Background: Epidemiological data indicate that 75% of subjects with major psychiatric disorders have their onset of illness in the age range of 17-24 years. An estimated 35-50% of college and university students drop out prematurely due to insufficient coping skills under chronic stress, while 85% of students receiving a psychiatric diagnosis withdraw from college/university prior to the completion of their education. In this study, we aimed at developing standardized means of identifying students with insufficient coping skills under chronic stress and at risk for mental health problems. Sampling and Methods: A sample of 1,217 college students from 3 different sites in the USA and Switzerland completed 2 self-report questionnaires: the Coping Strategies Inventory (COPE) and the Zurich Health Questionnaire (ZHQ), which assesses ‘regular exercises', ‘consumption behavior', ‘impaired physical health', ‘psychosomatic disturbances' and ‘impaired mental health'. The data were subjected to structure analyses by means of a neural network approach. We found 2 highly stable and reproducible COPE scales that explained the observed interindividual variation in coping behavior sufficiently well and in a socioculturally independent way. The scales reflected basic coping behavior in terms of ‘activity-passivity' and ‘defeatism-resilience', and in the sense of stable, socioculturally independent personality traits. Results: Correlation analyses carried out for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health. This underlined the role of insufficient coping behavior as a risk factor for physical and mental health problems. Conclusion: The combined COPE and ZHQ instruments appear to constitute powerful screening tools for insufficient coping skills under chronic stress and for risks of mental health problems.

The question of how to quantify insufficient coping behavior under chronic stress is of major clinical relevance. In fact, chronic stress increasingly dominates modern work conditions and can affect nearly every system of the human body, as suggested by physical, cognitive, affective and behavioral symptoms. Since freshmen students experience constantly high levels of stress due to tight schedules and frequent examinations, we carried out a 3-center study of 1,303 students from Italy, Spain and Argentina in order to develop socioculturally independent means for quantifying coping behavior. The data analysis relied on 2 self-report questionnaires: the Coping Strategies Inventory (COPE) for the assessment of coping behavior and the Zurich Health Questionnaire which assesses consumption behavior and general health dimensions. A neural network approach was used to determine the structural properties inherent in the COPE instrument. Our analyses revealed 2 highly stable, socioculturally independent scales that reflected basic coping behavior in terms of the personality traits activity-passivity and defeatism-resilience. This replicated previous results based on Swiss and US-American data. The percentage of students exhibiting insufficient coping behavior was very similar across the study sites (11.5-18.0%). Given their stability and validity, the newly developed scales enable the quantification of basic coping behavior in a cost-efficient and reliable way, thus clearing the way for the early detection of subjects with insufficient coping skills under chronic stress who may be at risk of physical or mental health problems.

Background: Human speech is greatly influenced by the speakers' affective state, such as sadness, happiness, grief, guilt, fear, anger, aggression, faintheartedness, shame, sexual arousal, love, amongst others. Attentive listeners discover a lot about the affective state of their dialog partners with no great effort, and without having to talk about it explicitly during a conversation or on the phone. On the other hand, speech dysfunctions, such as slow, delayed or monotonous speech, are prominent features of affective disorders. Methods: This project was comprised of four studies with healthy volunteers from Bristol (English: n = 117), Lausanne (French: n = 128), Zurich (German: n = 208), and Valencia (Spanish: n = 124). All samples were stratified according to gender, age, and education. The specific study design with different types of spoken text along with repeated assessments at 14-day intervals allowed us to estimate the ‘natural' variation of speech parameters over time, and to analyze the sensitivity of speech parameters with respect to form and content of spoken text. Additionally, our project included a longitudinal self-assessment study with university students from Zurich (n = 18) and unemployed adults from Valencia (n = 18) in order to test the feasibility of the speech analysis method in home environments. Results: The normative data showed that speaking behavior and voice sound characteristics can be quantified in a reproducible and language-independent way. The high resolution of the method was verified by a computerized assignment of speech parameter patterns to languages at a success rate of 90%, while the correct assignment to texts was 70%. In the longitudinal self-assessment study we calculated individual ‘baselines' for each test person along with deviations thereof. The significance of such deviations was assessed through the normative reference data. Conclusions: Our data provided gender-, age-, and language-specific thresholds that allow one to reliably distinguish between ‘natural fluctuations' and ‘significant changes'. The longitudinal self-assessment study with repeated assessments at 1-day intervals over 14 days demonstrated the feasibility and efficiency of the speech analysis method in home environments, thus clearing the way to a broader range of applications in psychiatry.

Background. Computerized speech analysis (CSA) is a powerful method that allows one to assess stress-induced mood disturbances and affective disorders through repeated measurements of speaking behavior and voice sound characteristics. Over the past decades CSA has been successfully used in the clinical context to monitor the transition from "affectively disturbed" to "normal" among psychiatric patients under treatment. This project, by contrast, aimed to extend the CSA method in such a way that the transition from "normal" to "affected" can be detected among subjects of the general population through 10-20 self-assessments. Methods. Central to the project was a normative speech study of 5 major languages (English, French, German, Italian, Spanish). Each language comprised 120 subjects stratified according to gender, age, and education with repeated assessments at 14-day intervals (total n=697). In a first step, we developed a multivariate model to assess affective state and stress-induced bodily reactions through speaking behavior and voice sound characteristics. Secondly, we determined language-, gender-, and age-specific thresholds that draw a line between "natural fluctuations" and "significant changes". Thirdly, we implemented the model along with the underlying methods and normative data in a self-assessment "voice app" for laptops, tablets, and smartphones. Finally, a longitudinal selfassessment study of 36 subjects was carried out over 14 days to test the performance of the CSA method in home environments. Results. The data showed that speaking behavior and voice sound characteristics can be quantified in a reproducible and language-independent way. Gender and age explained 15-35% of the observed variance, whereas the educational level had a relatively small effect in the range of 1-3%. The self-assessment "voice app" was realized in modular form so that additional languages can simply be "plugged-in", once the respective normative data become available. Results of the longitudinal self-assessment study in home environments demonstrated that CSA methods work well under most circumstances. Conclusions. We have successfully developed and tested a self-assessment CSA method that can monitor transitions from "normal" to "affected" in subjects of the general population in the broader context of mood disorders. Our easy-to-use "voice app" evaluates sequences of 10-20 repeated assessments and watches for affect-and stress-induced deviations from baseline that exceed language-, gender-, and age-specific thresholds. Specifically, the "voice app" provides users with stress-related "biofeedback" and can help to identify that 10-15% subgroup of the general population that exhibits insufficient coping skills under chronic stress and may benefit from early detection and intervention prior to developing clinically relevant symptoms. Each language comprised 120 subjects stratified according to gender, age, and education with repeated assessments at 14-day intervals (total n = 697). In a first step, we developed...

No abstract

No abstract