Objectives: To conduct a scoping review to 1) describe findings and determinants of physical functioning in children during and/or after PICU stay, 2) identify which domains of physical functioning are measured, 3) and synthesize the clinical and research knowledge gaps.Data Sources: A systematic search was conducted in PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Library databases following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews guidelines.Study Selection: Two investigators independently screened and included studies against predetermined criteria.Data Extraction: One investigator extracted data with review by a second investigator. A narrative analyses approach was used.Data Synthesis: A total of 2,610 articles were identified, leaving 68 studies for inclusion. Post-PICU/hospital discharge scores show that PICU survivors report difficulties in physical functioning during and years after PICU stay. Although sustained improvements in the long-term have been reported, most of the reported levels were lower compared with the reference and baseline values. Decreased physical functioning was associated with longer hospital stay and presence of comorbidities. A diversity of instruments was used in which mobility and self-care were mostly addressed.CONCLUSIONS: The results show that children perceive moderate to severe difficulties in physical functioning during and years after PICU stay. Longitudinal assessments during and after PICU stay should be incorporated, especially for children with a higher risk for poor functional outcomes. There is need for consensus on the most suitable methods to assess physical functioning in children admitted to the PICU.
MULTIFILE
Abstract Background: People with severe mental illness (SMI) often suffer from long-lasting symptoms that negatively influence their social functioning, their ability to live a meaningful life, and participation in society. Interventions aimed at increasing physical activity can improve social functioning, but people with SMI experience multiple barriers to becoming physically active. Besides, the implementation of physical activity interventions in day-to-day practice is difficult. In this study, we aim to evaluate the effectiveness and implementation of a physical activity intervention to improve social functioning, mental and physical health. Methods: In this pragmatic stepped wedge cluster randomized controlled trial we aim to include 100 people with SMI and their mental health workers from a supported housing organization. The intervention focuses on increasing physical activity by implementing group sports activities, active guidance meetings, and a serious game to set physical activity goals. We aim to decrease barriers to physical activity through active involvement of the mental health workers, lifestyle courses, and a medication review. Participating locations will be divided into four clusters and randomization will decide the start of the intervention. The primary outcome is social functioning. Secondary outcomes are quality of life, symptom severity, physical activity, cardiometabolic risk factors, cardiorespiratory fitness, and movement disturbances with specific attention to postural adjustment and movement sequencing in gait. In addition, we will assess the implementation by conducting semi-structured interviews with location managers and mental health workers and analyze them by direct content analysis. Discussion: This trial is innovative since it aims to improve social functioning in people with SMI through a physical activity intervention which aims to lower barriers to becoming physically active in a real-life setting. The strength of this trial is that we will also evaluate the implementation of the intervention. Limitations of this study are the risk of poor implementation of the intervention, and bias due to the inclusion of a medication review in the intervention that might impact outcomes. Trial registration: This trial was registered prospectively in The Netherlands Trial Register (NTR) as NTR NL9163 on December 20, 2020. As the The Netherlands Trial Register is no longer available, the trial can now be found in the International Clinical Trial Registry Platform via: https:// trial search. who. int/ Trial2. aspx? Trial ID= NL9163.
Objective:The objective of this scoping review is threefold: (1) to describe outcomes of and determinants for physical functioning in pediatric intensive care unit (PICU) survivors evaluated during and/or after their PICU stay, (2) to provide an overview how physical functioning and its associated determinants in this population are reported, measured and classified in accordance with the International classification of Functioning, Disability and Health-Children and Youth framework (ICF-CY) components and (3) to synthesize key gaps in knowledge and research and clinical recommendations related to our review questions.Introduction:Optimal physical functioning in children is of major importance in their developmental trajectories and for the prevention and recovery of health problems across lifespan. PICU children are at high risk of poor physical functioning during and after critical illness. A recent overview of the literature, concerning evaluation of physical functioning in PICU survivors according to the ICF-CY components, is lacking. Inclusion criteria:This review includes empirical studies reporting outcomes and determinants of physical functioning in PICU survivors evaluated during and/or after PICU stay. All English language studies reporting empirical data will be included with no restrictions set on the types of study designs used.Methods:This review will be conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. To locate studies eligible for inclusion, the electronic databases Pubmed, EMBASE, CINAHL and Cochrane Library will be searched from the earliest records to October 2019. Study selection will be performed by two independent reviewers. Covidence software will be used to screen titles and abstracts as well as the full-text of included studies. Data extraction will be conducted using a customized form. The extracted data will be presented in diagrammatic or tabular form with an accompanying narrative summary.
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.
