## 4 Discussion
In the present study, the transient adherence and initial clinical profile were found to be important for the prediction in both the RF model and the decision tree one (Figures 8b and 9, respectively). However, the validation of the models using an external dataset is a necessary next step. While the decision tree model predicted that those who were adherent in the familiarization phase would continue to be adherent, it also shed light on the other cases, where the combination of adherence profile and clinical baseline seemed to play a role in subsequent adherence. For example, the patients with high cardiovascular risk seemed to be more susceptible to support and improved adherence, while patients with a low cardiovascular risk might need different handling, as they were predicted to continue being non-adherent, potentially because they had already established a physically active lifestyle and, perhaps, did not have the motivation to follow a specific program. This is an important point that recognizes and sheds some light on the gray zone profiles or behaviors, which is also supported by the network analysis. Different network structures of baseline characteristics showed more correlated features in the high-risk and fit group of patients, and potentially more room for intervention. Temporal analysis at familiarization showed an interplay between HR performance and adherence in the transient and non-adherent groups, with adherence influenced by \(HRtime\) or \(HRnorm\), which may also suggest further room for improvement and personalization of sessions. In the present study, exercise intensity was not a factor predicting adherence; however, in the temporal graph analysis a temporal link between HR and subsequent session duration was noted. The role of both the familiarization phase and patient self-confidence is closely linked to understanding the program and self-motivation of patients. This has also been highlighted by [63], who mentioned family support to help keep patients engaged in a home-based CR program and suggested educating both patients and families to improve adherence to home-based CR programs. In addition, several previous works have identified factors that affect either short-term or long-term adherence to home-based CR programs, such as self-motivation, physical activity levels, or perception of self-status [9; 21], and they propose patient-centered strategies to improve adherence to the exercise programs [18; 64]. On the other hand, few works have attempted to make a predictive model in a data-driven manner to increase the chances of a match between patient and CR program. Recently, predictive models using machine learning techniques have been proposed [26]. However, those studies are only able to perform short-term predictions, while our model provides a longer-term adherence prediction. A major advantage of the methodology presented in the present article was that the models were based on variables that are easily collected. In addition, the present study does not disregard the patients with intermediate behavior, such as transient adherence, to make future predictions, making our approach more generalizable compared with previous studies [28]. Addressing adherence to lifestyle changes, including exercise training, is significant and incredibly difficult, since participation rates in CR programs depend on several factors. Understanding those factors and predicting patients' behavior, such as exercise compliance, is important in clinical practice; thus, the clinical implications of our work could be substantial. Identifying areas for improvement in the interventions can increase adherence and the effectiveness of home-based CR; this, in turn, can lead to a better health status and quality of life for patients with CVD. In addition, since home-based CR methods have also proven to be more cost-effective, this could also help alleviate the financial stress placed on healthcare systems by the management of CVD patients [65].