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Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study

Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study

Packer et al [7] showed that using a combination of clinical variables and impedance cardiography features could be a predictor of a decompensation event in the following 14 days. Previous studies have also investigated the use of wearable devices adhered to the chest. In the “Multisensor Monitoring in Congestive Heart Failure” study [8], the authors propose an algorithm that uses physiological signals, reporting a sensitivity of 63% and a specificity of 92%.

Ayse S Cakmak, Erick A Perez Alday, Samuel Densen, Gabriel Najarro, Pratik Rout, Christopher J Rozell, Omer T Inan, Amit J Shah, Gari D Clifford

JMIR Form Res 2022;6(8):e36972