Effects of aerobic conditioning on cardiovascular sympathetic response to and recovery from challenge.

Exercise has widely documented cardioprotective effects, but the mechanisms behind these effects are still poorly understood. Here, we test the hypothesis that aerobic training lowers cardiovascular sympathetic responses to and speeds recovery from challenge. We conducted a randomized, controlled trial contrasting aerobic versus strength training on indices of cardiac (pre-ejection period, PEP) and vascular (low-frequency blood pressure variability, LF-BPV) sympathetic responses to and recovery from psychological and orthostatic challenge in 149 young, healthy, sedentary adults. Aerobic and strength training did not alter PEP or LF-BPV reactivity to or recovery from challenge. These findings, from a large randomized, controlled trial using an intent-to-treat design, show that moderate aerobic exercise training has no effect on PEP and LF-BPV reactivity to or recovery from psychological or orthostatic challenge. In healthy young adults, the cardioprotective effects of exercise training are unlikely to be mediated by changes in sympathetic activity.

Analyzing growth trajectories.

Growth trajectories play a central role in life course epidemiology, often providing fundamental indicators of prenatal or childhood development, as well as an array of potential determinants of adult health outcomes. Statistical methods for the analysis of growth trajectories have been widely studied, but many challenging problems remain. Repeated measurements of length, weight and head circumference, for example, may be available on most subjects in a study, but usually only sparse temporal sampling of such variables is feasible. It can thus be challenging to gain a detailed understanding of growth patterns, and smoothing techniques are inevitably needed. Moreover, the problem is exacerbated by the presence of large fluctuations in growth velocity during early infancy, and high variability between subjects. Existing approaches, however, can be inflexible because of a reliance on parametric models, require computationally intensive methods that are unsuitable for exploratory analyses, or are only capable of examining each variable separately. This article proposes some new nonparametric approaches to analyzing sparse data on growth trajectories, with flexibility and ease of implementation being key features. The methods are illustrated using data on participants in the Collaborative Perinatal Project.