Modeling Accelerometry Data Amber Watts 11/14/2014

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?

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)

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

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)

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

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

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

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?

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?

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

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?

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

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)

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

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

Michele Carlson!

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

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.

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

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

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