How are wearables and AI changing chronic disease management?

Digital biomarkers are health signals, such as heart rate variability, sleep patterns, glucose levels and activity, collected continuously by wearables and other connected devices. For people living with diabetes, heart disease or hypertension, these signals give care teams a view of the condition between office visits. Combined with AI, digital biomarkers can surface patterns that support earlier conversations and more personalized care plans, though they inform clinicians rather than replace them.

Digital biomarkers are data points collected from digital devices, including wearables, that provide information about a person’s health status, behavior, or biological state. Traditional biomarkers, such as blood tests or imaging, are typically measured during in-person medical visits; digital biomarkers are collected continuously and passively, which makes them useful for tracking health over time.

Examples of digital biomarkers include:

Digital biomarkers can give a more complete picture of a patient’s overall health and how a condition changes over time. When combined with AI, they open new possibilities in chronic disease management.

Wearables change chronic disease management by allowing continuous tracking of health indicators outside the clinic. Smartwatches, fitness trackers, and health sensors collect data on metrics such as heart rate, steps, and blood oxygen levels.

Examples by condition:

• Diabetes: continuous glucose monitors can alert patients and their healthcare providers when glucose levels move outside a set range, supporting earlier intervention.
• Cardiovascular disease: wearables can track heart rate variability, blood pressure, and physical activity levels, helping individuals with heart disease follow their condition more closely.
• Hypertension: wearable blood pressure monitors can provide consistent readings, giving healthcare providers an ongoing view of a patient’s condition rather than relying on sporadic office visits.

Continuous tracking lets patients take a more active role in managing their health. For healthcare providers, it means a steady stream of data that can inform treatment decisions and care plans.

AI helps make wearable data usable at scale by analyzing large volumes of digital biomarker data to identify patterns and flag readings for review. Three common applications:

• Risk patterns: AI models can flag trends in wearable data that a care team may want to review, such as changes that precede an acute event in some patients.
• Personalized care: by analyzing data continuously, AI can help clinicians adjust a care plan based on a patient’s activity levels or suggest lifestyle changes, including exercise or diet modifications.
• Automated alerts: algorithms can notify patients or healthcare providers when readings fall outside expected ranges, such as a spike in blood pressure or an irregular heart rhythm.

Together, wearables and AI make it possible to manage chronic diseases proactively rather than reactively, with clinicians interpreting the signals and making the decisions.

Combining wearables and AI gives care teams more frequent data and patients more engagement with their own health:

1. Earlier intervention: continuous data collection and AI analysis can help healthcare providers notice potential issues before they escalate.
2. Patient engagement: wearables give patients constant access to their own data, and AI can offer tailored feedback that helps them stay on track with treatment plans.
3. Fewer routine visits: with wearable data available between appointments, there may be less need for in-person visits whose only purpose is checking a reading, letting clinicians prioritize more critical cases.
4. Personalized treatment plans: AI-assisted analysis allows treatment plans based on individual data rather than generalized assumptions.
5. Continuous health tracking: wearables provide data 24/7, giving healthcare providers a deeper understanding of a patient’s daily habits and how a condition progresses.

Three challenges still limit wearables and AI in chronic disease management:

• Data privacy and security: with large volumes of personal health data being generated, storing and transmitting that data securely is critical to maintaining patient trust.
• Standardization: wearables and digital biomarkers use different devices and data formats, so standardizing them is essential for integration into EHR systems and for clinicians to trust the data.
• Cost and accessibility: wearables are becoming more affordable but are not universally accessible, and expanding access matters most for underserved communities.

Standardization is the layer ROOK works on: ROOK Connect normalizes data from 72 data sources and more than 500 devices into one format. ROOK moves and standardizes the data; it does not diagnose or replace clinical judgment. For how wearable data reaches clinical records, see how AI closes the gap between wearables and EHRs.

The future of chronic disease management lies in combining digital biomarkers, wearable technology, and AI, which together allow proactive, personalized care between visits.

As the technology advances, wearables are likely to become more integral to chronic disease programs, with data supporting the clinicians who make care decisions. To see how wearable data is normalized for these programs, start with the ROOK developer documentation.