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June 12, 2026European Journal of Preventive CardiologyOpen Access

Using wearable and lifestyle data to predict adverse cardiac events in patients with established coronary artery disease

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Why the study?

Do digital biomarkers from wearable and chatbot monitoring improve the prediction of adverse cardiac events compared to SMART2 risk score and lifestyle questionnaires in patients with established coronary artery disease after revascularization?

Population

66 patients with coronary artery disease recovering from coronary revascularisation

Comparison

Digital biomarkers from wearable and chatbot… vs SMART2 risk score clinical parameters and…

Design

Cohort

Follow-up

two years

Authors

FBF J C Van BlerckVEV A A Van EsWGW F Goevaerts

Discussion

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Overview

Should not yet change post-revascularization care; leaves open whether wearable and chatbot data enhance adverse event prediction.

Key Points

  • This research aims to assess the predictive capabilities of lifestyle, wearable, and clinical data for adverse cardiac events in coronary artery disease patients.
  • Monitored 66 patients post-coronary revascularization for 21 days using a smartwatch and a chatbot platform.
  • Compared predictive performance of SMART2 parameters, lifestyle questionnaires, and wearable/chatbot data.
  • Developed a scoring system based on identified predictors for classifying risk of adverse cardiac events.
  • 39% of participants experienced adverse cardiac events during follow-up.
  • Wearable and chatbot model achieved highest AUC of 0.75, significantly outperforming other models (p < 0.01).
  • The new scoring system classified patients into moderate (17-24%) and high-risk (25-85%) tiers based on predictive factors.

Structured PICO

Do digital biomarkers from wearable and chatbot monitoring improve the prediction of adverse cardiac events compared to SMART2 risk score and lifestyle questionnaires in patients with established coronary artery disease after revascularization?

P
Population
66 patients with coronary artery disease (median age 60 years; 88% male) recovering from coronary revascularisation
I
Intervention
Digital biomarkers from wearable (smartwatch) and chatbot monitoring (activity, sleep, heart-rate metrics and self-reported lifestyle) for 21 days after coronary revascularisation
C
Comparator
SMART2 risk score clinical parameters and validated lifestyle questionnaires
O
Outcome
Adverse cardiac events (composite of CV death, re-event, new CV disease, or unplanned admission) during two years of follow upcomposite

Early post-discharge digital monitoring of sleep, activity, and circadian HR dynamics predicts adverse cardiac events more accurately than standard clinical risk scores in patients recovering from coronary revascularization.

Cite This Study

Blerck et al. (2026) studied this question.

synapsesocial.com/papers/6a2bd1386550ea4541ffe9dchttps://doi.org/10.1093/eurjpc/zwag249.177
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Development of an integrated PHR-EHR alert system for near-term prevention of sudden cardiac death and cardiovascular events: a preliminary descriptive analysis from a prospective cohort study2026
  2. 2Cardia-AI: Passive Cardiac Event Monitoring Using Smartwatch Sensors and Predictive Analysis via Large Language Models2025 · 1 citations
  3. 3AI-Powered Cardiovascular Risk Prediction: Integrating Wearable Biosensors and Machine Learning Algorithms2025
  4. 4Multi-sensor wearables re-shaping care of chronic heart-failure: A narrative review2025 · 3 citations
  5. 5Interactive Digital Visualization Counseling for Lifestyle Change in Patients at Risk of Cardiovascular Diseases: Randomized Controlled Trial2026