1. Modeling Accelerometry Data
Amber Watts
11/14/2014

2. Some Initial Research Questions
Does the amount of time spent in sedentary and physical activity influence health and cognitive function in older adults?
Do people with Alzheimer’s disease (AD) have different patterns of sedentary and physical activity?
Is light intensity activity sufficient to produce health and cognitive benefits in older adults or must it be vigorous activity?

3. Ongoing Data Collection
Goal is 50 AD & 50 Healthy Controls
Target date Feb 15, 2015 (currently more than 2/3 of our goal)
Neighborhood Data
Objective GIS data (connectivity & integration)
Subjective data on neighborhoods (safety, aesthetics, destinations, etc.)
Physical activity (1 week)
Accelerometry (intensity of movement) & Inclinometry (sitting/standing)
Activity diaries
Self report questionnaire of habitual physical activity (brief)

4. Available Outcome Measures include
Cardiorespiratory fitness- VO2 testing
Annual Cognitive Assessment Battery
Biomarkers (BMI, BP, cholesterol)
Physical performance test (walking speed, leg strength, etc.)
Neuroimaging

5. Accelerometry Types of data acquired by accelerometry
Time spent in light, mod, vigorous intensity activity
Time spent awake/asleep
Step counts
Step cadence
Time in sedentary (sitting) behavior
% waking hours spent in activity (sit, light, mod, vig)
Length of sedentary bouts, break rate
(Postural ; ActivPal- placement on thigh)

7. Accelerometers: ActivPAL
Advantage: placed on thigh for better discrimination between sitting and standing

8. Accelerometry Details
15 second epochs
Every 15 seconds for 7+ days
50 AD; 50 Healthy Controls
4- 15-sec epochs per minute
60 minutes per hour
24 hours per day (5760 epochs per day)
7 days
40,320 epochs per week

9. Standard Analysis Approach
Essentially collapsing 40,320 data points into one data point
Avg. minutes per day spent in moderate intensity activity over a week
Ignores variability patterns throughout the day
Remove time spent in sleep
Apply standard algorithms for intensity cutoffs for light, moderate, vigorous activity
Note this is not well established for applicability to older adults
Amount of time spent in X activity / Total waking hours = % of waking time spent in X activity

10. Some Questions
Are 15 second epochs actually more informative than daily averages?
At what length of epochs is the information most valuable?
If people are relatively sedentary, and spend long durations sitting, are we actually learning anything?

11. More Research Questions
Do AD spend more time sitting than Controls?
How long are the bouts of sitting (on average)?
For the whole group
For individuals
When people are sitting, what are they doing?
Technology use? Commute times?
When people are active, what are they doing?
Types of activities that are popular, safe, beneficial in this population
How do the diaries compare to the objective data?
Environmental & social predictors of sedentary time
How strongly related are sedentary time & active time in this population?

12. Adjustments
Variations by season, time of day, weekend vs. weekday

13. What could be done?
What are the patterns of activity associated with healthy outcomes?
What is the daily pattern followed by an individual?
Do people with AD have different patterns of activity, sitting, sleep/wake patterns?
What amount / intensity of activity is appropriate for older adults with and without AD?

14. What are the goals?
Long term goal: understand individual patterns of activity to allow for targeted interventions to reduce sitting time and/or increase activity
Describe the characteristics of people AD to identify differences from people without AD

15. MLM Activity Monitoring Level 1 (between persons)
AD or Control
Age, sex, education, wealth
Neighborhood characteristics
VO2
Level 2 (within person)
Multiple observations of activity intensity, duration, etc.
Neighborhood Nesting?
Level 1 (between neighborhoods)
Level 2 (addresses within a neighborhood)

16. Other suggestions
Using raw continuous activity counts instead of cutoffs (cutoffs not appropriate to older adults)
e.g., <100 cpm considered sedentary, but based on teenage girls
Log transformation of activity times
Rank ordering of all activity in a day, loses temporal ordering, but allows for smoother function to plot and analyze

18. More Research Questions
Decompose triaxial information
e.g., active sitting time axis 1 low, but axis 2 & 3 not sedentary

20. Michele Carlson!

21. Physical Activity Diaries
Accelerometers don’t tell us WHAT participants do
Activity diaries used to describe movement
Accelerometers don’t tell us what activities people do

22. Neighborhood Walking
Walking is the most common physical activity of older adults (safe, inexpensive, etc.)
Walking is related to healthy outcomes: focus on metabolic biomarkers & cognitive function
Most common place to walk is one’s own neighborhood
Neighborhood characteristics influence walking
Sidewalks, available destinations, traffic, etc.

23. Pilot Study: Methods Retrospective analysis of secondary data
64 older adults with & without mild Alzheimer’s disease (AD)
Neighborhood characteristics: integration & connectivity (GIS & space syntax analysis)
Self-reported physical activity & walking
Biomarkers (BMI, glucose, BP, etc.)
Factor analytically derived cognitive scores

24. Pilot Study: Results
Neighborhood integration & connectivity predicted health biomarkers and changes in cognitive performance over 2 year follow up
Neighborhood did not predict self-reported walking
Self reported walking recall bias & measurement error
Or other possible mechanisms?
Next Steps: Collect objective walking data via accelerometry

25. Neighborhood Data May share same neighborhoods (nested)
We used 0.5 mile radius around participant address
Connectivity: # of paths, streets, nodes directly linked to individual street or node in the network
Higher connectivity  more destinations
Integration: directness of path between points A & B
# of turns required to reach all other locations in network using shortest paths
Higher integration  less cognitively complex