The future of personalized cardiovascular disease detection and monitoring

Professor Allan Lawrie from Imperial College London discusses the future of personalized detection and monitoring of cardiovascular disease, including comments on wearable technology and AI

The rapid spread of digital consumer technology has significantly increased access to smartphones and connected devices across various socioeconomic groups and age groups. With smartphones came the ability to connect and collect data from various devices, including smartwatches, rings, scales, and blood pressure monitors. Originally developed by major technology companies targeting the health and fitness market, these devices have evolved to comprehensively monitor our health and literally put health monitoring in our hands.

Evolving Wearable Technology: From Fitness Trackers to Disease Detection and Monitoring

The technology developers initially focused on promoting a healthy lifestyle, particularly targeting those interested in health and fitness. However, the potential for monitoring cardiovascular health and disease (and other diseases) soon became clear.

Consumer wearables now provide continuous, real-time data on various physiological parameters, allowing users to track their cardiovascular fitness and identify potential health problems. Many of the functions measured by these wearables use photoplethysmography (PPG – the green/red light on the back of your device), which records heart rate (HR) and rhythm and estimates blood oxygenation and temperature.

Accelerometers in the devices provide additional context to movement and help estimate effort, speed and calories burned. Regular physical activity is an important part of cardiovascular fitness and reduces the risk of heart disease, obesity and diabetes. These devices undoubtedly allow users to track their activity and fitness.

HR provides insight into the cardiovascular system’s response to physical activity, stress and rest.

A lower resting heart rate typically indicates better cardiovascular fitness, while an increased resting heart rate can indicate potential problems such as high blood pressure or cardiac arrhythmias. Heart rate variability (HRV), the temporal variation between heartbeats, is another important measurement.

High HRV is generally associated with good cardiovascular fitness and a well-functioning autonomic nervous system, while low HRV may indicate stress, fatigue or underlying cardiovascular disease. Wearable devices continuously track these metrics, providing users with insight into changes in cardiovascular health over time.

Wearables equipped with electrocardiogram (ECG) capability can detect irregular heart rhythms such as atrial fibrillation (AFib), a common but serious condition that increases the risk of stroke and other complications. By detecting irregularities early, wearables can enable timely medical intervention and potentially prevent serious consequences.

Sleep tracking, including sleep disorders like sleep apnea, has come into focus recently. Good sleep is essential for health. Wearable devices can monitor sleep patterns, including duration and stages of sleep (light, deep, and REM sleep). Poor quality or insufficient sleep can negatively impact cardiovascular health and lead to conditions such as high blood pressure and heart disease.

By monitoring sleep, wearables help users make informed decisions to improve their sleep habits and overall health. Some wearables also have features to monitor stress levels and mental health indicators by assessing HR and HRV to estimate stress.

Because of these developments, many people can now recall at least one person in their family and friends who benefited from alerts on a wearable device, prompting them to seek medical attention.

Data exchange with healthcare providers

Most people are aware of the close connection between exercise and health. While a sedentary lifestyle is associated with an increased risk of cardiovascular disease, reduced activity can also be a symptom of cardiovascular problems or other health reasons, such as musculoskeletal problems, psychological problems, chronic illnesses or changes in the personal circumstances.

Early detection of cardiovascular disease is crucial for optimal treatment. However, caution is required to avoid overwhelming healthcare systems due to false positive results. There have been recent calls for personal data to be included in health records (which is already happening in some cases). This has the potential to improve care by providing physicians with detailed information about the patient’s health status over time, leading to more informed diagnoses and treatment plans. However, it is important to ensure that this data is contextualized with standardized processing (best practice) to ensure data equality (device independence).

Wearables and AI

The key to efficient use of large time series datasets is the development of artificial intelligence (AI) and machine learning (ML). AI approaches to clinical data are revolutionizing disease diagnosis, and many of them can be applied to data from wearable devices. For example, AI ECG models trained on large clinical datasets can identify disease risks and provide prognostic insights to monitor disease progression or response to treatment for cardiovascular and respiratory diseases.

Using AI based on wearable data can provide tailored recommendations to improve cardiovascular fitness and address potential health problems before they fully manifest. These insights enable users to take proactive steps toward better health, such as: B. adjust their exercise routines, improve their diet or seek medical advice. These AI models must be transparent and technology independent.

Wearable technology has the potential to transform cardiovascular health monitoring by providing continuous, real-time data on various physiological parameters. This data allows users to take control of their health and make informed decisions to improve their fitness and reduce their risk of cardiovascular disease.

However, there is still work to be done to engage and empower users. There is significant potential for providing personalized AI nudges (e.g. through large language models) to change activity and behavior.

It is also important to recognize that activity is only one factor that influences health. It is critical to understand how diet and other environmental influences interact with activity and our biology to influence disease.

As technology advances, wearables will provide even more precise health insights into metrics like blood pressure and blood sugar. However, while wearable technology provides valuable insights for certain metrics such as heart rate and step counting, it is less reliable for others, such as calorie consumption and sleep tracking.

Therefore, the use of these devices for general health monitoring should be encouraged. For critical health metrics, it is always recommended to consult medical professionals and use medical devices.

There is still much work to be done and large standardized datasets are needed to understand how to fully integrate this data into our health records. Nevertheless, they will undoubtedly become a crucial part of our healthcare digital twin.