Serious games, mHealth rehabilitation technology in neurological rehabilitation, remote monitoring, gait analysis in a virtual reality environment.
The use of serious games (exergames) in rehabilitation is becoming increasingly popular among clinicians. These games are either embedded in commercial entertainment applications or specifically designed for clinical purposes. Maintaining a high level of therapy adherence during the rehabilitation process is a significant challenge for clinicians. Lack of motivation and difficulty in monitoring progress are two key issues they face. Therefore, the use of serious games could offer an interesting supplement to conventional treatments for these patients.
At REVAL, we test various types of interventions (non-immersive, semi-immersive, and fully immersive) for both motor and cognitive rehabilitation across different patient groups (e.g., multiple sclerosis, children with cerebral palsy, orthopedic conditions, normal and pathological aging).
The development of mobile technology and mobile internet offers new possibilities in the field of rehabilitation and clinical evaluation from a longitudinal perspective for patient management. The number of health interventions via personal mobile devices (mHealth) has exponentially increased due to the widespread availability of mobile technology (there are currently more than six billion smartphone subscriptions worldwide, with hundreds of millions more expected in the coming years).The development and implementation of mHealth opens up new perspectives and opportunities in the healthcare sector.
Previous studies have shown that patients have already embraced mHealth. The main benefits reported by patients include easy access to personalized information, convenience, better insights into their health, and improved communication with healthcare providers. At REVAL, we test and implement both specially developed mHealth apps (e.g., Walk-With-Me apps, JOLO) and commercially available solutions.
Diabetes mellitus is a metabolic disorder characterized by high blood glucose levels due to either a lack of insulin production by the body (type 1) or a reduced effectiveness of the insulin produced (type 2). It is estimated that approximately 40,000 Belgians suffer from type 1 diabetes (IDF Diabetes Atlas, 8th Edition), a diagnosis that is typically made in childhood. If not properly managed, diabetes can lead to serious complications, including cardiovascular diseases, eye problems, kidney damage, and nerve conduction disorders.
While the exact cause of type 1 diabetes remains unclear, blood glucose levels can be stabilized through a combination of self-administered insulin injections, a healthy diet, and adequate exercise. As a result, individuals with type 1 diabetes can lead normal lives with a low risk of complications, provided they adhere to their prescribed treatment. Frequent monitoring of blood glucose levels and adjusting insulin doses based on current glucose readings is critical to this process.
Today, the majority of type 1 diabetes patients in Belgium use the FreeStyle Libre system to monitor their blood glucose levels. The Libre system is a flash monitoring method where a coin-sized sensor is worn on the upper arm. Glucose levels can be measured at any time by scanning the sensor, which needs to be replaced every 14 days. In addition to its ease of use, the advantage of this system over traditional finger-prick methods is that it not only displays the current glucose readings but also provides up to 8 hours of glucose history (Bailey, Bode, Christiansen, Klaff, & Alva, 2015).
Although this technology seems to encourage better adherence to therapy (patients check their glucose levels more frequently compared to the traditional finger-prick method; Ish-Shalom, Weinstein, Raz, & Mosenzon, 2016), clinical experience shows that this effect tends to decrease after about a year of use. Given the popularity of this technology and the critical importance of glucose monitoring in the self-management of type 1 diabetes, the aim of this study is to identify the factors that contribute to (or hinder) adherence to therapy among diabetes patients using the Libre monitoring system.
Virtual reality environments can be used in various rehabilitation applications for individuals with a range of conditions (e.g., people with multiple sclerosis or children with developmental coordination disorder). For instance, within a training program, visual (and even auditory) feedback can be provided to the patient to help them learn and perform more effectively. Additionally, we can explore how best to deliver visual feedback so that individuals can optimally integrate this information into their movement programs.
Artificial intelligence is increasingly being integrated into everyday applications and has the potential to be used more extensively in rehabilitation settings. One example is the use of deep learning algorithms to calculate joint angles based on simple 2D video footage. This means that, without the need for expensive equipment or complex protocols, it is possible to gain insights into how someone performs a specific movement—and whether it aligns with expectations. These deep learning techniques are often applied to pre-recorded footage.
A new development is the implementation of such calculations at the chip level (embedded intelligence). This enables real-time processing, allowing for immediate monitoring in healthcare settings, improving patient care through instant feedback and assessment.
One of the positive aspects of using technology in rehabilitation is the ability to collect a large amount of clinical data. This data can be used to facilitate communication between the various healthcare professionals involved in a patient's care, improving the quality of care while also reducing costs by avoiding unnecessary tests. Furthermore, this data can be integrated into the electronic health record (EHR), allowing other healthcare providers to access the patient's data and deliver optimal, patient-centered care based on interdisciplinary expertise.
With advanced machine learning and AI methods, we aim to develop a pipeline that can automatically adjust rehabilitation programs to the actual needs of patients, evaluated in real-time during their rehabilitation exercises. This approach would enable more personalized and adaptive care.
If health is a human right, and human rights are "the rights of individuals simply because they are part of the human species," then all people, regardless of where they live, should have access to the same collective efforts that protect or improve their health. At REVAL, we believe that translating research into practice is a key milestone in our civic mission. According to the WHO, one of the main barriers in the rehabilitation process is the lack of access to specialized facilities or healthcare professionals. Therefore, we have sought to implement new and affordable solutions to expand access to rehabilitation programs.
The use of mobile technologies and electronic health (eHealth) can provide an alternative solution for rehabilitation or complement existing programs. We are already actively involved in several low- and middle-income countries (LMICs), including, but not limited to, Benin, Burundi, Ethiopia, and Nepal.
Exercise therapy is of great importance for people with cardiovascular diseases or with an increased risk of these. It contributes to better control of cardiovascular risk factors, a better quality of life, a reduction in the number of hospital admissions, a reduced chance of (premature) death, and greater cost-effectiveness of healthcare.
However, there appears to be a large discrepancy between healthcare providers (doctors, physiotherapists, etc.) in how exercise is prescribed to people with (increased risk of) cardiovascular diseases (https://pubmed.ncbi.nlm.nih.gov/29486587/). That is why UHasselt, in collaboration with an international working group (EXPERT Network group: https://cvd-expert-network.eu), has developed the Exercise Prescription in Everyday practice & Rehabilitative Training (EXPERT) tool (https://pubmed.ncbi.nlm.nih.gov/28420250/).
This tool is intended to optimize rehabilitation prescriptions for patients with cardiovascular diseases by means of an algorithm that provides interactive training and decision support. This is based on the most recent EAPC (European Association of Preventive Cardiology) and ESC (European Society of Cardiology) guidelines.
That is why the Exercise Prescription in Everyday Practice & Rehabilitative Training (EXPERT) tool was developed at UHasselt (collaboration between Prof. Dr. Karin Coninx, Prof. Dr. Paul Dendale and Prof. Dr. Dominique Hansen), in collaboration with an international working group (EXPERT Network group: https://cvd-expert-network.eu) (https://pubmed.ncbi.nlm.nih.gov/28420250/).