public Health**
**University of South Carolina**
**Columbia, SC 29208**
**cematthe@sph.sc.edu**

## Overview

**BCC Physical Activity (PA) Workgroup Goals**
**Triangulation/Conceptual Issues**
**BCC Studies with Multiple PA Measures**
**Applied Example:**
- Just for You: The Gift of Healthy Eating and Physical Activity (aka, Reducing Disease Risk in  Low-income Postpartum Women, K.E. Peterson, PI)
**Analyses of Multiple PA Measures**

## Determine physical activity outcome similarities across BCC studies 
Develop common operational definitions of activity/exercise for cross project use 
Promote the use of a few common measures to allow comparisons across studies 
Establish methods for triangulation of measures to increase the validity of the findings in different studies 
Serve as a repository of all measures of activity used across studies and to determine the pros and cons of each measure. 

- Develop common operational definitions of activity/exercise for cross project use
- Promote the use of a few common measures to allow comparisons across studies
**Establish methods for triangulation of measures to increase the validity of the findings in different studies**_** **_
- Serve as a repository of all measures of activity used across studies and to determine the pros and cons of each measure.

**Goals**

**BCC Physical Activity Workgroup**

## Triangulation

- Known distance (K, 10 m)

- 90o

- 70o

- Distance
- X

**What is distance X?**
- Tan = opp (X) / adj (K)
- Tan 70 = X / 10 m
- 2.75 = X / 10 m
- X = 27.5 m

- 

- 

- 

**Notes:**
Method to estimate unknown quantity (distance between points 1 & 3 “distance x”) using information you can measure (distance between and angles of angles 1 & 2, or “distance k”).  

Trigonometry problem using the Tangent function and the relationship of the side opposite and side adjacent angles to the “line of position” (distance between 2 measured angles [1 & 2]).
Obviously not going to use this exact method in to measure PA, but we retain the idea that it is possible with multiple measures of a PA, we can improve our ability to obtain valid results for behavioral outcome measures of PA – almost certain to come from self-report

TRIANGULATION:modern uses
Trigonometry homework
Navigation
GPS calculations
Industrial robotics (precise measurements)

## Conceptual Issues

**PA Assessment Methods**
**Direct measure** (PALDLW)
**Self-report** (self-, interview-administered)
**Physiologic markers** (fitness, HDL, body mass/composition, strength, heart rate)
**Motion sensors** (pedometers, accelerometers)

## Conceptual Issues

**Uncorrelated Errors**
**Self- vs. interview **– minimize bias due to over/under reporting
**Short- vs. long-term recall** – minimize recall errors (e.g., episodic vs. generic memory)
**Physiologic/objective measure vs. self-report****	**

## Studies with Multiple PA Measures

**	****	****	****	****	****1****o**** Assessment****	****Verification**
_**U. Rhode Island**_**	****	**Yale PAS		Fitness
- 65+ yrs (N=1300)					Stage of change
_**Kansas St. U.**_
- 11-15 yrs (N=3200)		PDPAR			Accelerometer
_**U. Tennessee**_			Baecke PAQ		Accelerometer
- 25-65 yrs (N=360)		Fitness
_**Stanford U.**_**	****	****	**7DPAR, CHAMPS	Fitness,
- 55+ yrs (N=225)		 			Accelerometer
_**U. Maryland**_**	****	**
- 65+ yrs (N=240)		 Yale PAS		Step Monitor
_**Emory U.**_  			Avellone, MONICA-occp	  24PAR, Fitness
- 18-72 yrs (N=1000)		Paff (walking)

## Just for You: Study Design	

**Low-income postpartum women (N=680)**
- 2-weeks postpartum  18 months
- Women Infants and Children (WIC) participants
- Boston & Springfield/Holyoke, MA
**Randomized Controlled Trial**
- WIC+Expanded Food & Nutrition Education Program (EFNEP+PA)
- Usual WIC Care

## Measurement Objective

## Just for You: PA Measures

**Baseline**

**6-**

**12-**

**18-mo.**

**Measure**

- 7DPAR1	X		X	     X		   X
- CSA2		X		X	     X		   X
- 24PAR2	X			     X	     	   X

- 1 **Main Study (N=680)**
- Seven-day PA Recall (7DPAR)
- 2 **Ancillary Study (n=140, 20% random sample) **
-  CSA, 7 days/period
-  24PAR, 3 recalls/period

## CSA vs. Digi-walker (3-7 d, 12 hrs/d)

**	****	****	****           ****Digi-walker (steps/d, 1000s (K))***
_**CSA Measures**__**	**__**< 6.3    6.4-9.0   9.1-12.7  **__**≥**__**12.8 K**_
**Cts/min/d****	****	**** 255.0      333.4       502.2       569.5*******
**Inactive (min/d)****	**** 712.1      716.8       669.8       688.2**
**(0-499 cts/min)**
**Light (min/d) ****	****   99.2      114.3****	**** 136.4        121.0**
**(****500-1951 cts/min)**
**Mod-Vig (min/d) ****	****   21.0        35.1****	****   57.7          78.8*******
**(1952+ cts/min)      **

*******Quartiles, n=13 per group, middle-aged adults**
******* ****P < 0.01**

## Analyses

- Parallel analyses
- Modeling approaches
- Measurement error models
- Structural equation modeling

**Assumptions:**
- One measure is “true” measure, informs
- values measured with error

## Parallel Analysis: PACE

**Adapted from Calfas et. al. Prev. Med. 25: 225, 1996**

**p < 0.005**
**(n=56)**

**p < 0.05**
**(N=212)**

## Measurement Error Models

- 	Xi = Ti + b + Ei
- 	Where,
- 		Xi = measured PA (e.g., 7DPAR)
- 		Ti = “true” PA (e.g., accelerometer, 24PAR)
- 		Ei  = random error (biologic, measurement)
- 		b  = bias

**Notes:**

X = measured PA value (7DPAR)
T = “true” PA value (gold standard, accelerometer)
E = random error (mean=0)
B = bias coefficient

## 	b = Xi - Ti 

**Measurement Error Model**

- Xi = Ti + b

-  **Bias coefficient (b) used to adjust measured values using “true” measure **
**Adjustments in modeling process **
- Missing data problem (“missing at random”)
- Bias information obtained in randomly selected sub-sample applied to entire sample

**Notes:**
b = X – T
so, if you could estimate this difference, could correct measured values (X) using the reference value (T)
Statistical methods for missing data (“multiple matrix sampling”) – when “missing at random” may be capable of making this adjustment in full sample, using information (b) derived from sub-sample population with multiple PA measures (7DPAR, accelerometer, 24PAR) 

**Discussant:  **
- Deborah Riebe, Ph.D.
- University of Rhode Island
**Behavior Change Consortium**
- www1.od.nih.gov/behaviorchange

## Seven-day PA Recall (7DPAR)

**Recall last 7days, by day segment**
- 	Sleep, naps (1.0 METS [M])
- 	Sitting (1.0-1.4 M)
- 	Moderate (3.0-5.9 M)
- 	Vigorous  (6.0-7.9 M)
- 	Very Vigorous  (8.0+ M)

**Total Energy Expenditure ****(kcal/kg/d) **
**Moderate-vigorous PA Expenditure ****(kcal/kg/d)**

- Light (1.5-2.9 M) - subtraction

## Twenty-four Hour PA Recall (24PAR)

**Recall previous day, by day segment**
- 	Sleep, naps
- 	Sitting
- 	PA – open-ended report (standing+)

**Total & Moderate-vigorous PA ****(kcal/kg/d)**
**By PA domain and/or intensity **(e.g., walking for exercise (3+ mph), walking for transportation)

- Compendium interface
- 	Inside, Outside House
- 	Occupation/volunteer
- 	Recreation/leisure

**Notes:**
Advantages of 24PAR
Short recall period (what did you do yesterday – vs. what did you do everyday last week?)
Reporting bias may be minimized (over-, under-reporting)
Obtain more detail about PA
Open-ended report (activities) vs. “duration” at specific intensity
More precise MET weighting (compendium)
Quantification of day-to-day variation in PA patterns

Limitations of 24PAR
Intensive data collection effort (training, making calls – COMBINE with 24-hr diet recall, +$10/call)
More difficult to obtain frequency domain (days/week)
Reliability of measures (is  3 days enough?  decrease precision of group mean)
	adjustments possible with knowledge of day-to-day variation (measured)