Predicting endodontic flare-ups using wearable physiological data (heart rate variability, stress, sleep quality)

Abstract

Endodontic flare-ups remain one of the most challenging postoperative complications following root canal treatment, often resulting in severe pain, swelling, and unexpected emergency visits. Traditional prediction of flare-up risk primarily relies on clinical findings and patient history, which may not adequately capture dynamic physiological changes associated with stress and recovery. Recent advances in wearable health technologies provide an opportunity to continuously monitor physiological parameters such as heart rate variability, heart rate, stress levels, and sleep quality, offering valuable insights into autonomic nervous system activity and patient well-being. When integrated with artificial intelligence and predictive analytics, these real-time data streams may enable early identification of patients at increased risk of postoperative complications and facilitate personalized postoperative care. This review examines the physiological basis linking wearable-derived biomarkers with endodontic flare-ups, explores the potential application of machine learning models for risk prediction, and discusses the opportunities and challenges associated with incorporating wearable technologies into endodontic practice. Although current evidence remains limited, wearable physiological monitoring represents a promising adjunct to conventional clinical assessment and may contribute to more proactive, patient-centered management of postoperative endodontic outcomes.