On The General Distance Measure

Clustered Associations between Musculoskeletal Fitness Tests and Functional Movement Screen in Physically Active Men

Functional Movement Screen (FMS) is a tool used to assess fundamental movement patterns. There are relationships between musculoskeletal fitness (MSF) and a FMS. However, there is limited data regarding the multidimensional associations between these two constructs. This study is aimed at investigating the associations and detailed structures of FMS components and MSF components and identifying a deeper detailed structure of their relations to physical fitness. The study sample included 114 physically active men with an average body height of 1.81 ± 0.07 (cm), body weight of 80.61 ± 9.49 (kg), and a BMI of 24.65 ± 2.46 (kg/m²). Each participant performed a FMS test, sit-and-reach (S&R) test, sit-ups (ABS) evaluation, balance evaluation on an AMTI platform, handgrip strength (HG) testing, and standing broad jump (SBJ) ability. The Kendall's Tau correlation was performed to identify simple associations between FMS and MSF. Cluster analysis was used to determine the multidimensional nature of the relationships. In the vast majority, a positive correlation was observed suggesting that a high level of MSF improved FMS test results. A cluster analysis revealed 4 separate clusters. Shoulder mobility (SM) was identified as a single cluster. A strong similarity was observed between SBJ and trunk stability push-up (TSPU) forming a second cluster. This cluster joined to another consisting of the S&R test and active straight leg raise (ASLR) test. The last cluster consisted of HG and hurdle step (HS). These results confirmed that FMS and MSF tests measure the same constructs-a foundation for an individual's motor coordination, muscle strength, postural stability, and dynamic balance. This knowledge could be helpful in effectively enhancing physical performance based on combining similar constructs to accelerate the achievement of established goals.

AC-PCoA: Adjustment for confounding factors using principal coordinate analysis

Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data.

In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.

With today’s unprecedented amount of data, researchers are challenged by the need to enhance meaningful signals without the interference of unwanted confounders hidden inside the data. Data visualization is an important step toward exploring and explaining data in order to intuitively identify the dominant patterns. Principal coordinate analysis (PCoA), as a visualization tool, allows flexible ways to define pairwise distances and project the samples into lower dimensions without changing the distances. However, when visualizing large-scale biological datasets, the true patterns are often hindered by unwanted confounding variations, either biologically or technically in origin. To eliminate these confounding factors and recover underlying signals, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, and showed that it significantly outperforms existing methods in visualization through three simulation studies and five real datasets. We further showed that the low-dimensional representations given by AC-PCoA provide promising results in statistical testing, clustering, and classification as well.

Being Old Doesn’t Mean Acting Old

Most studies on adapting voice interfaces to older users work top-down by comparing the interaction behavior of older and younger users. In contrast, we present a bottom-up approach. A statistical cluster analysis of 447 appointment scheduling dialogs between 50 older and younger users and 9 simulated spoken dialog systems revealed two main user groups, a “social” group and a “factual” group. “Factual” users adapted quickly to the systems and interacted efficiently with them. “Social” users, on the other hand, were more likely to treat the system like a human, and did not adapt their interaction style. While almost all “social” users were older, over a third of all older users belonged in the “factual” group. Cognitive abilities and gender did not predict group membership. We conclude that spoken dialog systems should adapt to users based on observed behavior, not on age.