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Xu J, Zhou J, Luo P, Mao D, Xu W, Nima Q, Cui C, Yang S, Ao L, Wu J, Wei J, Chen G, Li S, Guo Y, Zhang J, Liu Z, Zhao X. Associations of long-term exposure to ambient air pollution and physical activity with insomnia in Chinese adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 792:148197. [PMID: 34144234 DOI: 10.1016/j.scitotenv.2021.148197] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Air pollution is a potential environmental risk for sleep disturbance. However, the evidence is very limited in China. On the other hand, physical activity (PA) is a preventive behavior that can improve insomnia, but whether PA mitigates the negative impact of air pollution on insomnia is unknown. METHODS We obtained data from the baseline of China Multi-Ethnic Cohort (CMEC) survey, and examined the association between air pollution and insomnia, as well as PA's modification effect of on this association. We included 70,668 respondents and assessed insomnia by self-reported symptoms collected using electronic questionnaires. Using satellite data, we estimated the residence-specified, three-year average PM1, PM2.5, PM10 (particulate matter with aerodynamic diameters of ≤1 μm, ≤2.5 μm and 10 μm, respectively), O3 (ozone), and NO2 (nitrogen dioxide) concentrations. We established the associations between air pollutants and insomnia through logistic regression. We evaluated the modification impact of total and domain-specific PA (leisure, occupation, housework, transportation) by introducing an interaction term. RESULTS Positive associations were observed between long-term exposure to PM1, PM2.5, PM10, and O3 and insomnia symptoms, with ORs (95% CI) of 1.09 (1.03-1.16), 1.11 (1.07-1.15), 1.07 (1.05-1.10) and 1.15 (1.11-1.20), respectively. As total PA increased, the ORs of air pollution for insomnia tended to decrease and then rise. We observed varying modification effects of domain-specific PA. With an increase in leisure PA, the ORs for PM2.5 and PM10 significantly declined. However, increased ORs of air pollutants were related to insomnia among participants with higher levels of occupational and housework PA. CONCLUSION Long-term exposure to higher concentrations of PM1, PM2.5, PM10, and O3 increases the risk of insomnia symptoms. Moderate to high levels of leisure PA alleviate the harmful effects of air pollution on insomnia, while high levels of occupation and housework PA intensify such effects.
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Affiliation(s)
- Jiayue Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - Junmin Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - Peng Luo
- School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China.
| | - Deqiang Mao
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing 400042, China
| | - Wen Xu
- Yunnan Center for Disease Control and Prevention, Kunming 650022, China
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention CN, Lhasa 850000, China
| | | | - Shujuan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - Linjun Ao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China.
| | - Jialong Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - Jing Wei
- Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Juying Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China.
| | - Zhu Liu
- Chengdu Center for Disease Control and Prevention, Chengdu 610041, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China.
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Seet V, Abdin E, Asharani PV, Lee YY, Roystonn K, Wang P, Devi F, Cetty L, Teh WL, Verma S, Mok YM, Subramaniam M. Physical activity, sedentary behaviour and smoking status among psychiatric patients in Singapore - a cross-sectional study. BMC Psychiatry 2021; 21:110. [PMID: 33602151 PMCID: PMC7893878 DOI: 10.1186/s12888-021-03103-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 01/27/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Unhealthy behaviours such as physical inactivity, sedentary behaviour and smoking have been found to be more prevalent in people with psychiatric disorders than in the general population, leading to increased mortality risk. The present study seeks to identify correlates of physical activity and sedentary behaviour among psychiatric patients in Singapore, as well as investigate differences in their physical activity patterns by smoking status. METHODS Participants (n = 380) were recruited from a tertiary psychiatric hospital in Singapore as part of a study on the prevalence and correlates of smoking among psychiatric patients. Physical activity levels and sedentary behaviour were measured using the Global Physical Activity Questionnaire (GPAQ) and analysed based on GPAQ guidelines. Chi-square analyses were conducted to examine differences in physical activity by smoking status, and logistic regression analyses to yield sociodemographic correlates of meeting physical activity guidelines (as recommended by the World Health Organization) and sedentary behaviour. RESULTS Education was found to be significantly associated with meeting recommended physical activity levels, while age and marital status were significantly associated with excessive sedentary behaviour. Additionally, while no significant differences were found among current, former and non-smokers across all types of physical activity engagement levels, there was a high prevalence of inadequate physical activity (43.2%) and excessive sedentary behaviour (38.8%) among participants. CONCLUSION Given the high prevalence of inadequate physical activity and excessive sedentary behaviour among current, former and non-smokers with psychiatric disorders, programmes aimed at increasing physical activity and lowering sedentary behaviour levels should be integrated into targeted treatment plans to improve clinical outcomes.
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Affiliation(s)
- Vanessa Seet
- Research Division, Institute of Mental Health, Singapore, Singapore.
| | - Edimansyah Abdin
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - P. V. Asharani
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Ying Ying Lee
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Kumarasan Roystonn
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Peizhi Wang
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Fiona Devi
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Laxman Cetty
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Wen Lin Teh
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
| | - Swapna Verma
- grid.414752.10000 0004 0469 9592Early Psychosis Intervention Programme, Institute of Mental health, Singapore, Singapore
| | - Yee Ming Mok
- grid.414752.10000 0004 0469 9592Department of Mood and Anxiety, Institute of Mental Health, Singapore, Singapore
| | - Mythily Subramaniam
- grid.414752.10000 0004 0469 9592Research Division, Institute of Mental Health, Singapore, Singapore
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Holowatyj AN, Langston ME, Han Y, Viskochil R, Perea J, Cao Y, Rogers CR, Lieu CH, Moore JX. Community Health Behaviors and Geographic Variation in Early-Onset Colorectal Cancer Survival Among Women. Clin Transl Gastroenterol 2020; 11:e00266. [PMID: 33512797 PMCID: PMC7678794 DOI: 10.14309/ctg.0000000000000266] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 10/12/2020] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Despite overall reductions in colorectal cancer (CRC) morbidity and mortality, survival disparities by sex persist among young patients (age <50 years). Our study sought to quantify variance in early-onset CRC survival accounted for by individual/community-level characteristics among a population-based cohort of US women. METHODS Geographic hot spots-counties with high early-onset CRC mortality rates among women-were derived using 3 geospatial autocorrelation approaches with Centers for Disease Control and Prevention national mortality data. We identified women (age: 15-49 years) diagnosed with CRC from 1999 to 2016 in the National Institutes of Health/National Cancer Institute's Surveillance, Epidemiology, and End Results program. Patterns of community health behaviors by hot spot classification were assessed by Spearman correlation (ρ). Generalized R values were used to evaluate variance in survival attributed to individual/community-level features. RESULTS Approximately 1 in every 16 contiguous US counties identified as hot spots (191 of 3,108), and 52.9% of hot spot counties (n = 101) were located in the South. Among 28,790 women with early-onset CRC, 13.7% of cases (n = 3,954) resided in hot spot counties. Physical inactivity and fertility were community health behaviors that modestly correlated with hot spot residence among women with early-onset CRC (ρ = 0.21 and ρ = -0.23, respectively; P < 0.01). Together, individual/community-level features accounted for distinct variance patterns in early-onset CRC survival among women (hot spot counties: 33.8%; non-hot spot counties: 34.1%). DISCUSSION Individual/community-level features accounted for approximately one-third of variation in early-onset CRC survival among women and differed between hot spot vs non-hot spot counties. Understanding the impact of community health behaviors-particularly in regions with high early-onset CRC mortality rates-is critical for tailoring strategies to reduce early-onset CRC disparities.
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Affiliation(s)
- Andreana N. Holowatyj
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Marvin E. Langston
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Yunan Han
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Richard Viskochil
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, USA
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Jose Perea
- Fundacion Jimenez Diaz University Hospital, Madrid, Spain
| | - Yin Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
- Alvin J. Siteman Cancer Center, St. Louis, Missouri, USA
| | - Charles R. Rogers
- Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Christopher H. Lieu
- Division of Medical Oncology, University of Colorado Denver, Aurora, Colorado, USA
| | - Justin X. Moore
- Division of Epidemiology, Augusta University at the Medical College of Georgia, Augusta, Georgia, USA
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Merrill RM. Explaining the Inverse Association between Altitude and Obesity. J Obes 2020; 2020:1946723. [PMID: 32566273 PMCID: PMC7285248 DOI: 10.1155/2020/1946723] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 04/28/2020] [Accepted: 05/12/2020] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To better understand the inverse association between altitude and adult obesity. METHODS An ecological study design was used, involving 3,108 counties in the contiguous United States. Data were from several national sources, and assessment involved various statistical techniques, including multiple regression analysis. RESULTS Living in counties at higher altitude is associated with lower adult obesity. Compared with counties <500 meters, the percent of adult obesity decreases by 5.18% at 500-999 meters, 9.69% at 1,000-1,499 meters, 16.77% at 1,500-1,999 meters, 24.14% at 2,000-2,499 meters, and 35.28% at ≥2,500 meters. After adjusting for physical inactivity, smoking, and other variables, corresponding decreases in adult obesity with higher altitude groupings are 3.87%, 5.64%, 8.03%, 11.41%, and 17.54%, respectively. Various mechanisms are presented as possible explanations for the association between higher altitude and lower obesity. In addition, altitude may indirectly influence adult obesity, primarily through its relationship with physical inactivity and smoking. In an adjusted regression model, adult obesity was most strongly associated with physical inactivity followed by adult smoking and then altitude. Together they explain 39.04% of the variation in adult obesity. After accounting for these variables, sunlight, precipitation, ambient air temperature, education, income, food insecurity, limited access to healthy foods, race, sex, and rural living explain an additional 4.68% of the variation in adult obesity. CONCLUSIONS The inverse association between altitude and adult obesity remains significant after adjustment for several variables.
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Affiliation(s)
- Ray M. Merrill
- Department of Public Health, College of Life Sciences, Brigham Young University, Provo, USA
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