Integration of a Heart Rate Prediction Model into a Personal Health Record to Support the Telerehabilitation Training of Cardiopulmonary Patients

Chronic obstructive pulmonary disease (COPD) and coronary artery disease are severe diseases with increasing prevalence. Studies show that regular endurance exercise training affects the health state of patients positively. Heart Rate (HR) is an important parameter that helps physicians and (tele-) rehabilitation systems to assess and control exercise training intensity and to ensure the patients’ safety during the training. On the basis of 668 training sessions (325 F, 343 M), we created linear models predicting the training HR during five application scenarios. Personal Health Records (PHRs) are tools to support users to enter, manage and share their own health data, but usage of current products suffers under interoperability and acceptance problems. To overcome these problems, we implemented a PHR that is physically localized in the user’s home environment and that uses the predictive linear models to support physicians during the training plan creation process. The prediction accuracy of the model varies with a median root mean square error (RMSE) of ≈11 during the training plan creation scenario up to ≈3.2 in the scenario where the prediction takes place at the beginning of a training phase.