PATROL: Participatory Activity Tracking and Risk Assessment for Anonymous Elderly Monitoring.

There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring technologies are therefore on the rise. In this study, we propose an anonymous elderly monitoring system to track potential risks in everyday activities such as sleep, medication, shower, and food intake using a smartphone application. We design and implement an activity visualization and notification strategy method to identify risks easily and quickly. For evaluation, we added risky situations in an activity dataset from a real-life experiment with the elderly and conducted a user study using the proposed method and two other methods varying in visualization and notification techniques. With our proposed method, 75.2% of the risks were successfully identified, while 68.5% and 65.8% were identified with other methods. The average time taken to respond to notification was 176.46 min with the proposed method, compared to 201.42 and 176.9 min with other methods. Moreover, the interface analyzing and reporting time was also lower (28 s) in the proposed method compared to 38 and 54 s in other methods.

Comparison of the effects of indoor and outdoor exercise on creativity: an analysis of EEG alpha power.

Previous research finds that natural environments and exercise enhance creativity. In this within-subjects design study, we examined the influence of outdoor exercise that combined a natural environment with exercise on creativity compared to an indoor exercise control condition by analyzing cognitive activities related to creativity. The participants performed an Alternative Uses Test (AUT), in which ordinary objects are presented to the participants (e.g., a brick), to prompt as many ideas for alternative uses as possible, which are transformed into a creativity score, after indoor running and outdoor running. During the test, brain activity was recorded using electroencephalography (EEG) and a short version flow state scale (FSS) was completed after the experiment. Results showed that while AUT scores did not significantly differ between conditions, alpha band activity at the parietal occipital region involved in divergent creativity increased during the AUT after outdoor exercise while it did not during the AUT after indoor exercise. In addition, FSS scores for positive emotional experience and absorption were higher after outdoor exercise than after indoor exercise. Our results from the FSS suggest that exercise in a natural environment is perceived subjectively differently from indoor exercise, participants report greater experiences of flow compared to indoor exercise, and the EEG measures objectively indicate enhanced cognitive activity in a creativity task after outdoor exercise. This study suggests that outdoor exercise increases neuronal activity in brain regions related to creativity. Further research is needed to understand how this can lead to increased creativity.

Criteria for detection of possible risk factors for mental health problems in undergraduate university students.

Introduction: Developing approaches for early detection of possible risk clusters for mental health problems among undergraduate university students is warranted to reduce the duration of untreated illness (DUI). However, little is known about indicators of need for care by others. Herein, we aimed to clarify the specific value of study engagement and lifestyle habit variables in predicting potentially high-risk cluster of mental health problems among undergraduate university students. Methods: This cross-sectional study used a web-based demographic questionnaire [the Utrecht Work Engagement Scale for Students (UWES-S-J)] as study engagement scale. Moreover, information regarding life habits such as sleep duration and meal frequency, along with mental health problems such as depression and fatigue were also collected. Students with both mental health problems were classified as high risk. Characteristics of students in the two groups were compared. Univariate logistic regression was performed to identify predictors of membership. Receiver Operating Characteristic (ROC) curve was used to clarify the specific values that differentiated the groups in terms of significant predictors in univariate logistic analysis. Cut-off point was calculated using Youden index. Statistical significance was set at p < 0.05. Results: A total of 1,644 students were assessed, and 30.1% were classified as high-risk for mental health problems. Significant differences were found between the two groups in terms of sex, age, study engagement, weekday sleep duration, and meal frequency. In the ROC curve, students who had lower study engagement with UWES-S-J score < 37.5 points (sensitivity, 81.5%; specificity, 38.0%), <6 h sleep duration on weekdays (sensitivity, 82.0%; specificity, 24.0%), and < 2.5 times of meals per day (sensitivity, 73.3%; specificity, 35.8%), were more likely to be classified into the high-risk group for mental health problems. Conclusion: Academic staff should detect students who meet these criteria at the earliest and provide mental health support to reduce DUI among undergraduate university students.