BACKGROUND: Apart from clinical experience and theoretical considerations, there is a lack of evidence that the level of adherence to in-hospital mobilization protocols is related to functional recovery in patients after resection for lung cancer. The objectives of the study were to determine (1) the relationship between adherence to the in-hospital mobilization protocol and physical fitness at hospital discharge and (2) the value of physical fitness measures at discharge in predicting physical functioning 6 weeks and 3 months postoperatively.METHODS: This observational study included 62 patients who underwent surgical resection for lung cancer. Adherence to the in-hospital mobilization protocol was abstracted from patients' records. Physical fitness measures before the operation and at hospital discharge included handgrip strength, 30-second sit-to-stand test, and 6-minute walk test (6MWT). Self-reported physical functioning was assessed preoperatively and 6 weeks and 3 months postoperatively, using the Medical Outcome Study 36-Item Short Form (SF-36) Physical Function subscale (RAND Corp, Santa Monica, CA). Linear regression analyses were used to estimate the relationships of interest, adjusting for potential confounders.RESULTS: Level of adherence to the mobilization protocol was significantly and independently related to handgrip strength, sit-to-stand test, and 6MWT at discharge. Handgrip strength and 6MWT at discharge significantly predicted SF-36 Physical Function at 6 weeks and 3 months postoperatively. The sit-to-stand test only predicted SF-36 Physical Function at 6 weeks.CONCLUSIONS: Suboptimal postoperative mobilization after surgical resection for lung cancer negatively affects physical fitness at discharge. Our results underline the importance of adherence to early postoperative mobilization protocols. Measuring physical fitness at discharge may be useful to inform clinicians on elective referral of patients for postdischarge rehabilitation.
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.
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.
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.
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.
