[Screen time, obesity and cardiovascular disease among elderly Brazilians: 2013 and 2019 National Health Surveys].
Elaine Cristina LopesLetícia Martins CândidoRafaela Aguiar RosaVanessa PavanateKatia Jakovljevic Pudla WagnerNúbia Carelli Pereira de AvelarAna Lucia DanielewiczPublished in: Ciencia & saude coletiva (2023)
The scope of this paper was to analyze the association between the time spent watching television (TV) and the presence of obsessive-compulsive disorder (OCD) obesity and obesity associated with cardiovascular diseases (CVD) among elderly Brazilians, according to gender, comparing data from the 2013 and 2019 National Health Surveys (NHS). It involved a cross-sectional study, with data from 23,815 and 43,554 elderly people, respectively, from the 2013 and 2019 NHS. Self-reported TV screen time was categorized into: <3, 3-6, and ≥6 per day. Obesity was assessed by body mass index ≥27 kg/m² and CVD by self-reported medical diagnosis. In 2013, elderly women who watched TV ≥6 hours/day were more likely to have OCD obesity (OR=1.87; 95%CI=1.32;2.64) and obesity associated with CVD (OR=6.30; 95%CI=3.38;11.74). In 2019, elderly women who watched TV between 3-6 hours/day (OR=1.44; 95%CI=1.25;1.65) and ≥6 hours/day (OR=1.55; 95%CI=1.28;1.88) were more likely to have OCD obesity, while the incidence of obesity associated with CVD was higher for ≥6 hours/day (OR=2.13; 95%CI=1.48;3.06). In 2019, men were more likely to have obesity associated with CVD watching TV between 3-6 hours/day (OR=1.76; 95%CI=1.20;2.56) and ≥6 hours/day (OR=2.13; 95%CI=1.27;3.57). The importance of reducing screen time by the elderly is clearly evident.
Keyphrases
- insulin resistance
- metabolic syndrome
- weight loss
- weight gain
- type diabetes
- high fat diet induced
- obsessive compulsive disorder
- cardiovascular disease
- body mass index
- middle aged
- polycystic ovary syndrome
- high throughput
- risk factors
- physical activity
- machine learning
- coronary artery disease
- skeletal muscle
- deep brain stimulation
- big data
- artificial intelligence
- cardiovascular events
- data analysis