BACKGROUND: Hospital stays are associated with high levels of sedentary behavior and physical inactivity. To objectively investigate physical behavior of hospitalized patients, these is a need for valid measurement instruments. The aim of this study was to assess the criterion validity of three accelerometers to measure lying, sitting, standing and walking. METHODS: This cross-sectional study was performed in a university hospital. Participants carried out several mobility tasks according to a structured protocol while wearing three accelerometers (ActiGraph GT9X Link, Activ8 Professional and Dynaport MoveMonitor). The participants were guided through the protocol by a test leader and were recorded on video to serve as reference. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were determined for the categories lying, sitting, standing and walking. RESULTS: In total 12 subjects were included with a mean age of 49.5 (SD 21.5) years and a mean body mass index of 23.8 kg/m2 (SD 2.4). The ActiGraph GT9X Link showed an excellent sensitivity (90%) and PPV (98%) for walking, but a poor sensitivity for sitting and standing (57% and 53%), and a poor PPV (43%) for sitting. The Activ8 Professional showed an excellent sensitivity for sitting and walking (95% and 93%), excellent PPV (98%) for walking, but no sensitivity (0%) and PPV (0%) for lying. The Dynaport MoveMonitor showed an excellent sensitivity for sitting (94%), excellent PPV for lying and walking (100% and 99%), but a poor sensitivity (13%) and PPV (19%) for standing. CONCLUSIONS: The validity outcomes for the categories lying, sitting, standing and walking vary between the investigated accelerometers. All three accelerometers scored good to excellent in identifying walking. None of the accelerometers were able to identify all categories validly.
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Introduction: Previous longitudinal studies indicate that physical activity (PA) significantly declines from primary-to secondary school, and report both changes in individual and environmental determinants of PA. In order to understand this transition and to prevent this negative trend, it is important to gather contextually rich data on possible mechanisms that drive this decline. Therefore, the aim of this study was to investigate changes of PA patterns in transition between primary and secondary school, and to add domain-specific insights of how, where, and when these changes occur. Methods: In total, 175 children participated in a 7-day accelerometer- and Global Positioning System (GPS) protocol at their last year of primary and their first year of secondary school. GPS data-points were overlaid with Geographical Information Systems (GIS) data using ArcGIS 10.1 software. Based on the GPS locations of individual data-points, we identified child’s PA at home, school, local sports grounds, shopping centers, and other locations. Also, trips in active and passive transport were identified according to previously validated GPS speed-algorithms. Longitudinal multi-level linear mixed models were fitted adjusting for age, gender, meteorological circumstances, and the nested structure of days within children and children within schools. Outcome measures were minutes spent in light PA and moderate-to-vigorous PA, specified for the time-segments before school, during school, after school and weekend days. Results: Total PA significantly declined from primary to secondary school. Although transport-related PA increased before- and during school, decreases were found for especially afterschool time spent at sports grounds and transport-related PA during weekends.
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The objective of this study was to assess relationships between children's physical environment and afterschool leisure time physical activity (PA) and active transport. Methods: Children aged 10-12 years participated in a 7-day accelerometer and Global Positioning Systems (GPS) protocol. Afterschool leisure time PA and active transport were identified based on locationand speed-algorithms based on accelerometer, GPS and Geospatial Information Systems (GIS) data. We operationalized children's exposure to the environment by combining home, school and the daily transport environment in individualized daily activity-spaces. Results: In total, 255 children from 20 Dutch primary schools from suburban areas provided valid data. This study showed that greenspaces and smaller distances from the children's home to school were associated with afterschool leisure time PA and walking. Greater distances between home and school, as well as pedestrian infrastructure were associated with increased cycling. Conclusion: We demonstrated associations between environments and afterschool PA within several behavioral contexts. Future studies are encouraged to target specific behavioral domains and to develop natural experiments based on interactions between several types of the environment, child characteristics and potential socio-cognitive processes. LinkedIn: https://www.linkedin.com/in/sanned/
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Gebruik van sensoren en data voor het monitoren van welzijn en gezondheid van mens en dier, raakt steeds meer ingeburgerd. Ook voor de paardenhouderij is het interessant om met behulp van sensoren de gezondheid en het welzijn van de paarden te volgen en in geval van ziekte of stress preventief te kunnen handelen. In tegenstelling tot het ruime aanbod voor de veehouderij, zijn er voor paarden nog weinig of geen sensoren beschikbaar voor gezondheidsmonitoring. In dit project zullen halsbanden voor paarden worden ontwikkeld met activiteitssensoren (accelerometers), die gedragsdata verzamelen. Deze data worden vertaald in informatie over het normale en afwijkende gedrag van de paarden. Activiteit en gedrag worden gekoppeld aan gezondheid en het welzijn van het paard. Doel is om een systeem te ontwikkelen waarbij gezondheid en welzijn van de paarden gemonitord wordt met behulp van deze sensor, en waarbij de eigenaar gewaarschuwd wordt wanneer veranderingen in gedrag optreden die voorspellend zijn voor ziekte, stress of afwijkingen.
The structure will be monitored real-time and reasons behind the damages will be found. Proposals for protecting the structure against earthquakes will be made. - Damage scenario of the building, in relation to the induced seismicity effects on structures in the region- Establishment of a real-time structural monitoring toolThe building will be instrumented with accelerometers and displacement crack sensors. Additionally to the monitoring efforts, the structure will also be modelled in FE computer simulations in an effort trying to find out possible future response of the monument to strong earthquakes. The monitoring data will be combined with FE simulations in concluding the response of the structure to recursive induced seismic events.
