Dana Wolff-Hughes, PhD

Dana Wolff-Hughes, PhD, is a Program Director in the Health Behaviors Research Branch (HBRB) of the Behavioral Research Program (BRP) in NCI's Division of Cancer Control and Population Sciences (DCCPS). In this capacity, she leads a portfolio focused on novel technologies and analytical approaches for assessment of health behaviors including physical activity, sleep/circadian rhythm, sedentary behavior, and the built environment.  Her research focuses on improving methods for assessment and profiling of cancer-related health behaviors (e.g., physical activity and sleep) from wearable sensors and temporally linked contextual data.

Dr. Wolff-Hughes is involved in numerous cross-agency and trans-NIH/NCI scientific initiatives including serving as the NIH lead on the NSF-NIH interagency Smart and Connected Health Initiative and a federal representative on the US Physical Activity Guidelines. Dr. Wolff-Hughes has been recognized for her scientific service and leadership receiving the HHS Stronger Together Award, NIH Director’s Awards, OD Honor and Service Awards, DPCPSI Director’s Awards, NCI and NHLBI Director’s Awards, and the 2025 DCCPS Mentor of the Year award. Dr. Wolff-Hughes is an elected fellow of the Washington Academy of Sciences and received their 2023 Leadership in Behavioral and Computer Science Award, recognizing work of merit and distinction of scientists and leaders in the greater Washington area.

Prior to joining NCI, Dr. Wolff-Hughes was a health scientist administrator for the NIH’s Office of Behavioral and Social Sciences Research where she coordinated trans-NIH initiatives focused on innovative technologies and data science approaches to advance behavioral and social sciences research. She received her Ph.D. in kinesiology with a concentration in physical activity epidemiology, a M.S. in Exercise Physiology from the University of Tennessee, and a B.S. in exercise science from Elon University. 


Scientific Interests

  • Physical activity
  • Sleep/circadian rhythm
  • Digital health technologies
  • Digital and artificial intelligence biomarkers
  • Novel data science technologies such as artificial intelligence/machine learning, computer vision, digital twins, multimodal data fusion

Selected Publications and Presentations