“…This factor, which is comprised of 4 items, assesses deep breathing and clean atmospheric environments. Brunt et al's study [27] showed that air pollution is significantly and positively correlated with respiratory health. Therefore, they recommended that it is important to obtain an air quality index to assess the health status of vulnerable individuals.…”
Objective: The aim of the present study was to develop a valid and reliable scale that measures the healthy life styles among young adults. Design: A methodological study design was employed to develop and validate the Healthy Lifestyle Screening Tool (HLST). Methods: The validity and reliability of the HLST were established in accordance with DeVellis' 8 steps guideline for tool development. The question items were generated based on literature reviews and interviews, which were then classified into 12 categories. The HLST was administered to 272 students attending a Korean university. The reliability was tested using Cronbach's alpha. The validity of the scale was examined with the mean inter-item correlations (MIIC) and factor analysis, and was also examined for content validity by experts. Results: The reliability of the HLST was found to be acceptable, as indicated by a Cronbach's alpha of 0.71. In the validity test, items with less than 80% "agreement" ratings on the content validity index by experts were revised. The MIIC values were greater than 0.25. A factor analysis of 36 items extracted 9 factors (i.e., four items per factor), which together explained 50.4% of the variance. The HLST consists of 36 items that measure 9 factors based on a 4-point Likert rating scale, with 4 items per factor, as follows: sunlight, water, air, rest, exercise, nutrition, temperance, trust, and general physical condition. High scores on the HLST are indicative of a healthy lifestyle (HL). Conclusions: The HLST is a valid and reliable scale that can be used to measure HL among young adults. Identification of HL by using the HLST can provide guidance to integrated therapeutic approaches along with conventional physical therapy.
“…This factor, which is comprised of 4 items, assesses deep breathing and clean atmospheric environments. Brunt et al's study [27] showed that air pollution is significantly and positively correlated with respiratory health. Therefore, they recommended that it is important to obtain an air quality index to assess the health status of vulnerable individuals.…”
Objective: The aim of the present study was to develop a valid and reliable scale that measures the healthy life styles among young adults. Design: A methodological study design was employed to develop and validate the Healthy Lifestyle Screening Tool (HLST). Methods: The validity and reliability of the HLST were established in accordance with DeVellis' 8 steps guideline for tool development. The question items were generated based on literature reviews and interviews, which were then classified into 12 categories. The HLST was administered to 272 students attending a Korean university. The reliability was tested using Cronbach's alpha. The validity of the scale was examined with the mean inter-item correlations (MIIC) and factor analysis, and was also examined for content validity by experts. Results: The reliability of the HLST was found to be acceptable, as indicated by a Cronbach's alpha of 0.71. In the validity test, items with less than 80% "agreement" ratings on the content validity index by experts were revised. The MIIC values were greater than 0.25. A factor analysis of 36 items extracted 9 factors (i.e., four items per factor), which together explained 50.4% of the variance. The HLST consists of 36 items that measure 9 factors based on a 4-point Likert rating scale, with 4 items per factor, as follows: sunlight, water, air, rest, exercise, nutrition, temperance, trust, and general physical condition. High scores on the HLST are indicative of a healthy lifestyle (HL). Conclusions: The HLST is a valid and reliable scale that can be used to measure HL among young adults. Identification of HL by using the HLST can provide guidance to integrated therapeutic approaches along with conventional physical therapy.
“…Previous U.S. studies have found that lower income households are more often located in areas with higher air pollution (Clark et al, 2014;Grineski et al, 2007; Morello-Frosch, Pastor, & Sadd, 2001). Similar results are found in Canada (Buzzelli & Jerrett, 2003;Carrier, Apparicio, Kestens, et al, 2016;Carrier et al, 2014b;Jerrett et al, 2007;Pinault et al, 2016), and in Europe (Briggs et al, 2008;Brunt et al, 2016;Havard et al, 2009).…”
Section: Introductionsupporting
confidence: 78%
“…Satellite data can also be used with LUR to generate concentrations surfaces over a larger geographical area. This was done for PM2.5 and NO2 in Massachusetts (Rosofsky, Levy, Zanobetti, Janulewicz, & Fabian, 2018); NO2 across Western Europe (Temam et al, 2017); NO2, PM10, and PM2.5 in Wales (Brunt et al, 2016); PM2.5 across Canada (Pinault, van Donkelaar, & Martin, 2017), and for NO2 across the United States (Clark et al, 2014;Clark, Millet, & Marshall, 2017).…”
Section: Methodologies For Assessing Air Pollutionmentioning
confidence: 99%
“…To measure environmental equity, many studies focus only on populations that live in near proximity to monitoring locations, and interpolate concentrations based on measurements at specific locations (Bell & Ebisu, 2012;Miranda et al, 2011). In other cases, studies use land-use regression (LUR) models to provide concentrations at a refined spatial resolution (Brunt et al, 2016;Clark et al, 2014Clark et al, , 2017Rosofsky et al, 2018;Temam et al, 2017). Other studies rely on point-source emissions, generating concentrations through source-receptor matrices , or through the use of atmospheric dispersion models (Martenies et al, 2017;Poorfakhraei et al, 2017;Pratt et al, 2015;Tayarani et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…These studies require SES and pollution datasets at a refined spatial scale. Many studies rely on land-use regression models (Brunt et al, 2016;Clark et al, 2014;Rosofsky et al, 2018;Temam et al, 2017) or atmospheric dispersion models (Martenies et al, 2017;Poorfakhraei et al, 2017;Pratt et al, 2015;Tayarani et al, 2016) to provide the necessary spatial resolution, while a small number of papers model concentrations using Chemical Transport Models (CTMs) (Fann et al, 2011;Marshall et al, 2014;Nguyen & Marshall, 2018). The benefit of using a CTM is the flexibility to change parameters and model future scenarios.…”
The field of environmental equity investigates how environmental risk factors such as air pollution are associated with socioeconomic status (SES). This thesis examines current levels of inequity across income groups of the health risk caused by fine particulate matter (PM2.5) air pollution, in New York City (NYC) and surrounding areas, and identifies emission control measures that can improve equity in this region.
There are growing concerns about the impact of pollution on maternal and infant health. Despite an extensive correlational literature, observational studies which adopt methods that seek to address potential biases due to unmeasured confounders draw mixed conclusions. Using a population database of births in Northern Ireland (NI) linked to localized geographic information on pollution in mothers' postcodes (zipcodes) of residence during pregnancy, we examine whether prenatal exposure to PM2.5 is associated with a comprehensive range of birth outcomes, including placental health. Overall, we find little evidence that particulate matter is related to infant outcomes at the pollution levels experienced in NI, once we implement a mother fixed effects approach that accounts for time‐invariant factors. This contrasts with strong associations in models that adjust for observed confounders but without fixed effects. While reducing ambient air pollution remains an urgent public health priority globally, our results imply that further improvements in short‐run levels of prenatal PM2.5 exposure in a relatively low‐pollution, higher‐income country context, are unlikely to impact on birth outcomes at the population level.
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