Wireless sensor networks are becoming popular in the field of ambient assisted living. In this paper we report our study on the relationship between a functional health metric and features derived from the sensor data. Sensor systems are installed in the houses of nine people who are also quarterly visited by an occupational therapist for functional health assessments. Different features are extracted and these are correlated with a metric of functional health (the AMPS). Though the sample is small, the results indicate that some features are better in describing the functional health in the population, but individual differences should also be taken into account when developing a sensor system for functional health assessment.
BACKGROUND: Generalized Joint Hypermobility (GJH) has been found to be associated with musculoskeletal complaints and disability. For others GJH is seen as a prerequisite in order to excel in certain sports like dance. However, it remains unclear what the role is of GJH in human performance. Therefore, the purpose of the study was to establish the association between GJH and functional status and to explore the contribution of physical fitness and musculoskeletal complaints to this association.METHODS: A total of 72 female participants (mean age (SD; range): 19.6 (2.2; 17-24)) were recruited among students from the Amsterdam School of Health Professions (ASHP) (n = 36) and the Amsterdam School of Arts (ASA), Academy for dance and theater (n = 36) in Amsterdam, The Netherlands. From each participant the following data was collected: Functional status performance (self-reported Physical activity level) and capacity (walking distance and jumping capacity: side hop (SH) and square hop (SQH)), presence of GJH (Beighton score ≥4), muscle strength, musculoskeletal complaints (pain and fatigue) and demographic characteristics (age and BMI).RESULTS: GJH was negatively associated with all capacity measures of functional status. Subjects with GJH had a reduced walking distance (B(SE):-75.5(10.5), p = <.0001) and jumping capacity (SH: B(SE):-10.10(5.0), p = .048, and SQH: B(SE):-11.2(5.1), p = .024) in comparison to subjects without GJH, when controlling for confounding: age, BMI and musculoskeletal complaints. In participants with GJH, functional status was not associated with performance measures.CONCLUSION: GJH was independently associated with lower walking and jumping capacity, potentially due to the compromised structural integrity of connective tissue. However, pain, fatigue and muscle strength were also important contributors to functional status.
A growing number of older patients undergo cardiac surgery. Some of these patients are at increased risk of post-operative functional decline, potentially leading to reduced quality of life and autonomy, and other negative health outcomes. First step in prevention is to identify patients at risk of functional decline. There are no current published tools available to predict functional decline following cardiac surgery. The objective was to validate the identification of seniors at risk—hospitalised patients (ISAR-HP), in older patients undergoing cardiac surgery. A multicenter cohort study was performed in cardiac surgery wards of two university hospitals with follow-up 3 months after hospital admission. Inclusion criteria: consecutive cardiac surgery patients, aged ≥65. Functional decline was defined as a decline of at least one point on the Katz ADL Index at follow-up compared with preadmission status.
In the context of sustainability, the use of biocatalysis in organic synthesis is increasingly observed as an essential tool towards a modern and ‘green’ chemical industry. However, the lack of a diverse set of commercially available enzymes with a broad selectivity toward industrially-relevant substrates keeps hampering the widespread implementation of biocatalysis. Aminoverse B.V. aims to contribute to this challenge by developing enzymatic screening kits and identifying novel enzyme families with significant potential for biocatalysis. One of the most important, yet notoriously challenging reaction in organic synthesis is site-selective functionalization (e.g. hydroxylation) of inert C-H bonds. Interestingly, Fe(II)/α-ketoglutarate-dependent oxygenases (KGOs) have been found to perform C-H hydroxylation, as well as other oxyfunctionalization, spontaneously in nature. However, as KGOs are not commercially available, or even extensively studied in this context, their potential is not readily accessible to the chemical industry. This project aims to demonstrate the potential of KGOs in biocatalysis. In order to achieve this, the following challenges will be addressed: i) establishing an enzymatic screening methodology to study the activity and selectivity of recombinant KGOs towards industrially relevant substrates, ii) establishing analytical methods to characterize KGO-catalyzed substrate conversion and product formation. Eventually, the proof-of-principle demonstrated during this project will allow Aminoverse B.V. to develop a commercial biocatalysis kit comprised of KGO enzymes with a diverse activity profile, allowing their application in the sustainable production of either commodity, fine or speciality chemicals. The project consortium is composed of: i) Aminoverse B.V, a start-up company dedicated to facilitate chemical partners towards implementing biocatalysis in their chemical processes, and ii) Zuyd University, which will link Aminoverse B.V. with students and (bio)chemical professionals in creating a novel collaboration which will not only stimulate the development of (bio)chemical students, but also the translation of academic knowledge on KGOs towards a feasible biocatalytic application.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
