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      KCI등재 SCOPUS SCIE

      Air Pollution Has a Significant Negative Impact on Intentional Efforts to Lose Weight: A Global Scale Analysis

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      https://www.riss.kr/link?id=A105911730

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      다국어 초록 (Multilingual Abstract)

      Background: Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.
      Methods: Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.
      Results: Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m2 in average (95% confidence interval, –2.26 to –2.04). From a multilevel model, PM10 (β=0.04, P=0.002) and PM2.5 (β=0.08, P<0.001) had a significant negative effect on weight loss when collected per year. The results were confirmed with the validation (βAQI*time=1.5×10-5; P<0.001) by mixed effects model.
      Conclusion: This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.
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      Background: Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validate...

      Background: Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.
      Methods: Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.
      Results: Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m2 in average (95% confidence interval, –2.26 to –2.04). From a multilevel model, PM10 (β=0.04, P=0.002) and PM2.5 (β=0.08, P<0.001) had a significant negative effect on weight loss when collected per year. The results were confirmed with the validation (βAQI*time=1.5×10-5; P<0.001) by mixed effects model.
      Conclusion: This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.

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      참고문헌 (Reference)

      1 Elfhag K, "Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain" 6 : 67-85, 2005

      2 Klenk J, "Walking on sunshine: effect of weather conditions on physical activity in older people" 66 : 474-476, 2012

      3 Ridgers ND, "Too hot to move? Objectively assessed seasonal changes in Australian children's physical activity" 12 : 77-, 2015

      4 Tucker P, "The effect of season and weather on physical activity: a systematic review" 121 : 909-922, 2007

      5 Kimberley L. Way, "The Effect of Regular Exercise on Insulin Sensitivity in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis" 대한당뇨병학회 40 (40): 253-271, 2016

      6 Chin SO, "Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity" 6 : 34563-, 2016

      7 Patel R, "Smartphone apps for weight loss and smoking cessation: quality ranking of 120 apps" 128 : 73-76, 2015

      8 O'Hare C, "Seasonal and meteorological associations with depressive symptoms in older adults: a geo-epidemiological study" 191 : 172-179, 2016

      9 Peng RD, "Seasonal analyses of air pollution and mortality in 100 US cities" 161 : 585-594, 2005

      10 Mokdad AH, "Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001" 289 : 76-79, 2003

      1 Elfhag K, "Who succeeds in maintaining weight loss? A conceptual review of factors associated with weight loss maintenance and weight regain" 6 : 67-85, 2005

      2 Klenk J, "Walking on sunshine: effect of weather conditions on physical activity in older people" 66 : 474-476, 2012

      3 Ridgers ND, "Too hot to move? Objectively assessed seasonal changes in Australian children's physical activity" 12 : 77-, 2015

      4 Tucker P, "The effect of season and weather on physical activity: a systematic review" 121 : 909-922, 2007

      5 Kimberley L. Way, "The Effect of Regular Exercise on Insulin Sensitivity in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis" 대한당뇨병학회 40 (40): 253-271, 2016

      6 Chin SO, "Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity" 6 : 34563-, 2016

      7 Patel R, "Smartphone apps for weight loss and smoking cessation: quality ranking of 120 apps" 128 : 73-76, 2015

      8 O'Hare C, "Seasonal and meteorological associations with depressive symptoms in older adults: a geo-epidemiological study" 191 : 172-179, 2016

      9 Peng RD, "Seasonal analyses of air pollution and mortality in 100 US cities" 161 : 585-594, 2005

      10 Mokdad AH, "Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001" 289 : 76-79, 2003

      11 Ng M, "Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013" 384 : 766-781, 2014

      12 Davis B, "Fifty years of fat: news coverage of trends that predate obesity prevalence" 15 : 629-, 2015

      13 Pagoto S, "Evidence-based strategies in weight-loss mobile apps" 45 : 576-582, 2013

      14 Ustulin M, "Effects of climatic variables on weight loss: a global analysis" 7 : 40708-, 2017

      15 Jacob DJ, "Effect of climate change on air quality" 43 : 51-63, 2009

      16 Wei Y, "Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: findings from a natural experiment in Beijing" 30 : 2115-2122, 2016

      17 김소헌, "Brown Fat and Browning for the Treatment of Obesity and Related Metabolic Disorders" 대한당뇨병학회 40 (40): 12-21, 2016

      18 Jerrett M, "Automobile traffic around the home and attained body mass index: a longitudinal cohort study of children aged 10-18 years" 50 (50): S50-S58, 2010

      19 Lewis LK, "At the mercy of the gods: associations between weather, physical activity, and sedentary time in children" 28 : 152-163, 2016

      20 Rundle A, "Association of childhood obesity with maternal exposure to ambient air polycyclic aromatic hydrocarbons during pregnancy" 175 : 1163-1172, 2012

      21 손윤정, "Association of Waist-Height Ratio with Diabetes Risk: A 4-Year Longitudinal Retrospective Study" 대한내분비학회 31 (31): 127-133, 2016

      22 AirNow, "Air quality index (AQI) basics"

      23 World Health Organization, "Air pollution"

      24 The Weather Company, "A weather API designed for developers"

      25 Patterson RE, "A comprehensive examination of health conditions associated with obesity in older adults" 27 : 385-390, 2004

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2017-12-01 평가 SCIE 등재 (기타) KCI등재
      2011-05-30 학술지명변경 한글명 : KOREAN DIABETES JOURNAL -> Diabetes and Metabolism Journal KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2005-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2004-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2002-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2000-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.55 0.55 0.55
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.49 0.5 1.018 0.21
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