Shift Work and Obesity Risk—Are There Sex Differences?

Smith, Kevin L.; Danyluk, Alexandria; Munir, Sanah S.; Covassin, Naima

doi:10.1007/s11892-022-01474-z

Cited by 3 publications

(1 citation statement)

References 94 publications

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“…Shift work systems have been recognized as significant contributors to the heightened risk of overweight and obesity in healthcare professionals ( 4 , 5 ). Studies have shown that shift workers are more than twice as likely to have a BMI of ≥25 compared to those in traditional work settings ( 6 ), with an increased duration of shift work correlating with a higher risk of being overweight or obese ( 7 , 8 ). A large-scale epidemiological study demonstrated that every 5 years of rotating night shifts is associated with a 0.17 unit increase in BMI (95% CI 0.14–0.19) and a weight gain of 0.45 kg (95% CI: 0.38–0.53) ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study

Huang,

Yu,

Liu

et al. 2024

Front. Public Health

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ObjectiveThis study aimed to analyze the relationship between the sleep quality of healthcare professionals and the incidence of overweight and obesity, exploring the potential impact of sleep quality on the onset of overweight and obesity in order to provide a scientific basis for formulating effective health intervention measures.MethodsA convenience sampling method was used to conduct a survey on the sleep characteristics and obesity status among healthcare professionals at Peking Union Medical College Hospital and Tianjin Dongli District Traditional Chinese Medicine Hospital. The survey was conducted via online questionnaires, which included demographic data, the Pittsburgh Sleep Quality Index (PSQI), height, weight, and related sleep, exercise, and dietary habits. Univariate and multivariate logistic regression analyses were applied to study the relationship between sleep quality and overweight/obesity among healthcare professionals.ResultsA total of 402 questionnaires were distributed, with a 100% retrieval rate, yielding 402 valid questionnaires. The average body mass index of the 402 participants was 23.22 ± 3.87 kg/m^2. Among them, 144 cases were overweight or obese, accounting for 35.8% (144/402) of the total. The prevalence of poor sleep quality among healthcare professionals was 27.4% (110/402), with an average PSQI score of 8.37 ± 3.624. The rate of poor sleep quality was significantly higher in the overweight and obese group compared to the normal weight group (36.1% vs. 22.5%, p = 0.003). The multivariate analysis indicated that gender, marital status, lower education level, sleep duration (odds ratio [OR] =1.411, 95% confidence interval [CI] 1.043–1.910, p = 0.026), and sleep disturbances (OR = 1.574, 95%CI 1.123–2.206, p = 0.008) were significant risk factors for overweight and obesity among healthcare professionals.ConclusionOverweight or obese healthcare professionals had poorer sleep quality compared to those with a normal weight. Sleep duration and sleep disorders were identified as independent risk factors for overweight or obesity in healthcare professionals. Increasing sleep duration and improving sleep disorders may play a positive role in controlling overweight and obesity among healthcare professionals.

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Section: Introductionmentioning

confidence: 99%

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study

Huang,

Yu,

Liu

et al. 2024

Front. Public Health

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Does daily energy and macronutrient intake differ between work and non-workdays in shift workers? A mixed methods study

Saggi,

Phillips,

Comas

et al. 2023

Chronobiology International

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Prevalence and Influencing Factors of Central Obesity among Adults in China: China Nutrition and Health Surveillance (2015–2017)

Nan,

Chen,

Yuan

et al. 2024

Nutrients

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The purpose of this study was to describe the prevalence of central obesity and its influencing factors among Chinese adults aged 18 or older. The data were from China Nutrition and Health Surveillance (2015–2017), which used a stratified, multistage, random sampling method. A total of 145,298 adults aged 18 years or older from 31 provinces were included in this study. The Criteria of Weight for Adults promulgated by China in 2013 were used to determine central obesity. Out of all the adults investigated, 48,342 were identified with central obesity, with a prevalence rate of 33.3%. A logistic analysis suggested that the following factors were associated with central obesity: female sex [odds ratio (OR) = 1.329, 95%CI = 1.277~1.384]; increasing age [OR (95%CI): 1.146 (1.061~1.238), 1.254 (1.167~1.348), 1.774 (1.651~1.907), 2.041 (1.894~2.198), 2.434 (2.239~2.647)]; being married [OR = 1.184, 95%CI = 1.077~1.302]; being divorced or widowed [OR = 1.132, 95%CI = 1.006~1.273]; an urban setting [OR = 1.096, 95%CI = 1.061~1.132]; BMI [OR (95%CI): 0.159 (0.095~0.266), 12.645 (11.388~14.042), 180.989 (153.025~214.064)]; drinking [OR = 1.069, 95%CI = 1.031~1.109]; and screen time > 5 h [OR = 1.088, 95%CI = 1.036~1.141] were risk factors for central obesity; while education above primary school [OR (95%CI): 0.905 (0.875~0.936), 0.857 (0.802~0.915)] and sufficient physical activity [OR = 0.819, 95%CI = 0.782~0.858] were protective factors for central obesity. This study revealed that the prevalence of central obesity, which differed by gender and age, is still high. Large differences between different groups and geographic regions exist persistently. Effective, sustainable, and culturally targeted interventions are needed.

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Shift Work and Obesity Risk—Are There Sex Differences?

Cited by 3 publications

References 94 publications

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study

Does daily energy and macronutrient intake differ between work and non-workdays in shift workers? A mixed methods study

Prevalence and Influencing Factors of Central Obesity among Adults in China: China Nutrition and Health Surveillance (2015–2017)

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