Relationship of physical activity with motor skills, aerobic fitness and body fat in preschool children: a cross-sectional and longitudinal study (Ballabeina).

Adiposity, low aerobic fitness and low levels of activity are all associated with clustered cardiovascular disease risk in children and their high prevalence represents a major public health concern. The aim of this study is to investigate the relationship of objectively measured physical activity (PA) with motor skills (agility and balance), aerobic fitness and %body fat in young children. This study is a cross-sectional and longitudinal analyses using mixed linear models. Longitudinal data were adjusted for baseline outcome parameters. In all, 217 healthy preschool children (age 4-6 years, 48% boys) participated in this study. PA (accelerometers), agility (obstacle course), dynamic balance (balance beam), aerobic fitness (20-m shuttle run) and %body fat (bioelectric impedance) at baseline and 9 months later. PA was positively associated with both motor skills and aerobic fitness at baseline as well as with their longitudinal changes. Specifically, only vigorous, but not total or moderate PA, was related to changes in aerobic fitness. Higher PA was associated with less %body fat at baseline, but not with its change. Conversely, baseline motor skills, aerobic fitness or %body fat were not related to changes in PA. In young children, baseline PA was associated with improvements in motor skills and in aerobic fitness, an important determinant of cardiovascular risk.

Psychosocial Quality-of-Life, Lifestyle and Adiposity: A Longitudinal Study in Pre-schoolers (Ballabeina Study).

PURPOSE: In obesity prevention, understanding psychosocial influences in early life is pivotal. Reviews reported contradictory results and a lack of longitudinal studies focusing on underlying lifestyle factors. This study tested whether psychosocial Quality-Of-Life (QOL) was associated with pre-schoolers' lifestyle and adiposity changes over one school year and whether lifestyle moderated the latter. It was hypothesised that QOL might not impact adiposity in everybody but that this might depend on preceding lifestyle. METHOD: Longitudinal data from 291 Swiss pre-schoolers (initially 3.9-6.3 years) was available. The following measures were used in longitudinal regressions: psychosocial QOL by PedsQL, adiposity (BMI z-score, waist, fat%), diet (food frequency), sedentary time and accelerometer-based activity. RESULTS: Concerning lifestyle, low psychosocial QOL was only related to unfavourable changes in diet (less fruit β = 0.21 and more fat intake β = -0.28) and lower physical activity (β = 0.21). Longitudinal QOL-adiposity relations appeared only after moderation by lifestyle factors (beta-range 0.13-0.67). Low psychosocial QOL was associated with increased adiposity in children with an unhealthy diet intake or high sedentary time. By contrast, low psychosocial QOL was associated with decreasing adiposity in high fruit consumers or more physically active pre-schoolers. CONCLUSION: Results emphasise the need for testing moderation in the QOL-adiposity relation. An unhealthy diet can be a vulnerability factor and high physical activity a protective factor in QOL-related adiposity. Consequently, QOL and lifestyle should be targeted concurrently in multi-factorial obesity prevention. The environment should be an 'activity encouraging, healthy food zone' that minimises opportunities for stress-induced eating. In addition, appropriate stress coping skills should be acquired.

Modelling structural zeros in compositional data

This analysis was stimulated by the real data analysis problem of household expenditure data. The full dataset contains expenditure data for a sample of 1224 households. The expenditure is broken down at 2 hierarchical levels: 9 major levels (e.g. housing, food, utilities etc.) and 92 minor levels. There are also 5 factors and 5 covariates at the household level. Not surprisingly, there are a small number of zeros at the major level, but many zeros at the minor level. The question is how best to model the zeros. Clearly, models that try to add a small amount to the zero terms are not appropriate in general as at least some of the zeros are clearly structural, e.g. alcohol/tobacco for households that are teetotal. The key question then is how to build suitable conditional models. For example, is the sub-composition of spending excluding alcohol/tobacco similar for teetotal and non-teetotal households? In other words, we are looking for sub-compositional independence. Also, what determines whether a household is teetotal? Can we assume that it is independent of the composition? In general, whether teetotal will clearly depend on the household level variables, so we need to be able to model this dependence. The other tricky question is that with zeros on more than one component, we need to be able to model dependence and independence of zeros on the different components. Lastly, while some zeros are structural, others may not be, for example, for expenditure on durables, it may be chance as to whether a particular household spends money on durables within the sample period. This would clearly be distinguishable if we had longitudinal data, but may still be distinguishable by looking at the distribution, on the assumption that random zeros will usually be for situations where any non-zero expenditure is not small. While this analysis is based on around economic data, the ideas carry over to many other situations, including geological data, where minerals may be missing for structural reasons (similar to alcohol), or missing because they occur only in random regions which may be missed in a sample (similar to the durables)

Educational participation post-16: a longitudinal analysis of intentions and outcomes

The issue of levels of participation in post-compulsory education has been emphasised by the current policy initiatives to increase the age to which some form of participation is compulsory. One of the acknowledged weaknesses of research in the field of children's intentions with regard to participation is the lack of longitudinal data. This paper offers a longitudinal analysis using the Youth Survey from the British Household Panel Survey. The results show that most children can express intentions with regard to future participation very early in their secondary school careers and that these intentions are good predictors of actual behaviour five years later. Intentions to stay on are more consistent than intentions to leave and most children who finally leave at 16 have at some point said they want to remain in education post-16. The strongest association with participation levels is attainment at GCSE. However, there are also influences of gender and parental background and these remain, even after attainment is held constant. The results show the value of focusing on intentions for participation at a very early stage of children's school careers and also the importance of current attempts to reform curriculum and assessment for the 14-19 age group.

Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting data for each individual. Assuming a CD4 counting data set in HIV-infected patients, we develop a hierarchical Bayesian analysis considering the different proposed models and using Markov Chain Monte Carlo methods. We also discuss some Bayesian discrimination aspects for the choice of the best model.

Contrasting longitudinal and cross-sectional relationships between insulin resistance and percentage of body fat, fitness, and physical activity in children—the LOOK study

Background: Knowledge of individual changes in insulin resistance (IR) and longitudinal relationships of IR with lifestyle-associated factors are of important practical significance, but little longitudinal data exist in asymptomatic children. We aimed to determine (a) changes in the homeostatic model of insulin resistance (HOMA-IR) over a 2-yr period and (b) comparisons of longitudinal and cross-sectional relationships between HOMA-IR and lifestyle-related risk factors.

Methods: Our subjects, 241 boys and 257 girls, were assessed at age 8.1 yr (SD 0.35) and again 2 yr later for fasting blood glucose and insulin, dual X-ray absorptiometry-assessed percentage of body fat (%BF), pedometer-assessed physical activity (PA), and cardio-respiratory fitness (CRF) by multistage running test.

Results: HOMA-IR was initially 9% greater in girls than boys and 27% greater 2 yr later. There was no evidence of longitudinal relationships between HOMA-IR and %BF in boys or girls, despite significant cross-sectional relationships (p < 0.001). In boys, there was evidence of a longitudinal relationship between HOMA-IR and both PA (p < 0.001) and CRF (p = 0.05). In girls, we found a cross-sectional relationship between HOMA-IR and CRF (p < 0.001).

Conclusions: HOMA-IR increases between 8 and 10 yr of age and to a greater extent in girls. Longitudinal, unlike cross-sectional, relationships do not support the premise that body fat has any impact on HOMA-IR during this period or that PA or CRF changes affect HOMA-IR in girls. These data draw attention to difficulties in interpreting observational studies in young children.

Change in active travel and changes in recreational and total physical activity in adults : longitudinal findings from the iConnect study

Background : To better understand the health benefits of promoting active travel, it is important to understand the relationship between a change in active travel and changes in recreational and total physical activity.

Methods : These analyses, carried out in April 2012, use longitudinal data from 1628 adult respondents (mean age 54 years; 47% male) in the UK-based iConnect study. Travel and recreational physical activity were measured using detailed seven-day recall instruments. Adjusted linear regression models were fitted with change in active travel defined as ‘decreased’ (<−15 min/week), ‘maintained’ (±15 min/week) or ‘increased’ (>15 min/week) as the primary exposure variable and changes in (a) recreational and (b) total physical activity (min/week) as the primary outcome variables.

Results : Active travel increased in 32% (n=529), was maintained in 33% (n=534) and decreased in 35% (n=565) of respondents. Recreational physical activity decreased in all groups but this decrease was not greater in those whose active travel increased. Conversely, changes in active travel were associated with commensurate changes in total physical activity. Compared with those whose active travel remained unchanged, total physical activity decreased by 176.9 min/week in those whose active travel had decreased (adjusted regression coefficient −154.9, 95% CI −195.3 to −114.5) and was 112.2 min/week greater among those whose active travel had increased (adjusted regression coefficient 135.1, 95% CI 94.3 to 175.9).

Conclusion : An increase in active travel was associated with a commensurate increase in total physical activity and not a decrease in recreational physical activity.

A longitudinal analysis of self-prescribed complementary and alternative medicine use by a nationally representative sample of 19,783 Australian women, 2006-2010

BACKGROUND: Complementary and alternative medicine (CAM) use is increasingly popular amongst general populations around the world with women constituting substantial CAM users. However, self-prescribed CAM use does raise potential safety concerns and so it is important to identify those risk factors associated with self-prescribed CAM use. METHODS: Data was obtained from the Australian Longitudinal Study on Women's Health (ALSWH). Longitudinal data analyses were conducted on questionnaire data from the 1973-78 cohort (n=9,145) and the 1946-51 cohort (n=10,638), collected over the period 2006-2010. RESULTS: In the 1973-78 cohort, use of self-prescribed CAM was 73.2% in 2006 and 75.3% in 2009. For the 1946-51 cohort, use of self-prescribed CAM was 73.9% in 2007 and 74.7% in 2010. There were similar levels of use of individual self-prescribed CAM, with the exception that the use of herbal medicine was much higher among the 1946-51 cohort (20% vs. 27%). There was a substantial increase over three years in the use of vitamins/minerals in both cohorts (21% and 19%, respectively). In contrast, there was a considerable decline over three years in use of aromatherapy oils in both cohorts (34% and 28%, respectively). CONCLUSION: Self-prescribed CAM use is popular amongst women in Australia and it is important that conventional practitioners providing women's health care be cognizant of such use amongst their patients. In order to ensure effective practice, there is a need for further research to explore women's decision-making and experiences around self-prescribed CAM use.

Using Spatiotemporal Methods to Fill Gaps In Energy Usage Interval Data

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

Poverty dynamics in Costa Rica with panel data from cross-sections

Includes bibliography

"From Dartboards to Dashboards: The Imperative of using Data to Improve Student Achievement"

Aimee Guidera, Director of the National Data Quality Campaign, delivered the second annual Lee Gurel '48 Lecture in Education, "From Dartboards to Dashboards: The Imperative of Using Data to Improve Student Outcomes." Aimee Rogstad Guidera is the Founding Executive Director of the Data Quality Campaign. She manages a growing partnership among national organizations collaborating to improve the quality, accessibility and use of education data to improve student achievement. Working with 10 Founding Partners, Aimee launched the DQC in 2005 with the goal of every state having a robust longitudinal data system in place by 2009. The Campaign is now in the midst of its second phase focusing on State Actions to ensure effective data use. Aimee joined the National Center for Educational Accountability as Director of the Washington, DC office in 2003. During her eight previous years in various roles at the National Alliance of Business, Aimee supported the corporate community's efforts to increase achievement at all levels of learning. As NAB Vice President of Programs, she managed the Business Coalition Network, comprised of over 1,000 business led coalitions focused on improving education in communities across the country. Prior to joining the Alliance, Aimee focused on school readiness, academic standards, education goals and accountability systems while in the Center for Best Practices at the National Governors Association. She taught for the Japanese Ministry of Education in five Hiroshima high schools where she interviewed educators and studied the Japanese education system immediately after receiving her AB from Princeton University’s Woodrow Wilson School of Public & International Affairs. Aimee also holds a Masters Degree in Public Policy from Harvard’s John F. Kennedy School of Government.

Bayesian Hidden Markov Modeling of Array CGH Data

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Since the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme and breast cancer are analyzed, and comparisons are made with some widely-used algorithms to illustrate the reliability and success of the technique.