https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43720 Wed 28 Sep 2022 10:29:43 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38560 Wed 02 Aug 2023 11:51:56 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46311 Tue 15 Nov 2022 11:17:49 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39885 2), nitric oxide (NO), nitrogen dioxide (NO₂), and NOx (sum of NO and NO₂). Forty-six air monitoring stations were grouped into 6 rural and urban regional areas. Linear regression models examined pollution levels in association with rural and urban regions and meteorological covariates. Results: Findings show that daily mean pollutant levels in the rural Upper Hunter were the highest of all regions, and were significantly higher than metropolitan Sydney, with and without control for weather conditions, for every pollutant. For example, daily mean PM2.5 was 8.64 µg/m³ in the rural Upper Hunter, compared to 7.23 μg/m³ in metropolitan Sydney. Conclusions: Results highlight the need to consider both urban and rural sources of pollution in air quality studies, and appropriate policy steps to address likely rural air pollution from coal mining.]]> Thu 21 Jul 2022 09:48:20 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35868 Thu 14 Apr 2022 11:01:33 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38451 Mon 29 Jan 2024 17:57:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40735 Mon 18 Jul 2022 12:51:18 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:46119 N = 8,177) were followed up every 2 to 3 years to ages 37 to 42 using the Australian Longitudinal Study on Women's Health. A latent class analysis identified obesity-risk classes based on time-dependent measures of income, education, physical activity, sleep quality, dietary behavior, depression, stressful events, and social functioning. Cox proportional hazards regression models examined associations between incident obesity and latent classes, controlling for covariates. Results: Four latent classes were identified, including a lower-risk referent class and classes characterized by poor exercise and diet, stress and low income, and multiple intermediate-level risks. Compared with the referent, all three risk classes had significantly higher obesity risk, with the highest risk in the stress and low-income group (hazard ratio = 2.22; 95% CI: 1.92-2.56). Individual risks associated with obesity included lower education, stressful life events, and lower vigorous physical activity. Conclusions: Young women without baseline obesity were at risk of developing obesity when they experienced co-occurring behavioral, socioeconomic, and psychosocial risks. Both latent classes and individual risk indicators offer insights into prevention strategies.]]> Fri 11 Nov 2022 15:01:11 AEDT ]]>