Male prairie voles with different avpr1a microsatellite lengths do not differ in courtship behaviour.

Females are generally expected to be selective when choosing their social and sexual partners. In a previous laboratory study, female prairie voles (Microtus ochrogaster) showed significant social and sexual preferences for males with longer microsatellite DNA within the avpr1a gene encoding the vasopressin 1a receptor, as predicted if females select mates whose parental behaviour should increase female reproductive success. We tested the hypothesis that males with short versus long avpr1a microsatellite alleles exhibit differences in courtship behaviour, which could act as cues for female mate preference. The only behavioural difference we detected between males with short versus long avpr1a microsatellite alleles in mate preference trials was that males with short avpr1a microsatellite alleles sniffed the anogenital region of females more frequently during the first two days of the trials. Our results did not strongly support the hypothesis that a male's avpr1a genotype predicts the courtship behaviours we measured and suggests that other courtship behaviours or traits, such as odour and vocalizations, may be more important to female prairie voles when choosing mates. Additional studies using a wider array of species are needed to assess the degree to which male mammal courtship behaviour provides information on mate quality to females.

Body fat has no effect on the maximal fat oxidation rate in young, normal, and overweight women.

The purpose of this study was to examine how fat mass affects the maximal fat oxidation rates of women. Fourteen active, healthy women (age, 21-31 years) with body composition ranging from 18.6 to 30.0% fat were divided into 2 groups (15-24.9% = lower-fat group; 25-35% = higher-fat group). On day 1, subjects performed a graded exercise test on the treadmill to determine maximal oxygen consumption (VO2max). On day 2, subjects were measured for % fat and performed a maximal fat oxidation test. Fat and carbohydrate oxidation rates were determined using gas exchange analysis. Fat oxidation in absolute (in gram per minute) and relative to fat-free body mass (in milligram per kilogram of fat-free mass per minute) was determined using stoichiometric equations and appropriate energy equivalents. There were no significant differences (p > 0.05) in maximal fat oxidation rates between the women in lower-fat (0.39 ± 0.10 g·min-1, 8.52 ± 2.69 mg·kg FFM·min-1) and higher-fat (0.49 ± 0.13 g·min-1, 10.81 ± 2.80 mg·kg-1 FFM·min-1) groups. Maximal fat oxidation occurred at an exercise intensity of 55.7 ± 11.1% and 59.1 ± 5.4% VO2max for the lower-fat and higher-fat groups, respectively, with no significant difference between groups (p > 0.05). The maximal fat oxidation rate (g·min-1 and mg·kg-1 FFM·min-1) was not significantly correlated with any of the descriptive variables (fat mass, fat-free mass, percent body fat, or VO2max). In conclusion, personal trainers and health practitioners can use the exercise intensities that elicited the highest rate of fat oxidation to prescribe exercise programs to women, despite their body composition, that prevent weight gain and/or promote body fat and body weight loss.

Risk factors of falls in community-dwelling older adults: logistic regression tree analysis.

PURPOSE OF THE STUDY: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. DESIGN AND METHODS: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used. Logistic Tree with Unbiased Selection, a computer algorithm for tree-based modeling, recursively split the entire group in the data set into mutually exclusive subgroups and fit a logistic regression model in each subgroup to generate an easily interpreted tree diagram. RESULTS: A subgroup of older adults with a fall history and either no activities of daily living (ADL) limitation and at least one instrumental activity of daily living or at least one ADL limitation was classified as at high risk of falling. Additionally, within each identified subgroup, the best predictor of falls varied over subgroups and was also evaluated. IMPLICATIONS: Application of tree-based methods may provide useful information for intervention program design and resource allocation planning targeting subpopulations of older adults at risk of falls.

Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis.

Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources. With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c testing. Using Andersen's health behavior model, need factors appear to dominate the definition of which sub-groups were at greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their self-care behaviors. Limitations and contributions of the study are discussed.

The psychosocial effects of cleft lip and palate in non-Anglo populations: a cross-cultural meta-analysis.

OBJECTIVE: A meta-analytic study was conducted to examine the cross-cultural psychosocial impact of cleft lip and/or palate in non-Anglo populations. DESIGN: A total of 333 citations were initially identified for review using electronic and hand-search strategies. Of the six studies that met inclusion criteria, two were later excluded due to insufficient data. The four remaining studies represented a combined sample size of 2276 adolescents and adults with cleft lip and/or palate from China, Hong Kong, Taiwan, and Norway. MAIN OUTCOME MEASURES: Effect size using Cohen's d and confidence intervals were estimated using data from four studies of empirical, controlled study designs. RESULTS: The magnitude of effect sizes indicated that men (d = -0.75) with cleft lip and/or palate in non-Anglo cultures are more prone to psychosocial issues than women (d = -0.33). Adults (d = -0.50) are more impacted than adolescents (d = -0.04). Overall, regardless of age, gender, or culture, individuals with cleft lip and/or palate have lower psychosocial development than individuals without cleft lip and/or palate (d = -0.42). CONCLUSION: The effects for the cross-cultural comparisons were moderated by the age group and gender of the participants; however, most studies resulted in negative effect sizes. Health care teams for cleft lip and/or palate should recognize the importance of psychological intervention and family support in the treatment of all patients with cleft lip and/or palate throughout the life span.

Comparing methods for analyzing overdispersed count data in aquatic toxicology.

Endpoints in aquatic toxicity tests can be measured using a variety of measurement scales including dichotomous (survival), continuous (growth) and count (number of young). A distribution is assumed for an endpoint and analyses proceed accordingly. In certain situations, the assumed distribution may be incorrect and this may lead to incorrect statistical inference. The present study considers the analysis of count effects, here motivated by the Ceriodaphnia dubia reproduction study. While the Poisson probability model is a common starting point, this distribution assumes that the mean and variance are the same. This will not be the case if there is some extraneous source of variability in the system, and in this case, the variability may exceed the mean. A computer simulation study was used to examine the impact of overdispersion or outliers on the analysis of count data. Methods that assumed Poisson or negative binomially distributed outcomes were compared to methods that accommodated this potential overdispersion using quasi-likelihood (QL) or generalized linear mixed models (GLMM). If the data were truly Poisson, the adjusted methods still performed at nominal type I error rates. In the cases of overdispersed counts, the Poisson assumed methods resulted in rejection rates that exceeded nominal levels and standard errors for regression coefficients that were too narrow. The negative binomial methods worked best in the case when the data were, in fact, negative binomial but did not maintain nominal characteristics in other situations. In general, the QL and GLMM methods performed reasonably based on the present study, although all procedures suffered some impact in the presence of potential outliers. In particular, the QL is arguably preferred because it makes fewer assumptions than the GLMM and performed well over the range of conditions considered.

Comparing methods for analyzing overdispersed binary data in aquatic toxicology.

Historically, death is the most commonly studied effect in aquatic toxicity tests. These tests typically employ a gradient of concentrations and exposure with more than one organism in a series of replicate chambers in each concentration. Whereas a binomial distribution commonly is employed for such effects, variability may exceed that predicted by binomial probability models. This additional variability could result from heterogeneity in the probabilities across the chambers in which the organisms are housed and subsequently exposed to concentrations of toxins. Incorrectly assuming a binomial distribution for the statistical analysis may lead to incorrect statistical inference. We consider the analysis of grouped binary data, here motivated by the study of survival. We use a computer simulation study to examine the impact of overdispersion or outliers on the analysis of binary data. We compare methods that assume binomial or generalizations that accommodate this potential overdispersion. These generalizations include adjusting the standard probit model for clustering/correlation or using alternative estimation methods, generalized estimating equations, or generalized linear mixed models (GLMM). When data were binomial or overdispersed binomial, none of the models exhibited any significant bias when estimating regression coefficients. When the data were truly binomial, the probit model controlled type I errors, as did the Donald and Donner method and the GLMM method. When data were overdispersed, the probit model no longer controlled type I error, and the standard errors were too small. In general, the Donald and Donner and the GLMM methods performed reasonably based on this study, although all procedures suffered some impact in the presence of potential outliers.