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.
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BackgroundLittle is known about the association between fear of movement (kinesiophobia) and objectively measured physical activity (PA), the first 12 weeks after cardiac hospitalization.PurposeTo assess the longitudinal association between kinesiophobia and objectively measured PA and to assess the factor structure of kinesiophobia.MethodsWe performed a longitudinal observational study. PA was continuously measured from hospital discharge to 12 weeks using the Personal Activity Monitor. The PAM measures time spent per day in PA-intensity categories: light, moderate and heavy. Kinesiophobia was assessed with the Tampa Scale for Kinesiophobia (TSK) at four time points (hospital discharge, 3, 6 and 12 weeks). The longitudinal association between PA-intensity and kinesiophobia was studied with a random intercept cross lagged panel model (RI-CLPM). A RI-CLPM estimates effects from kinesiophobia on objectively measured PA and vice versa (cross-over effects), and autoregressive effects (e.g. kinesiophobia from one occasion to the next).ResultsIn total, 116 patients (83.6% male) with a median age of 65.5 were included in this study. On no occasion did we find an effect of kinesiophobia on PA and vice versa. Model fit for the original model was poor (X2: = 44.646 P<0.001). Best model fit was found for a model were kinesiophobia was modelled as a stable between factor (latent variable) and PA as autoregressive component (dynamic process) (X2 = 27.541 P<0.12).ConclusionKinesiophobia and objectively measured PA are not associated in the first 12 weeks after hospital discharge. This study shows that kinesiophobia remained relatively stable, 12 weeks after hospital discharge, despite fluctuations in light to moderate PA-intensity.
Movement behaviors, that is, both physical activity and sedentary behavior, are independently associated with health risks. Although both behaviors have been investigated separately in people after stroke, little is known about the combined movement behavior patterns, differences in these patterns between individuals, or the factors associated with these patterns. Therefore, the objectives of this study are (1) to identify movement behavior patterns in people with first-ever stroke discharged to the home setting and (2) to explore factors associated with the identified patterns.
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.
