The purpose of this study was to study the association between the presence of generalized joint hypermobility (GJH) and anxiety within a non-clinical high performing group of adolescents and young adults. Second, to study the impact of GJH and/or anxiety on physical and psychosocial functioning, 168 adolescents and young adults (mean (SD) age 20 (2.9)) were screened. Joint (hyper)mobility, anxiety, and physical and psychosocial functioning were measured. In 48.8% of all high performing adolescents and young adults, GJH was present, whereas 60% had symptoms of anxiety. Linear models controlled for confounders showed that adolescents and young adults with GJH and anxiety had decreased workload (ß (95%CI) -0.43 (-0.8 to -0.08), p-value 0.02), increased fatigue (ß (95%CI) 12.97 (6.3-19.5), p-value < 0.01), and a higher level of pain catastrophizing (ß (95%CI) 4.5 (0.5-8.6), p-value 0.03). Adolescents and young adults with only anxiety had increased fatigue (ß (95%CI) 11 (4.9-19.5). In adolescents and young adults with GJH alone, no impact on physical and psychosocial functioning was found. Adolescents and young adults with the combination of GJH and anxiety were significantly more impaired, showing decreased physical and psychosocial functioning with decreased workload, increased fatigue, and pain catastrophizing. Presence of GJH alone had no negative impact on physical and psychosocial functioning. This study confirms the association between GJH and anxiety, but especially emphasizes the disabling role of anxiety. Screening for anxiety is relevant in adolescents and young adults with GJH and might influence tailored interventions.
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.
Background: Improving physical activity, especially in combination with optimizing protein intake, after surgery has a potential positive effect on recovery of physical functioning in patients after gastrointestinal and lung cancer surgery. The aim of this randomized controlled trial is to evaluate the efficacy of a blended intervention to improve physical activity and protein intake after hospital discharge on recovery of physical functioning in these patients. Methods: In this multicenter single-blinded randomized controlled trial, 161 adult patients scheduled for elective gastrointestinal or lung cancer surgery will be randomly assigned to the intervention or control group. The purpose of the Optimal Physical Recovery After Hospitalization (OPRAH) intervention is to encourage self-management of patients in their functional recovery, by using a smartphone application and corresponding accelerometer in combination with coaching by a physiotherapist and dietician during three months after hospital discharge. Study outcomes will be measured prior to surgery (baseline) and one, four, eight, and twelve weeks and six months after hospital discharge. The primary outcome is recovery in physical functioning six months after surgery, and the most important secondary outcome is physical activity. Other outcomes include lean body mass, muscle mass, protein intake, symptoms, physical performance, self-reported limitations in activities and participation, self-efficacy, hospital readmissions and adverse events. Discussion: The results of this study will demonstrate whether a blended intervention to support patients increasing their level of physical activity and protein intake after hospital discharge improves recovery in physical functioning in patients after gastrointestinal and lung cancer surgery. Trial registration: The trial has been registered at the International Clinical Trials Registry Platform at 14–10-2021 with registration number NL9793. Trial registration data are presented in Table 1.
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.
In the Netherlands, 125 people suffer a stroke every day, which annually results in 46.000 new stroke patients Stroke patients are confronted with combinations of physical, psychological and social consequences impacting their long term functioning and quality of live. Fortunately many patients recover to their pre-stroke level of functioning, however, almost half of them never will. Consequently, rehabilitation often means that patients need to adapt to a new reality in their lives, requiring not only physical but also psychosocial adjustments. Nurses play a key role during rehabilitation of stroke patients. However, when confronted with psychosocial problems, they often feel insecure about identifying the specific psycho-social needs of the individual patient and providing adequate care. In our project ‘Early Detection of Post-Stroke Depression’, (SIA RAAK; 2010-12-36P), we developed a toolkit focusing on early identification of depression after stroke continued with interventions nurses can use during hospitalisation. During this project it became clear that evidence regarding possible interventions is scarce and inclusive. Moreover feasibility of interventions is often not confirmed. Our project showed that during the period of hospital admission patients and health care providers strongly focus on surviving the stroke and on the physical rehabilitation. Therefore, we concluded that to make one step beyond we first have to go one step back. To strengthen psychosocial care for patients after stroke we have to add, reconsider and shape knowledge in context of health care practices in a systematic way, resulting in evidence based and practice informed stepping stones. With this project we aim to collect these stepping stones and develop a nursing care programme that improves psychosocial well-being of patients after stroke, is tailored to the particular concerns and needs of patients, and is considered feasible for use in the usual care process of nurses in the stroke rehabilitation pathway.
