Correlation of Smoking Behavior with Body Mass Index (BMI) in Palm Oil Mill Workers

Authors

  • Yuziani Medical Faculty, Universitas Malikussaleh, North Aceh, Indonesia
  • Rizka Sofia Medical Faculty, Universitas Malikussaleh, North Aceh, Indonesia
  • Harvina Sawitri Medical Faculty, Universitas Malikussaleh, North Aceh, Indonesia

DOI:

https://doi.org/10.29103/micoms.v3i.185

Keywords:

BMI, Smokers, weight

Abstract

The Smokers tend to weigh less than never smokers, while successful quitting leads to an increase in body weight. Because smokers and non-smokers may differ in genetic and environmental family background, we analysed data from twin pairs in which the co-twins differed by their smoking behaviour to evaluate if the association between smoking and body mass index (BMI) remains after controlling for family background. The research will be carried out at the palm oil mill of PT. Syaukath Sejahtera, Bireuen Regency. Sampling was carried out by means of simple random sampling on selected subjects using a cross-sectional design. . The end result of this study is smoking is associated with lower BMI and smoking cessation with higher BMI.

References

Suaeb A., "Occupational health and safety (case study: cleaning windscreens)", Jakarta Universitas Gunadarma, (http://www.gunadarma.ac.id/library/articles/graduate/Artikel 30403013.pdf, accessed on 25 September 2013).

Sholihah Q., Anward HH.. "Textbook ergonomics and human factors (basic concepts)", First edition. Banjarmasin, P3A1 Lambung Mangkurat University in cooperation with Nusa Media Bandung, 2012 [3] C. Baier, J-P. Katoen, Principles of Model Checking, MIT Press, 2008.

Buchari, "The management of occupational health and personal protective equipment". Medan: Sumatera Utara University, 2007.

V. Forejt, M. Kwiatkowska, G. Norman, D. Parker, Automated verification techniques for probabilistic systems, in: M. Bernardo, V. Issarny (Eds.), Proceedings of the Formal Methods for Eternal Networked Software Systems (SFM), Springer, Berlin, Heidelberg, 2011, pp. 53–113. DOI: https://doi.org/10.1007/978-3-642-21455-4_3

GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016; 388(10053):1659- 724. Epub 2016/10/14. https://doi.org/10.1016/S0140-6736(16)31679-8 PMID: 27733284.

Smoking prevalence and attributable disease burden in 195 countries and territories, 1990-2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet. 2017. Epub 2017/04/10. https://doi.org/10.1016/S0140-6736(17)30819-x PMID: 28390697.

Trends in adult body mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016; 387(10026):1377-96. Epub 2016/04/27. https://doi.org/10.1016/S0140-6736(16)30054-X PMID: 27115820.

Tian J, Venn A, Otahal P, Gall S. The association between quitting smoking and weight gain: a systemic review and meta-analysis of prospective cohort studies. Obes Rev. 2015; 16(10):883-901. Epub 2015/ 06/27. https://doi.org/10.1111/obr.12304 PMID: 26114839.

Downloads

Published

2022-12-17