Ian J. Barnett

About

Ian J. Barnett, Ph.D., is an Adjunct Assistant Professor of Biostatistics and Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is also a Senior Scholar at the Center for Clinical Epidemiology and Biostatistics, a Core Center Faculty member at the Center for Statistics in Big Data, and an Affiliate Member of the Wharton Sports Analytics and Business Initiative. Dr. Barnett serves as the Co-director of the Biostatistics and Data Management Core of the Penn Mental Health AIDS Research Center (PMHARC) and is the Director of the Penn Unit for Mobile App Sensing (PUMAS) within the Department of Biostatistics, Epidemiology, and Informatics. His educational background includes a B.S. in Mathematical and Computational Sciences from Stanford University (2010) and a Ph.D. in Biostatistics from Harvard University (2014). His research focuses on biostatistics, epidemiology, digital health, and data science, with particular emphasis on mobile sensing, digital phenotyping, and behavioral interventions. Dr. Barnett's work involves developing and applying statistical methods to understand health behaviors, mental health, and injury recovery, often utilizing innovative digital and mobile technologies.

Research topics

• Medicine
• Computer Science
• Data Mining
• Psychiatry
• Machine Learning
• Neuroscience
• Clinical psychology
• Medical education
• Mathematics
• Psychology

Selected publications

• Towards clinically actionable digital phenotyping targets in schizophrenia

  Schizophrenia · 2020 · 62 citations

  Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well a…

  DOI

• Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care

  World Psychiatry · 2020 · 39 citations

  Imagine a smartphone app that knows when a patient is at risk of relapsing on alcohol use based on geolocation data indicating proximity to a liquor store and real-time surveys suggesting elevated craving. The smartphone detects this imminent risk, alerts a clinician, and the patient receives a personal check-in within minutes. Such a system does not sound futuristic in 2020, neither was it a decade ago, when the Alcohol - Comprehensive Health Enhancement Support System (A-CHESS) study, describe…

  DOI

• Determining sample size and length of follow-up for smartphone-based digital phenotyping studies

  Journal of the American Medical Informatics Association · 2020 · 32 citations

  1st authorCorresponding

  OBJECTIVE: Studies that use patient smartphones to collect ecological momentary assessment and sensor data, an approach frequently referred to as digital phenotyping, have increased in popularity in recent years. There is a lack of formal guidelines for the design of new digital phenotyping studies so that they are powered to detect both population-level longitudinal associations as well as individual-level change points in multivariate time series. In particular, determining the appropriate bal…

  DOI

• Gut metagenomes reveal interactions between dietary restriction, ageing and the microbiome in genetically diverse mice

  Nature Microbiology · 2025-03-31 · 9 citations

  articleOpen access
  PublisherOA PDFDOI

• Naturalistic Tobacco Retail Exposure and Smoking Outcomes in Adults Who Smoke Cigarettes Daily

  JAMA Network Open · 2025-09-29 · 2 citations

  articleOpen access

  Importance: The tobacco industry spends more than $8 billion annually in the US on marketing at the point of sale. Exposure to tobacco retail has been associated with smoking outcomes, but substantially less is known about how objectively logged everyday tobacco retail exposure is associated with smoking outcomes. Objective: To assess preregistered hypotheses that individuals would report (1) greater craving and (2) more cigarettes smoked on days when their exposure to tobacco retail is higher t…

  PublisherOA PDFDOI

Recent grants

• Statistical methods in mHealth to signal interventional needs for mental health patients

  NIH · $1.6M · 2019–2023

Frequent coauthors

• John Torous

  Harvard University

  31 shared

• Jukka‐Pekka Onnela

  21 shared

• Matcheri S. Keshavan

  Harvard University

  19 shared

• Catherine C. McDonald

  Philadelphia University

  16 shared

• Christina L. Master

  13 shared

• Theodore D. Satterthwaite

  Children's Hospital of Philadelphia

  12 shared

• M. Kit Delgado

  University of Pennsylvania

  12 shared

• Patrick Staples

  12 shared

Labs

• Biostatistics and EpidemiologyPI

Education

• B.S., Mathematical and Computational Sciences

  Stanford University

  2010

• Ph.D., Biostatistics

  Harvard University T.H. Chan School of Public Health

  2014

Awards & honors

• Senior Scholar, Center for Clinical Epidemiology and Biostat…
• Affiliate Member, Wharton Sports Analytics and Business Init…
• Co-director, Biostatistics and Data Management Core of Penn…
• Director, Penn Unit for Mobile App Sensing (PUMAS), Departme…

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