Bio
Dr. Perera is a biostatistician with interests in aging, clinical trials, time series analysis, item response theory, and classification and regression trees. His work has involved estimating criteria for clinically meaningful change in physical performance measures of the elderly, examining their association with future outcomes using large data sets, and using item response theory to analyze rating scales used in elderly populations. Dr. Perera co-leads the Data Management, Analysis and Informatics Core of our Pepper Center, in addition to serving as lead statistician in multiple funded NIH, AHRQ and VA grants within and outside of the Division.
Research
Publications
Peer-Reviewed Article
2023
Brain and Cognition
Exercise Interventions, postural control, and prefrontal cortex activation in older adults
Peer-Reviewed Article
2022
JAMA Network Open
Effect of Timing and Coordination Training on Mobility and Physical Activity Among Community-Dwelling Older Adults A Randomized Clinical Trial
Peer-Reviewed Article
2020
Contemporary Clinical Trials
Program to improve mobility in aging (PRIMA) study: Methods and rationale of a task-oriented motor learning exercise program
Peer-Reviewed Article
2019
Journal of Biomechanics
Predictive regression modeling of body segment parameters using individual-based anthropometric measurements
Peer-Reviewed Article
2019
Journal of Biomechanics
Age and body mass index associations with body segment parameters
Peer-Reviewed Article
2017
Experimental Brain Research
Effects of acute peripheral/central visual field loss on standing balance
Peer-Reviewed Article
2016
Journals of Gerontology Series A-Biological Sciences and Medical Sciences
Cerebral amyloid deposition and dual-tasking in cognitively normal, mobility unimpaired older adults
Peer-Reviewed Article
2015
IEEE Journal of Translational Engineering in Health and Medicine
Extraction of Stridge Events from Gait Accelerometry During Treadmill Walking
Peer-Reviewed Article
2015
Computers in Biology and Medicine
Understanding the effects of pre-processing on extracted signal features from gait accelerometry signals
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