Smart Monitoring Tool: intelligent model for monitoring colorectal cancer patients in the active phase of treatment

Abstract

Colorectal cancer is one of the most prevalent in men and women, and its development is associated with several risk factors, such as a sedentary lifestyle and eating habits. In addition, it directly impacts the individual's quality of life and daily routine (work, study, leisure, among others), especially when diagnosed in advanced stages. Currently, during the period between chemotherapy sessions, there is no follow-up to verify if the patient is following the treatment, as instructed by the medical team, which contributes to low engagement in actions to improve their clinical condition and self-manage the adverse effects of the treatment. This work aimed to develop a computational model, based on Artificial Intelligence and the Internet of Things, for monitoring cancer patients undergoing active treatment to ensure greater patient engagement in treatment through individualized and automated interactions and feedback between the patient and the virtual assistant and/or multidisciplinary team responsible for your treatment. Data were stored in a database, and the multidisciplinary team was notified when the patient's clinical condition indicated deterioration. The model worked both passively and actively, and the study was carried out in three phases. The first phase was carried out in December 2021, when the Sinop Cancer Center team evaluated one of the computational model tools. In the second phase, the model was applied to colorectal cancer patients undergoing active treatment from July to December 2022. All patients who addressed the inclusion criteria were invited to participate. For 8 weeks, patients were encouraged to self-report symptoms and adverse effects related to treatment, physical activity, and data about their diet. The outcome assessment was based on the comparison between the intervention and control groups. The patients evaluated the model through the User Experience Questionnaire (UEQ) and System Usability Scale (SUS) surveys. In the third phase, the application of a recommendation system integrated to the proposed model was evaluated. The results of the first phase showed that the model was effective in addressing usability and user experience. We evaluated the UEQ attractiveness and efficiency scales as excellent and the others as good. The usability evaluated by the SUS obtained a mean of 75 ± 7.14 and a median of 72.5 (70-77.5). In the second phase, patients who participated in the model reported signs and symptoms more accurately (control: 64.7%; intervention: 92.3%; p=0.1038). In the intervention group, the practice of physical activity was more effective, and most patients (61.5%) interacted with the chatbot for at least 62.5% of the period. There was also a statistical reduction in the consumption of alcoholic beverages and fast food, and a statistical increase in fruit consumption in the intervention group. Finally, in the third phase, the results suggest that the recommender system can positively address user expectations. Therefore, results indicate that the model contributed to more assertive data collection and greater patient engagement in self-management of symptoms and adverse effects of treatment and cancer. Moreover, the model contributed to increasing the practice of light physical activity. UEQ and SUS scores indicate that the model met users' expectations and had acceptable usability.