This longitudinal study aimed to assess the impact of COVID-19 containment measures on perceived health, health protective behavior and risk perception, and investigate whether chronic disease status ...and urbanicity of the residential area modify these effects. Participants (n = 5420) were followed for up to 14 months (September 2020-October 2021) by monthly questionnaires. Chronic disease status was obtained at baseline. Urbanicity of residential areas was assessed based on postal codes or neighborhoods. Exposure to containment measures was assessed using the Containment and Health Index (CHI). Bayesian multilevel-models were used to assess effect modification of chronic disease status and urbanicity by CHI. CHI was associated with higher odds for worse physical health in people with chronic disease (OR = 1.09, 95% credibility interval (CrI) = 1.01, 1.17), but not in those without (OR = 1.01, Crl = 0.95, 1.06). Similarly, the association of CHI with higher odds for worse mental health in urban dwellers (OR = 1.31, Crl = 1.23, 1.40) was less pronounced in rural residents (OR = 1.20, Crl = 1.13, 1.28). Associations with behavior and risk perception also differed between groups. Our study suggests that individuals with chronic disease and those living in urban areas are differentially affected by government measures put in place to manage the COVID-19 pandemic. This highlights the importance of considering vulnerable subgroups in decision making regarding containment measures.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
Although there is scientific evidence for an increased prevalence of sleep disorders during the coronavirus disease 2019 (COVID-19) pandemic, there is still limited information on how ...lifestyle factors might have affected sleep patterns. Therefore, we followed a large cohort of participants in the Netherlands (n = 5,420) for up to 1 year (September 2020–2021) via monthly Web-based questionnaires to identify lifestyle changes (physical activity, cigarette smoking, alcohol consumption, electronic device use, and social media use) driven by anti–COVID-19 measures and their potential associations with self-reported sleep (latency, duration, and quality). We used the Containment and Health Index (CHI) to assess the stringency of anti–COVID-19 measures and analyzed associations through multilevel ordinal response models. We found that more stringent anti–COVID-19 measures were associated with higher use of electronic devices (per interquartile-range increase in CHI, odds ratio (OR) = 1.47, 95% confidence interval (CI): 1.40, 1.53), less physical activity (OR = 0.94, 95% CI: 0.90, 0.98), lower frequency of alcohol consumption (OR = 0.63, 95% CI: 0.60, 0.66), and longer sleep duration (OR = 1.11, 95% CI: 1.05, 1.16). Lower alcohol consumption frequency and higher use of electronic devices and social media were associated with longer sleep latency. Lower physical activity levels and higher social media and electronic device use were related to poorer sleep quality and shorter sleep duration.
Electronic media (eMedia) devices along with exposure to transportation noise are integral to the daily routines of adolescents. The concerns associated with excessive eMedia usage extend beyond ...sleep deprivation to include the heightened exposure to radiofrequency electromagnetic fields (RF-EMF) emitted by these wireless devices. The aim of HERMES (Health Effects Related to Mobile PhonE Use in AdolescentS) study is to better understand biophysical and psychological pathways in relation to eMedia, RF-EMF exposure use and transportation noise that may effect on cognitive, behavioral, sleep and mental health, as well as non-specific symptoms. Following two previous HERMES cohorts conducted between 2012 and 2015 we have initiated the third wave of HERMES study as a prospective cohort with intermediate (every four months) and one year follows-up. Eligible participants are adolescents attending 7 th or 8 th school grades in Northwest and Central Switzerland. Baseline examinations are a questionnaire on eMedia usage and selected health outcomes, as well as computerized cognitive tests. In addition, parents/guardians are asked to fill in a questionnaire about their child’s health and potential eMedia use determinants. Far-field RF-EMF exposure and transportation noise at the place of residence and school are predicted based on a propagation model. Cumulative RF-EMF brain dose is calculated based on self-reported eMedia use, mobile phone operator data, and RF-EMF modelling. A follow-up visit is conducted one year later, and two interim questionnaires are sent to adolescents to be completed at home. Between baseline and 1-year follow-up, a subsample of 150 study participants is invited to collect personal RF-EMF measurements as well as sleep and physical activity data using accelerometers. This new recruitment wave of HERMES study provides a greater understanding of causal pathways between eMedia, RF EMF, and transportation noise exposure and their effects on health outcomes, with relevant implications for both governmental health policy and lay people alike.
