BACKGROUND: Cystic fibrosis is an inherited life-limiting disorder, characterised by pulmonary infections and thick airway secretions. Chest physiotherapy has been integral to clinical management in facilitating removal of airway secretions. Conventional chest physiotherapy techniques (CCPT) have depended upon assistance during treatments, while more contemporary airway clearance techniques are self-administered, facilitating independence and flexibility.OBJECTIVES: To compare CCPT with other airway clearance techniques in terms of their effects on respiratory function, individual preference, adherence, quality of life and other outcomes.SEARCH STRATEGY: We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group trials register which comprises references identified from comprehensive electronic database searches and handsearching of relevant journals and abstract books of conference proceedings. We also searched CINAHL from 1982 to 2002 and AMED from 1985 to 2002. Date of most recent search: January 2004.SELECTION CRITERIA: Randomised or quasi-randomised clinical trials including those with a cross-over design where CCPT was compared with other airway clearance techniques. Studies of less than seven days duration were excluded.DATA COLLECTION AND ANALYSIS: Two reviewers allocated quality scores to relevant studies and independently extracted data. If we were unable to extract data, we invited authors to submit their data. We excluded studies from meta-analysis when data were lost or study design precluded comparison. For some continuous outcomes, we used the generic inverse variance method for meta-analysis of data from cross-over trials and data from parallel-designed trials were incorporated for comparison. We also examined efficacy of specific techniques and effects of treatment duration.MAIN RESULTS: Seventy-eight publications were identified by the searches. Twenty-nine of these were included, representing 15 data sets with 475 participants. There was no difference between CCPT and other airway clearance techniques in terms of respiratory function measured by standard lung function tests. Studies undertaken during acute exacerbations demonstrated relatively large gains in respiratory function irrespective of airway clearance technique. Longer-term studies demonstrated smaller improvements or deterioration over time. Ten studies reported individual preferences for technique, with participants tending to favour self-administered techniques. Heterogeneity in the measurement of preference precluded these data from meta-analysis.AUTHORS' CONCLUSIONS: This review demonstrated no advantage of CCPT over other airway clearance techniques in terms of respiratory function. There was a trend for participants to prefer self-administered airway clearance techniques. Limitations of this review included a paucity of well-designed, adequately-powered, long-term trials.
This study provides an in-depth understanding of the perceptions of patients with T2DM before use (acceptability) and after use (acceptance) regarding 4 different mobile health apps for diabetes control and self-management. This study was part of the TOPFIT Citizenlab project. TOPFIT Citizenlab is a 3-year research and innovation program in the eastern part of the Netherlands. Citizens, health care professionals (HCPs), and companies have joined forces with researchers to develop and implement technology for health and well-being.
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Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.
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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.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
