An Intelligent Approach for Prediction of Life Style Conditions in Adolescent Girls by Analyzing Health Parameters

Monitoring health parameters has become a challenging task due to unpredictable diseases and related symptoms. Lifestyle is a crucial factor to decide to be healthy, in adolescent girls especially. This chapter presents a work in progress on prediction of lifestyle of adolescent girls based on problems like unhealthy routines of eating habits, sleep patterns, stress, etc. Therefore, an IT-enabled system is presented to assess current lifestyle of adolescent girls in an easy and faster way. A systematic survey is conducted with specially designed survey form by consulting medical practitioners and physical trainers. Twenty-one factors related to age, diet habits, exercise habits, sleeping habits, health history, etc. are included in the expert-guided form. One hundred fifty-five individual responses are collected and assessed manually by medical experts to annotate as healthy or unhealthy types. The healthy lifestyle prediction accuracy with support vector machine is 83.87% whereas it is 80.64% using logistic regression.

Surf or sleep?

Poor sleep habits may cause serious problems of mind and body, and it is a commonly observed issue for college students due to study workload as well as peer and social influence. Understanding its impact and identifying students with poor sleep habits matters a lot in educational management. Most of the current researches is either based on self-reports and questionnaires, suffering from small sample size and social desirability bias, or the methods used are not suitable for the education system. In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects. First, we design a Possion-based probabilistic mixture model to cluster students according to the distribution of bedtime and identify students who are used to stay up late. Second, we profile students from five aspects (including eight dimensions) based on campusbehavior data and build Bayesian networks to explore the relationship between behavioral characteristics and sleeping habits. Finally, we test the predictability of sleeping habits. This paper not only contributes to the understanding of student sleep from a cognitive and behavioral perspective but also presents a new approach that provides an effective framework for various educational institutions to detect the sleeping patterns of students.

Impact of Remote Teaching on Japanese Medical University Students' Sleeping Habits : a Longitudinal Study

Background: The coronavirus disease 2019 (COVID-19) pandemic has changed our daily life. Owing to the imposed restrictions, many educational facilities have introduced remote teaching. This study aims to understand the impact of remote teaching on Japanese university students' sleeping habits.Methods: The participants were medical university students. We used data from an ongoing longitudinal sleeping habits survey. For 684 participants who enrolled in the university during 2018–2020, multilevel analyses of sleep duration during weekdays and weekends across 3 years were conducted, adjusting for gender, grade, place of stay, sleep problems and lifestyle habits. Results: Among the participants, 356 male (mean ± standard deviation: 22 ± 3, 18–37 years old) and 288 female (22 ± 3, 18–32 years old) students in 2018, 365 male (24 ± 3,18–36 years old) and 284 female (22 ± 2, 18–33 years old) students in 2019, and 226 male (20 ± 3,18-36 years old) and 167 female (21 ± 2, 18–34 years old) students in 2020 answered the questionnaire. The mean sleep duration during weekdays (in minutes) was 407.6 ± 60.3 in 2018, 406.9 ± 63.0 in 2019, and 417.3 ± 80.9 in 2020. The mean sleep duration during weekends (in minutes) was 494.5 ± 82.5 in 2018, 488.3 ± 87.9 in 2019, and 462.3 ± 96.4 in 2020. The analysis showed that sleep duration during weekdays was associated with the place of stay and survey year. Moreover, students reported significantly longer sleep duration during weekdays in 2020 than 2019, but no significant difference in sleep duration between 2018 and 2019. Sleep duration during weekends was found to be associated with the survey year, gender and always doing something before going to bed. Sleep duration during weekends was shorter in 2020 than 2019 and longer in male students and students who always do something before going to bed. Ten students were reported to have a delayed sleep phase in 2020. Conclusions: Students' sleep duration increased during weekdays and decreased during weekends in 2020. This difference could be explained by the COVID-19 pandemic and the introduction of remote teaching.

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.

Physical Activity, Screen Time, Sedentary and Sleeping Habits of Polish Preschoolers during the COVID-19 Pandemic and WHO’s Recommendations: An Observational Cohort Study

Background: Restrictions related to the COVID-19 pandemic may lead to a significant decrease in physical activity, an increase in sedentary behavior, and thus also such things as screen time or a change in health behavior patterns. The survey aimed to compare levels of physical activity, screen time, hours spent sitting and sleeping time among Polish children aged 3–5 years of age before and during the COVID-19 pandemic. Methods: We identified 3000 respondents under five years of age, at Polish kindergartens. The questionnaire consists of 62 questions according to the recommendations of health behavior in school-aged children. The questionnaire was completed by the parents of these children. Results: Only 30.77% of children complied with WHO criteria before the pandemic. During the pandemic, the percentage of children meeting the recommendations for physical activity decreased even more. Children spent much more time in a sitting position before the restrictions. The children slept as recommended 10–13 h a day, and the pandemic caused an increase in sleep duration of 10–18%. Most children had a limited time allowed for the use of electronic devices already before the pandemic, but during the pandemic the results negatively decreased by 71.54%. Conclusions: The results clearly indicate decreased physical activity and increased screen time. It is also crucial to develop recommendations for prevention management strategies of sedentary lifestyles in the youngest group.

How Does COVID 19 Alter Children Life?

COVID 19 (Coronavirus 2019) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in December 2019 in Wuhan, Hubei, China and resulted in an ongoing pandemic. It has put the world on a crisis footing and children could be the hidden victim of the pandemic. Due to this pandemic, the impact on children is severe. Children may be more vulnerable due to school closures, lockdowns and other stressors. Access to a computer and the internet at home could determine whether education comes to a halt or if children can continue to learn outside of the classroom. And also raising the cases of malnutrition, hunger and may suffer from extended ill-consequences of this pandemic, such as child labor, child trafficking, child marriage, sexual exploitation and death etc. soap and running water may make the difference between whether children get sick or not. School closure, lack of outdoor activity, aberrant dietary and sleeping habits are likely to disrupt children's usual lifestyle and can potentially promote monotony, distress, impatience, annoyance and varied neuropsychiatric manifestations. Incidences of domestic violence, child abuse, adulterated online contents are on the rise. The COVID-19 pandemic is potentially catastrophic for many children around the world. For children caught at the apex of this crisis, there is a genuine prospect that its effects will permanently alter their lives.

Precocious Puberty and Covid-19 Into Perspective: Potential Increased Frequency, Possible Causes, and a Potential Emergency to Be Addressed

A significant increase in precocious puberty, rapidly progressive puberty and precocious menarche has been reported in Italy since the initial lockdown because of the pandemic, and this could represent a new emergency to be addressed during this pandemic. There is a need, therefore, for further understanding and research. Many causes could account for this. Initially, it was thought that the changes in life-style, in screen time, and sleeping habits could be the cause but if considered individually these are insufficient to explain this phenomenon. Likely, changes in central nervous mediators, and an increase in catecholamines could contribute as a trigger, however, these aspects are poorly studied and understood as well as the real perceptions of these children. Finally, staying more indoors has certainly exposed these children to specific contaminants working as endocrine disruptors which could also have had an effect. It would be of utmost importance to compare this phenomenon worldwide with appropriate studies in order to verify what is happening, and gain a new insight into the consequences of the covid-19 pandemic and into precocious puberty and for future prevention.

PaLS Study of Sleep Deprivation and Mental Health Consequences of the COVID-19 Pandemic among University Students: A Cross-Sectional Survey

The COVID-19 pandemic has changed the way many people live. To assess its impact on sleep quality and quantity, blue light exposure, and the mental health of Polish university students, a cross-sectional survey was conducted. Almost half of the participants were medical students (47.62%; n = 630). The majority of students were suffering from insomnia (58.13%, n = 769). Almost every third student was sleeping less than 7 h a day (30.39%, n = 402). Our study showed that a short sleep duration correlates with poorer mental health outcomes. Respondents who declared sadness and depression were more likely to suffer from insomnia (OR = 5.6997, 95% CI: 4.3641–7.4441). Difficulty with tasks was also more likely to co-occur with insomnia (OR = 5.4723, 95% CI: 4.3007 to 6.9630). The results of this study showed that the COVID-19 pandemic contributed to the deterioration of sleep quality and quantity as well as the psychological well-being of Polish students. It is important to take steps to promote proper sleeping habits to alleviate the risk of mental health disorders in this group of people.