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Smartwatch-based algorithm for early detection of pulmonary infection: Validation and performance evaluation.

Yibing Chen1, Danyang She1, Yutao Guo2

  • 1College of Pulmonary and Critical Care Medicine, The Eighth Medical Center of Chinese PLA General Hospital, Beijing, China.

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A smartwatch algorithm can detect pulmonary infections (PI) early. This wearable system, using multi-source features, shows high accuracy in identifying PI risk in adults.

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Area of Science:

  • Biomedical Engineering
  • Digital Health
  • Infectious Disease Monitoring

Background:

  • Smart devices offer potential for early detection of pulmonary infections (PI).
  • This study focuses on validating a smartwatch-based algorithm for PI risk monitoring in adults.

Purpose of the Study:

  • To validate a smartwatch algorithm for early detection of pulmonary infections (PI).
  • To assess the algorithm's accuracy, sensitivity, and specificity in identifying PI risk.

Main Methods:

  • An algorithm was developed and tested on smartwatches, analyzing heart rate variability, respiratory rate, oxygen saturation, body temperature, and cough sound.
  • The algorithm was embedded in the Respiratory Health Study app and validated against clinical diagnosis (gold standard).

Main Results:

  • The algorithm achieved an area under the curve of 0.86 for predicting PI (P < 0.001).
  • Overall accuracy was 85.9%, with 81.4% sensitivity and 90.4% specificity.
  • Incorporating cough sound improved accuracy to 82.6% compared to other vital signs alone (68.2%).

Conclusions:

  • A wearable system using smartwatches can facilitate early detection of pulmonary infection risk.
  • Multi-source features significantly enhance the performance of lung infection screening algorithms.