Childhood Growth, the Midgrowth Spurt, and the Adiposity Rebound (Ages 4–9 Years)
Alan D. Rogol, M.D., Ph.D., University of Virginia
Dr. Rogol reviewed the main factors that control human growth:
- General health and nutrition
- Intrauterine environment
- Genetics.
He also briefly discussed the regulatory role played by hormones. Dr. Rogol described growth as a sensitive indicator of health, nutrition, and genetic potential. He noted that growth has incremental, hypertrophic, and reparative properties.
Dr. Rogol referred to the infant, childhood, puberty (ICP) model of linear growth as an example of one of the various methods currently being used. He explained that the ICP is a mathematical model of the human growth curve that attempts to relate growth to the underlying dynamics of hormonal and other control.
Next, Dr. Rogol summarized findings from various studies that have identified several key characteristics of the mid-childhood growth spurt:
- It tends to occur around age 7 in boys and 6.7 in girls.
- It may possibly be cyclical.
- It may possibly be seasonal.
- If there is a hormonal basis, it is largely unknown.
If the mid-childhood growth spurt is cyclical and/or seasonal, Dr. Rogol cautioned that it cannot be represented in regular population-based growth charts.
Dr. Rogol defined adiposity rebound (AR) as the point of maximal leanness or minimal BMI, expressed as the ratio of weight to height squared. Children have a rapid increase in BMI during the first year. After a child reaches 9 to 12 months of age, BMI declines and reaches a minimum, usually around age 5 or 6. At that point, BMI begins to gradually increase through adolescence and most of adult life. Dr. Rogol also noted that:
Dr. Rogol listed several methods of measurement and suggested timeframes for conducting those measures within the Study:
- Height: twice each year
Weight
BMI
Upper-to-lower ratio
Abdominal circumference
Skinfolds: three measurements, once each year
DXA body composition
Peripheral quantitative computed tomography (QCT)
Hormonal measurements, specifically, DHEA-S (consider using saliva) and IGF-I.
He closed by noting that DXA, peripheral QCT, and hormonal measurements would likely not be realistic for the entire Study cohort. Therefore, he suggested that these measures could be taken using a small, but representative, cohort.
Puberty, Growth, and Body Composition Development in Adolescence (Ages 8–18 Years)
John H. Himes, Ph.D., M.P.H., University of Minnesota, Minneapolis
Dr. Himes noted that his discussion would focus on reviewing general patterns and issues that could help frame the Study, given the extended timeframe for studying subjects in this age group. He began by presenting working definitions of key terms:
Growth is an increase in size, number, or mass.
Maturation is achievement of adult status, morphology, or function.
Body composition is the absolute and relative contributions of elements, molecules, or tissue masses to the whole body.
Dr. Himes underscored that puberty is a process, a series of interactions related to endocrinal and hormonal changes. Puberty is a time of rapid growth and maturation. Because there is so much change occurring during puberty, the Study will need to be very selective about what will be measured, as well as how and when. Dr. Himes pointed out that the Study will examine morphological manifestations of puberty––skeletal growth and body composition.
Dr. Himes next discussed several seminal studies, including the Child Research Council longitudinal study of Denver children that compared different patterns of growth and maturity among boys and girls. The CRC compared median combined radiographic thicknesses of subcutaneous fat from the forearm, thigh, calf, deltoid, and hip in girls and boys. The researchers found that boys tend to lose fat whereas girls gain fat during adolescence.
Dr. Himes summarized several issues of maturational status relative to somatic growth and body composition during adolescence:
- Timing of peak adolescent growth velocity varies among individuals and among body dimensions and composition components.
Maturation status is associated with variation in growth and body composition variables even within small chronological age groups.
Amount of maturation-related variation varies but may be substantial.
Maturation-related variation is not random. That is, components respond in a predictable way to changes in maturity.
He also outlined the implications of the effects of maturation-related variation in growth and body composition during adolescence:
- For individuals, there is an increase in misclassification and bias when using reference data based only on chronological age.
For groups, there is an increase in the standard deviation (SD); there is a decrease in precision of estimates; likewise, there is a decrease in detection of differences, effects, and change.
Dr. Himes presented data from several studies, including NHANES III, which assessed stages of sexual maturity independent of chronological age against certain variables, such as stature, FFM, and variation in percent body fat. He also reviewed findings from a study of white 1 to 13-year-old girls in Minnesota, pointing out that girls with later onset of puberty tended to have less mean total body fat than girls with earlier onset of puberty.
Dr. Himes concluded by summarizing several implications of puberty on assessing growth and body composition in the Study. In particular, the Study will need to:
- Conduct more frequent measurements than in childhood to accommodate the rapid growth and change that occur during puberty.
Measure a wide range of dimensions and components of body composition.
Measure maturational status on multiple occasions.
Develop valid and reliable noninvasive measures of maturation.
Use maturation variables in analyses.
Dr. Himes again emphasized that if attempting to detect differences among adolescents, what is being measured, as well as when it is being measured, will be significant in terms of the magnitude of those changes being detected.
Lean Body Mass, Skeletal Muscle, and Body Water
Jack Wang, M.S., Columbia University, and Ira Bernstein, M.D., University of Vermont
Dr. Bernstein focused his presentation on reviewing some of the specific considerations related to fetal measures of body composition that could possibly be included in the Study. He pointed out that fetal measures are unique and somewhat specific. He discussed the basic framework for current techniques used to assess fetal body composition:
- Standard obstetrical ultrasound equipment
Well-defined anatomic planes
Limited to subcutaneous fat depots (80 percent of fat in neonate)
Anatomic locations
– Abdominal wall
– Proximal upper and lower extremity.
Dr. Bernstein noted that currently data are restricted to 2-D imaging. He also explained that the discussion centered on two measurement sites––the abdominal wall and the proximal upper and lower extremities—because these measures have been validated based on comparisons among newborns in multiple clinical studies.
He next summarized relevant validation studies that examined abdominal wall thickness (to measure neonatal fat) and proximal extremities, that is, the humerus/femur (to measure neonatal fat and lean body mass).
Dr. Bernstein offered two considerations for the Study:
He concluded by noting preferred timeframes for observations and measurements:
- Observations should occur over the second half of pregnancy (beginning at or beyond 20 weeks gestation).
Serial scans should occur every 4 to 6 weeks until delivery, based on discretely defined gestational age windows (for example, at weeks 20–24, 25–28, 29–32, and 33–36).
Mr. Wang began by explaining that his presentation would focus on discussing currently available techniques that could be used to measure FFM, TBW, skeletal muscle mass (SMM), and body dimensions during the five phases of the Study. Each method had been scored based on several criteria:
Risk
Participant burden
Reliability
Accuracy
Feasibility
Cost.
First, Mr. Wang described current methods for measuring FFM, as well as specific variables measured by each technique:
- Anthropometry: weight, length, circumference, width, skinfold thickness
UWW: body volume (BV)
Tracer dilution: TBW, ECW
40K counting: TBK
Creatinine: 24-hour or 28-hour urinary excretion
3-methylhistidine: 48-hour urinary excretion
CT: total/regional adipose tissue and skeletal muscle volume, organ sizes
BIA (SF, MF): bioelectrical impedance
Ultrasound: subcutaneous fat thickness (prenatal growth and development)
IR: subcutaneous thickness
IVNA: body elements
DXA: total/regional body fat, FFM, bone mineral
MRI: total/regional adipose tissue and skeletal muscle volume, organ sizes
GNRA: body elements
ADP (PP, BP): BV
TBSS: total/regional BV, circumference, length, width.
Mr. Wang then discussed the issue of reliability of skinfold measurements to determine body circumference. He reported on a study of 26 clinical nurses who were trained to measure body circumference at five body locations and skinfold at five different body locations. After observing the trainer’s demonstration and practicing on at least 10 subjects, 73 percent of the nurses achieved skill levels for body circumferences less than 2 percent different from the trainer’s reading. These same nurses achieved skill levels for skinfold measurements that differed by less than 20 percent from the trainer’s reading.
Mr. Wang also reviewed findings from a number of studies that examined various other measurement techniques:
Ultrasound assessment of body composition in obese adults––chiefly overcoming the limitations of the skinfold caliper
UWW as a technique to measure percent of body fat
- 3-D photonic scanning to assess body volume in the head, torso, left arm, right arm, left leg, and right leg
BIA to predict TBW and FFM in healthy and HIV-infected children and teens
BIA to estimate SMM.
Mr. Wang next discussed the scale used to rate the various methods used to measure FFM, TBW, SMM, and body dimensions. He explained that each method was assigned a score based on the six previously defined criteria. The highest score was 12; the lowest was 0.
He concluded by summarizing the following issues for consideration by the Study when finalizing how body composition will be measured:
Minimize participant’s burden to help ensure long-term retention.
Minimize participant’s risk from participation.
Establish study sites convenient to participants.
Use a multiple-measurement approach.
Standardize protocol and instrumentation.
Centralize data production and management.
Establish a long-term quality assurance protocol.
- Conduct pilot studies.
Establish a similar database in a normal cohort.
Bone Mineral Content and Density
Steven B. Heymsfield, M.D., Columbia University
Dr. Heymsfield discussed the dynamics of the relation among body weight, skeletal muscle, and bone. Bone mass increases early in life, peaks in adulthood, and then declines. A modern view is that bone actually is generated by mechanical forces––body weight or physical activity. Force exerts tension on skeletal muscle. In turn, skeletal responds by pulling on bone thereby influencing bone mass and bone density. Dr. Heymsfield emphasized that these factors are all interrelated, although the relation between bone and muscle stays more or less stable.
He next briefly described changes in bone properties due to age:
Dr. Heymsfield discussed factors associated with inadequate bone mass accumulation in children:
- Systemic steroid use for longer than 1 month over a year
Immunosuppressive medication use
Prior malignancy
Systemic burns
Chronic pulmonary diseases
Chronic renal disease
Malabsorptive disorders
Chronic liver disease
Muscular weakness
Chronic rheumatic diseases
Immobility
Chronic anticonvulsant therapy
Poorly controlled diabetes mellitus
Thyroid hormone use
Anorexia nervosa
Genetic hypercalciuria
Inadequate nutrition
Unhealthy lifestyle.
Dr. Heymsfield also discussed the benefits and weaknesses of methods used to assess bone mineral content and density:
- Quantitative computed tomography (QCT) and peripheral QCT (pQCT): bone composition and architecture, as well as skeletal muscle; expensive; only appendicular bones can be studied; independent of chronological age and body size; false-low bone mineral density (BMD) in small subjects; radiation exposure; reproducibility of BMD +/- 0.3–1.2 percent
Dual-energy x-ray absorptiometry (DXA): bone composition of multiple bones and body composition; expensive; radiatio exposure; cannot separate trabecular and cortical bone; pediatric influences; reproducibility of BMD +/- 1–1.5 percent
Quantitative ultrasound (QUS): no radiation; relatively easy to use; inexpensive; unlimited repetition; many pediatric influences including macrostructure effects.
Adiposity and Regional Fat Distribution
Henry S. Kahn, M.D., National Center for Chronic Disease Prevention and Health Promotion, CDC, DHHS
Dr. Kahn began by reiterating the longitudinal implications of examining adipose tissue (AT) for the Study:
- Relation of AT to the etiology of adult chronic disease
- Causal and protective roles
- Marker roles - Environmental origins of AT accumulation
- Total AT
- Regional distribution
- Relationships to accumulated lean mass.
Dr. Kahn also emphasized the need to consider conventional field methods, as well as high-techoptions, in terms of feasibility, cost, and relevance to public health.
He reviewed the concepts of lipid overaccumulation and lipotoxicity. Lipid overaccumulation occurs when the flux of lipid fuels exceeds the capacity of peripheral AT to buffer and store energy. This leads to larger waist size and an increase in circulating triglycerides.
Lipotoxicity, a consequence of lipid overaccumulation, is associated with deposition of lipid in ectopic tissues, leading to:
Dr. Kahn next discussed the concept of caudal diminution and the suppression of caudal growth. He presented several possible meanings of caudal diminution:
- Marker of growth restraint in specific time windows
Reduced lower-body AT causes decreased ability to buffer and store lipid fuels
Reduced lower-body muscle leads to decreased disposal of calories (decreased locomotion)
Reduced lower-body bone length provides reduced frame for support of functioning AT.
Dr. Kahn followed with a rationale for studying reduced lower-body size (caudal diminution):
He also pointed out that lower-body measurements are noninvasive and inexpensive.
Dr. Kahn discussed several studies as examples of critical outcomes among an older population for which the predecessor state could have been in the fetus or due to some childhood occurrence. He also reported on a study of persons with type 2 diabetes, which showed an inverse association with hip and thigh circumference.
Dr. Kahn further suggested that measuring head diameter at 18 weeks gestational age and femur length at 20 weeks could be used to track peak growth velocity. These measures might be able to identify environmental factors that could be linked to the failure of the fetus to grow and develop.
Dr. Kahn next discussed fingerprint ridge counts as a measurement technique. He explained that fingerprints are formed by the 19th week of gestation. Thus, if it can be demonstrated that a particular pediatric or adult outcome is associated with fingerprints, then it is logical to conclude that the outcome represents a dynamic that occurred before the 19th week. Dr. Kahn also pointed out that fingerprints are sequentially related to spinal cord segments. Finally, fingerprint measurement is inexpensive, and current technologies are more efficient and less cumbersome than earlier methods.
Dr. Kahn described several measurement options:
- External anthropometry
- Hand morphology: fingerprints and digit lengths
- Imaging: fetal ultrasound; MRI, CT, or liver fat in children and teens
- BIA
- DXA.
He reiterated that external anthropometry is relatively inexpensive, and available for later clinical adoption. This method can be used to measure weight, height, circumferences, sagittal abdominal diameter (SAD), and subcutaneous AT.
Dr. Kahn concluded his remarks by suggesting timeframes and measurement methods for examinations that would include adiposity and regional fat distribution in the Study cohort:
- Mother (external anthropometry): at enrollment, 17 weeks gestational age, and at 37 weeks gestational age
Fetus (ultrasound): at 17 weeks, 27 weeks, and 37 weeks gestational age
Newborn (external anthropometry): placental weight and assay for LPL activity, FABP expression
Child and adolescent (external anthropometry): at ages 3 months and at 1, 3, 5, 8, 13, and 18 years.
Obesity and Biomarkers of Insulin Resistance
Stephen R. Daniels, M.D., Ph.D., Cincinnati Children’s Hospital Medical Center
Dr. Daniels explained that he would focus on the “downstream effects” of obesity, noting that obesity and physical development is a priority outcome for the Study.
Given that the most widely used definition of obesity is based on BMI, Dr. Daniels suggested that BMI may be the most useful way to identify obesity in the clinical setting. He also noted that BMI is simple, inexpensive, and relatively easy to interpret. In discussing the best method to evaluate overweight and obese children in the Study, Dr. Daniels suggested that multiple methods may be needed.
Dr. Daniels emphasized the broad metabolic impact of obesity, noting that obesity affects virtually every organ system. He listed adverse outcomes related to obesity:
- Metabolic: type 2 diabetes mellitus, metabolic syndrome, dyslipidemia
- Orthopedic: slipped capital femoral epiphysis, Blount’s disease
Cardiovascular: hypertension, left ventricular hypertrophy, atherosclerosis
Psychologic: depression, poor quality of life
Neurologic: pseudotumor cerebri
Hepatic: nonalcoholic fatty liver disease, nonalcoholic steatohepatitis
Pulmonary: obstructive sleep apnea, asthma (exacerbation)
Renal: proteinuria, end-stage renal disease.
Dr. Daniels discussed the relationship of obesity with lipids and lipoproteins, noting that obesity:
He also summarized studies that examined the relation of obesity and cardiovascular disease (CVD), noting that even small differences in blood pressure in obese persons can substantially increase the risk of CVD. Other studies examined obesity and left ventricular mass (LVM), finding obesity to be an important determinant of LVM.
Dr. Daniels discussed whether obesity contributes to development of atherosclerosis. He cited the Bogalusa study that found that obesity accelerates coronary atherosclerosis. He noted that increased BMI was associated with increased prevalence of fatty structures and fibrous plaque. Furthermore, childhood weight and BMI were found to be significant predictors for development of coronary artery disease. Dr. Daniels pointed out that a number of noninvasive methods are now available to evaluate the development of atherosclerosis, suggesting that some of these methods may be feasible for the Study.
Dr. Daniels described insulin resistance syndrome (IRS), noting that IRS is not a specific clinical disease, entity, or diagnosis. He explained that the primary cause is resistance to insulin action, resulting in ß cell compensation, leading to increased insulin production and secretion. Dr. Daniels discussed the two models currently used to assess insulin resistance––homeostasis assessment model (HOMA) and QUICKI.
Dr. Daniels next discussed the relation of obesity with metabolic syndrome. He summarized findings from studies, including NHANES III and the Bogalusa study, indicating that there was a substantial increase in the prevalence of metabolic syndrome in obese 12 to 19-year-olds.
Dr. Daniels ended by pointing out that:
- That the definition of obesity may be complex.
Obesity results in numerous adverse effects.
Insulin resistance is important and probably best assessed in a large epidemiologic study by measuring fasting insulin and glucose.
There is increasing interest in and concern about the metabolic syndrome.
It is not clear at present how to best define the metabolic syndrome in young individuals.
Air Displacement Plethysmography
Alessandro Urlando, M.S., Life Measurement, Inc.
Mr. Urlando explained the overall design and operational concepts of the BOD POD® Body Composition Tracking System. This technology measures body composition through densitometry. The BOD POD determines volume through application of air displacement plethysomography using a precision load cell scale system.
Mr. Urlando cited published research on use of BOD POD in adults. To date, there are more than 50 published studies on body composition assessment in adults using the BOD POD. The adult populations in these studies ranged in age from 18 to 86 years. BMI ranged from 17 to 40 kg/m2. Specific population groups were included in the studies, such as the disabled, chronically ill, severely obese, and the elderly. Most of these studies evaluated the performance of the BOD POD using UWW, DXA, or multicompartment models (3- or 4-C) as the reference methods.
A summary of 25 studies comparing percent of body fat using the BOD POD versus the reference methods in adults showed that on average the BOD POD and the reference methods agreed within 1 percent body fat for adults. Individual agreement between BOD POD and the reference methods was considered “excellent to ideal.”
Mr. Urlando also pointed out that to date, there have been more than 12 published studies on body composition assessment in children using the BOD POD. The children studied ranged in age from 5 to 19 years. The BMI ranged from 13 to 45 kg/m2. Younger children, aged 5 to 7 years, overweight and obese children, various ethnic groups, and children with cystic fibrosis were also studied. Mr. Urlando reported that percent body fat by DXA was highly correlated with the BOD POD, for both baseline and follow-up measurements.
Mr. Urlando next described the PEA POD® Infant Body Composition System, which is based on the same air displacement technology as the BOD POD. He noted that the PEA POD was designed to address all the issues associated with more traditional measurement methods, such as practical limitations, training requirements, limited availability, cost, accuracy, and safety.
Mr. Urlando emphasized that the PEA POD can be used to:
- Assess infant growth
Optimize nutritional and pharmacological interventions
Investigate and optimize release criteria in NICU settings
Establish normative body composition data.
He also listed various benefits of this technology. That is, the PEA POD:
- Is accurate and precise
Is safe and noninvasive
Provides immediate feedback
Requires a short test time
Accommodates most infant behaviors
Is comfortable, with a heated testing environment
Is mobile
Is user-friendly, with minimal operator training requirements
Includes menu-driven software with data management capabilities.
Mr. Urlando also summarized data from validation studies of the PEA POD compared with the two-compartment model.
Optical Detection of Subcutaneous Fat Thickness
Kenneth J. Ellis, Ph.D., Baylor College of Medicine
Dr. Ellis discussed the preliminary evaluation of the Lipometer by the Children’s Nutrition Research Center (CNRC). He described the basic components and operation of the Lipometer as a tool to measure body composition, including total body fat and FFM. Dr. Ellis pointed out that the Lipometer is a mobile system with few component parts. He then presented examples of screen shots and printouts showing detailed measurements from 15 body sites.
He reported on an assessment comparing DXA and the Lipometer as methods for monitoring body fatness in school-aged children. This study, which is ongoing, is following 325 children and young adults between the ages of 3 and 26. Dr. Ellis presented preliminary findings, pointing out several benefits of the Lipometer, as well as potential drawbacks, and noted that the weaknesses will likely be resolved and clarified with further study.
- Strengths:
- Is easy to use
- Alerts the operator if the procedure is not correct
- Takes only about 5 minutes to measure all 15 sites
- Results are not operator dependent
- Has good reproducibility
- Correlation with skinfold measurements at each site (r = 0.30–0.85)
- Can be used to predict percent fat with reasonable accuracy. - Weaknesses:
- Occasional low reading (especially among African Americans)
- Change in skin pigmentation may influence values
- Anatomical site must be correct.
Quantitative Ultrasound
David J. Helowicz, Sunlight Medical, Inc.
Mr. Helowicz described several new products that use ultrasound technology to assess skeletal development in children and adults. He pointed out that bone age testing can help identify growth patterns and deficiencies in young children. Mr. Helowicz pointed out several strengths of the BonAge product. It is:
Mr. Helowicz explained that BonAge measures the ossifying cartilage structures of the wrist, which provides a view of skeletal development. BonAge measures the velocity of an ultrasound wave transmitted through the wrist, using a proprietary gender-and ethnically based algorithm to generate a numeric bone age score. These scores are available on site, and do not require interpretation by the physician. A full-color report presents bone age, height, and adult height prediction, compared with age-, gender-, and ethnicity-matched normal curves.
Mr. Helowicz presented a video demonstrating operation of the BonAge assessment tool, as well as overviews of several new prototypes of other products currently under development that utilize ultrasound technology for body composition assessment.
Questions and Open Discussion
At the close of the morning and afternoon sessions, participants asked questions and offered comments relevant to the Study design, hypotheses, and outcomes that would influence suggestions and findings regarding measuring body composition within the Study. These discussion points included:
- Recognition that it will be critical not only to define measurements, but also the timing/staging for when those measurements will be taken
What measurements fit best within the Study will depend on each specific question or hypothesis
New technologies are emerging that may alter the entire approach to measuring body composition
Whether “localized findings” will be excluded due to IRB guidelines
The need to recognize that what is being measured may be an outward manifestation of an internal dynamic related to growth, and to connect internal dynamics with observable occurrences
Accommodation of sample collection and storage
What is currently considered a topic of interest for Study inclusion may cease to be of interest within the next 5–10 years
Reiteration that the Study hypotheses have been formulated in the broadest terms to capture as much information from as many subjects as possible
Emphasis on defining types of feasible measurements, populations to be measured, and timing of measurements within a cohort of 100,000
Measurement continuity, in particular, transitioning from the prenatal to postnatal stage
Determining measures of predictable risk and understanding that those measures may be different in children than in adults
Establishment of norms and how to define them within the Study context
Recognition that “normal” ranges have not been determined for some types of measurement
Limitations of technologies currently being used to measure body composition, particularly in special populations (for example, children with structural malformations or physical disabilities).
Breakout Sessions
Robert J Kuczmarski, Dr.P.H., R.D., National Institute of Diabetes and Digestive and Kidney Diseases, NIH, DHHS
Dr. Kuczmarski explained that the workshop, so far, had:
Provided an overview of the Study
Described the conceptual framework of growth and body composition developed by the Nutrition, Growth, and Pubertal Development Working Group
Described the three levels of appropriate measurements
Offered presentations on variety of growth and development topics (the “what” of the workshop)
Listed the workshop objectives
Presented findings from other Study workshops.
According to Dr. Kuczmarski, the purpose of the breakout sessions was to focus on the “how” by developing a consensus on the most promising methods for measuring growth and body composition across the lifespan of a large longitudinal study. Workshop participants were assigned to one of the following age/status groups:
Pregnancy
Fetal growth and preterm infants
Infants (birth to age 3 years)
Childhood (ages 4–9 years)
Adolescence (ages 8–21 years).
(There is an overlap in age ranges for childhood and adolescence.)
Dr. Kuczmarski commented that the challenges for the breakout groups increased with increasing age of Study participants. He noted that a particular challenge for the Study is incorporating evolving technologies that will become state-of-the-art methods over the next
15–20 years. Each group considered the following questions:
- Which methods do you consider to be most promising for use in this age range for the Study? Why?
How often and when (at what ages) should measurements be taken? Optimally? Mandatory minimum?
Are there any unique opportunities for data collection in this age range? Are there any special concerns? For example, should neonates be measured on a gestation-corrected or chronological age schedule? If measured at gestation-corrected ages, when should the approach change to chronological age?
What are the current problems or barriers, if any, with using these methods in the Study? Do you believe it is possible to overcome these problems/barriers? How?
For those methods that are most promising, what research, additional validation, or pilot study would be needed to make this instrument(s) a viable option for measuring growth and/or body composition in the Study?
Are these instruments or methods sufficiently developed to provide continuity across time?
In their discussions, the groups specifically identified the following information:
At the conclusion of discussions, each group reported its findings in a measurement rating form, providing information on:
Anthropometric dimension or body composition
Measurement method
Relative ratings:
– Risk (min = 2, med = 1, max = 0)
– Participant burden (min = 2, med = 1, max = 0)
– Reliability (high = 2, acceptable = 1, low = 0)
– Accuracy (high = 2, acceptable = 1, low = 0)
– Feasibility (high = 2, med = 1, low = 0)
– Cost (low = 2, med = 1, high = 0)
Time points for measurement:
– Specify as year of age, time interval, and age category
– Rate the frequency of measurement as realistic/feasible, absolute minimum, etc.
Rate longitudinal continuity between ages:
– From adolescence to childhood (high = 2, acceptable = 1, unacceptable = 0)
– From childhood to infancy (high = 2, acceptable = 1, unacceptable = 0)
– From infancy to fetal period (high = 2, acceptable = 1, unacceptable = 0)
– From adolescence to adulthood (high = 2, acceptable = 1, unacceptable = 0)
Appropriateness of the measure:
– Whole Study or substudy only
– Field method or restricted to laboratory
Associated technical issues (for example, technician training, standardization, quality control issues, etc.—specify issue and level)
– None/easily resolved (a )*
– Moderately difficult (b)*
– Complex (c)*
Status of method for the Study
– Ready for use (a)*
– Requires pilot study (specify) (b)*
Other considerations, notes, comments.
*See breakout group reports.