Mobile Romberg test assessment (mRomberg)

Background The diagnosis of frailty is based on physical impairments and clinicians have indicated that early detection is one of the most effective methods for reducing the severity of physical frailty. Maybe, an alternative to the classical diagnosis could be the instrumentalization of classical functional testing, as Romberg test or Timed Get Up and Go Test. The aim of this study was (I) to measure and describe the magnitude of accelerometry values in the Romberg test in two groups of frail and non-frail elderly people through instrumentation with the iPhone 4®, (II) to analyse the performances and differences between the study groups, and (III) to analyse the performances and differences within study groups to characterise accelerometer responses to increasingly difficult challenges to balance. Methods This is a cross-sectional study of 18 subjects over 70 years old, 9 frail subjects and 9 non-frail subjects. The non-parametric Mann–Whitney U test was used for between-group comparisons in means values derived from different tasks. The Wilcoxon Signed-Rank test was used to analyse differences between different variants of the test in both independent study groups. Results The highest difference between groups was found in the accelerometer values with eyes closed and feet parallel: maximum peak acceleration in the lateral axis (p < 0.01), minimum peak acceleration in the lateral axis (p < 0.01) and minimum peak acceleration from the resultant vector (p < 0.01). Subjects with eyes open and feet parallel, greatest differences found between the groups were in the maximum peak acceleration in the lateral axis (p < 0.01), minimum peak acceleration in the lateral axis (p < 0.01) and minimum peak acceleration from the resultant vector (p < 0.001). With eyes closed and feet in tandem, the greatest differences found between the groups were in the minimum peak acceleration in the lateral axis (p < 0.01). Conclusions The accelerometer fitted in the iPhone 4® is able to study and analyse the kinematics of the Romberg test between frail and non-frail elderly people. In addition, the results indicate that the accelerometry values also were significantly different between the frail and non-frail groups, and that values from the accelerometer accelerometer increased as the test was made more complicated.

Portrait of Moritz Romberg; Employee of Mendelssohn Bank, Berlin Portraits Men

"1.10.1891 Moritz Romberg geb. 4.6.1870"

O modelo de Romberg e o percurso metodológico de uma pesquisa qualitativa em educação de uma pesquisa qualitativa em educação

The objective of this study is to present the trajectory of a research project (ALLEVATO, 2005) whose phenomenon of interest is the teaching of mathematics using problem solving with computers. The text is an attempt to portray this trajectory, from the point of view of the methodological route followed by the researcher, which was based on two main axes: the guidance of the educator Thomas A. Romberg (1992), and the guidelines provided by the foundations of qualitative research. The study was developed during a doctoral course offered by the Graduate Program in Mathematics Education at the State University of São Paulo (UNESP), Rio Claro campus.

Bernhard Romberg, Kniestück

Signatur des Originals: S 36/F01672

Andreas Romberg, Brustbild

Signatur des Originals: S 36/G02046

Bernhard Romberg, Brustbild

Signatur des Originals: S 36/G02047

Bernhard Romberg, Brustbild, mit Violoncello

Signatur des Originals: S 36/G02048

Bernhard Romberg, Brustbild, mit Violoncello

Signatur des Originals: S 36/G02050

Bernhard Romberg, Brustbild

Signatur des Originals: S 36/G02049

Bernhard Romberg, Brustbild

Signatur des Originals: S 36/G03562

Bernhard Romberg, Brustbild, mit Violoncello

Signatur des Originals: S 36/G04340

Sophie Schwerin; ein lebensbild aus ihren eigenen hinterlassenen papieren zusammengestellt von ihrer jüngeren schwester Amalie v. Romberg.

Mode of access: Internet.

Reconceptualizing statistical learning in the early years

This chapter argues for the need to restructure children’s statistical experiences from the beginning years of formal schooling. The ability to understand and apply statistical reasoning is paramount across all walks of life, as seen in the variety of graphs, tables, diagrams, and other data representations requiring interpretation. Young children are immersed in our data-driven society, with early access to computer technology and daily exposure to the mass media. With the rate of data proliferation have come increased calls for advancing children’s statistical reasoning abilities, commencing with the earliest years of schooling (e.g., Langrall et al. 2008; Lehrer and Schauble 2005; Shaughnessy 2010; Whitin and Whitin 2011). Several articles (e.g., Franklin and Garfield 2006; Langrall et al. 2008) and policy documents (e.g., National Council of Teachers ofMathematics 2006) have highlighted the need for a renewed focus on this component of early mathematics learning, with children working mathematically and scientifically in dealing with realworld data. One approach to this component in the beginning school years is through data modelling (English 2010; Lehrer and Romberg 1996; Lehrer and Schauble 2000, 2007)...