Proof of concept of a 45-second cardiorespiratory fitness self-test for coronary artery disease patients based on accelerometry

G. Papini, A.G. Bonomi, W. Stout, Jos J. Kraal, Hareld M.C. Kemps, F. Sartor

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Cardiorespiratory fitness (CRF) provides important diagnostic and prognostic information. It is measured directly via laboratory maximal testing or indirectly via submaximal protocols making use of predictor parameters such as submaximal , heart rate, workload, and perceived exertion. We have established an innovative methodology, which can provide CRF prediction based only on body motion during a periodic movement. Thirty healthy subjects (40% females, 31.3 ± 7.8 yrs, 25.1 ± 3.2 BMI) and eighteen male coronary artery disease (CAD) (56.6 ± 7.4 yrs, 28.7 ± 4.0 BMI) patients performed a test on a cycle ergometer as well as a 45 second squatting protocol at a fixed tempo (80 bpm). A tri-axial accelerometer was used to monitor movements during the squat exercise test. Three regression models were developed to predict CRF based on subject characteristics and a new accelerometer-derived feature describing motion decay. For each model, the Pearson correlation coefficient and the root mean squared error percentage were calculated using the leave-one-subject-out cross-validation method (rcv, RMSEcv). The model built with all healthy individuals’ data showed an rcv = 0.68 and an RMSEcv = 16.7%. The CRF prediction improved when only healthy individuals with normal to lower fitness (CRF<40 ml/min/kg) were included, showing an rcv = 0.91 and RMSEcv = 8.7%. Finally, our accelerometry-based CRF prediction CAD patients, the majority of whom taking β-blockers, still showed high accuracy (rcv = 0.91; RMSEcv = 9.6%). In conclusion, motion decay and subject characteristics could be used to predict CRF in healthy people as well as in CAD patients taking β-blockers, accurately. This method could represent a valid alternative for patients taking β-blockers, but needs to be further validated in a larger population.
Original languageEnglish
Article numbere0183740
Number of pages13
JournalPLoS ONE
Issue number9
Publication statusPublished - 7 Sept 2017


  • cardiovascular disease
  • vo2max
  • Wearable technology
  • Accelerometer
  • fitness exercise
  • Motion
  • Cardiorespiratory Fitness
  • Models, Cardiovascular
  • Humans
  • Oxygen Consumption
  • Linear Models
  • Coronary Artery Disease/diagnosis
  • Accelerometry/instrumentation
  • Aged


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