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Highlights • Decreased systolic blood pressure and heart rate are observed with time. • Increased vigor and decreased negative emotions are observed in forest settings. • Increased fatigue and decreased self-esteem were reported in urban settings. • Greater benefits were found when immersing in forest settings. Abstract Previous studies used pictures or movies to investigate the impact of virtual nature environments on physiological and psychological health, providing inferior immersive experiences. The latest virtual reality (VR), launched in 2016, allows users to be fully immersed in simulated surroundings. However, the effects of the simulated environments created by the latest VR technology on health were not yet known. This study employed both cross-over and pretest-posttest design to examine the influence of forest and urban VR environments on restoration (N = 30). Both physiological and psychological responses were collected. The results show that participants' systolic blood pressure and heart rate decreased with time, regardless of environmental differences. About psychological responses, an increased level of fatigue and a decreased level of self-esteem were reported in simulated urban environments. In contrast, an increased level of vigor and a decreased level of negative emotions (i.e., confusion, fatigue, anger-hostility, tension, and depression) were observed in simulated forest environments. In sum, greater benefits were found when immersing in forest settings. The latest VR technology can serve as an alternative way to access nature environments for restoration. [ABSTRACT FROM AUTHOR]

Objective: This study examined the effect of two e-cigarette warning labels on college students' perceived advantages and risks of e-cigarette use, as well as students' intentions to use e-cigarettes. The company-produced e-cigarette warning label carries abundant information with small font size while the governmental warning label has only two sentences presented in large font size. The effect of both labels have not yet been examined and verified.Methods: Data were collected in October 2015 from college students at a Midwestern university. A pretest-posttest design was employed with 338 students exposed to the warning label proposed by the FDA and 328 students exposed to the label created by e-cigarette companies. Structural equation modeling analysis was implemented to examine the effect of warning labels with the analytical model grounded in the Theory of Planned Behavior.Results: Findings showed that college students' perceived advantages of e-cigarette use were positively related to their intentions to use e-cigarettes, while perceived risks were negatively associated with their intentions. When comparing two labels, the governmental label was found to reduce college students' intentions to use e-cigarettes via increasing perceived risks of e-cigarette use (β=0.10, p<0.05), however, not via decreasing perceived advantages of e-cigarette use. The warning label currently used by e-cigarette companies showed no influence on beliefs about or intentions to use e-cigarettes.Conclusions: The warning label proposed by the FDA is more effective than that created by e-cigarette companies, however, has room for improvement to make a greater impact on e-cigarette use intention. [ABSTRACT FROM AUTHOR]

Objective: This study investigated characteristics of potential and current e-cigarette users based on four different levels of use acceptability along with the determinants that promote e-cigarette use acceptability among college students.Methods: College students (N=1198) aged 18-25years at a Midwestern university were surveyed in September-October 2015. Participants were categorized into four groups based on e-cigarette use acceptability adapted from the Diffusion of Innovation Theory (i.e., laggards, late majority, early majority, and adopters). Multinomial logistic regressions and Heckman two-step selection procedures were performed to examine the determinants that promote e-cigarette use acceptability.Results: Approximately 40% of the participants reported ever using e-cigarettes. E-cigarette adopters agreed that e-cigarettes are more socially acceptable than traditional tobacco cigarettes (relative risk ratio [RRR]=1.43, p<0.01). Unique features such as flavor appeared to encourage college students' experimentation with e-cigarettes (ps<0.05). Participants mentioned positive sensory experiences as a reason for e-cigarette use (ps<0.01) and reported caring about their appearance more than their health (ps<0.05) when asked about possible outcomes of e-cigarette use.Conclusions: Study findings indicate a possible explosive increase in e-cigarette experimentation or use among college students. Unique features of e-cigarettes such as flavor and USB rechargeability appear to be strong factors making e-cigarettes more acceptable and appealing to young adults regardless of their smoking status. Concerted efforts should be initiated to effectively counter or eliminate attractive features that lure young adults to experiment with e-cigarettes. [ABSTRACT FROM AUTHOR]

There have been many studies associating various aspects of greenspaces with physical health. Very few of these investigations are available for developing countries such as Indonesia. Our study focused on evaluating the association between greenspace and the incidence rate of non-communicable diseases (NCDs) in terms of ischemic heart disease (IHD), diabetes mellitus (DM), rheumatoid arthritis (RA), and chronic kidney disease (CKD). Greenspace was presented by satellite-derived normalized difference vegetation index (NDVI) and forest-related green cover datasets to define exposures to the resolution of 250-m. The Institute for Health Metrics and Evaluation provided age and gender incident data of NCDs at the province level. A generalized additive mixed model coupled with sensitivity test was used to evaluate the exposure-outcome association. Stratified analyses were also employed. After adjusting for covariates, there was a significant negative association for incidence of NCDs and greenspace. We found that an interquartile unit increase of NDVI, and a percentage of forest were closely related to a decrease in the risk of NCDs by 0.3–9.4% and 0.6–6.2%, respectively. Stratified by exposure level, a greater effect of greenspace on reducing NCDs risk occurred in high exposure areas. Considering the socioeconomic factors, greenspace could influence on reducing NCD risks in high urbanization, low-high poverty, and low-high literacy areas. An increment unit of greenspace was associated with a decreased risk of NCDs. This study underscores important health benefits associated with exposures to nature supporting efforts to preserve greenspaces in Indonesia. • A study assessing the relationship between greenspace and physical health diseases. • High levels of greenspace were associated with 0.3%−9.4% lower NCD rates. • Proximity to greenspace may encourage physical work, which in turn reduce NCD risks. • Greenspace may serve as free healthcare in supporting quality of life in Indonesia. • The link of greenspace and NCD remained after adjusting for socioeconomic factors. [ABSTRACT FROM AUTHOR]

For indoor air modelling, difficulties in collecting indoor parameters including life activity patterns and building characteristics are dilemmas when conducting a large-area study. Land-use/land cover information which is easier to obtain could represent as surrogates of emission sources for assessing indoor air quality. Moreover, low-cost sensors and machine learning provide a better way to enhance model accuracy. This study proposed an alternative estimation approach to assess daily PM 2.5 concentration for indoor environments of schools in a large area by integrating low-cost sensors, land-use/land cover predictors, and machine learning-based modelling approaches. Indoor PM 2.5 data was collected from 145 indoor AirBox sensors in Kaohsiung and Pingtung Counties of Taiwan. Geospatial predictors were extracted from the circular buffers surrounding each AirBox sensor. Spearman correlation analysis and stepwise variable selection procedures were performed to select variables for land-use regression (LUR) and integrated with XGBoost, Random Forest (RF), and LGBM machine learning models. The results revealed that outdoor PM 2.5 and distance to the nearest thermal power plant were the main determinants of indoor estimation variations, when there were no indoor sources. When incorporating machine learning, the R2 increased from 0.59 for LUR to 0.85 for LUR-XGBoost while the RMSE decreased from 8.63 to 5.27 μg/m3, which performed better than both LUR-RF and LUR-LGBM. This study demonstrates the value of the proposed alternative approach by incorporating data from a low-cost sensor with LUR model and machine learning algorithm in estimating the spatiotemporal variability of indoor PM 2.5 for a large area. [Display omitted] • An alternative approach was proposed to estimate indoor PM 2.5 for school campuses. • Low-cost sensor and land-use/land cover were incorporated using machine learning. • Important land-use/land cover variables affecting indoor PM 2.5 were identified. • The R2 of indoor LUR-XGBoost model was 0.85 with RMSE 5.27 μg/m3. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM 2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM 2.5. Daily average PM 2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM 2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM 2.5 exposures. [Display omitted] • Estimating long-term daily PM 2.5 concentration with machine learning models. • Land-use patterns were included in machine learning models by using land-use regression. • Explanatory power of daily PM 2.5 concentration was increased from 0.58 to 0.94. • XGboost outperformed random forest and deep neural network algorithms. [ABSTRACT FROM AUTHOR]

• Participants expressed lower fatigue and depression after viewing nature scenes. • Virtual nature scenes rated higher in perceived restorativeness. • Virtual natural environments were described as appeasing and relaxing. • VR natural settings can be a restorative experience for middle-aged and elderly group. Previous studies have demonstrated health benefits result from exposure to natural environments. Virtual reality (VR) may offer an alternative to actual outdoor immersion by generating a simulated health-promoting environment. Given that health issues are more prevalent in older adults, this study investigated the restorative effects of virtual natural settings on middle-aged and elderly adults. A cross-over pretest-posttest design was used to measure changes in participants' mood levels, physiological and psychological responses, and attentional measures of cognitive functioning (N = 34). Semi-structured interviews after the VR experiences were conducted to evaluate participants' experiences. Physiological responses to VR did not differ between virtual natural and urban settings. In contrast, participants expressed more positive feelings and lower levels of fatigue and depression after viewing virtual nature settings than after viewing virtual urban settings. Virtual nature settings were also rated as more restorative than virtual urban settings. Further, participants described virtual natural settings as appeasing and relaxing, so much so that they were motivated to travel outdoors to experience the settings shown in VR. Our findings provide additional evidence that viewing simulated natural settings in VR can be beneficial for this population. Perhaps the most promising finding is that VR may motivate older adults to experience nature outdoors, thus promoting synergistic benefits first during virtual exposure and then during actual exposure. [ABSTRACT FROM AUTHOR]

It is likely that exposure surrogates from monitoring stations with various limitations are not sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal resolution of air pollution modelling approaches must be improved in order to achieve more accurate estimates. If not, the exposure assessments will not be applicable in future health risk assessments. To deal with this challenge, this study featured Land-Use Regression (LUR) models that use machine learning to assess the spatial-temporal variability of Nitrogen Dioxide (NO 2). Daily average NO 2 data was collected from 70 fixed air quality monitoring stations, belonging to the Taiwanese EPA, on the main island of Taiwan. Around 0.41 million observations from 2000 to 2016 were used for the analysis. Several datasets were employed to determine spatial predictor variables, including the EPA environmental resources dataset, the meteorological dataset, the land-use inventory, the landmark dataset, the digital road network map, the digital terrain model, MODIS Normalized Difference Vegetation Index database, and the power plant distribution dataset. Regarding analyses, conventional LUR and Hybrid Kriging-LUR were performed first to identify important predictor variables. A Deep Neural Network, Random Forest, and XGBoost algorithms were then used to fit the prediction model based on the variables selected by the LUR models. Lastly, data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were applied to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 65% and 78%, respectively, of NO 2 variation. When the XGBoost algorithm was further incorporated in LUR and hybrid-LUR, the explanatory power increased to 84% and 91%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed all other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm to estimate the spatial-temporal variability of NO 2 exposure. For practical application, the associations of specific land-use/land cover types selected in the final model can be applied in land-use management and in planning emission reduction strategies. [Display omitted] • Estimating long-term daily NO 2 concentration with machine learning models. • Land-use patterns were included in machine learning models by using land-use regression. • The most contributed predictors were identified by stepwise variable selection. • Explanatory power of daily NO 2 concentration was increased from 0.65 to 0.91. • XGboost outperformed RF and DNN machine learning algorithms. Capsule: The explanatory power of Hybrid Kriging-LUR coupled with XGBoost algorithm on daily NO 2 variations reached 91% and outperformed all other integrated methods. [ABSTRACT FROM AUTHOR]

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.e., disability-adjusted life year loss and income) were used to represent the benefits. The incidence rate of bipolar disorder was obtained from Taiwan's National Health Insurance Research Database. Normalized different vegetation index (NDVI) was measured as a proxy for the greenspace availability. A generalized additive mixed model coupled with a sensitivity test were applied to evaluate the statistical association. The prevented fraction for the population (PFP) was then applied to develop a scenario for quantifying benefit. The result showed a significant negative association between greenspace and bipolar disorder in Taiwan. Compared to low greenspace, areas with medium and high greenspace may reduce the bipolar risk by 21% (RR = 0.79; 95% CI = 0.76–0.83) and 51% (RR = 0.49; 95% CI = 0.45–0.53). Calculating benefits, we found that the development of a scenario by increasing greenspace adjusted for availability indicator in township categorized as low greenspace could save in DALY loss due to bipolar disorder up to10.97% and increase in income up to 11.04% from the current situation. Lastly, this was the first study in Asia-Pacific to apply a customized greenspace increment scenario to quantify the benefits to a particular health burden such as bipolar disorder. • Greenspace has a significant association with bipolar disorder. • Increased greenspace is related to a reduced risk of bipolar disorder in Taiwan. • 50 of 348 townships in Taiwan did not meet the greenspace availability standard. • Benefits in terms of savings DALY loss and increasing income were estimated. • Greenspace can save DALY loss up to 10.97% and increase in income up to 11.04%. [ABSTRACT FROM AUTHOR]