Relevant demographic and environmental conditions need to be understood before tailoring policies to improve mental health. Using community health survey data from 25 communities in Seoul, 2013, cross-sectional associations between mental health and community level environments were assessed. Mental health outcomes (self-rated stress levels (SRS) and depressive symptoms (DS)) were analyzed. Community environmental factors included green space, green facilities, and annual PM₁₀ level (AnnPM₁₀); socio-demographic factors included sex, age, education, labor market participation, comorbidity, sleep hours, physical activity, smoking, and drinking. A total of 23,139 people with the following characteristics participated: men (44.2%); age groups 19−39 (36.0%), 40−59 (39.4%), 60−74 (19.2%), and 75+ (5.4%). Women had higher odds ratios (OR) for SRS [OR 1.22, 95% Confidence interval (CI) 1.17–1.27] and DS [OR 1.55, 95% CI 1.42–1.71]. Regular physical activity predicted SRS [OR 0.90, 95% CI 0.84–0.95] and DS [OR 0.98, 95% CI 0.88–1.10]; current smoking and drinking were adversely associated with both SRS and DS. Higher accessibility to green space (Q4) was inversely associated with DS [OR 0.89, 95% CI 0.81−0.97] compared to lower accessibility (Q1). AnnPM₁₀, annual levels for particles of aerodynamic diameter <10 µm (PM₁₀), among communities was associated with poorer SRS [OR 1.02, 95% CI 1.00–1.04] by 10 μg/m³ increases. Therefore, both demographic and environmental factors should be considered to understand mental health conditions among the general population.

As sleep duration and quality are associated with various diseases, many devices have been developed to assess sleep. We reviewed the devices used to assess sleep and their performance, focusing on Fitbit, one of the most popular devices for tracking sleep. Although previous studies have reported conflicting results regarding Fitbit’s estimation of the sleep index, most studies have shown that it has acceptable accuracy and high sensitivity. Its performance could be affected by various factors such as participants’ sleep characteristics, sleep hours and stages, analysis methods, devices compared, device models and settings, and the dominant/non-dominant wrist wearing the device. Therefore, these factors should be considered when using Fitbit to assess sleep patterns.

This study aimed to investigate the prevalence pattern of pressure injuries (PIs), or ‘sores’, in South Korea and investigate the factors affecting its development. We estimated the annual prevalence of PIs using the National Inpatient Sample (NIS) data extracted from the Health Insurance Review and Assessment Service (HIRA) database from 2009 to 2015. Multivariable logistic regression was performed to examine the association between hospitalization and socio-demographic characteristics, such as sex, age, type of health insurance, Charlson Comorbidity Index (CCI), and plegia comorbidity. We found that inpatients with PIs make up to 0.86% of the total population in South Korea in 2015, which had shown a steady increase from the previous years. And male, old age, low socioeconomic status (SES), and the patients’ severity such as high CCI and the plegia comorbidity were strongly associated with hospitalization due to PI. Based on our results, it would be anticipated that the medical cost for treatment and management of PIs will increase in the future, and it will be accelerated due to the rapidly aging society. In addition, patients in low SES and patients with severe comorbidities would be relatively more burdensome, threatening their household economy and further reducing the quality of life. Therefore, PIs should not be overlooked as the responsibility of just the nursing care professionals but should be recognized as one of the serious societal problems. The establishment of an intense medical care system is needed not only to reduce the prevalence of PIs but also to increase the awareness in people with PI patients.