Background Physical activity after bariatric surgery is associated with sustained weight loss and improved quality of life. Some bariatric patients engage insufficiently in physical activity. The aim of this study was to examine whether and to what extent both physical activity and exercise cognitions have changed at one and two years post-surgery, and whether exercise cognitions predict physical activity. Methods Forty-two bariatric patients (38 women, 4 men; mean age 38 ± 8 years, mean body mass index prior to surgery 47 ± 6 kg/m²), filled out self-report instruments to examine physical activity and exercise cognitions pre- and post surgery. Results Moderate to large healthy changes in physical activity and exercise cognitions were observed after surgery. Perceiving less exercise benefits and having less confidence in exercising before surgery predicted less physical activity two years after surgery. High fear of injury one year after surgery predicted less physical activity two years after surgery. Conclusion After bariatric surgery, favorable changes in physical activity and exercise cognitions are observed. Our results suggest that targeting exercise cognitions before and after surgery might be relevant to improve physical activity.
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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.
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.
