## 1 Introduction
[21] presented a list of factors that are associated with adherence to home-based physical therapies, where it was reported that the perception of health status, self-motivation, or current physical activity level presents a strong positive association with adherence, while daily stress has a strong negative association with it. On the other hand, the incorporation of data-driven or rule-based models can guide decisions, leading to improved adherence [24]. It would be beneficial to integrate objective patient information from a short period of time to predict long-term adherence to exercise and, thus, proceed with appropriate targeted modifications and better use of resources. In [25] the Discontinuation PredictionScore (DiPS) was introduced, which compares each week's average steps with those of the first week of the program. It can be used to score the probability of dropping out from exercise programs during the week. The prediction model used objectively collected physical activity data from 210 physically inactive women aged 25 to 69 years, and it applied logistic regression and support-vector machines to predict the DiPS. As found, the adherence rate decreased as the program progressed, whereas daily steps at the start of the program and the steps measured during the previous week were significant predictors of DiPS. In a study performed by [26], three different clusters of participants were created based on basic individual characteristics and training data collected during the first three months of the application's use. Deep learning techniques were applied for the prediction of adherence to exercise during the fourth month of the program. Finally, [27] applied data-characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs) to predict outdoor physical activity, taking into account patient profiles and environmental characteristics, such as weather, temperature, and humidity. However, in all of these studies, the focus was on the short-term prediction. It would be beneficial to identify the patients who are likely to be non-adherent at an early stage of the program, so as to modify the motivation strategies and to use the resources efficiently. In a previous study performed by our research group, adherence to a short familiarization period for home-based CR was combined with clinical characteristics to predict future adherence [28]. A support-vector machine (SVM) classifier was trained using the most significant features. However, in that study, only those patients who could clearly be considered to be adherent or non-adherent during the familiarization phase were included. This resulted in the exclusion of a considerable number of patients who were moderately adherent, hindering the generalization of the method and results. In this paper, we hypothesize that a predictive model based on machine learning techniques, which integrates (i) patient clinical characteristics, (ii) data from self-reports, and (iii) objective physical activity information gathered during a short familiarization phase, can predict longer-term adherence to a home-based CR program for CVD patients. Therefore, the specific aims of this study were as follows: (1) to cluster patients into distinct groups based on the adherence to the system during a 6-week familiarization period, (2) to investigate significant differences between the groups during the aforementioned period, and (3) to implement a model that could predict the use of the system during a 6-month CR program. Following a data-driven approach, while adherence prediction was considered to be a discrete problem (N classes), the number of classes was not predefined but, rather, identified during the analysis pipeline via clustering. Finally, the predictive model needed to be explainable so that it could be used by the clinical experts to better support patients in adhering to home-based CR or to search for other CR alternatives if predicted adherence to home-based CR was low.