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I am an Associate Professor at Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, a faculty member of Statistical Methodology and Applications in Technology (SMART) group, a co-leader of the Wearable and Implantable Technology (WIT) group, and the biostatistics co-director of the Motor Activity Research Consortium for Health (mMARCH).
I am working on methods development for multi-modal data generated by wearables (such as fitness and sleep trackers, heart rate monitors, GPS, and electronic diaries) and interfacing these data with larger databases of more traditionally collected information (by large national epidemiological studies, health surveys, and Electronic Medical Records). I am currently focusing on the understanding these continuously monitored physiological signals via engineering of interpretable and predictive biomarkers to be able to parsimoniously model interactions between various physiological systems and relating them to clinical outcomes. I am currently interested in quantifying: i) frailty and fatigue in clinical populations with heart disease (such as Congestive Heart Failure and Atrial Fibrillation), ii) physical disability in individuals with Multiple Sclerosis, iii) disruption of circadian rhythms in populations with Mood Disorders (bipolar disorder and major depression disorder) and dementia, iv) quality of sleep in impaired aging populations including individuals with dementia and Alzheimer’s Disease.
I am also deeply involved in analyzing ecological momentary assessment (EMA, also known as burst measurements, intensive longitudinal data, experience sampling, daily electronic diaries) data. EMA is a very popular tool in Psychology to assess cognitive function and emotional circumplex (such as self-reported levels of sadness, anxiety, energy, etc) in daily life. Daily electronic diaries available through smart-phones/watches are now extensively used beyond Psychology for ecological momentary sampling that taps patterns of many homeostatic systems including sleep, emotional states, dietary intake and others to assess behavioral components of human homeostatic systems.
My research gets a lot of immediate exposure via close connections with multiple research groups and labs through Johns Hopkins University (Departments of Epidemiology, Mental Health, Anesthesiology, Rehabilitation, Neurology, and Psychiatry) as well as through intramular research programs at National Institute of Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, and National Institute of Drug Abuse.
I am working on methods development for multi-modal data generated by wearables (such as fitness and sleep trackers, heart rate monitors, GPS, and electronic diaries) and interfacing these data with larger databases of more traditionally collected information (by large national epidemiological studies, health surveys, and Electronic Medical Records). I am currently focusing on the understanding these continuously monitored physiological signals via engineering of interpretable and predictive biomarkers to be able to parsimoniously model interactions between various physiological systems and relating them to clinical outcomes. I am currently interested in quantifying: i) frailty and fatigue in clinical populations with heart disease (such as Congestive Heart Failure and Atrial Fibrillation), ii) physical disability in individuals with Multiple Sclerosis, iii) disruption of circadian rhythms in populations with Mood Disorders (bipolar disorder and major depression disorder) and dementia, iv) quality of sleep in impaired aging populations including individuals with dementia and Alzheimer’s Disease.
I am also deeply involved in analyzing ecological momentary assessment (EMA, also known as burst measurements, intensive longitudinal data, experience sampling, daily electronic diaries) data. EMA is a very popular tool in Psychology to assess cognitive function and emotional circumplex (such as self-reported levels of sadness, anxiety, energy, etc) in daily life. Daily electronic diaries available through smart-phones/watches are now extensively used beyond Psychology for ecological momentary sampling that taps patterns of many homeostatic systems including sleep, emotional states, dietary intake and others to assess behavioral components of human homeostatic systems.
My research gets a lot of immediate exposure via close connections with multiple research groups and labs through Johns Hopkins University (Departments of Epidemiology, Mental Health, Anesthesiology, Rehabilitation, Neurology, and Psychiatry) as well as through intramular research programs at National Institute of Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, and National Institute of Drug Abuse.
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BMJ EVIDENCE-BASED MEDICINEpp.bmjebm-2023, (2024)
Neurologyno. 4 (2024): e208102-e208102
MENTAL HEALTH AND PHYSICAL ACTIVITY (2024): 100573
Jennifer Glaus,Sun Jung Kang,Wei Guo,Femke Lamers,Marie-Pierre F Strippoli, Andrew Leroux,Debangan Dey,Kerstin J Plessen,Julien Vaucher, Peter Vollenweider,Vadim Zipunnikov,Kathleen R Merikangas,
Journal of psychiatric research (2023): 325-336
arXiv (Cornell University) (2023)
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Journal of the American Academy of Child & Adolescent Psychiatryno. 10 (2023): S372-S373
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