This study explored self-reported changes in caregiving intensity (CI) and caregiver burden (CB) among informal caregivers due to the COVID-19 pandemic overall and by gender. Informal caregivers for someone age 50+ completed a survey via Amazon’s MTurk in June 2020. Participants reported changes in CI and CB due to COVID-19 and provided demographic information. Multinomial logistic regression models assessed changes in CI and CB attributed to the COVID-19 pandemic overall and by gender. The sample (n = 835) was 68.5% male and had an average age of 34 years (SD 9.8); 55.7% had increased CI, and 53.1% had increased CB attributed to the pandemic. Increased CB due to COVID-19 was associated with increased CI (OR 5.67, 95% CI 3.92–8.00). Male caregivers with decreased CI due to COVID-19 were nearly seven times as likely as those with no change in CI to have reduced CB due to COVID-19 (OR 6.91, 95% CI 3.29–14.52). Women with decreased CI due to COVID-19 were over eight times as likely to have reduced CB due to COVID (OR 8.30, 95% CI 2.66–25.91). Results indicate that many caregivers experienced increases in CI and CB since the start of the COVID-19 pandemic, and that these changes are complex and vary by gender.

Obesity is a critical public health issue, affecting over one-third of all Americans, and is an underlying cause of numerous health issues across the lifespan. For older adults, obesity is linked to premature declines in physical and mental health and cognitive functioning. The occurrence of obesity and related health behaviors and chronic diseases are higher in rural areas than in urban areas. Furthermore, rural areas of the United States have a higher proportion of older adults than urban areas. Few studies, to date, have explored rural-urban differences in the relationships between dietary patterns and obesity among older adults. Therefore, the purpose of this study is to assess rural-urban differences in obesity rates in older adults, and the potential for the associations between obesity and physical activity and dietary patterns to vary by rural-urban status. Data were abstracted from respondents aged 65 and above from the 2012 Behavioral Risk Factor Surveillance System (BRFSS) database linked to Census-based county-level information on rural-urban status and socioeconomic status. Generalized linear models were utilized to assess rural-urban disparities in obesity, and the potential for associations between obesity and known risk factors (fruit consumption, green vegetable consumption and physical activity) to vary by rural-urban status, accounting for complex sampling and confounders. Obesity rates were highest and fruit consumption was lowest in the most rural areas. However, for older adults in the most urban areas, there was a significant negative association between obesity and fruit and green vegetable consumption. This association was not observed in more rural older adults. These findings underscore the need to take into account place-based factors such as rural-urban status, when designing and implementing policies and interventions designed to reduce obesity through risk factor mitigation in older adults. To reduce rural-urban disparities in older adults, all policies, programs, and interventions should address the unique barriers and needs specific to rural and urban older adults.

Despite the health benefits associated with physical activity (PA), screen time reduction, and sleep quantity and quality, the relationships between PA, screen time, and sleep quantity and quality remain unclear in adolescents. The present study is a cross-sectional analysis of data from adolescents aged 16–19 years who participated in the 2005–2006 National Health and Nutrition Examination Survey (n = 542). Multivariable logistic regression models, adjusted for confounders, examined the relationship between objectively measured PA, self-reported screen time, and sleep quantity and quality. Respondents who met the current PA recommendation had 50% lower odds of having sufficient sleep (≥8 h) than those not meeting the recommendation (OR = 0.50, 95% CI: 0.26, 0.94). Respondents who met the screen time recommendation (≤2 h/day) had 55% lower odds of reporting poor sleep quality than those whose screen time exceeded the recommendation (OR = 0.45, 95% CI: 0.22, 0.91), with similar patterns observed for females and males. However, males who met both PA and screen time recommendations had 73% lower odds of reporting poor sleep quality than males who met neither recommendation (OR = 0.27, 95% CI: 0.07, 0.99). In conclusion, PA and screen time are associated with sleep quantity or sleep quality in adolescents, and there are differences in these associations by sex.