Gender Differences in Unhealthy Lifestyle Behaviors among Adults with Diabetes in the United States between 1999 and 2018.
Yu WangPeihua CaoFengyao LiuYilin ChenJingyu XieBingqing BaiQuanjun LiuHuan MaQingshan GengPublished in: International journal of environmental research and public health (2022)
Lifestyle management is important to patients with diabetes, but whether gender differences exist in lifestyle management is unclear. Data from the US National Health and Nutrition Examination Survey (NHANES 1999 to 2018) was used for this research. Gender differences were evaluated descriptively and using an odds ratio (OR) with a 95% confidence interval (CI). A total of 8412 participants (48% women) were finally included. Across these surveys, the incidences of poor diet (OR: 1.26 (95% CI, 1.12, 1.43)), smoking (1.58 (1.35, 1.84)), alcohol consumption (1.94 (1.68, 2.25)) and sedentary behavior (1.20 (1.04, 1.39)) were more common in men, while depression (0.47 (0.37, 0.59)), obesity (0.69 (0.61, 0.78)) and insufficient physical activity (0.56 (0.49, 0.65)) were more common in women. Reductions in poor diet were greater in men between 1999 and 2000 and 2017 and 2018 ( p = 0.037), while the mean body mass index (BMI) levels ( p = 0.019) increased more among women. Furthermore, several gender differences were found to be related to age, race/ethnicity and marital/insurance/employment statuses. Our research found gender differences in diabetes-related unhealthy lifestyle behaviors and provides reference data for implementing measures to reduce the gender differences. Further work to reduce gender-specific barriers to a healthy lifestyle is warranted in order to further improve diabetes management.
Keyphrases
- physical activity
- weight loss
- body mass index
- cardiovascular disease
- metabolic syndrome
- type diabetes
- glycemic control
- polycystic ovary syndrome
- alcohol consumption
- weight gain
- sleep quality
- pregnancy outcomes
- insulin resistance
- electronic health record
- cervical cancer screening
- depressive symptoms
- healthcare
- middle aged
- breast cancer risk
- pregnant women
- mental health
- skeletal muscle
- data analysis
- health insurance
- quality improvement
- machine learning
- deep learning