Background Physical activity after bariatric surgery is associated with sustained weight loss and improved quality of life. Some bariatric patients engage insufficiently in physical activity. The aim of this study was to examine whether and to what extent both physical activity and exercise cognitions have changed at one and two years post-surgery, and whether exercise cognitions predict physical activity. Methods Forty-two bariatric patients (38 women, 4 men; mean age 38 ± 8 years, mean body mass index prior to surgery 47 ± 6 kg/m²), filled out self-report instruments to examine physical activity and exercise cognitions pre- and post surgery. Results Moderate to large healthy changes in physical activity and exercise cognitions were observed after surgery. Perceiving less exercise benefits and having less confidence in exercising before surgery predicted less physical activity two years after surgery. High fear of injury one year after surgery predicted less physical activity two years after surgery. Conclusion After bariatric surgery, favorable changes in physical activity and exercise cognitions are observed. Our results suggest that targeting exercise cognitions before and after surgery might be relevant to improve physical activity.
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ObjectiveMany patients with coronavirus disease 2019 (COVID-19) infections were admitted to an intensive care unit (ICU). Physical impairments are common after ICU stays and are associated with clinical and patient characteristics. To date, it is unknown if physical functioning and health status are comparable between patients in the ICU with COVID-19 and patients in the ICU without COVID-19 3 months after ICU discharge. The primary objective of this study was to compare handgrip strength, physical functioning, and health status between patients in the ICU with COVID-19 and patients in the ICU without COVID-19 3 months after ICU discharge. The second objective was to identify factors associated with physical functioning and health status in patients in the ICU with COVID-19. Methods In this observational, retrospective chart review study, handgrip strength (handheld dynamometer), physical functioning (Patient-Reported Outcomes Measurement Information System Physical Function), and health status (EuroQol 5 Dimension 5 Level) were compared between patients in the ICU with COVID-19 and patients in the ICU without COVID-19 using linear regression. Multilinear regression analyses were used to investigate whether age, sex, body mass index, comorbidities in medical history (Charlson Comorbidity Index), and premorbid function illness (Identification of Seniors At Risk-Hospitalized Patients) were associated with these parameters in patients in the ICU with COVID-19. Results In total, 183 patients (N = 92 with COVID-19) were included. No significant between-group differences were found in handgrip strength, physical functioning, and health status 3 months after ICU discharge. The multilinear regression analyses showed a significant association between sex and physical functioning in the COVID-19 group, with better physical functioning in men compared with women. Conclusion Current findings suggest that handgrip strength, physical functioning, and health status are comparable for patients who were in the ICU with COVID-19 and patients who were in the ICU without COVID-19 3 months after ICU discharge. Impact Aftercare in primary or secondary care in the physical domain of postintensive care syndrome after ICU discharge in patients with COVID-19 and in patients without COVID-19 who had an ICU length of stay >48 hours is recommended. Lay Summary Patients who were in the ICU with and without COVID-19 had a lower physical status and health status than healthy people, thus requiring personalized physical rehabilitation. Outpatient aftercare is recommended for patients with an ICU length of stay >48 hours, and functional assessment is recommended 3 months after hospital discharge.
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Parental involvement is a crucial force in children’s development, learning and success at school and in life [1]. Participation, defined by the World Health Organization as ‘a person’s involvement in life situations’ [2] for children means involvement in everyday activities, such as recreational, leisure, school and household activities [3]. Several authors use the term social participation emphasising the importance of engagement in social situations [4, 5]. Children’s participation in daily life is vital for healthy development, social and physical competencies, social-emotional well-being, sense of meaning and purpose in life [6]. Through participation in different social contexts, children gather the knowledge and skills needed to interact, play, work, and live with other people [4, 7, 8]. Unfortunately, research shows that children with a physical disability are at risk of lower participation in everyday activities [9]; they participate less frequently in almost all activities compared with children without physical disabilities [10, 11], have fewer friends and often feel socially isolated [12-14]. Parents, in particular, positively influence the participation of their children with a physical disability at school, at home and in the community [15]. They undertake many actions to improve their child’s participation in daily life [15, 16]. However, little information is available about what parents of children with a physical disability do to enable their child’s participation, what they come across and what kind of needs they have. The overall aim of this thesis was to investigate parents’ actions, challenges, and needs while enhancing the participation of their school-aged child with a physical disability. In order to achieve this aim, two steps have been made. In the first step, the literature has been examined to explore the topic of this thesis (actions, challenges and needs) and to clarify definitions for the concepts of participation and social participation. Second, for the purposes of giving breadth and depth of understanding of the topic of this thesis a mixed methods approach using three different empirical research methods [17-19], was applied to gather information from parents regarding their actions, challenges and needs.
Alcohol use disorder (AUD) is a pattern of alcohol use that involves having trouble controlling drinking behaviour, even when it causes health issues (addiction) or problems functioning in daily (social and professional) life. Moreover, festivals are a common place where large crowds of festival-goers experience challenges refusing or controlling alcohol and substance use. Studies have shown that interventions at festivals are still very problematic. ARise is the first project that wants to help prevent AUD at festivals using Augmented Reality (AR) as a tool to help people, particular festival visitors, to say no to alcohol (and other substances). ARise is based on the on the first Augmented Reality Exposure Therapy (ARET) in the world that we developed for clinical treatment of AUD. It is an AR smartphone driven application in which (potential) visitors are confronted with virtual humans that will try to seduce the user to accept an alcoholic beverage. These virtual humans are projected in the real physical context (of a festival), using innovative AR glasses. Using intuitive phone, voice and gesture interactions, it allows users to personalize the safe experience by choosing different drinks and virtual humans with different looks and levels of realism. ARET has been successfully developed and tested on (former) AUD patients within a clinical setting. Research with patients and healthcare specialists revealed the wish to further develop ARET as a prevention tool to reach people before being diagnosed with AUD and to extend the application for other substances (smoking and pills). In this project, festival visitors will experience ARise and provide feedback on the following topics: (a) experience, (b) awareness and confidence to refuse alcohol drinks, (c) intention to use ARise, (d) usability & efficiency (the level of realism needed), and (e) ideas on how to extend ARise with new substances.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
