Objective To systematically summarize the literature on the course of pain in patients with knee osteoarthritis (OA), prognostic factors that predict deterioration of pain, the course of physical functioning, and prognostic factors that predict deterioration of physical functioning in persons with knee OA. Methods A search was conducted in PubMed, CINAHL, Embase, Psych‐INFO, and SPORTDiscus up to January 2014. A meta‐analysis and a qualitative data synthesis were performed. Results Of the 58 studies included, 39 were of high quality. High heterogeneity across studies (I2 >90%) and within study populations (reflected by large SDs of change scores) was found. Therefore, the course of pain and physical functioning was interpreted to be indistinct. We found strong evidence for a number of prognostic factors predicting deterioration in pain (e.g., higher knee pain at baseline, bilateral knee symptoms, and depressive symptoms). We also found strong evidence for a number of prognostic factors predicting deterioration in physical functioning (e.g., worsening in radiographic OA, worsening of knee pain, lower knee extension muscle strength, lower walking speed, and higher comorbidity count). Conclusion Because of high heterogeneity across studies and within study populations, no conclusions can be drawn with regard to the course of pain and physical functioning. These findings support current research efforts to define subgroups or phenotypes within knee OA populations. Strong evidence was found for knee characteristics, clinical factors, and psychosocial factors as prognostics of deterioration of pain and physical functioning.
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.
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.
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.
