Dataset Information

Evaluating the influential factors for life preserver donning tests.

ABSTRACT: Life preservers often play a vital role in ensuring passenger safety in water-related accidents, while the difficulty of donning life preservers has been repeatedly proved even in a donning test. To evaluate the influencing factors for life preserver donning tests, 109 college students and 42 villagers were chosen as subjects. A total of fourteen variables with seven categorical variables and seven continuous variables were considered as potential influencing factors. T-test and one-way analysis of variance (ANOVA, for three or more categories) were used to judge whether grouping in categorical variables had a significant effect on the donning performance. Then all variables were offered into the stepwise linear regression (SLR) to evaluate the influential factors for life preserver donning tests. Results showed that four of fourteen variables, including gender, instruction condition, age group, and tool test time (representing the subject's flexibility), had a significant effect on the donning performance. To evaluate the relationship between the donning performance and influencing factors, models of the retrieving time, the opening time, and the donning time were built based on the SLR analysis. The paper also highlights recommendations for modification of the donning test procedure, which helps to improve the validation and reliability of life preserver donning tests.

SUBMITTER: Yang R

PROVIDER: S-EPMC7870007 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluating the influential factors for life preserver donning tests.

Yang Ruiliang R Wu Zijiang Z Qian Xiaoming X

PloS one 20210208 2

Life preservers often play a vital role in ensuring passenger safety in water-related accidents, while the difficulty of donning life preservers has been repeatedly proved even in a donning test. To evaluate the influencing factors for life preserver donning tests, 109 college students and 42 villagers were chosen as subjects. A total of fourteen variables with seven categorical variables and seven continuous variables were considered as potential influencing factors. T-test and one-way analysis ...[more]

PMID: 33556145

Similar Datasets

Project description:IntroductionStudies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association.MethodsIn addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF).ResultsAll outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF.DiscussionIn summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Project description:BackgroundEpidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability.MethodsWe obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight.ResultsHigher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability.ConclusionsOur Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.

Dataset Information

Evaluating the influential factors for life preserver donning tests.

Publications

Evaluating the influential factors for life preserver donning tests.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets