Identifying dietary and physical activity (PA) patterns in Russian youths and examining their dependence on gender, age, family characteristics and area of residence features. The research involved ...783 school students 10-17 years of age and their parents living in the cities of Moscow and Murmansk. Using the principal component analysis, four integral indices were identified that characterized the habits and dietary patterns, PA and lifestyle. Boys, compared with girls, were more likely to consume unhealthy foods but less likely to practice malnutrition. Within the age ranges of 13-15 years old and 16-17 years old, the proportions of school students with a poor sleep pattern and low PA were higher than in children 10-12 years of age. In smoking families, children were less likely to consume healthy foods and more likely to eat unhealthy foods. In Murmansk school students, compared with their Moscow peers, a reduction in both sleep time and PA was observed less often. Our study demonstrated that the most significant factors of a balanced and healthy diet, rational daily routine and lifestyle in school students were their gender and age characteristics, as well as some contextual factors.
The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify ...deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.
•Aggressive behaviours in individuals with Autism Spectrum Disorder (ASD) reduced during COVID-19 social restrictions.•The extent of perceived care burden among caregivers increased during COVID-19 ...social restrictions.•Individuals with coexisting ASD and Intellectual disability coped relatively better compared to the group with ASD alone.•Caregivers of male individuals with ASD reported an increase in the level of perceived care burden more often.
Previous research has established an association between changes to the daily routine of individuals with Autism Spectrum Disorder (ASD) and increase in maladaptive behaviours. The relationship between maladaptive behaviours in autistic individuals and increase in care burden among their caregivers is also well established. However, no study has yet examined these associations in the context of the COVID-19 pandemic. The main aim of this study was to explore the impact of COVID-19 restrictions on autistic individuals and their caregivers.
A questionnaire-based cross-sectional study conducted with the caregivers of 58 autistic individuals across the mental health services at Hamad Medical Corporation, Doha, Qatar. The extent of care burden was measured using the Care Burden Interview, whereas changes in behaviour in autistic individuals was assessed using the Revised Overt Aggression Scale.
A total of 58 caregivers participated in the study. Out of these, 24 (41 %) reported a clinically significant increase in their care burden. Among caregivers reporting an increase in care burden, two-third were caring for individuals whose behaviour either remained unchanged or improved during social restrictions. Nine autistic people (15.5 %) were reported to have no aggression prior to the implementation of COVID-19 social restrictions compared to 13 (22.4%) individuals during COVID-19 social restrictions. Minimal, mild and moderate aggression were reported in 27 (46.6 %), 21 (36.2 %), and 1 (1.7 %) patients respectively, before COVID-19 social restrictions compared to 29 (50 %), 15 (25.9), and 1 (1.7 %) during COVID-19 restrictions. Severe aggression was not reported in any patient either before or during COVID-19 social restrictions.
This study showed reduced levels of aggression in autistic individuals but an increase in care burden among their caregivers during the COVID-19 social restrictions highlighting the need of supporting patients and caregivers alike.
The study examines certain aspects of the physiological and mental states of students caused by the conditions of social isolation caused by the COVID-19 pandemic and the need to study remotely. 189 ...respondents (84.6% female, 15.4% male) aged 17 to 27 years were interviewed. The data was collected remotely (using a Google form) between November and December 2020. The following methods were used: the author’s questionnaire aimed at evaluating the regime moments, eating habits and physical activity of students, as well as their academic load; the questionnaire “Well-Being, Activity, Mood”; the Beck Depression Inventory; the Spielberger-Hanin anxiety scale. The results showed that in the conditions of distance learning, the lifestyle of a significant proportion of students is characterized by pronounced deviations from the recommended values. Sleep deprivation, an unbalanced diet, a decrease in physical activity — all this, along with a pronounced academic load of medical students, leads to a deterioration in their functional state. A close relationship between the physical and emotional well-being of students was revealed. At the same time, the main predictors of physical well-being are regular physical activity and commitment to a healthy life- style, while emotional well-being is largely determined by the personal characteristics of students and the ability to adapt to a new learning regime.
Blood pressure (BP) is an important indicator of an individual's health status and is closely related to daily behaviors. Thus, a continuous daily measurement of BP is critical for hypertension ...control. To assist continuous measurement, BP prediction based on non-physiological data (ubiquitous mobile phone data) was studied in the research. An algorithm was proposed that predicts BP based on patients’ daily routine, which includes activities such as sleep, work, and commuting. The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP. A half-year data set from October 2017 of 320 individuals, including telecom data and BP measurement data, was analyzed. Two hierarchical Bayesian topic models were used to extract individuals’ location-driven daily routine patterns (topics) and calculate probabilities among these topics from their day-level mobile trajectories. Based on the topic probability distribution and patients’ contextual data, their BP were predicted using different models. The prediction model comparison shows that the long short-term memory (LSTM) method exceeds others when the data has a high dependency. Otherwise, the Random Forest regression model outperforms the LSTM method. Also, the experimental results validate the effectiveness of the topics in BP prediction.
This study analyzes the impact of experiencing a disaster on subsequent risk recognition and evacuation behavior using data collated from the interview of victims of the flood and landslides that ...followed the 2014 Hiroshima Heavy Rain Disaster. The high accuracy of the storm and flood damage prediction system has made it possible to limit human casualties by routinizing advance evacuation behavior. The study explores conditions for the routinization of evacuation behavior and its findings are as follows: (i) a series of experiences such as timing of incidental awareness, evacuation, housing damage, and human damage define the damage recognition of each victim. The difference between each damage recognition has different influences on their post-disaster risk recognition and behaviors; (ii) experiencing severe disasters generally enhances disaster risk recognition. However, whether it promotes advanced evacuation behavior is dependent on the magnitude of the damage and pre-disaster risk recognition. If risk recognition is ambiguous, the effect of the experience is minimal even if the damage is severe; (iii) for disaster victims to inculcate an evacuation behavior in preparation for the next disaster, they must first have clear pre-disaster risk recognition mechanisms. It is also necessary to have a reliable destination that is incorporated into the daily life of the residents, which can serve as an evacuation site.
The authors assessed the impact of lockdown in response to the COVID-19 pandemic on routine-oriented lifestyle behaviors and symptoms of depression, anxiety, and insomnia in South Africans.
In this ...observational study, 1048 adults (median age = 27 y; n = 767 females; n = 473 students) responded to an online survey on work, exercise, screen, alcohol, caffeine and sleep behaviors, depression, anxiety, and insomnia before and during lockdown. Comparisons were made between males and females, and students and nonstudents.
During lockdown, males reported larger reductions in higher intensity exercise and alcohol use than females, while depressive symptoms increased more among females, more of whom also reported poorer sleep quality. Students demonstrated larger delays in work and sleep timing, greater increases in sitting, screen, sleep duration, napping, depression and insomnia and larger decreases in work hours, exercise time, and sleep regularity compared with nonstudents.
Students experienced more changes in their routine-oriented behaviors than nonstudents, coupled with larger increases in depression and insomnia. The dramatic change in their work and sleep timing suggests habitual routines that are at odds with their chronotype, with their sleep changes during lockdown likely reflecting "catch-up" sleep in response to accumulated sleep debt under usual routines.
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