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

• Published in: PloS one 2017-09, Vol.12 (9), p.e0183740-e0183740
• Main Authors: Papini, Gabriele, Bonomi, Alberto G, Stut, Wim, Kraal, Jos J, Kemps, Hareld M C, Sartor, Francesco
• Format: Article
• Language: English
• Subjects: Accelerometers; Accelerometry - instrumentation; Accelerometry - methods; Aged; Biology and Life Sciences; Body mass; Cardiac patients; Cardiology; Cardiorespiratory Fitness; Cardiovascular disease; Chronic illnesses; Coronary artery; Coronary artery disease; Coronary Artery Disease - diagnosis; Correlation coefficient; Correlation coefficients; Decay; Diagnostic systems; Engineering and Technology; Exercise; Exercise tests; Females; Fitness; Fitness equipment; Health aspects; Heart attacks; Heart diseases; Heart rate; Humans; Linear Models; Mathematical models; Medicine; Medicine and Health Sciences; Models, Cardiovascular; Motion; Movement; Neural networks; Oxygen Consumption; Patients; Personal health; Physical fitness; Physiology; Regression analysis; Regression models; Rehabilitation; Respiratory equipment; Sports medicine
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0183740

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 [Formula: see text], 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 [Formula: see text] 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