Wearable Devices in Healthcare Beyond the One-Size-Fits All Paradigm

Area of Science:

• Biomedical Engineering
• Clinical Informatics
• Digital Health

Background:

• Wearable devices (WDs) are increasingly used for continuous, non-invasive vital signs monitoring in clinical settings.
• Integration with Artificial Intelligence (AI) enables predictive analytics for early patient deterioration detection and personalized interventions.
• The value of WDs lies in trend analysis relative to a patient's baseline, not absolute measurements, for real-time deterioration prediction.

Purpose of the Study:

• To analyze nine clinically validated wearable devices for vital signs monitoring.
• To highlight how differences in device configurations influence suitability across clinical environments.
• To emphasize context-driven selection of WDs based on clinical needs.

Main Methods:

• Narrative review of nine specific wearable devices: Radius VSM, BioButton, Portrait Mobile, VitalPatch, CardioWatch 287-2, Cosinuss C-Med Alpha, SensiumVitals, Isansys Lifetouch, and CheckPoint Cardio.
• Analysis focused on sensor configurations, battery life, connectivity, and validation contexts.
• Evaluation of device suitability across diverse clinical settings and patient populations.

Main Results:

• Wearable devices are not interchangeable despite shared multiparametric capabilities.
• Differences in sensor configurations, battery life, connectivity, and validation contexts impact device suitability.
• Each device exhibits unique strengths and limitations influenced by patient population and operational demands.

Conclusions:

• Context-driven selection is crucial for implementing wearable devices effectively.
• Considerations for selection include care setting, patient profile, infrastructure, and interoperability.
• Matching wearable device capabilities to specific clinical needs maximizes utility, safety, and efficiency in patient care.