Predictability of Fall Risk Assessments in Community-Dwelling Older Adults: A Scoping Review.

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method's validity needs to be confirmed by large prospective studies.

Power in sports: A literature review on the application, assumptions, and terminology of mechanical power in sport research.

The quantification of mechanical power can provide valuable insight into athlete performance because it is the mechanical principle of the rate at which the athlete does work or transfers energy to complete a movement task. Estimates of power are usually limited by the capabilities of measurement systems, resulting in the use of simplified power models. This review provides a systematic overview of the studies on mechanical power in sports, discussing the application and estimation of mechanical power, the consequences of simplifications, and the terminology. The mechanical power balance consists of five parts, where joint power is equal to the sum of kinetic power, gravitational power, environmental power, and frictional power. Structuring literature based on these power components shows that simplifications in models are done on four levels, single vs multibody models, instantaneous power (IN) versus change in energy (EN), the dimensions of a model (1D, 2D, 3D), and neglecting parts of the mechanical power balance. Quantifying the consequences of simplification of power models has only been done for running, and shows differences ranging from 10% up to 250% compared to joint power models. Furthermore, inconsistency and imprecision were found in the determination of joint power, resulting from inverse dynamics methods, incorporation of translational joint powers, partitioning in negative and positive work, and power flow between segments. Most inconsistency in terminology was found in the definition and application of 'external' and 'internal' work and power. Sport research would benefit from structuring the research on mechanical power in sports and quantifying the result of simplifications in mechanical power estimations.

Getting in shape: Reconstructing three-dimensional long-track speed skating kinematics by comparing several body pose reconstruction techniques.

In gait studies body pose reconstruction (BPR) techniques have been widely explored, but no previous protocols have been developed for speed skating, while the peculiarities of the skating posture and technique do not automatically allow for the transfer of the results of those explorations to kinematic skating data. The aim of this paper is to determine the best procedure for body pose reconstruction and inverse dynamics of speed skating, and to what extend this choice influences the estimation of joint power. The results show that an eight body segment model together with a global optimization method with revolute joint in the knee and in the lumbosacral joint, while keeping the other joints spherical, would be the most realistic model to use for the inverse kinematics in speed skating. To determine joint power, this method should be combined with a least-square error method for the inverse dynamics. Reporting on the BPR technique and the inverse dynamic method is crucial to enable comparison between studies. Our data showed an underestimation of up to 74% in mean joint power when no optimization procedure was applied for BPR and an underestimation of up to 31% in mean joint power when a bottom-up inverse dynamics method was chosen instead of a least square error approach. Although these results are aimed at speed skating, reporting on the BPR procedure and the inverse dynamics method, together with setting a golden standard should be common practice in all human movement research to allow comparison between studies.

Design and verification of a simple 3D dynamic model of speed skating which mimics observed forces and motions.

Advice about the optimal coordination pattern for an individual speed skater, could be addressed by simulation and optimization of a biomechanical speed skating model. But before getting to this optimization approach one needs a model that can reasonably match observed behaviour. Therefore, the objective of this study is to present a verified three dimensional inverse skater model with minimal complexity, which models the speed skating motion on the straights. The model simulates the upper body transverse translation of the skater together with the forces exerted by the skates on the ice. The input of the model is the changing distance between the upper body and the skate, referred to as the leg extension (Euclidean distance in 3D space). Verification shows that the model mimics the observed forces and motions well. The model is most accurate for the position and velocity estimation (respectively 1.2% and 2.9% maximum residuals) and least accurate for the force estimations (underestimation of 4.5-10%). The model can be used to further investigate variables in the skating motion. For this, the input of the model, the leg extension, can be optimized to obtain a maximal forward velocity of the upper body.