posted on 2021-11-01, 08:42authored byJacob D. Meyer, John O'Connor, Cillian P. McDowell, Jeni E. Lansing, Cassandra S. Brower, Matthew P. Herring
The COVID-19 pandemic has elicited increased sedentary behaviors, decreased
moderate-to-vigorous physical activity (MVPA), and worsened mental health, yet the
longitudinal impact of these changes and their inter-relations remains unknown. Our
purpose was to examine associations between changes in self-reported activity
behaviors and mental health over an 8-week period following the COVID-19 outbreak.
Participants from all 50 states and the District of Colombia were recruited through
convenience and snowball sampling at baseline April 3–10, 2020. Prospective data from
2,327 US adults with ≥2 responses (63.8% female; 74.3% response rate) were collected
weekly via online survey for eight consecutive weeks (April 3–June 3, 2020). Primary
exposures were self-reported time spent sitting, viewing screens and in MVPA, with
primary outcomes being depressive symptoms, anxiety symptoms, and positive mental
health (PMH). A significant sitting-by-time interaction (p < 0.05) showed slightly higher
marginal effects for depressive symptoms for the 90th-percentile of sitting time than the
10th-percentile at baseline (5.8 [95% confidence interval = 5.5–6.2] vs. 5.7 [5.4–6.1]),
with the difference magnifying over time (week 8: 3.5 [3.2–3.9] vs. 2.7 [2.4–2.9]). No
other interactions over time were significant. Screen time was negatively associated with
PMH and positively associated with depressive and anxiety symptoms (p < 0.05). Sitting
time was negatively associated with PMH (p < 0.05). Rapid changes in sitting patterns
(e.g., due to a pandemic) may have lasting effects on depressive symptoms. Strategies
targeting those most affected (i.e., young adults, females) and/or focused on reducing
sitting time may be critical for preventing long-term mental health effects resulting from
COVID-19 or other large-scale behavior changes in the general population.
Funding
Development of a structure identification methodology for nonlinear dynamic systems