Predictors of weight stability in women and men: The aerobic center longitudinal study, 1987--1995

It is estimated that more than half the U.S. adult population is overweight or obese as classified by a body mass index of 25.0–29.9 or ≥30 kg/m 2, respectively. Since the current treatment approaches for long-term maintenance of weight loss are lacking, the National Institutes of Health state that an effective approach may be to focus on weight gain prevention. There is a limited body of literature describing how adults maintain a stable weight as they age. It is hypothesized that weight stability is the result of a balance between energy consumption and energy expenditure as influenced by diet, lifestyle, behavior, genetics and environment. The purpose of this research was to examine the dietary intake and behaviors, lifestyle habits, and risk factors for weight change that predict weight stability in a cohort of 2101 men and 389 women aged 20 to 8 7 years in the Aerobic Center Longitudinal Study regardless of body weight at baseline. At baseline, participants completed a maximal exercise treadmill test to determine cardiorespiratory fitness, a medical history questionnaire, which included self-reported measures of weight, dietary behaviors, lifestyle habits, and risk factors for weight change, a three-day diet record, and a mail-back version of the medical history questionnaire in 1990 or 1995. All analyses were performed separately for men and women. Results from multivariate regression analyses indicated that the strongest predictor of follow-up weight for men and women was previous weight, accounting for 87.0% and 81.9% of the variance, respectively. Age, length of follow-up and eating habits were also significant predictors of follow-up weight in men, though these variables only explained 3% of the variance. For women, length of follow-up and currently being on a diet were significantly associated with follow-up weight but these variables explained only an additional 2% of the variance. Understanding the factors that influence weight change has tremendous public health importance for developing effective methods to prevent weight gain. Since current weight was the strongest predictor of previous weight, preventing initial weight gain by maintaining a stable weight may be the most effective method to combat the increasing prevalence of overweight and obesity. ^

Design of mixed-longitudinal studies

Cross-sectional designs, longitudinal designs in which a single cohort is followed over time, and mixed-longitudinal designs in which several cohorts are followed for a shorter period are compared by their precision, potential for bias due to age, time and cohort effects, and feasibility. Mixed longitudinal studies have two advantages over longitudinal studies: isolation of time and age effects and shorter completion time. Though the advantages of mixed-longitudinal studies are clear, choosing an optimal design is difficult, especially given the number of possible combinations of the number of cohorts and number of overlapping intervals between cohorts. The purpose of this paper is to determine the optimal design for detecting differences in group growth rates.^ The type of mixed-longitudinal study appropriate for modeling both individual and group growth rates is called a "multiple-longitudinal" design. A multiple-longitudinal study typically requires uniform or simultaneous entry of subjects, who are each observed till the end of the study.^ While recommendations for designing pure-longitudinal studies have been made by Schlesselman (1973b), Lefant (1990) and Helms (1991), design recommendations for multiple-longitudinal studies have never been published. It is shown that by using power analyses to determine the minimum number of occasions per cohort and minimum number of overlapping occasions between cohorts, in conjunction with a cost model, an optimal multiple-longitudinal design can be determined. An example of systolic blood pressure values for cohorts of males and cohorts of females, ages 8 to 18 years, is given. ^

Growth and development of body fat distribution from skinfold measurements: Longitudinal principal components

Longitudinal principal components analyses on a combination of four subcutaneous skinfolds (biceps, triceps, subscapular and suprailiac) were performed using data from the London Longitudinal Growth Study. The main objectives were to discover at what age during growth sex differences in body fat distribution occur and to see if there is continuity in body fatness and body fat distribution from childhood into the adult status (18 years). The analyses were done for four age sectors (3mon-3yrs, 3yrs-8yrs, 8yrs-18yrs and 3yrs-18yrs). Longitudinal principal component one (LPC1) for each age interval in both sexes represents the population mean fat curve. Component two (LPC2) is a velocity of fatness component. Component three (LPC3) in the 3mon-3yrs age sector represents infant fat wave in both sexes. In the next two age sectors component three in males represents peaks and shifts in fat growth (change in velocity), while in females it represents body fat distribution. Component four (LPC4) in the same two age sectors is a reversal in the sexes of the patterns seen for component three, i.e., in males it is body fat distribution and in females velocity shifts. Components five and above represent more complicated patterns of change (multiple increases and decreases across the age interval). In both sexes there is strong tracking in fatness from middle childhood to adolescence. In males only there is also a low to moderate tracking of infant fat with middle to late childhood fat. These data are strongly supported in the literature. Several factors are known to predict adult fatness among the most important being previous levels of fatness (at earlier ages) and the age at rebound. In addition we found that the velocity of fat change in middle childhood was highly predictive of later fatness (r $\approx -$0.7), even more so than age at rebound (r $\approx -$0.5). In contrast to fatness (LPC1), body fat distribution (LPC3-LPC4) did not track well even though significant components of body fat distribution occur at each age. Tracking of body fat distribution was higher in females than males. Sex differences in body fat distribution are non existent. Some sex differences are evident with the peripheral-to-central ratios after age 14 years. ^