Background: Osteoarthritis is one of the most common chronic joint diseases, mostly affecting the knee or hip through pain, joint stiffness and decreased physical functioning in daily life. Regular physical activity (PA) can help preserve and improve physical functioning and reduce pain in patients with osteoarthritis. Interventions aiming to improve movement behaviour can be optimized by tailoring them to a patients' starting point; their current movement behaviour. Movement behaviour needs to be assessed in its full complexity, and therefore a multidimensional description is needed. Objectives: The aim of this study was to identify subgroups based on movement behaviour patterns in patients with hip and/or knee osteoarthritis who are eligible for a PA intervention. Second, differences between subgroups regarding Body Mass Index, sex, age, physical functioning, comorbidities, fatigue and pain were determined between subgroups. Methods: Baseline data of the clinical trial 'e-Exercise Osteoarthritis', collected in Dutch primary care physical therapy practices were analysed. Movement behaviour was assessed with ActiGraph GT3X and GT3X+ accelerometers. Groups with similar patterns were identified using a hierarchical cluster analysis, including six clustering variables indicating total time in and distribution of PA and sedentary behaviours. Differences in clinical characteristics between groups were assessed via Kruskall Wallis and Chi2 tests. Results: Accelerometer data, including all daily activities during 3 to 5 subsequent days, of 182 patients (average age 63 years) with hip and/or knee osteoarthritis were analysed. Four patterns were identified: inactive & sedentary, prolonged sedentary, light active and active. Physical functioning was less impaired in the group with the active pattern compared to the inactive & sedentary pattern. The group with the prolonged sedentary pattern experienced lower levels of pain and fatigue and higher levels of physical functioning compared to the light active and compared to the inactive & sedentary. Conclusions: Four subgroups with substantially different movement behaviour patterns and clinical characteristics can be identified in patients with osteoarthritis of the hip and/or knee. Knowledge about these subgroups can be used to personalize future movement behaviour interventions for this population.
LINK
Background Movement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time. Objectives 1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke. Methods Accelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified. Results The composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers. Conclusions The composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.
MULTIFILE
Objective: To describe the development of a goal-directed movement intervention in two medical wards, including recommendations for implementation and evaluation. Design: Implementation Research. Setting: Pulmonology and nephrology/gastroenterology wards of the University Medical Centre Utrecht, The Netherlands. Participants: Seven focus groups were executed including 28 nurses, 7 physical therapists and 15 medical specialists. Patients' perceptions were repeatedly assessed during the iterative steps of the intervention development. Intervention: Interventions were targeted to each ward's specific character, following an Intervention Mapping approach using literature and research meetings. Main measures: Intervention components were linked to Behavior Change Techniques and implementation strategies will be selected using the Expert Recommendation Implementing Change tool. Evaluation outcomes like number of patients using the movement intervention will be measured, based on the taxonomy of Proctor. Results: The developed intervention consists of: insight in patients movement behavior (monitoring & feedback), goal setting (goals & planning) and adjustments to the environment (associations & antecedents). The following implementation strategies are recommended: to conduct educational meetings, prepare & identify champions and audit & provide feedback. To measure service and client outcomes, the mean level of physical activity per ward can be evaluated and the Net Promoter Score can be used. Conclusion(s): This study shows the development of a goal-directed movement intervention aligned with the needs of healthcare professionals. This resulted in an intervention consisting of feedback & monitoring of movement behavior, goal setting and adjustments in the environment. Using a step-by-step iterative implementation model to guide development and implementation is recommended.
Creating and testing the first Brand Segmentation Model in Augmented Reality using Microsoft Hololens. Sanoma together with SAMR launched an online brand segmentation tool based on large scale research, The brand model uses several brand values divided over three axes. However they cannot be displayed clearly in a 2D model. The space of BSR Quality Planner can be seen as a 3-dimensional meaningful space that is defined by the terms used to typify the brands. The third axis concerns a behaviour-based dimension: from ‘quirky behaviour’ to ‘standardadjusted behaviour’ (respectful, tolerant, solidarity). ‘Virtual/augmented reality’ does make it possible to clearly display (and experience) 3D. The Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences has created the BSR Quality Planner in Virtual Reality – as a hologram. It’s the world’s first segmentation model in AR. Breda University of Applied Sciences (professorship Digital Media Concepts) has deployed hologram technology in order to use and demonstrate the planning tool in 3D. The Microsoft HoloLens can be used to experience the model in 3D while the user still sees the actual surroundings (unlike VR, with AR the space in which the user is active remains visible). The HoloLens is wireless, so the user can easily walk around the hologram. The device is operated using finger gestures, eye movements or voice commands. On a computer screen, other people who are present can watch along with the user. Research showed the added value of the AR model.Partners:Sanoma MediaMarketResponse (SAMR)
Wildlife crime is an important driver of biodiversity loss and disrupts the social and economic activities of local communities. During the last decade, poaching of charismatic megafauna, such as elephant and rhino, has increased strongly, driving these species to the brink of extinction. Early detection of poachers will strengthen the necessary law enforcement of park rangers in their battle against poaching. Internationally, innovative, high tech solutions are sought after to prevent poaching, such as wireless sensor networks where animals function as sensors. Movement of individuals of widely abundant, non-threatened wildlife species, for example, can be remotely monitored ‘real time’ using GPS-sensors. Deviations in movement of these species can be used to indicate the presence of poachers and prevent poaching. However, the discriminative power of the present movement sensor networks is limited. Recent advancements in biosensors led to the development of instruments that can remotely measure animal behaviour and physiology. These biosensors contribute to the sensitivity and specificity of such early warning system. Moreover, miniaturization and low cost production of sensors have increased the possibilities to measure multiple animals in a herd at the same time. Incorporating data about within-herd spatial position, group size and group composition will improve the successful detection of poachers. Our objective is to develop a wireless network of multiple sensors for sensing alarm responses of ungulate herds to prevent poaching of rhinos and elephants.
To what extent does receiving information about either popular attractions or less-visited at-tractions, presented as “highlights” of the city, influence the movement of tourists to popular or less-visited attractions, and how does this differ by information channel through which the information is presented? To what extent does receiving information about either popular attractions or less-visited at-tractions, presented as “highlights” of the city, influence tourists’ experience, including their evaluations of the destination, their visit as a whole, and the specific information channel they received, and how does this differ by information channel through which the information is presented? What implementation models and financing mechanisms are available for DMO’s to spread tourists using the information channels tested, contingent on their effectiveness as measured by the previous experiment?Societal issueDestination Management Organisations (DMOs) are looking for interventions that effectively discourage tourists from visiting crowded hotspots and entice them to visit less crowded locations. Interventions like changing infrastructure, charging entrance fees and re-serving site access are either too expensive, too invasive or politically controversial. It is much easier to intervene on tourists' behaviour by informing them about alternatives.Collaborative partnersNHL Stenden, Travel with Zoey, Amsterdam and Partners, Wonderful Copenhagen, Mobidot.
