Putting the individual back into individual growth curves.

2000 ◽  
Vol 5 (1) ◽  
pp. 23-43 ◽  
Author(s):  
Paras D. Mehta ◽  
Stephen G. West
Keyword(s):  
Growth Curves ◽  
Individual Growth ◽  
The Individual

PATTERNS OF GROWTH IN HEIGHT AND WEIGHT FROM BIRTH TO EIGHTEEN YEARS OF AGE

PEDIATRICS ◽  
1959 ◽  
Vol 24 (5) ◽  
pp. 904-921
Author(s):  
Robert B. Reed ◽  
Harold C. Stuart
Keyword(s):  
Growth Curves ◽  
Growth Patterns ◽  
Individual Growth ◽  
Growth Spurt ◽  
Adolescent Growth Spurt ◽  
Wide Range ◽  
Basic Facts ◽  
The Individual ◽  
Rates Of Growth ◽  
Mature Size

In this report is displayed the range of variation observed in the growth curves of height and weight in a series of 134 children observed from birth to 18 years. For purposes of simplification the individuals have been classified on the basis of their rates of growth during three successive 6-year intervals. Even in terms of this crude classification several basic facts about individual growth patterns of height and weight are apparent. The wide range of differences between individuals applies not only to facts about size at specific ages but also to the pattern of change followed from age period to age period. The rate of growth during early childhood, i.e. before 6 years of age, is associated with, but not specifically predictive of, size at maturity and timing of the adolescent growth spurt. Individuals with rapid growth before 6 years of age tend to have large mature size and early adolescent growth spurt. It will be the objective of future reports from this research project to determine the manner in which the individual differences in growth demonstrated and classified here are related to aspects of physical development, to environmental influences such as dietary intake and to the level of health of the child.

scholarly journals Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model

2020 ◽  
Vol 33 (12) ◽  
pp. 1589-1595
Author(s):  
Mariana del Pino ◽  
Virginia Fano ◽  
Paula Adamo
Keyword(s):  
General Population ◽  
Growth Curve ◽  
Growth Velocity ◽  
Adult Height ◽  
Growth Curves ◽  
Peak Height ◽  
Height Growth ◽  
Individual Growth ◽  
Height Velocity ◽  
Peak Height Velocity

AbstractObjectivesIn general population, there are three phases in the human growth curve: infancy, childhood and puberty, with different main factors involved in their regulation and mathematical models to fit them. Achondroplasia children experience a fast decreasing growth during infancy and an “adolescent growth spurt”; however, there are no longitudinal studies that cover the analysis of the whole post-natal growth. Here we analyse the whole growth curve from infancy to adulthood applying the JPA-2 mathematical model.MethodsTwenty-seven patients, 17 girls and 10 boys with achondroplasia, who reached adult size, were included. Height growth data was collected from birth until adulthood. Individual growth curves were estimated by fitting the JPA-2 model to each individual’s height for age data.ResultsHeight growth velocity curves show that after a period of fast decreasing growth velocity since birth, with a mean of 9.7 cm/year at 1 year old, the growth velocity is stable in late preschool years, with a mean of 4.2 cm/year. In boys, age and peak height velocity in puberty were 13.75 years and 5.08 cm/year and reach a mean adult height of 130.52 cm. In girls, the age and peak height velocity in puberty were 11.1 years and 4.32 cm/year and reach a mean adult height of 119.2 cm.ConclusionsThe study of individual growth curves in achondroplasia children by the JPA-2 model shows the three periods, infancy, childhood and puberty, with a similar shape but lesser in magnitude than general population.

scholarly journals Individual growth of Heleobia piscium in natural populations (Gastropoda: Cochliopidae) from the multiple use natural Reserve Isla Martin Garcia, Buenos Aires, Argentina

2008 ◽  
Vol 68 (3) ◽  
pp. 617-621 ◽  
Author(s):  
SM. Martin
Keyword(s):  
Buenos Aires ◽  
Natural Populations ◽  
Dynamic Structure ◽  
Reproductive Period ◽  
Individual Growth ◽  
Growth Equation ◽  
Multiple Use ◽  
Natural Reserve ◽  
Drainage Channels ◽  
The Individual

The present work analyses the individual growth of Heleobia piscium in natural conditions in coastal drainage channels of the Multiple Use Natural Reserve Isla Martín García, Buenos Aires, Argentina. Isla Martín García is located in the Upper Río de la Plata, to the south of the mouth of the Uruguay river (34° 11' 25" S and 58° 15' 38" W). Monthly collections were made from July 2005 to July 2006 in the eastern part of the island (Arena Beach). The population of H. piscium showed a complex and dynamic structure of sizes during a long period of the annual cycle. Two cohorts could be detected. The Bertalanffy growth equation was: Lt = 6 (1-e -1.85 (t+0.38)) and Lt = 3.9 (1-e -0.19 (t+4.84)) for cohorts 1 and 2, respectively. The pattern of population growth displayed a staggered model, where the greatest growth is observed during the summer. The reproductive period occurred during six months, from the beginning of summer to middle of fall. Based on only one reproductive effort, this pattern is not similar to that of other cogeneric species already studied.

Individual Growth and Reproduction

2019 ◽  
pp. 38-56
Author(s):  
Ken H. Andersen
Keyword(s):  
Life History ◽  
Growth Model ◽  
Life Histories ◽  
Maximum Size ◽  
Individual Growth ◽  
Energy Budgets ◽  
Asymptotic Size ◽  
The Individual ◽  
Growth And Reproduction

This chapter develops descriptions of how individuals grow and reproduce. More specifically, the chapter seeks to determine the growth and reproduction rates from the consumption rate, by developing an energy budget of the individual as a function of size. To that end, the chapter addresses the question of how an individual makes use of the energy acquired from consumption. It sets up the energy budgets of individuals by formulating the growth model using so-called life-history invariants, which are parameters that do not vary systematically between species. While the formulation of the growth model in terms of life-history invariants is largely successful, there is in particular one parameter that is not invariant between life histories: the asymptotic size (maximum size) of individuals in the population. This parameter plays the role of a master trait that characterizes most of the variation between life histories.

Development of a Model To Predict Growth of Clostridium perfringens in Cooked Beef during Cooling

2005 ◽  
Vol 68 (2) ◽  
pp. 336-341 ◽  
Author(s):  
SARAH SMITH-SIMPSON ◽  
DONALD W. SCHAFFNER
Keyword(s):  
Food Safety ◽  
Clostridium Perfringens ◽  
Exponential Growth ◽  
Growth Curves ◽  
Policy Implications ◽  
Individual Growth ◽  
Performance Standard ◽  
Square Root ◽  
Time Model ◽  
New Model

The objective of this work was to develop a new model to predict the growth of Clostridium perfringens in cooked meat during cooling. All data were collected under changing temperature conditions. Individual growth curves were fit using DMFit. Germination outgrowth and lag (GOL) time was modeled versus temperature at the end of GOL using conservative assumptions. Each growth curve was used to estimate a series of exponential growth rates at a series of temperatures. The square-root model was used to describe the relationship between the square root of the average exponential growth rate and effective temperature. Predictions from the new model were in close agreement with the data used to create the model. When predictions from the model were compared with new observations, fail-dangerous predictions were made a majority of the time. When GOL time was predicted exactly, many fail-dangerous predictions shifted toward the fail-safe direction. Two important facts regarding C. perfringens should impact future modeling research with this organism and may have broader food safety policy implications: (i) the normal variability in the response of the organism from replicate to replicate may be quite large (1 log CFU) and may exceed the current U.S. Food Safety Inspection Service performance standard, and (ii) the accuracy of the GOL time model has a profound influence upon the overall prediction, with small differences in GOL time prediction (~1 h) having a very large effect on the predicted final concentration of C. perfringens.

MATHEMATICAL MODELLING OF INDIVIDUAL GROWTH CURVES

1981 ◽  
Vol 37 (3) ◽  
pp. 247-252 ◽  
Author(s):  
M A PREECE ◽  
INGEBORG HEINRICH
Keyword(s):  
Mathematical Modelling ◽  
Growth Curves ◽  
Individual Growth

Visualization of Individual Growth-Related Craniofacial Changes Based on Cephalometric Landmark Data: A Pilot Study

2002 ◽  
Vol 39 (3) ◽  
pp. 341-352 ◽  
Author(s):  
Christopher J. Lux ◽  
Jens Starke ◽  
Jan Rübel ◽  
Angelika Stellzig ◽  
Gerda Komposch
Keyword(s):  
Dynamic Behavior ◽  
Growth Process ◽  
Shape Change ◽  
Individual Growth ◽  
Craniofacial Skeleton ◽  
Size And Shape ◽  
Lateral Cephalograms ◽  
Landmark Data ◽  
The Individual ◽  
Shape Changes

Objective: An approach based on Euclidean distances between cephalometric landmarks is presented (1) to visualize and localize the individual shape changes of the complex craniofacial skeleton during growth and (2) to depict the individual dynamic behavior of developmental size and shape changes. Patients and Method: Growth-related craniofacial changes were investigated exemplarily for two male orthodontically untreated subjects from the Belfast Growth Study on the basis of lateral cephalograms at 7, 9, 11, 13, and 15 years. The interlandmark distances among seven skeletal cephalometric landmarks served as a database for the study. A modified Karhunen-Loèvedecomposition based on orthogonal modes and time-dependent scalar amplitudes was used to describe the growth process. The individual shape changes of the various craniofacial regions were visualized by allocation of colors to the respective distances, and overdrawn representations were reconstructed by means of multidimensional scaling. Results and Conclusions: This visualization technique allows anatomical regions to be characterized with respect to reduced or strengthened growth, compared with pure size changes. The clinically relevant mechanisms of craniofacial changes are visualized (e.g., shifts in the anteroposterior or vertical dimensions of the jaws in relation to cranial base and structural imbalances during development). In addition, overdrawing the effects of shape change on the skeletal structures gives a more readily comprehensible impression of the growth process. Taking account of the methodical limitations of this approach (e.g., the restrictions concerning the number of landmarks), the clinician may take advantage of this technique in orthodontic or surgical diagnostics to gain additional insight into the individual complex size and shape changes during development along with their dynamic behavior.

scholarly journals Hypothesized, Directly-Coded Curve Shapes in Growth Curve Analysis: An Example

2013 ◽  
Vol 3 (2) ◽  
pp. 13 ◽  
Author(s):  
Patricia M. Herman ◽  
Lee Sechrest
Keyword(s):  
Hierarchical Linear Modeling ◽  
Growth Curve ◽  
Degrees Of Freedom ◽  
Growth Curves ◽  
Curve Analysis ◽  
Small Samples ◽  
Growth Curve Analysis ◽  
Linear Modeling ◽  
Individual Level ◽  
The Individual

Growth curve analysis provides important informational benefits regarding intervention outcomes over time. Rarely, however, should outcome trajectories be assumed to be linear. Instead, both the shape and the slope of the growth curve can be estimated. Non-linear growth curves are usually modeled by including either higher-order time variables or orthogonal polynomial contrast codes. Each has limitations (multicollinearity with the first, a lack of coefficient interpretability with the second, and a loss of degrees of freedom with both) and neither encourages direct testing of alternative hypothesized curve shapes. Especially in studies with relatively small samples it is likely to be useful to preserve as much information as possible at the individual level. This article presents a step-by-step example of the use and testing of hypothesized curve shapes in the estimation of growth curves using hierarchical linear modeling for a small intervention study. DOI:10.2458/azu_jmmss_v3i2_herman

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