were combined (r2 = 0.76 for TEE, r2 = 0.83 for PAL). Studies validating HR-body movement models in free-living conditions showed comparable results 34,35. Our results revealed that the accuracy of energy expenditure estimation improves when specific patient characteristics are included in the model. Although HR and oxygen consumption are linearly related, our results showed that variation in individual patient characteristics (e.g. fitness, age and gender) cause a high between-subject variation. Adding these patient characteristics and using individual calibration protocols (e.g. step-test, cycling protocol) can capture these variations and improve the accuracy of TEE/PAL estimation. In particular, the HR-Flex method, using HR data and a cycling protocol for calibration proved to be more accurate in TEE and PAL estimation than the models combining body movement and HR data only. These results are in line with the literature, indicating the beneficial effects of individual calibration to capture variances 34,36. However, individual calibration also requires procedures, and is therefore unsuitable for large-scale studies and clinical practice. In addition, branched equation models, used in several studies to distinguish between low- and high intensity activities based on the transition between 20, are unsuitable for cardiac patients with limited physical fitness levels. Therefore, we aimed at developing an energy expenditure model that could be applicable to patient data that are available at the start of a rehabilitation program to successfully monitor physical activity in these patients in daily life. Currently, general patient characteristics (i.e. age, body weight, BMI, medication use) are available in the electronic patient record and a symptom limited exercise test is performed at the start of each program to determine physical fitness (peakVO2). With this data available, only
were combined (r2 = 0.76 for TEE, r2 = 0.83 for PAL). Studies validating HR-body movement models in free-living conditions showed comparable results 34,35. Our results revealed that the accuracy of energy expenditure estimation improves when specific patient characteristics are included in the model. Although HR and oxygen consumption are linearly related, our results showed that variation in individual patient characteristics (e.g. fitness, age and gender) cause a high between-subject variation. Adding these patient characteristics and using individual calibration protocols (e.g. step-test, cycling protocol) can capture these variations and improve the accuracy of TEE/PAL estimation. In particular, the HR-Flex method, using HR data and a cycling protocol for calibration proved to be more accurate in TEE and PAL estimation than the models combining body movement and HR data only. These results are in line with the literature, indicating the beneficial effects of individual calibration to capture variances 34,36. However, individual calibration also requires procedures, and is therefore unsuitable for large-scale studies and clinical practice. In addition, branched equation models, used in several studies to distinguish between low- and high intensity activities based on the transition between 20, are unsuitable for cardiac patients with limited physical fitness levels. Therefore, we aimed at developing an energy expenditure model that could be applicable to patient data that are available at the start of a rehabilitation program to successfully monitor physical activity in these patients in daily life. Currently, general patient characteristics (i.e. age, body weight, BMI, medication use) are available in the electronic patient record and a symptom limited exercise test is performed at the start of each program to determine physical fitness (peakVO2). With this data available, only