Association of electrocardiographic left and right ventricular hypertrophy with physical fitness of military males: The CHIEF study.

AIM: To investigate the association of electrocardiographic (ECG) left and/or right ventricular hypertrophy (LVH and RVH) with physical fitness of military males. METHODS: We used a military cohort of 2587 males, who were on average 29.1 years of age, from the cardiorespiratory fitness and hospitalization events in armed forces (CHIEF) study in Taiwan for the analysis. Isolated ECG-LVH (n = 779) was diagnosed by either the Sokolow-Lyon or Cornell voltage criteria. Isolated ECG-RVH (n = 234) was defined by either the Sokolow-Lyon or Myers et al. voltage criteria. Combined ECG-LVH/RVH (n = 140) was defined as those who met the voltage criteria for both LVH and RVH. The other ECGs were defined as unaffected (n = 1434). Physical fitness was evaluated by the upper and lower 16% exercise performance (beyond 1-standard deviation) in 3000-meter run, 2-minute sit-ups, and 2-minute push-ups. All procedures were standardized and monitored by unified computerized scoring systems. A multiple logistic regression was used to determine the relationship. RESULTS: Compared with unaffected participants, those with an isolated ECG-LVH were tended to have a better 3000-meter run performance (odds ratio (OR) and 95% confidence intervals: 1.22 [0.99-1.50], p-value = 0.064) after adjusting for age, service specialty, body mass index, mean blood pressure, smoking status, alcohol intake, hemoglobin level, and exercise frequency. By contrast, those with an isolated ECG-RVH were tended to have a worse 2-minute sit-up performance (OR: 1.46 [0.99-2.16], p-value = 0.054). CONCLUSION: Military males with ECG-LVH and/or ECG-RVH compared to unaffected participants may have diverse exercise performances. However, these observations narrowly failed to reach statistical significance.

A stochastic regression model for general trend analysis of longitudinal continuous data.

A predictive continuous time model is developed for continuous panel data to assess the effect of time-varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein-Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first-order autoregressive regression model with time-varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi-likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi-likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow-up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.