Although many wearable devices are used to assess sleep, their accuracy remains controversial. This study aimed to investigate the accuracy of the Actiwatch, a research-grade device, and the Fitbit, a consumer-grade device, against sleep diaries to assess sleep patterns. Methods: Twenty participants wore Fitbit and Actiwatch for two weeks and tracked their sleep patterns using sleep diaries. Total sleep time (TST), time-in-bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) from the two devices and sleep diaries were analyzed using analysis of variance and Bland-Altman analysis. Results: The TIB measured by the sleep log, Fitbit, and Actiwatch were 420.9 minutes, 417.3 minutes, and 567.4 minutes, respectively. Compared to the sleep log, the Fitbit underestimated TST, TIB, and SE, with significant differences observed for TST (p<0.001) and SE (p<0.001), but not for TIB. The Actiwatch overestimated TIB (p<0.001) and TST (p=0.02) and underestimated SE (p<0.001) compared to the sleep log. The difference between the Fitbit and Actiwatch was significant for TST, TIB, and SE (all p<0.001). Conclusions: The Fitbit showed a smaller difference than the Actiwatch when compared with the sleep logs. The Fitbit could be used as a tool to assess sleep patterns in the clinic as well as in daily life.

Although many wearable devices are used to assess sleep, their accuracy remains controversial. This study aimed to investigate the accuracy of the Actiwatch, a research-grade device, and the Fitbit, a consumer-grade device, against sleep diaries to assess sleep patterns. Methods: Twenty participants wore Fitbit and Actiwatch for two weeks and tracked their sleep patterns using sleep diaries. Total sleep time (TST), time-in-bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) from the two devices and sleep diaries were analyzed using analysis of variance and Bland-Altman analysis. Results: The TIB measured by the sleep log, Fitbit, and Actiwatch were 420.9 minutes, 417.3 minutes, and 567.4 minutes, respectively. Compared to the sleep log, the Fitbit underestimated TST, TIB, and SE, with significant differences observed for TST (p<0.001) and SE (p<0.001), but not for TIB. The Actiwatch overestimated TIB (p<0.001) and TST (p=0.02) and underestimated SE (p<0.001) compared to the sleep log. The difference between the Fitbit and Actiwatch was significant for TST, TIB, and SE (all p<0.001). Conclusions: The Fitbit showed a smaller difference than the Actiwatch when compared with the sleep logs. The Fitbit could be used as a tool to assess sleep patterns in the clinic as well as in daily life.

Although many wearable devices are used to assess sleep, their accuracy remains controversial. This study aimed to investigate the accuracy of the Actiwatch, a research-grade device, and the Fitbit, a consumer-grade device, against sleep diaries to assess sleep patterns. Methods: Twenty participants wore Fitbit and Actiwatch for two weeks and tracked their sleep patterns using sleep diaries. Total sleep time (TST), time-in-bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) from the two devices and sleep diaries were analyzed using analysis of variance and Bland-Altman analysis. Results: The TIB measured by the sleep log, Fitbit, and Actiwatch were 420.9 minutes, 417.3 minutes, and 567.4 minutes, respectively. Compared to the sleep log, the Fitbit underestimated TST, TIB, and SE, with significant differences observed for TST (p<0.001) and SE (p<0.001), but not for TIB. The Actiwatch overestimated TIB (p<0.001) and TST (p=0.02) and underestimated SE (p<0.001) compared to the sleep log. The difference between the Fitbit and Actiwatch was significant for TST, TIB, and SE (all p<0.001). Conclusions: The Fitbit showed a smaller difference than the Actiwatch when compared with the sleep logs. The Fitbit could be used as a tool to assess sleep patterns in the clinic as well as in daily life.

Objectives: This study examined the association between the experience of disease management education and the use of nutrition labels according to the sociodemographic characteristics and health behaviors of people diagnosed with hypertension and diabetes living in the community. Methods: Among the participants from the Community Health Survey (2018), 74,283 individuals diagnosed with hypertension or diabetes were included in the study population. According to gender, this study evaluated nutrition label use by the experience of disease management education, individual sociodemographic characteristics, and health behavior. Finally, using multiple logistic regression analysis, the association between disease management education and nutrition labels was calculated using the odds ratio (OR) and 95% confidence interval (CI). Results: Males (24.5%) experienced more disease management education than females (22.6%). In addition, younger age, higher education level, and higher equalized personal income experienced more disease management education (P < 0.001). The educational experience rate was higher in the male subjects who did not smoke or were involved in high-risk alcohol consumption (P < 0.001). In addition, the rate of disease management education experience was significantly higher for both men and women who exercised by walking (P < 0.001). The use of nutrition labels was higher in females (9.9%) than males (5.8%), and both males and females were significantly higher in young age, high education, high income, and professional and office positions (P < 0.001). The utilization rate of nutrition labels was high in non-smoking male subjects and high-risk-drinking female subjects. In addition, the utilization rate of nutrition labels was significantly higher in males and females who exercised by walking and those who experienced disease management education (P < 0.001). After adjusting for individual sociodemographic characteristics, health behavior, and disease management education, the use of nutrition labels was high among females (OR 3.19, 95% CI 2.85-3.58), high income (Q4; OR 1.62, 95% CI 1.41-1.87, Q5; OR 1.58, 95% CI 1.37-1.84) and highly educated (high school; OR 2.87, 95% CI 2.62-3.14, above college; OR 5.60, 95% CI 5.02-6.23) while it was low in the elderly (OR 0.43, 95% CI 0.40-0.47), and economically inactive (OR 0.86, 95% CI 0.76-0.96). The use of nutrition labels was high in non-smokers (OR 1.29, 95% CI 1.13-1.48), nonhigh-risk drinkers (OR 1.22, 95% CI 1.08-1.38), and subjects who exercised walking (OR 1.44, 95% CI 1.34-1.54). There was no difference in the utilization rate of nutrition labels according to obesity, and the utilization rate of nutrition labels was significantly higher in subjects who had experienced disease education (OR 1.34, 95% CI 1.24-1.44). Conclusions Education on the use of nutrition labels, which contributes to food selection for healthy eating, might be a tool for dietary management. Moreover, the utilization rate can be a good indicator for predicting the proportion of the population practicing the guide for disease management. Improving the utilization rate of nutrition labels through disease management education can be a useful intervention for people with chronic diseases who need healthy eating habits for disease management and preventing complications, particularly those diagnosed with hypertension and diabetes.

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 dominanton-dominant wrist wearing the device. Therefore, these factors should be considered when using Fitbit to assess sleep patterns.

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 dominanton-dominant wrist wearing the device. Therefore, these factors should be considered when using Fitbit to assess sleep patterns.

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 dominanton-dominant wrist wearing the device. Therefore, these factors should be considered when using Fitbit to assess sleep patterns.

The Korean Early Childhood Education and Care Panel Study (K-ECEC panel study), commencing in 2021, aimed to collect data related to early life growth and development. In particular, based on Bronfenbrenner’s ecological model, the study aims to collect intrinsic and extrinsic parental factors, educational and caregiving experiences in institutions, and environmental factors from the prenatal stage through infancy, toddlerhood, and early childhood. This is important for understanding child development with varied dimensional aspects. The K-ECEC panel study uses a stratified sampling strategy in order to recruit pregnant women based on regions and numbers of deliveries to individual hospitals. This resulted in the participation of 3,372 women, 95 of whom were from low-income families and 72 from multicultural families. The K-ECEC panel study is based on a household survey collecting information related to pregnancy, birth, and childrearing. The survey included an annual follow-up for individual households of newborns from 2022, and the second round of the survey was conducted in 2023. A total of 2,775 children participated in the first round of the survey, 1,423 of whom were boys. As there are few birth cohort studies considering policy-oriented aspects in Korea, the K-ECEC panel study is expected to provide data to understand childhood trajectories for growth and development. Furthermore, in terms of childcare and education, it is expected to be utilized to evaluate the policy effects of early life and parenting support.