The feasibility of pelvic floor training to treat urinary incontinence in women with breast cancer : a telehealth intervention trial

Purpose: To investigate the feasibility of recruiting into a pelvic floor muscle training (PFMT) program delivered via telehealth to treat urinary incontinence (UI) in women with breast cancer on aromatase inhibitors. Methods: We conducted a pre-post single cohort clinical trial with 54 women with breast cancer. Participants underwent a 12-week PFMT program using an intra-vaginal pressure biofeedback device: femfit®. The intervention included eight supervised individual PFMT sessions over Zoom™ and a 12-week home exercise program. The primary outcome of this study was feasibility, specifically consent rate. Secondary outcomes which included prevalence and burden of UI measured using the International Consultation on Incontinence Questionnaire–Urinary Incontinence Short Form (ICIQ-UI SF), and pelvic floor muscle (PFM) strength measured as intravaginal squeeze pressure were compared using McNemar’s and paired t tests. Results: The mean age of participants was 50 years (SD ± 7.3). All women who were eligible to participate in this study consented (n = 55/55, 100%). All participants reported that the program was beneficial and tailored to their needs. The results showed a statistically significant decline in the prevalence (percentage difference 42%, 95% CI 28, 57%) and burden (ICIQ-UI SF score mean change 9.4, 95% CI 8.5, 10.4) of UI post intervention. A significant increase in PFM strength was observed post-intervention (mean change 4.8 mmHg, 95% CI 3.9, 5.5). Conclusion: This study indicated that PFMT delivered via telehealth may be feasible and potentially beneficial in treating stress UI in women with breast cancer. Further studies such as randomized controlled trials are required to confirm these results. © 2022, The Author(s)

Cerebral Palsy

Nowadays, cerebral palsy (CP) rehabilitation, along with medical and surgical interventions in children with CP, leads to better motor and postural control and can ensure ambulation and functional independence. In achieving these improvements, many modern practices may be used, such as comprehensive multidisciplinary assessment, clinical decision making, multilevel surgery, botulinum toxin applications, robotic ambulation applications, treadmill, and other walking aids to increase the quality and endurance of walking. Trainings are based on neurodevelopmental therapy, muscle training and strength applications, adaptive equipment and orthotics, communication, technological solves, and many others beyond the scope of this book. In the years of clinical and academic experiences, children with cerebral palsy have shown us that the world needs a book to give clinical knowledge to health professionals regarding these important issue. This book is an attempt to fulfill and to give “current steps” about CP. The book is intended for use by physicians, therapists, and allied health professionals who treat/rehabilitate children with CP. We focus on the recent concepts in the treatment of body and structure problems and describe the associated disability, providing suggestions for further reading. All authors presented the most frequently used and accepted treatment methods with scientifically proven efficacy and included references at the end of each chapter

A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

Motion sensors for knee angle recognition in muscle rehabilitation solutions

The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.This work was funded by European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Portugal 2020 in the framework of the NanoStim (POCI-01-0247-FEDER-045908) project, and Fundação para a Ciência e a Tecnologia under Projects UIDB/05757/2020, UIDB/00319/2020, and PhD grant 2020.05704.BD

Biofeedback and Anxiety Reduction: An Occupational Therapy Intervention for Persons with Long Covid

Background: People who acquire Covid 19 may have symptoms lasting over three months, called Long Covid. It is estimated that one in five people in the United States has Long Covid. Unpleasant symptoms of Long Covid are many including anxiety. People with Long Covid have a difficult time engaging in everyday activities and have a poor quality of life. Occupational therapy practitioners using heart rate variability (HRV) biofeedback may help decrease anxiety in the Long Covid population. There is limited research on occupational therapy and HRV biofeedback. Purpose: The problem the study addressed was to investigate the use of HRV biofeedback therapy delivered by an occupational therapy practitioner and its effectiveness to decrease anxiety levels in people with Long Covid. The research aimed to discover if HRV biofeedback decreased anxiety and increased the quality of life in persons with Long Covid. Methods: This study was quasi-experimental in the form of a pre-test-post-test design. Each participant was administered two pre-tests/posttests, the Generalized Anxiety Disorder (GAD) and the Quality of Life Scale (QOLS). Each participant received eight biofeedback sessions. Results: The results of this study found that HRV biofeedback demonstrated potential in decreasing anxiety and improving quality of life in persons with Long Covid. This study yielded a low sample size; therefore, more data needs to be collected in order to determine if the results are statistically significant. A minimal clinical difference of a 4-point change is considered clinically meaningful. Ten out of eleven participants in this study reported a decrease in anxiety based on the post-test outcome measures of the GAD-7 which is considered clinically meaningful. An increase of 8-9 points in the QOLS is considered a 60% improvement in quality of life. Nine out of eleven participants reported an improvement in their quality of life and as a group mean score, which was found to be clinically meaningful. Conclusions: The results of this pilot study appear promising for the use of HRV biofeedback to reduce anxiety and improve QOLS in persons with Long Covid. The study will continue to collect data until there are sufficient participants to perform paired-sample t-tests to determine the statistical significance of pre-test/post-test scores for both the GAD-7 and the QOLS

Gait Analysis and Rehabilitation Using Web-Based Pose Estimation

Gait abnormalities are one of the most common health conditions in the elderly population, with almost one in three people over 60 experiencing symptoms that disrupt their movement [1]. These symptoms can cause disability [2] and present an increased fall risk [3] [4]. Detecting these abnormalities early is, therefore, crucial as it reduces the likelihood of injuries and accidents. Current treatments for gait abnormalities depend on the condition, but many treatment plans commonly incorporate some form of physiotherapy. Clinicians typically deliver physiotherapy in the form of gait assessments and targeted exercises or therapies. Recent research has also shown that virtual reality (VR) treadmill walking, using motion capture technology, can be an effective method of treating certain gait abnormalities [5] [6] [7]. This thesis covers the development of a web-based VR treadmill walking system to make VR physiotherapy cheaper and more accessible. The system uses convolutional neural networks to assess the patient’s gait from an RGB webcam feed and provides them with live feedback on their body position within a VR environment. The system’s gait assessment capabilities are validated by comparing it to a gold standard – the OptiTrack motion capture system. The results demonstrate that the system’s percentage error (ϵ˜%) was much less for temporal gait metrics (0.24 < ϵ˜< 12.40) than it was for spatial ones (70.90 < ϵ˜% < 79.72). Four out of five spatial metrics also had a “very strong correlation” (0.74 < r < 0.86) when compared to the OptiTrack’s metrics, meaning the accuracy could be increased using a gain factor. These findings establish the basis for a similar study with a larger sample size. They also raise the possibility that this system could analyse gait in the clinic and the home without specialist motion capture equipment or facilities

Tailored virtual reality and mobile application for motor rehabilitation

The present work presents a measurement system and methodology for hand and finger motor rehabilitation. The interaction with serious games developed in Unity 3D game engine is performed using a natural user interface based on Leap Motion Controller. The storage and management of data related to patient identification, established training plans and training results in LeaPhysio system, is realized in a client server architecture. The stored information can be accessed through a developed LeaPhysio App for Android OS platform, which also allows configuration of training plan by a therapist. Different metrics were included in the measurement system to provide to users the possibility to evaluate in an objective way the motor rehabilitation. The tests have shown that the developed system can provide accurate data on hand and finger movements in a meaningful and motivating exercise environment.info:eu-repo/semantics/acceptedVersio

Smart Biofeedback

Smart biofeedback is receiving attention because of the widespread availability of advanced technologies and smart devices that are used in effective collection, analysis, and feedback of physiologic data. Researchers and practitioners have been working on various aspects of smart biofeedback methodologies and applications by using wireless communications, the Internet of Things (IoT), wearables, biomedical sensors, artificial intelligence, big data analytics, clinical virtual reality, smartphones, and apps, among others. The current paradigm shift in information and communication technologies (ICT) has been propelling the rapid pace of innovation in smart biofeedback. This book addresses five important topics of the perspectives and applications in smart biofeedback: brain networks, neuromeditation, psychophysiological psychotherapy, physiotherapy, and privacy, security, and integrity of data

Development of a transformer-based electrical stimulation driver for a wearable physiotherapy system

This work presents the development of an electrical stimulation driver for two wearable systems: NanoStim and NanoID. These systems are designed for physiotherapy purposes, with NanoStim focusing on the treatment of knee pathologies and NanoID on balance control. The goal of this work is to create a compact electrical stimulation driver with low processing consumption capable of inducing muscle contractions in a wide range of applications. The proposed solution involves the development of two transformer-based stimulation drivers, each controlled by a microcontroller and a mobile application. These drivers are based on distinct transformer models, each tailored to produce muscle contractions through the application of electrical pulses and are designed to meet the requirements for integration into wearable systems. Both alternatives underwent testing and validation with volunteers, and the collected data was analyzed to determine the optimal circuit alternative for implementation in both wearable systems