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Recent progress in modelling individual growth has been achieved by combining the principal component analysis and the maximum likelihood principle. This combination models growth even in incomplete sets of data and in data obtained at irregular intervals. We re-analysed late 18th century longitudinal growth of German boys from the boarding school Carlsschule in Stuttgart. The boys aged 6-23 years, were measured at irregular 3-12 monthly intervals during the period 1771-1793. At the age of 18 years, mean height was 1652 mm, but height variation was large. The shortest boy reached 1474 mm, the tallest 1826 mm. Measured height closely paralleled modelled height, with mean difference of 4 mm, SD 7 mm. Seasonal height variation was found. Low growth rates occurred in spring and high growth rates in summer and autumn. The present study demonstrates that combining the principal component analysis and the maximum likelihood principle enables growth modelling in historic height data also.
Twenty-two scientists met at Krobielowice, Poland, to discuss the impact of the social environment, spatial proximity, migration, poverty, but also psychological factors such as body perception and satisfaction, and social stressors such as elite sports, and teenage pregnancies, on child and adolescent growth. The data analysis included linear mixed effects models with different random effects, Monte Carlo analyses, and network simulations. The work stressed the importance of the peer group, but also included historic material, some considerations about body proportions, and growth in chronic liver, and congenital heart disease.
Objective: The age of menarche is usually considered to be affected by nutritional, health-related, social, and economic factors and has significantly decreased since the mid-19th century. The present study was performed to investigate whether the timing of menarche paralleled the general acceleration of physical development, or whether this pattern differed.
Study Design: In all, 30 German studies on menarcheal age (n = > 200) since 1848 were collected. Frequency distributions were analyzed.
Results: During the second half of the 19th and the early 20th century, mean menarcheal age decreased from 18 to 12-13 years in Europe. Yet, the data fail to support the conventional hypothesis that menarcheal age mainly depends on nutritional, health, and economic factors.
Conclusions: We suggest that later than usual menarche may not necessarily be regarded as a physical illness, but in view of the apparently physiological delay of menarche in the 19th century, may be viewed as "collective social amenorrhea."
Target Audience: Obstetricians & Gynecologists and Family Physicians.
Learning Objectives: After participating in this CME activity, physicians should be better able to evaluate menarche as an indicator of developmental tempo in both historical and modern settings, compare menarche in healthy mid-19th century girls with menarche in average modern girls, and assess the marked sensitivity of full pubertal development to environmental circumstances.
Sexuelle Reifeentwicklung & Menarchealter : Bedeutung des psychosozialen Umfeldes damals und heute
(2012)
Twenty-four scientists met at Aschauhof, Altenhof, Germany, to discuss the associations between child growth and development, and nutrition, health, environment and psychology. Meta-analyses of body height, height variability and household inequality, in historic and modern growth studies published since 1794, highlighting the enormously flexible patterns of child and adolescent height and weight increments throughout history which do not only depend on genetics, prenatal development, nutrition, health, and economic circumstances, but reflect social interactions. A Quality of Life in Short Stature Youth Questionnaire was presented to cross-culturally assess health-related quality of life in children. Changes of child body proportions in recent history, the relation between height and longevity in historic Dutch samples and also measures of body height in skeletal remains belonged to the topics of this meeting. Bayesian approaches and Monte Carlo simulations offer new statistical tools for the study of human growth.