Physical activity monitoring with wearable technology has the potential to support stroke rehabilitation. Little is known about how physical therapists use and value the use of wearable activity monitors. This cross-sectional study explores the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Over 300 physical therapists in primary and geriatric care and rehabilitation centers in the Netherlands were invited to fill in an online survey that was developed based on previous studies and interviews with experts. In total, 103 complete surveys were analyzed. Out of the 103 surveys, 27% of the respondents were already using activity monitoring. Of the suggested treatment purposes of activity monitoring, 86% were perceived as useful by more than 55% of the therapists. The most recognized barriers to clinical implementation were lack of skills and knowledge of patients (65%) and not knowing what brand and type of monitor to choose (54%). Of the non-users, 79% were willing to use it in the future. In conclusion, although the concept of remote activity monitoring was perceived as useful, it was not widely adopted by physical therapists involved in stroke care. To date, skills, beliefs, and attitudes of individual therapists determine the current use of wearable technology.
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In dit project wordt er voortgeborduurd op de kennis en expertise omtrent beweeginterventies die een brug van zorg naar bewegen slaan (sportzorgprogramma’s). Hier is er veel expertise opgedaan binnen Physical Activity Centre (PAC), een project vanuit het Lectoraat Fysieke Activiteit en Gezondheid van Fontys Sporthogeschool. PAC is ontstaan vanuit twee invalshoeken. Enerzijds als een verbeteractie om een optimale koppeling te realiseren tussen de theoretische kennis (medisch-biologisch en gedragswetenschappelijke theorieën) en het praktisch handelen van een student. Anderzijds was er de maatschappelijke vraag naar ‘beweegaanbod op maat’ omtrent de problematiek rondom chronische ziekten, vergrijzing en bewegingsarmoede.
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Purpose: The primary aim of this study was to investigate the concurrent validity of the PAM AM400 accelerometer for measuring physical activity in usual care in hospitalized patients by comparing it with the ActiGraph wGT3X-BT accelerometer. Materials and methods: This was a prospective single centre observational study performed at the University Medical Centre Utrecht in The Netherlands. Patients admitted to different clinical wards were included. Intraclass Correlation Coefficients (ICCs) were computed using a two-way mixed model with random subjects. Additionally, Bland-Altman plots were made to visualize the level of agreement of the PAM with the ActiGraph. To test for proportional bias, a regression analysis was performed. Results: In total 17 patients from different clinical wards were included in the analyses. The level of agreement between the PAM and ActiGraph was found strong with an ICC of 0.955. The Bland-Altman analyses showed a mean difference of 1.12min between the two accelerometers and no proportional bias (p¼0.511). Conclusions: The PAM is a suitable movement sensor to validly measure the active minutes of hospitalized patients. Implementation of this device in daily care might be helpful to change the immobility culture in hospitals.
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Tot op heden is er weinig tot geen inzicht in de beweegredenen achter routekeuzes. Deze informatie is van belang om fietsgedrag te kunnen beïnvloeden en stimuleren. Ter verbetering van de bikeability zijn in Groningen de Slimme Routes bedacht, waarbij fietsers om drukke plekken heen worden geleid. Hiermee moeten drukke en onveilige situaties tussen weggebruikers voorkomen worden. Met behulp van objectieve monitoring is getracht zowel kwantitatieve als kwalitatieve data te verzamelen over het fietsgedrag richting het Zernikecomplex met als dit gedrag te kunnen beïnvloeden.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
In societies where physical activity levels are declining, stimulating sports participation in youth is vital. While sports offer numerous benefits, injuries in youth are at an all-time high with potential long-term consequences. Particularly, women football's popularity surge has led to a rise in knee injuries, notably anterior cruciate ligament (ACL) injuries, with severe long-term effects. Urgent societal attention is warranted, supported by media coverage and calls for action by professional players. This project aims to evaluate the potential of novel artificial intelligence-based technology to enhance player monitoring for injury risk, and to integrate these monitoring pathways into regular training practice. Its success may pave the way for broader applications across different sports and injuries. Implementation of results from lab-based research into practice is hindered by the lack of skills and technology needed to perform the required measurements. There is a critical need for non-invasive systems used during regular training practice and allowing longitudinal monitoring. Markerless motion capture technology has recently been developed and has created new potential for field-based data collection in sport settings. This technology eliminates the need for marker/sensor placement on the participant and can be employed on-site, capturing movement patterns during training. Since a common AI algorithm for data processing is used, minimal technical knowledge by the operator is required. The experienced PLAYSAFE consortium will exploit this technology to monitor 300 young female football players over the course of 1 season. The successful implementation of non-invasive monitoring of football players’ movement patterns during regular practice is the primary objective of this project. In addition, the study will generate key insights into risk factors associated with ACL injury. Through this approach, PLAYSAFE aims to reduce the burden of ACL injuries in female football players.
