Keiko Tanida

Abstract Objective Discrepancies between sleep timing on work/school and free days, also known as social jetlag (SJL), can cause health problems. These issues occur most often in individuals from adolescence to the early 20s, which is equivalent to the age of university students. This study was designed to explore the recommended level of physical activity required to minimize SJL and to examine the relationship between SJL and objective physical activity among female university students. Methods We assessed the SJL of 68 female students using the Japanese version of the Munich Chronotype Questionnaire. The objective physical activity and sleep variables of subjects were also evaluated at 3 to 4 weeks using a small triaxial accelerometer. Results A significant negative correlation was found between SJL and physical activity on both free (r = − 0.435, p < 0.001) and school days (r = − 0.341, p < 0.01). According to the linear regression analysis, physical activity of 11,174 steps on school days and 10,713 steps on free days had the lowest SJL value. Total sleep time on free days had a significant positive correlation with SJL (r = 0.399, p < 0.001) and a negative correlation with physical activity (r = − 0.520, p < 0.001). Discussion Our results suggest that substantial SJL may cause chronic fatigue and lead to a low level of physical activity in female university students. These results also imply that the recommended level of physical activity necessary to minimize SJL among these students is around 11,000 steps on both school and free days.

本学(県立大学)看護学部のプロジェクト「ビッグデータを活用した健康リスク予測と高度看護介入による新たなデータヘルス・システムの開発」の一環として実施した、糖尿病の悪化を予測するためのアルゴリズムの開発と特定保健指導で活用可能なアプリケーションの開発について報告した。糖尿病悪化予測アルゴリズムの開発は、A市の平成12年(2000年)から平成31年(2019年)までの健診データ19750人分をもとに、過去2回分のデータから3回目の糖尿病の総合判定の予測を行った。1回目のデータを「過去データ」、2回目のデータを「現在データ」、3回目のデータを「未来データ」とし、検査項目を「基本データ」「診断データ」「アンケートデータ」と区分し、区分ごとに「過去データ」「現在データ」「未来データ」「過去→現在への変化データ」「現在→未来への変化データ」に分け、3回目である「未来データ」が悪化するかしないかを機械学習などを用いた。上記のアルゴリズムを用いて糖尿病の悪化および発症のリスク変化を提示し、生活習慣の改善の動機づけとすることを目的に、保健指導アプリを構築した。その結果、糖尿病悪化予測アルゴリズムでは、「判定の悪化」を予測対象としてしまったため、判定が一段階悪化したばかりの者は次回に悪化しにくく、良いスコアが表示されてしまう結果となり、これだけでは生活習慣改善の動機づけにつながらないことが分かった。

Health-related behaviors during adolescence have lifelong impacts. However, there are unclear areas regarding the associations between health-related quality of life and demographic characteristics, as well as physical and psychosocial indicators. The aim of this study was to examine the associations between quality of life and body weight, sleep outcome, social support by age, and cohabitants, given that income, self-esteem, lifestyle, emotional, social and behavioral problems were taken into account among adolescents in East and Southeast Asia. A cross-sectional survey was conducted in Zhengzhou of China, Hong Kong, Kansai region of Japan, Taipei of Taiwan, Bangkok of Thailand and Manila of the Philippines between 2016 and 2017 among 21,359 urban adolescents aged between 9 and 16. The results showed that adolescents who had better self-esteem and control of emotions and behaviors had much higher level of perceived quality of life. Those who were overweight or obese, sleepy in the daytime, and not living with parents had worse quality of life compared with those who were not. In conclusion, psychosocial well-being should have a higher priority in the promotion of quality of life among Asian adolescents. Nevertheless, further studies are required to explore the differences in perceived quality of life between genders and countries.

[Purpose] This cross-sectional study aimed to identify the kinds of thermometers used by elderly individuals living in City A and whether they are able to 1) insert an axillary thermometer correctly, 2) wipe axillary sweat before inserting a thermometer, and 3) clean the thermometer after measuring their body temperature. [Methods] We collected data from July to September 2015 and recruited participants from senior centers managed by City A. Questionnaires were primarily collected by the placement method and by postal mail. We performed chi-square tests to determine the differences in thermometer use by sex and age group (young-elderly and old-elderly) and the relationship between participants’ sources of health information and thermometer use. [Results] We recruited 745 participants and obtained 627 responses (response rate 84.2%). After cleaning, data from 515 seniors (39.6% male, 73.7±5.4 years, 65–91 years) were used. The analyses showed that among seniors in the final sample, 416 (80.8%) used a digital axillary thermometer and 78 (15.1%) used a mercury thermometer. A total of 88 seniors (19.6%) answered the question on how to insert a thermometer, correctly. Significantly more seniors who answered the question correctly obtained health information at health seminars than those who answered incorrectly. We found that 78.6% of respondents wiped armpit sweat before measuring their temperature and 34.6% cleaned the thermometer after use; the rate of implementation of these actions was significantly lower among men aged 65–74 years (young-elderly) than among men or women aged ≥75 years (old-elderly). [Conclusion] These results show that less than 20% of seniors use a thermometer hygienically and, compared with other groups, young-elderly men were less likely to wipe their armpits and clean their thermometer. Mercury thermometers are still used in many households. We suggest that medical professionals take more care in educating seniors about appropriate thermometer use.

Background: Given the risk of physical and psychosocial health that emerge in adolescents that are continuing into adulthood, identifying and addressing early signs of health-related quality of life (HRQoL) decline provides an opportunity to ensure that young people have a healthier progression through adolescence. Aim: To investigate the association between demographic characteristics, lifestyle health behaviours, and HRQoL of adolescents who live in Asia Pacific region, including Hong Kong in China, Beijing in China, Akashi in Japan, Seoul in South Korea, and Bangkok in Thailand. Methods: A cross-sectional self-reported survey carried out in a sample of 2296 adolescents that were aged 9–16 years (mean= 12.0; standard deviation [SD] = 1.63) was conducted in the five cities of the Asia Pacific region between January and August 2017. Demographic characteristics, adolescent lifestyle behaviours, and HRQoL were measured with demographic questionnaire, Adolescent Lifestyle Questionnaire’s (ALQ) seven domains and Paediatric Quality of Life’s (PedsQL 4.0) global score, respectively. Mixed multilevel model (MMLM) was used to fit the data. Results: After adjusting the demographic variables, one score increase in ALQ physical participation, nutrition, social support, and identity awareness are associated with an increase in PedsQL global score. On the contrary, one score increase in ALQ health practices is associated with a decrease in the PedsQL global score. The estimated mean of PedsQL global score of South Korea, Beijing, and Japan were better, while the score of Thailand and Hong Kong were poorer. Conclusions: Differentiating the impacts of promoting health behaviours among different countries can help in better understanding the health needs of adolescents in each country, especially in the Asia Pacific region, so that adequate and relevant resources can be allocated to reduce health-risk taking behaviours among this vulnerable group for health-promoting strategies.

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

This study examined and compared the characteristics of the power spectral indices of heart rate variability (HRV) for the different stages of sleep in order to determine which indices are useful for sleep assessment. Sixteen sets of polysomnogram (PSG) and electrocardiogram data were collected synchronously from 8 healthy women (age range, 20-44 years). PSG data were analyzed in 1-min segments, while HRV data were analyzed in 1- and 5-min segments using the MemCalc method and calculated at 3 frequency band powers: higher area in very low frequency band (VLF-hi, 0.016-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz), and high frequency band (HF, 0.15-0.4 Hz). Significant differences were observed in LF/HF, HF/ (LF+HF), HF/(VLF-hi+LF+HF), and VLF-hi between all sleep stages. Compared to the HRV results obtained from 5-min segments, those from the 1-min segments showed a stronger relationship with sleep stage. Although HRV analysis during sleep typically involves the use of 5-min segments, the present results imply that improvements in technology enable the detection of sleep stages at shorter 1-min segments which correspond to PSG analysis segments.

This study investigated the correlations between subjective sleep assessed by the Oguri Shirakawa-Azumi (OSA) sleep inventory and objective indices based on electrocardiogram data obtained during sleep. Twenty-one sets of data were collected from 10 healthy women. As objective indices, total sleep time, sleep period time, sleep latency, and waking after sleep onset were assessed from polysomnogram data, and the averages of the power spectral indices of heart rate variability (HRV) -low frequency ［LF］, high frequency ［HF］ , ratio of LF to HF ［LF/HF］, and ratio of HF to the sum of LF and HF ［HF/ (LF＋HF) ］-were calculated. HF/ (LF＋HF) showed positive correlations with four OSA factors : factor I (sleepiness), factor II (sleep maintenance), factor III (worries), and factor IV (integrated sleep feeling). Negative correlations were found between LF/HF and factors II, III, and IV. Although factor V (sleep initiation) had a negative correlation with length of sleep latency, no power spectral indices were correlated with this factor. The results suggest that some aspects of subjective sleep can be assessed by means of HRV power spectral indices.

Recently, aromatherapy has been introduced to nursing care settings ; however, the efficacy of fragrance on pain relief remains unclear. The present study examines the effectiveness of inhalation of true lavender (Lavandula angustifolia) essential oil on an experimental pain threshold. Twenty-seven healthy adult women participated in the study. Three drops of the essential oil were placed on a small piece of cotton, and after a ten-minute rest the experimental group subjects inhaled the fragrance for 10 minutes while seated. Then, measurement of the pain threshold was made with a reaction time to the thermal stimulations to their palms. In the experimental group, the threshold was measured before and while inhaling of the aroma. In the control group, it was assessed twice without an exposure to the fragrance. Following the measurements, in order to find out a correlation between the degree of preference and the changes in the pain threshold, the experimental group subjects were asked to rate their preference for the fragrance using a 7 point scale where "1" was "I hate the fragrance" and "7" was "I like it very much." The results of the experimental group showed a statistically significant rise in the pain threshold. It was not found in the control group. Regarding the correlation, the p value did not reach the level of significance. The findings support the usefulness of the fragrance for pain management, and suggest that the analgesic effect is not strongly influenced by the preferences for the aroma.