ABSTRACT
After the acute disease, post-COVID-19 patients may present several and persistent symptoms, known as the new paradigm of 'post-acute COVID-19 syndrome'. This necessitates a multidisciplinary rehabilitation that has been proposed but whose effectiveness is still to be assessed. In this study, convalescent COVID-19 patients undergoing pulmonary rehabilitation (PR) after reporting long-term symptoms were consecutively enrolled. Then, they were grouped by laboratory parameters at admission through an unsupervised Machine Learning (ML) approach. We aimed to identify potential indicators that could discriminate several phenotypes leading to a different responsiveness to the rehabilitation program. A k-means clustering method was performed;then, statistical analysis was employed to compare clinical and hematochemical parameters of the obtained clusters. The dataset consisted of 78 patients (84.8% males, mean age 60.72 years). The optimal number for clustering was boldsymbol{mathrm{k}=2} with a silhouette coefficient of 0.85, and D-Dimer resulted the most discriminating parameter, thus confirming its role as a marker of inflammation. The phenotypes exhibited statistically significant differences in terms of age boldsymbol{(mathrm{p}=0.007)}, packs of cigarettes per year boldsymbol{(mathrm{p}=0.003)}, uricemia boldsymbol{(mathrm{p}=0.010)}, PCR boldsymbol{(mathrm{p}=0.026)}, D-Dimer boldsymbol{(mathrm{p} < 0.001)}, red blood cells boldsymbol{(mathrm{p}=0.005)}, hemoglobin boldsymbol{(mathrm{p}=0.039)}, hematocrit boldsymbol{(mathrm{p}=0.026), text{PaO}_{2} (mathrm{p}=0.006)},boldsymbol{text{SpO}_{2} (mathrm{p}=0.011)}. Overall, our findings suggest the effectiveness of ML in identifying personalized prevention, interventional and rehabilitation strategies. © 2022 IEEE.
ABSTRACT
Cardiac Rehabilitation Programs (CRPs) are an important tool of secondary prevention and their implementation within health services, despite the uneven geographical distribution, has been receiving attention from decision-makers in recent years. Adherence to the CRPs is one of the great challenges faced by the multidisciplinary team, and there are several strategies to maintain adherence, particularly in CRP-Phase III, which occurs outside the hospital environment. One of the strategies followed is the use of remote performance monitoring and recording of possible alert symptoms. With the pandemic due to COVID-19, these challenges have become even more evident as Phase II programs have been suspended, thus increasing the importance of the home-based CRPs. In this work, the results of a study aiming to understand the impact of the pandemic on adherence to the prescription of exercise and the perception of patients regarding the effects of physical activity on health conditions are shown. The results indicate that the pandemic did not have a major effect on the adherence to home-based exercises, in particular, in patients undergoing programs using a telemonitoring system. Moreover, the perception of the importance of physical activity for health and well-being was reinforced in the context of the pandemic. © 2022 Croatian Society MIPRO.
ABSTRACT
Parkinson’s Disease (PD) is a neurodegenerative disease affecting mainly the elderly. Patients affected by PD may experience slowness of movements, loss of automatic movements, and impaired posture and balance. Physical therapy is highly recommended to improve their walking where therapists instruct patients to perform big and loud exercises. Rhythmic Auditory Stimulation (RAS) is a method used in therapy where external stimuli are used to facilitate movement initiation and continuation. Aside from face-to-face therapy sessions, home rehabilitation programs are used by PD patients with mobility issues and who live in remote areas. Telerehabilitation is a growing practice amid the COVID-19 pandemic. This work describes the design and implementation of a wireless sensor network to remotely and objectively monitor the rehabilitation progress of patients at their own homes. The system, designed in consultation with a physical therapist, includes insole sensors which measure step parameters, a base station as a phone application which facilitates RAS training sessions and communication interface between the therapist and patients, and an online server storing all training results for viewing. Step data from the system’s real-time analysis were validated against post-processed and reconstructed signals from the raw sensor data gathered across different beats. The system has an accuracy of at least 80% and 72% for the total steps and correct steps respectively. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
COVID-19 pandemic adversely challenged the healthcare system in an unprecedented way. Access to neurorehabilitation programme for patients with stroke and other neurological disability was severely restricted including shutting down of most community-based and outpatient facilities. There is hardly any organised virtual programme of exploring any potential of stretching and exercising of muscles needed in a rehabilitation programme. There is an impetus to innovate service developments, while the risks and fear of contracting the coronavirus remain prevalent. We propose a framework for developing a novel tele-neurorehabilitation system that will guide the patients to perform therapeutic exercises, as proposed by the clinicians, remotely. The system will allow patients to directly interact with doctors through a secure audio–video online portal. Wearable motion tracking sensors will be integrated within a hardware-based home setting for gathering performance data live from patients while they are performing exercises. The paper describes the design components of the framework justifying the tools, hardware, and protocols required to implement a secure online portal for tele-neurorehabilitation. Specifications of the core architectural layers have been reported. Some preliminary work demonstrates how the framework specifies capturing and analysing of physiological data using wearable sensors, as well as displaying of gait parameters on a software dashboard. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.