Fast and Medium Tempo Resistance Training with a Low Number of Repetitions in Trained Men: Effects on Maximal Strength and Power Output.

This study aimed to investigate the effects of high load fast and medium tempo back squats using a low number of repetitions on maximal strength and power output. Seventeen participants completed a countermovement jump test and 1-repetition maximum (1-RM) assessment before and after an eight-week intervention. All participants were randomly divided into a fast tempo (FAS: 1/0/1/0) and a medium tempo (MED: 2/0/2/0) resistance training (RT) group and performed three repetitions per set of a Smith back squat exercise with 85% 1-RM intensity. Maximal strength, jump height, peak power and force of the two groups were significantly improved (p < 0.05). In addition, peak velocity significantly increased after the intervention in the FAS group (p < 0.05), but not in the MED group (p > 0.05). A significant interaction effect between training groups was observed for jump height (F (1, 30) = 5.49, p = 0.026, η2 = 0.155). However, no significant group by time interaction effects were found between training groups for maximal strength (F (1, 30) = 0.11, p = 0.742, η2 = 0.004). Therefore, the two groups showed similar effects in maximal strength, but, compared with the MED group, FAS resistance training with low repetitions caused favorable adaptations in power output in trained men.

Comparison between 2,000 m and 3,000 m time trials to estimate the maximal aerobic speed for collegiate runners.

Considered to be a lesser resource burden, 2,000 and 3,000 m time trials (TTs) have been recognized as alternatives to accurately estimate the maximal aerobic speed (MAS) derived from laboratory-graded exercise testing (GXT). Previous studies have commonly used ordinary least squares linear regression and the Bland-Altman method to compare the agreement between MAS and TT performance. The agreement analysis aimed to identify the systematic bias between the results of the two methods, rather than to identify similarities. The model II regression technique (ordinary least product regression) is increasingly favored by researchers in the field of physiology. Thus, we aimed to 1) use the ordinary least product (OLP) and bootstrap methods to determine the agreement between the average speed of 2,000 m TT (S2000) and the average speed of 3,000 m TT (S3000) and 2) determine whether S2000 or S3000 can accurately approximate the GXT-derived MAS. It is used as an alternative to estimate the MAS and prescribe training intensity. Thirty-five Beijing Sport University recreational male runners completed an MAS test in laboratory settings, followed by 2,000 and 3,000 m TTs randomly, with a 7-day interval. OLP regression was used to analyze the agreement between the GXT-derived MAS and S2000 and S3000. The bootstrap method was used to calibrate the equations. Differences between the GXT-derived MAS and S2000 and S3000 were compared using a one-way repeated measure analysis of variance (ANOVA) and a post hoc analysis (Bonferroni). The significance level was p < 0.05. The results showed that before calibration, the 95% CI of the OLP regression intercept and slope between the GXT-derived MAS and S2000 and S3000 did not include 0 and 1.00, respectively. These values, after calibration, included 0 and 1.00, respectively. Post hoc analysis revealed that S3000 closely approximated the GXT-derived MAS and underestimated 0.46% (0.06 km h-1 and p > 0.05), and S2000 overestimated 5.49% (0.81 km h-1 and p < 0.05) by the MAS. It concluded that the 3,000 m TT performance approximated the GXT-derived MAS compared to the 2,000 m TT performance. There exist fixed bias and proportional bias between the GXT-derived MAS and TT performance. More attention should be applied to calibration when using the TT performance to estimate the MAS.

Environmental Health Indicators for China: Data Resources for Chinese Environmental Public Health Tracking.

Many developed countries use environmental public health tracking to gain a better understanding of the link between environmental hazards and public health. To respond to complicated environmental health issues, the National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), has begun to build a Chinese Environmental Public Health Tracking (CEPHT) system. On behalf of the CEPHT, authors provide insight into the CEPHT's development, current status, and future plans. In the initial stage of CEPHT, an indicator framework linking environment and public health that included a list of publicly available data sources regarding environmental hazards, public health outcomes, and risk factors in China was developed. An analysis of data availability, along with a comparison between CEPHT's indicator system and other tracking networks, revealed the existence of barriers and gaps in data integration that affect China's ability to track environmental public health. The lack of access to data, combined with inadequate data quality, has led to difficulties linking environmental hazards to their effects on public health. Current CEPHT efforts will help integrate environmental factors and exposure data with public health outcomes. For the near future, CEPHT plans to focus on increasing collaboration among data tracking agencies, improving data quality, and expanding proper data sharing. https://doi.org/10.1289/EHP4319.

A random forest model to predict heatstroke occurrence for heatwave in China.

Extreme heat events have recently become more frequent and represent an increasing risk of damage to public health. However, the existing prediction of heatwave related health effects has limited representativeness and verification. Our study addressed the prediction of heatstroke occurrences based on three years' data of typical cities of hot temperature in China, and examined the importance ranks of model parameters including meteorological and socioeconomic status (SES) factors. The results show that meteorological factors contributed the most to model estimation of the parameters evaluated, and SES parameters, such as the search index, were also important indicators of heatstroke prediction. The model had a satisfying performance compared to traditional linear regression models. The model established in our study can be further applied to extreme weather-related impact research and reduce economic loss due to public health expenses.

Fine Particle Constituents and Mortality: A Time-Series Study in Beijing, China.

There is a rising concern that fine particle (PM2.5) compositions may play an important role in explaining PM2.5-related mortality risks. However, PM2.5 constituents responsible for these risks have not yet been determined. To date, there are few PM2.5 constituent health studies in developing countries. We adopted a time-series approach, using generalized linear regression models to examine associations between short-term exposure to PM2.5 constituents and mortality. We analyzed data stratified by sex and by age groups (<65, 65-74, and >74) from 2013 to 2015 in Beijing, China. We also investigated seasonal patterns of such associations. For a 0 day lag, interquartile range increases in potassium, calcium, magnesium, and organic carbon were associated with 0.51% (95% CI: 0.17-0.85), 2.07% (95% CI: 0.71-3.44), 0.26% (95% CI: 0.08-0.44), and 2.65% (95% CI: 0.18-5.18) increases in respiratory mortality, and sulfate with a 1.57% (95% CI: 0.04-3.12) increase in cardiovascular mortality. In the season-stratified analysis, the association of some constituents (potassium, calcium, magnesium, nitrate, sulfate, and organic carbon) with respiratory mortality appeared to be stronger in cold seasons than in warm seasons. Older adults (65-74) may be susceptible to certain compositions. Our findings provide evidence that link PM2.5 constituents with mortality and suggest that adverse effects vary among constituents in different seasons.

Modeling of residential indoor PM2.5 exposure in 37 counties in China.

It is critical to estimate the exposure to indoor air pollution of residents spending most of their time in such microenvironments. However, the understanding regarding PM2.5 exposure in residential indoor environments is very limited. In this study, we collected participants' basic information and time-activity patterns, as well as details of other factors related to indoor air pollution exposure, through questionnaires presented to a large population in 37 counties of China. Continuous monitoring of ambient PM2.5 concentrations was performed using an environmental fixed-site monitoring network. Residential indoor PM2.5 concentrations were predicted using a mass balance model based on the data obtained. Evaluation of continuous daily average residential indoor PM2.5 exposure doses for large populations during winter revealed concentrations ranged from 67 to 195 µg/m3. Finally, differences in residential indoor PM2.5 exposure between northern and southern China were investigated. The results suggested that residential indoor PM2.5 concentrations in northern China, associated with heating, were higher than in the south. The established model could be important for improved understanding of human exposure to indoor PM2.5 air pollution.