Cardiometabolic Health Is Shaped by Daily Physiology
Cardiovascular and cardiometabolic diseases, including hypertension, obesity, type 2 diabetes, and heart disease, develop gradually over time. Long before symptoms appear, subtle physiological changes begin to emerge. Detecting these early signals is critical for improving prevention, treatment, and long-term health outcomes.
Traditionally, however, researchers and clinicians have relied on measurements taken during occasional clinic visits to monitor cardiovascular health. Blood pressure readings, electrocardiograms, and fasting blood tests provide important information, but they capture only brief snapshots of a person’s physiology. In reality, many of the processes that influence cardiometabolic health, such as stress responses, sleep quality, and physical activity, fluctuate continuously throughout the day.
Digital biomarkers are helping move research beyond these traditional approaches.
What Are Digital Biomarkers?
A digital biomarker is an objective physiological or behavioral measure collected through connected technologies such as wearable sensors or smartphones. These tools allow researchers to collect health data continuously while individuals go about their normal daily lives.
Instead of relying on a few isolated measurements, researchers can now observe how physiological systems change over hours, days, and weeks in real-world environments (Coravos et al., 2019). This continuous perspective is particularly valuable in cardiometabolic research because many risk factors for heart and metabolic disease are closely tied to everyday behaviors and environmental influences.
Stress, sleep patterns, activity levels, and recovery all affect how the cardiovascular system functions. Continuous monitoring allows researchers to capture these fluctuations and identify patterns that may signal early physiological changes associated with disease risk.
Key Digital Biomarkers in Cardiovascular Research
Several physiological signals commonly used in wearable devices provide valuable insight into cardiovascular and metabolic regulation.
One of the most widely studied signals is heart rate variability (HRV), which measures the variation in time between heartbeats. HRV reflects how the autonomic nervous system regulates the heart and is often used as an indicator of stress, recovery, and cardiovascular resilience. Reduced HRV has been associated with increased cardiovascular risk and impaired autonomic regulation (Shaffer & Ginsberg, 2017).
Continuous heart rate monitoring is another important metric. Tracking heart rate throughout daily activities and sleep can reveal how the cardiovascular system responds to stress, exertion, and recovery. Even small changes in resting or nighttime heart rate may provide early signals of physiological strain (Zhao et al., 2025).
Researchers are also increasingly using electrodermal activity (EDA) to measure sympathetic nervous system activity. EDA reflects changes in skin conductance driven by sweat gland activity and provides insight into physiological stress responses and emotional arousal. Chronic stress and autonomic dysregulation are increasingly recognized as contributors to cardiometabolic disease risk (Vaccarino and Bremner, 2024).
In addition, wearable devices often include accelerometers, which measure movement and physical activity. These sensors help quantify daily activity levels and sedentary behavior, both of which play a major role in cardiometabolic disease risk (Booth et al., 2012).
Sleep monitoring is another important component. Poor sleep quality and disrupted circadian rhythms have been linked to increased risk of hypertension, obesity, and metabolic disorders (Tasali & Van Cauter, 2006).
A More Complete Picture of Cardiometabolic Health
By combining multiple measures, such as heart rate, HRV, EDA, activity, and sleep, digital biomarker platforms provide a more holistic view of human physiology. This multimodal approach allows researchers to study how behavior, stress physiology, and cardiovascular regulation interact over time.
Importantly, wearable sensors also allow physiological data to be collected in real-world environments rather than only in controlled laboratory settings. This provides more ecologically valid insights into how individuals respond to everyday stressors, lifestyle factors, and environmental conditions.
Supporting Personalized and Adaptive Treatment
Beyond improving research insights, digital biomarkers also have the potential to support more personalized approaches to healthcare. Continuous physiological monitoring allows clinicians and researchers to observe how individuals respond to medications, lifestyle interventions, or behavioral therapies in their daily lives.
For example, improvements in HRV, sleep patterns, or stress responses may indicate that a treatment is effective, while persistent physiological strain may suggest that adjustments are needed. By capturing real-world physiological responses over time, digital biomarkers can help guide treatment decisions, optimize interventions, and ultimately improve cardiometabolic health outcomes.
TIMESPAN’s ART-CARMA project is using Empatica’s EmbracePlus wearable, part of the Empatica Health Monitoring Platform, to study the management of chronic cardiometabolic disease and treatment discontinuity in adult ADHD patients. The project aims to understand the risks of cardiometabolic illnesses such as cardiovascular disease and obesity for adults with ADHD, and how their future health can be best improved through medication. To find out more about Empatica’s solutions and how thousands of research partners and institutions, including NASA, are using their technology, visit their website.
References
- Booth, F. W., Roberts, C. K., & Laye, M. J. (2012). Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology, 2(2), 1143–1211.
- Coravos, A., Khozin, S., & Mandl, K. (2019). Developing and adopting safe and effective digital biomarkers. NPJ Digital Medicine, 2, 14.
- Shaffer, F., & Ginsberg, J. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258.
- Tasali, E., & Van Cauter, E. (2006). Sleep and the metabolic syndrome. Chest, 130(2), 568–577.
- Vaccarino, V., & Bremner, J. D. (2024). Stress and cardiovascular disease: an update. Nature reviews. Cardiology, 21(9), 603–616.
- Zhao Y, Chen P, Zhang Y, Huo S, Yu D, Zeng X and Zhang W (2025) Heart rate variability and its modulation by nutrients: a narrative review on implications for cardiovascular aging. Front. Neurosci. 19:1654796.