27 research outputs found
Crisis or adaptation reconsidered: a comparison of East and West German fertility patterns in the first six years after the ´Wende´
Get PDFSimilar to other Eastern European countries, East Germany experienced a rapid decline in period fertility rates after the fall of communism. This decline has been discussed along the lines of a ´crisis´ and a ´adaptation´ to western demographic patterns. The aim of this paper is twofold. Firstly, we discuss the factors which foster and hamper a convergence of fertility behavior in East and West Germany. Secondly, we use data from the German micro-census to analyze the fertility patterns of the cohorts born 1961-1970. Major results from our empirical analysis are that East Germans who are still childless at unification are more rapid to have their first child in the subsequent years than comparable West Germans. However, regarding second parity births, the pattern reverses. Here, East Germans display a lower transition rate than their counterparts in the West.Germany, fertility
Development and validation of a new method to measure walking speed in free-living environments using the actibelt® platform.
No full textWalking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92-0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12-0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure
Visualization of coverage probability for normal walking in the indoor ecological validation.
No full text<p>The black solid line represents speed as measured by the mobile gold standard for normal walking. Green, yellow, red and blue lines in different linestyles represent different speed estimates by different algorithms and models. The filled areas colored from light to dark grey around the black solid line indicate coverage probality levels from 0.1 to 0.3 m/s. Speed intervals are sorted increasingly across all participants for reasons of clarity and readability.</p
Visualization of coverage probability for slow walking in the indoor ecological validation.
No full text<p>The black solid line represents speed as measured by the mobile gold standard for slow walking. Green, yellow, red and blue lines in different linestyles represent different speed estimates by different algorithms and models. The filled areas colored from light to dark grey around the black solid line indicate coverage probality levels from 0.1 to 0.3 m/s. Speed intervals are sorted increasingly across all participants for reasons of clarity and readability.</p
Mobile gold standard.
No full text<p>Example of a test subject operating the mobile gold standard.</p
Algorithm ranking (outdoor ecological validity).
No full text<p>Individual coverage probability with a maximum difference of 0.1 m/s (CP1) to 0.3 m/s (CP3) as well as concordance correlation coefficient (CCC) for each algorithm across all speed levels.</p
Visualization of coverage probability for participant 01 (male, 46 years) in the experiment for outdoor ecological validity.
No full text<p>The black solid line represents speed as measured by the mobile gold standard for running. Green, yellow, red and blue lines in different linestyles represent different speed estimates by different algorithms and models. The filled areas colored from light to dark grey around the black solid line indicate coverage probality levels from 0.1 to 0.3 m/s.</p
SVR models.
No full text<p>Parameters for the various SVR models which were found through 10-fold cross-validation using a grid search over a supplied range of values and total mean squared error (MSE) during cross-validation.</p
Visualization of coverage probability for running in the indoor ecological validation.
No full text<p>The black solid line represents speed as measured by the mobile gold standard for running. Green, yellow, red and blue lines in different linestyles represent different speed estimates by different algorithms and models. The filled areas colored from light to dark grey around the black solid line indicate coverage probality levels from 0.1 to 0.3 m/s. Speed intervals are sorted increasingly across all participants for reasons of clarity and readability.</p
Algorithm ranking (indoor ecological validation including running).
No full text<p>Individual coverage probability with a maximum difference of 0.1 m/s (CP1) to 0.3 m/s (CP3) as well as concordance correlation coefficient (CCC) including 95% confidence intervals (95% CI) for each algorithm across all speed levels.</p
