About

Jessilyn Dunn is an Associate Professor of Biomedical Engineering at Duke University. Her research focuses on physiologic monitoring, digital health, and risk factor discovery, contributing to the development of innovative tools and methods for health assessment and disease management. As a faculty member in the BIG IDEAs Lab, she collaborates with a team of researchers, students, and medical professionals to advance biomedical engineering solutions that improve health outcomes.

Research topics

• Artificial Intelligence
• Computer Science
• Embedded system
• Medicine
• Machine Learning
• Simulation
• Data science
• Engineering
• Internal medicine
• Surgery
• Telecommunications
• Pathology
• Human–computer interaction
• Mathematics

Selected publications

• Impact of daylight saving time on physical activity patterns

  Nature Health · 2026-04-23

  articleSenior authorCorresponding
  PublisherDOI

• The need to develop health data transaction disclosure requirements to balance transparency, privacy, and progressive use

  The Lancet Digital Health · 2026-02-01

  articleOpen access

  This Viewpoint critically evaluates the impact of commodifying health data in the era of electronic health records and the ethical challenges this practice raises. Additionally, the Viewpoint explores the complex interplay between the advancement of health-care artificial intelligence (AI) and the imperative for transparency in data practices, scrutinising the insufficiency of existing regulations in addressing the nuances of health data transactions. The January, 2025 US executive order on AI further emphasises the need for stringent data management and privacy measures, supporting our call for a patient-centric approach to data handling. Moreover, we advocate for comprehensive regulatory frameworks that promote transparency, protect patient data, and maintain trust in health technologies. Such efforts contribute to a health-care environment where data stewardship is synonymous with integrity and respect for patient rights.

  PublisherOA PDFDOI

• Population differences in wearable device wear time: Rescuing data to address biases and advance health equity

  medRxiv · 2026-03-06 · 1 citations

  articleOpen access

  Abstract Wearable devices present transformative opportunities for personalized healthcare through continuous monitoring of digital biomarkers; however, individual variations in device wear time could mask or otherwise impact signal identification. Despite the widespread adoption of wearable devices in research, no comprehensive framework exists for understanding how wear time varies across populations or for addressing wear time-related biases in analysis. Using Fitbit data from 11,901 participants in the All of Us Research Program, we conducted the first large-scale systematic assessment of wearable device wear time across demographics, social determinants of health, lifestyle factors, mental health symptoms, and disease. Our findings revealed that wear time was higher among males and increased with age, income, and education, but decreased with depressive, anxiety, and anhedonia symptoms, with reductions more pronounced following clinical diagnoses compared to symptom-based classifications. Individuals with chronic conditions displayed differential levels of wear time compared to healthy controls. Critically, we demonstrate that the widely used ≥10-hour daily compliance threshold, while appropriate for some research contexts, can disproportionately exclude days of data from disease populations: among individuals with major depressive disorder, 74.4% of data days were excluded compared to 20.9% for controls. We propose a flexible methodological framework including standard compliance thresholds, wear time covariate adjustment, metric normalization, propensity score matching, and adaptive thresholds that can be applied individually or in combination to optimize wearable data retention across diverse research contexts. These findings establish wear time as a critical methodological consideration for wearable device research and provide guidance for advancing equitable and rigorous digital health analytics.

  PublisherOA PDFDOI

• BIG IDEAs Lab Glycemic Variability and Wearable Device Data

  PhysioNet · 2026-04-11

  datasetOpen accessSenior author

  This study aimed to determine the feasibility and effectiveness of wearable devices in detecting early physiological changes prior to the development of prediabetes [1-3]. The study generated digital biomarkers for remote, mHealth- based prediabetes and hyperglycemia risk to classify which individuals should undergo further clinical testing. The primary inclusion criteria were subjects aged 35-65 years, inclusive, including only post-menopausal females, with a point of care A1C measurement between 5.2-6.4%, inclusive. Blood was collected during the study for measurement of glucose, hemoglobin A1C, lipoproteins, and triglycerides. Participants wore a Dexcom 6 continuous glucose monitor (CGM) and an Empatica E4 wristband for 10 days while receiving a standardized breakfast meal every other day. At the end of the 10 days, the participant returned to the clinic for an oral glucose tolerance test (OGTT). Research data collected includes physiological measurements from wearable devices such as heart rate, accelerometry, and electrodermal conductance.

  PublisherDOI

• Big data integration for enhanced epidemiological research: insights and directions from NHLBI's workshop

  Frontiers in Digital Health · 2026-04-22

  articleOpen access

  The landscape of epidemiological research is experiencing a technological transformation, driven by the rapid expansion of big data and advancements in artificial intelligence (AI) and machine learning (ML). This workshop explored the opportunities and challenges associated with integrating diverse data sources into population-based research at different levels, including electronic health records (EHRs), genomic and omics data, imaging, wearable device data, and social determinants of health measures, among others. AI/ML tools present powerful capabilities for analyzing these vast datasets, offering advancements in health risk prediction, disease pattern identification, and the development of personalized interventions. However, the integration of big data introduces technical barriers related to data heterogeneity, privacy and security concerns, and the potential to exacerbate health disparities through algorithmic biases. In September 2023, the National Institutes of Health's (NIH) National Heart, Lung, and Blood Institute (NHLBI), in collaboration with the National Cancer Institute (NCI) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), hosted a workshop to address these challenges and discuss the integration of big data into epidemiology and population-based studies. Key themes from the workshop emphasized interdisciplinary collaboration, data standardization, and the development of robust ethical frameworks, as well as the importance of advancing data governance, implementing transparent consent processes, and employing privacy-preserving techniques to maintain public trust. Additionally, the workshop highlighted the transformative potential of digital health technologies, such as wearable devices, which, when integrated with EHRs, enhance data granularity, facilitate early disease detection, and strengthen public health surveillance. Ethical, legal, and social issues (ELSI) are central to responsibly leveraging big data and AI in research, unbiased algorithms, the use of diverse datasets in AI training, and continuous human oversight to mitigate risk and ensure validity. The workshop also emphasized the need for workforce training and education in data science and bioinformatics to prepare researchers for utilizing these technologies effectively. The workshop concluded by recognizing the need for a balanced approach that addresses data integration challenges while harnessing AI/ML to improve healthcare outcomes. By fostering interdisciplinary collaboration, prioritizing privacy, and embracing data-driven methodologies, epidemiological research can unlock the full potential of big data to transform public health and clinical practice.

  PublisherOA PDFDOI

• Performance of Wearable Pulse Oximetry During Controlled Hypoxia Induction: Instrument Validation Study

  JMIR Formative Research · 2026-03-27

  articleOpen accessSenior author

  Background: Oxygen saturation is a crucial metric used for monitoring patients with lung disease or respiratory illness who are at risk of hypoxemia (low blood oxygen saturation). Early and accurate identification of abnormal oxygen saturation is important for these patients, who may develop significant desaturation and hypoxemia symptoms during their daily activities. Objective: This study aimed to evaluate the accuracy of Apple Watch Series 7 and a clinical-grade pulse oximeter, Masimo MightySat Rx, under hypoxemia and to assess whether measurement error is influenced by the oxygen desaturation rate (ODR). Methods: We calculated the ODR of each measurement and conducted a comparative analysis of the displayed oxygen saturation readings from both the Masimo MightySat Rx finger pulse oximeter and Apple Watch Series 7 with arterial blood oxygen saturation (SaO2) readings obtained from a blood gas analyzer. Results: Both the Masimo MightySat Rx pulse oximeter and the Apple Watch Series 7 tended to overestimate oxygen saturation. The pulse oximeter readings were more likely to fall within 2% of the acceptable (as specified by Masimo) peripheral oxygen saturation (SpO₂) error range than the Apple Watch (49.03% vs 32.14%). Notably, both devices had limitations under low oxygen saturation levels (<88%), with an accuracy root mean square difference (Arms) of 3.52% (95% CI 3.18%-3.86%) and 5.82% (95% CI 5.32%-6.31%) for the Masimo MightySat Rx and Apple Watch Series 7, respectively. Among the blood oxygen measurements taken during a high ODR (ie, ≥2% SpO2 per minute), which is a rate clinically correlated with sleep apnea, the Arms increased slightly by 0.75% for the Masimo MightySat Rx and decreased by 0.28% for the Apple Watch Series 7. Conclusions: Both devices consistently overestimated SpO2, with accuracy declining notably during hypoxemia. The Apple Watch Series 7 mean bias suggests a likelihood of missing instances of hypoxemia, particularly at arterial oxygen saturation values below but close to 88%. Both the Apple Watch Series 7 and Masimo MightySat Rx exhibited Arms values exceeding the US Food and Drug Administration threshold under conditions of hypoxemia. While past studies have implicated high ODRs in increasing measurement error, we found no statistically significant relationship between ODR and measurement error for either device. Overall, our findings of SpO2 overestimation and high Arms values underscore the need for caution when interpreting oxygen saturation values from these devices. The small sample size and limited diversity in skin tone and age restrict the generalizability of our findings. Future studies should include larger and more diverse populations to evaluate the performance of wearable-based pulse oximetry.

  PublisherDOI

• Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic

  Frontiers in Digital Health · 2025-01-09 · 1 citations

  articleOpen access1st author

  Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.

  PublisherDOI

• Unmasking Hidden Dysglycemia: A Mobile OGTT Approach Using Continuous Glucose Monitors

  medRxiv · 2025-12-15 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Prediabetes (PD), which includes impaired glucose tolerance (IGT) and impaired fasting glucose (IFG), is a dysfunctional metabolic state that often progresses to type 2 diabetes (T2D). Standard screening tools such as random glucose and hemoglobin A1c frequently miss early or intermittent dysglycemia and cannot distinguish underlying physiological differences relevant for targeted intervention. Although the oral glucose tolerance test (OGTT) detects more PD and T2D and identifies high-risk individuals earlier, its clinical use is limited by the need for repeated venous sampling and in-clinic administration. We introduce the mobile OGTT (mOGTT), which leverages continuous glucose monitoring (CGM) to capture high-resolution glycemic responses to a standardized glucose challenge outside clinical settings. In a population spanning a broad range of glycemic health, we establish preliminary normative mOGTT values, characterize their relationship to clinical OGTT thresholds, and assess concordance with A1c. We show that CGM-derived analogs of OGTT metrics improve detection and phenotyping of dysglycemia and propose the Glucose Challenge Response Index (GCRI), a composite measure of glycemic health. Finally, we demonstrate the generalizability of GCRI-based subphenotypes in an out-of-sample cohort. These results help facilitate an efficient and scalable approach for conveniently detecting and quantifying early-stage glucose dysregulation.

  PublisherOA PDFDOI

• Data from the All of Us research program reinforces existence of activity inequality

  npj Digital Medicine · 2025-01-04 · 10 citations

  articleOpen accessSenior authorCorresponding

  Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment. Quantifying activity inequality using the modified Gini index, we found a strong correlation with obesity prevalence (R2 = 0.804) and a moderate correlation with perceived walkability (R2 = 0.426) and the activity gender gap (R2 = 0.385). This study demonstrates the value of digital health technologies in exploring and understanding public health practices while highlighting the need to examine complexities, including biopsychosocial factors that may contribute to activity inequality.

  PublisherOA PDFDOI

• Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

  Circulation Genomic and Precision Medicine · 2025-05-09 · 20 citations

  reviewOpen access

  Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.

  PublisherOA PDFDOI

Recent grants

• Mobile technologies to screen for prediabetes and type 2 diabetes in asymptomatic adults

  NIH · $2.5M · 2023–2028

Frequent coauthors

• Brinnae Bent

  30 shared

• Md Mobashir Hasan Shandhi

  29 shared

• Hanjoong Jo

  21 shared

• Alanah Webb

  Johns Hopkins Medicine

  20 shared

• Lakshmi Santhanam

  Johns Hopkins University

  20 shared

• Dan E. Berkowitz

  University of Alabama at Birmingham

  20 shared

• M Snyder

  20 shared

• Sarah R. Gutbrod

  Boston Scientific (United States)

  20 shared

Labs

• BIG IDEAs LabPI