BackgroundWorking alliance can possibly influence patients’ experiences of pain and physical functioning. The aim of this systematic review is to merge evidence from literature regarding the influence of patients’ perceived working alliance on pain and physical functioning in patients with chronic musculoskeletal pain.MethodsA systematic review in which randomized controlled trials and cohort studies were included that assessed the influence of working alliance on either pain or physical functioning in patients with chronic musculoskeletal pain. The methodological quality of the included studies were rated by means of the PEDro score and STROBE statement.ResultsThe first step of the search process provided 1469 studies. After screening, five studies were included in this review including one RCT and four cohort studies of patients with chronic musculoskeletal pain. One cohort study was rated as low methodological quality and the other studies as high methodological quality. There was a significant effect of working alliance on the outcome of pain severity, pain interference, and physical functioning in all studies. Physical functioning was measured by means of questionnaires and functional capacity tests. The effect on questionnaires was positive; the effect was conflicting on functional capacity.ConclusionWhen influencing pain with treatment, a patient’s perceived working alliance during treatment does predict pain reduction and improvement in physical functioning. It is recommended to inquire about a patient’s working alliance during treatment in patients with chronic musculoskeletal pain.
BackgroundWorking alliance can possibly influence patients’ experiences of pain and physical functioning. The aim of this systematic review is to merge evidence from literature regarding the influence of patients’ perceived working alliance on pain and physical functioning in patients with chronic musculoskeletal pain.MethodsA systematic review in which randomized controlled trials and cohort studies were included that assessed the influence of working alliance on either pain or physical functioning in patients with chronic musculoskeletal pain. The methodological quality of the included studies were rated by means of the PEDro score and STROBE statement.ResultsThe first step of the search process provided 1469 studies. After screening, five studies were included in this review including one RCT and four cohort studies of patients with chronic musculoskeletal pain. One cohort study was rated as low methodological quality and the other studies as high methodological quality. There was a significant effect of working alliance on the outcome of pain severity, pain interference, and physical functioning in all studies. Physical functioning was measured by means of questionnaires and functional capacity tests. The effect on questionnaires was positive; the effect was conflicting on functional capacity.ConclusionWhen influencing pain with treatment, a patient’s perceived working alliance during treatment does predict pain reduction and improvement in physical functioning. It is recommended to inquire about a patient’s working alliance during treatment in patients with chronic musculoskeletal pain.
Background: Decline in physical activity and functioning is commonly observed in the older population and might be associated with biomarkers such as Advanced Glycation End-products (AGEs). AGEs contribute to age-related decline in the function of cells and tissues in normal aging and have been found to be associated with motor function decline. The aim of this study is to investigate the association between the levels of AGEs, as assessed by skin autofluorescence, and the amount of physical activity and loss of physical functioning in older participants.Methods: Cross-sectional data of 5,624 participants aged 65 years and older from the Lifelines cohort study was used. Linear regression analyses were utilized to study associations between skin autofluorescence/AGE-levels (AGE reader), the number of physically active days (SQUASH), and physical functioning (RAND-36), respectively. A logistic regression analysis was used to study associations between AGE-levels and the compliance with the Dutch physical activity guidelines (SQUASH).Results: A statistical significant association between AGE levels and the number of physically active days (β = -0.21, 95% CI: -0.35 to -0.07, P = .004), physical functioning (β = -1.60, 95% CI: -2.64 to -0.54, P = .003), and compliance with the Dutch physical activity guidelines (OR = 0.76, 95% CI: 0.62 to 0.94, P = .010) was revealed.Conclusions: This study indicates that high AGE levels may be a contributing factor as well as a biomarker for lower levels of physical activity and functioning in the older population.
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.
