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BackgroundIdentifying healthy lifestyle behaviours that promote psychological wellbeing is crucial to preventing mental disorders. The aim of the current study was to evaluate the individual and combined associations between different aspects of everyday life and mental health within a representative community sample in Germany.MethodThe study was conducted in 2012/2013 and included 7,937 participants representative of the German population. Lifestyle factors were assessed via self-report and included frequency of physical and mental activity, alcohol consumption, smoking, body mass index as well as circadian and social regularity. Outcome variables were depression, anxiety, stress and satisfaction with life.ResultsAll lifestyle factors were associated with the mental health outcomes. Better mental health was linked to higher frequency of physical and mental activity, moderate alcohol consumption (i.e. not increased or no alcohol consumption), non-smoking, a body mass index within the range of normal to overweight (i.e. not underweight or obese) and a regular life rhythm. The more healthy lifestyle choices an individual makes, the higher life satisfaction and lower psychological distress he or she tends to have.ConclusionsThe current study underlines the importance of healthy lifestyle choices in respect to psychological wellbeing.

The relationship between resilience and mental health was examined in three phases over 4 years in a sample of 314 college students in China. The present study aimed to gain insight into the reciprocal relationship of higher levels of resilience predicting lower levels of mental ill-being, and higher levels of positive mental health, and vice versa, and track changes in both resilience, mental ill-being and positive mental health over 4 years. We used the Depression Anxiety Stress, the Positive Mental Health, and the Resilience Scales. Results revealed that first-year students and senior year students experienced higher negative mental health levels and lower positive mental health levels than junior year students. Cross-lagged structural equation modeling analyses showed that resilience could significantly predict mental health status in the short term, namely within 1 year from junior to senior year. However, the predicting function of resilience for mental health is not significant in the long term, namely within 2 years from freshman to junior year. Additionally, the significant predicting function of individuals' mental health for resilience is fully verified for both the short and long term. These results indicate that college mental health education and interventions could be tailored based on students' year in college.

BackgroundSocial rhythm refers to the regularity with which one engages in social activities throughout the week, and has established links with bipolar disorder, as well as some links with depression and anxiety. The aim of the present study is to examine social rhythm and its relationship to various aspects of health, including physical health, negative mental health, and positive mental health.MethodQuestionnaire data were obtained from a large-scale multi-national sample of 8095 representative participants from the U.S., Russia, and Germany.ResultsResults indicated that social rhythm irregularity is related to increased reporting of health problems, depression, anxiety, and stress. In contrast, greater regularity is related to better overall health state, life satisfaction, and positive mental health. The effects are generally small in size, but hold even when controlling for gender, marital status, education, income, country, and social support. Further, social rhythm means differ across Russia, the U.S., and Germany. Relationships with mental health are present in all three countries, but differ in magnitude.ConclusionsSocial rhythm irregularity is related to mental health in Russia, the U.S., and Germany.

The present study introduces a short screening instrument for the measurement of experienced general daily stressors across different life domains that can be used in large-scale studies. The brief daily stressors screening tool (BDSST) assesses the experience of general daily stressors in eight distinct life domains. General daily stressors are indicated for the past 12-months on a five-point Likert scale. The present study evaluates the BDSST in two successive studies. The first study was conducted in a representative German sample (n = 7,849). The second study was conducted to assess one-month-retest-stability in another representative German sample (n = 1,294). The BDSST shows promising psychometric properties. It has a skewed positive distribution, internal consistency and stability are acceptable and its one-factor structure was confirmed in a bifactor confirmatory factor analysis. The BDSST is a reliable and valid short instrument for the assessment of experienced general daily stressors in large-scale studies and routine clinical practise. For in-depth clinical assessment, it can be used to identify relevant life domains for further investigation.

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Lithium battery energy storage system (ESS) has fast response speed, but it is in service for a long time. Therefore, in the research of secondary frequency regulation, it is necessary to establish a battery model that can reflect the short-term polarization and estimate SOC accurately under long-term service. Therefore, we propose a method of variable parameter loss model of lithium battery suitable for secondary frequency modulation of power system and optimize its control strategy based on the relationship between the independent variable SOC and the internal electrical parameters of the battery. Firstly, we discretize the state equation of single cell to obtain the PNGV model with variable parameters. Then the SOC estimation method is improved by using the modified extended Kalman filter method. Finally, we did an analysis about the economy of the ESS participating in secondary frequency regulation by using the loss model, and the optimal control strategy is designed. The HPPC experiment of the ESS unit is designed by Simulink, and the accuracy of the simulation and experimental curves has been analyzed. Finally, the economic differences between the traditional control strategy and the optimal control strategy considering loss are compared. The results show that the error of the variable parameter model in the fitting experiment is no more than 0.41%, and the accuracy of SOC estimation is 12.96% higher than that of the traditional estimation. The economy of the optimized control strategy is 15.38% higher than that of the traditional control strategy.