Monica Rincon-Roncancio

Introduction: Cardiac rehabilitation (CR) is an evidence-based secondary prevention strategy that improves functional capacity, quality of life, and cardiovascular outcomes. However, adherence to CR programs remains suboptimal, limiting their effectiveness. Methods: A retrospective multicenter cohort study was conducted among patients enrolled in outpatient CR programs at two specialized cardiovascular centers. Adherence was defined as attendance at ≥70% of the prescribed sessions (≥25 of 36 sessions). Bivariate analyses were performed to compare adherent and non-adherent participants. Variables with p < 0.20 were included in a multivariable logistic regression model to identify factors independently associated with adherence. Results: A total of 210 participants were included, of whom 77.6% (163/210) were classified as adherent. The median age was 68 years (IQR 60.3–75.0), and 64.3% were men. Most participants (70.5%) completed more than 30 rehabilitation sessions. Adherence rates were highest among patients undergoing surgical myocardial revascularization (80.6%) and valve surgery (76.0%). In multivariable analysis, program duration was the only factor significantly associated with adherence (OR = 4.59; 95% CI: 2.46–8.59; p < 0.001). History of dyslipidemia showed a non-significant trend toward higher adherence (OR = 3.29; 95% CI: 0.91–11.90; p = 0.069). Educational level, left ventricular ejection fraction, and health insurance affiliation were not independently associated with adherence. Conclusions: Adherence to CR was high in this cohort. Longer participation in the program was the only independent predictor of adherence, highlighting the importance of strategies that promote sustained engagement throughout the rehabilitation process.

Lack of motivation and low adherence rates are critical concerns of long-term rehabilitation programmes, such as cardiac rehabilitation. Socially assistive robots are known to be effective in improving motivation in therapy. However, over longer durations, generic and repetitive behaviours by the robot often result in a decrease in motivation and engagement, which can be overcome by personalising the interaction, such as recognising users, addressing them with their name, and providing feedback on their progress and adherence. We carried out a real-world clinical study, lasting 2.5 years with 43 patients to evaluate the effects of using a robot and personalisation in cardiac rehabilitation. Due to dropouts and other factors, 26 patients completed the programme. The results derived from these patients suggest that robots facilitate motivation and adherence, enable prompt detection of critical conditions by clinicians, and improve the cardiovascular functioning of the patients. Personalisation is further beneficial when providing high-intensity training, eliciting and maintaining engagement (as measured through gaze and social interactions) and motivation throughout the programme. However, relying on full autonomy for personalisation in a real-world environment resulted in sensor and user recognition failures, which caused negative user perceptions and lowered the perceived utility of the robot. Nonetheless, personalisation was positively perceived, suggesting that potential drawbacks need to be weighed against various benefits of the personalised interaction.

Introduction: This study investigates the impact of socially assistive robots and personalization in cardiac rehabilitation, in terms of physiological progress and perceptions of the patients and clinicians. Methods: We conducted a longitudinal study for 2.5 years with 43 patients. The intervention was classified into 3 conditions: control group (without a robot), a social robot and a personalized robot. Throughout the rehabilitation program (36 sessions), the initial heart rate, average heart rate during training, and heart rate recovery in the first minute. Heart rate recovery in the first minute was normalized with initial heart rate to homogenize the data: Heart rate recovery in the first minute normalized = heart rate recovery in the first minute/ initial heart rate. We apply Johansen's general formulation of the Welch-James statistic with approximate degrees of freedom on Heart rate recovery in the first minute normalized in 6 stages. Perceptions of the patients and clinicians are evaluated through surveys and interviews. Results: 26 patients completed the rehabilitation program. Heart rate recovery in the first minute normalized changed significantly in each stage of the program (TWJ (5, 305) = 14.36, p < .001). Patients assisted by social and personalized robots achieved a higher improvement in cardiovascular functioning than the control group. Both robots improved motivation and trust due to continuous monitoring and immediate feedback, facilitated prompt detection of critical conditions, and were recommended for future use by clinicians and patients. Conclusions: Our results suggest that robots improve cardiovascular functioning compared to the conventional cardiac rehabilitation program. In addition, robots have motivational benefits for patients, and increase awareness of clinicians to detect complications, as a result, they were recommended for future use by patients and clinicians.

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

What are the benefits of using a socially assistive robot for long-term cardiac rehabilitation? To answer this question we designed and conducted a real-world long-term study, in collaboration with medical specialists, at the Fundación Cardioinfantil-Instituto de Cardiología clinic (Bogotá, Colombia) lasting 2.5 years. The study took place within the context of the outpatient phase of patients' cardiac rehabilitation programme and aimed to compare the patients' progress and adherence in the conventional cardiac rehabilitation programme (control condition) against rehabilitation supported by a fully autonomous socially assistive robot which continuously monitored the patients during exercise to provide immediate feedback and motivation based on sensory measures (robot condition). The explicit aim of the social robot is to improve patient motivation and increase adherence to the programme to ensure a complete recovery. We recruited 15 patients per condition. The cardiac rehabilitation programme was designed to last 36 sessions (18 weeks) per patient. The findings suggest that robot increases adherence (by 13.3%) and leads to faster completion of the programme. In addition, the patients assisted by the robot had more rapid improvement in their recovery heart rate, better physical activity performance and a higher improvement in cardiovascular functioning, which indicate a successful cardiac rehabilitation programme performance. Moreover, the medical staff and the patients acknowledged that the robot improved the patient motivation and adherence to the programme, supporting its potential in addressing the major challenges in rehabilitation programmes.

This paper presents a longitudinal case study of Robot Assisted Therapy for cardiac rehabilitation. The patient, who is a 60-year old male that suffered a myocardial infarction and received angioplasty surgery, successfully recovered after 35 sessions of rehabilitation with a social robot, lasting 18 weeks. The sessions took place directly at the clinic and relied on an exercise regime which was designed by the clinicians and delivered with the support of a social robot and a sensor suite. The robot monitored the patient’s progress, and provided personalised encouragement and feedback. We discuss the recovery of the patient and illustrate how the use of a social robot, its sensory systems and its personalised interaction was instrumental to maintain engagement with the programme and to the patient’s recovery. Of note is a critical event that was promptly detected by the robot, which allowed fast intervention measures to be taken by the medical staff for the referral of the patient for further surgery.

Currently, Social Assistive Robotics (SAR) is widely explored in different areas and scenarios. In cardiac rehabilitation, SAR has been recently implemented as a tool to improve the quality of the procedures and support patients to boost their performance. As cardiac rehabilitation comprises numerous sessions, such systems must guarantee to be effective in the long term. Therefore, to achieve this goal, it is important to understand how users, namely patients and clinicians who mostly know the needs and the therapy environment, perceive this technology. In this context, this paper presents the assessment of the attitudes towards a social robot in order to evaluate the expectation of potential new users, and perception of users who interacted with the social robot during a period of 18 weeks performing cardiac rehabilitation. A total of 43 participants (28 patients and 15 clinicians) were included in the study, and acceptance and perception factors were evaluated through a modified UTAUT questionnaire model and open discussion sessions. Results show that 75% of patients have positive thoughts regarding the usefulness, utility, safety, and trust perceived of a social robot, and 80% of clinicians consider that the robot is a useful tool for cardiac rehabilitation. Similarly, a more positive perception was noticed after the users interacted with the robot. Furthermore, this perception study allows the enhancement of the social model of interaction in the future, aiming to provide a more natural interaction trough personalized features, increasing social abilities and engagement of the users during the therapy.

Socially Assistive Robotics (SAR) has shown to be an important tool to assist patients in physical rehabilitation. SAR is used to provide feedback about patient's state and performance to users and health professionals, therefore, patients are monitored by means of sensor interfaces. In this context, aiming to avoid over-training conditions, one of the most important parameter to monitor is the fatigue level. However, it is usually measured by subjective scales such as Borg scale, thus, there is a need to develop systems that are able to estimate fatigue with greater accuracy. It has been demonstrated that fatigue can be associated to the decreasing performance of the exercise. Hence, this work carried out a study to determinate which temporal and kinematic features are related to the fatigue level during a sit-to-stand test. The procedure consisted of sitting down and standing up from a chair while kinematic data were measured by a Kinect sensor, in order to relate kinematic data and fatigue. Results show that temporal features (time stand-to-stand and time sit-to-stand) and 3 kinematic features (max vertical-velocity of the spin base, max knee-flexo-extension velocity, and max hip-flexo-extension velocity), have a significant correlation with the fatigue level (p <; 0.05).

Cardiovascular disease is the leading cause of death in the world. A program of cardiac rehabilitation (CR) is related to physical activities or exercises to regain the optimal quality of life. CR relies on the necessity to evaluate, control and supervise a patient's status and progress. This work has two objectives: on the one hand, provide a tool for clinicians to assess the patient's status during CR. On the other hand, there is evidence that robots can motivate patients during therapeutic procedures. Our sensor interface explores the possibility to integrate a robotic agent into cardiac therapy. This work presents an exploratory experiment for on-line assessment of typical CR routines.