Call for the application of a biopsychosocial and interdisciplinary approach to the return-to-sport framework of snow sports athletes

Snow sports such as alpine skiing or snowboarding are associated with a high risk of injury and reinjury and are subject to a very special environment with specific rehabilitation challenges that must be addressed. Due to geographic decentralisation, seasonal climatic limitations, alternation of training in off-snow and on-snow settings and unique loading patterns of practising these sports, special rehabilitation structures and processes are required compared with other sports. In addition, returning to preinjury performance requires a high level of confidence and a resumption of risk-taking in demanding situations such as high-speed skiing and high-amplitude jumps. A biopsychosocial and interdisciplinary approach can be viewed as a holistic, athlete-centred approach that promotes interprofessional communication and collaboration. This is particularly central for managing the physical/biological, psychological and social demands of injury management for snow sports. It can help ensure that rehabilitation content is well coordinated and tailored to individual needs. This is because transitions between different rehabilitation phases and caring professionals are well aligned, and rehabilitation is understood not only as purely 'physical recovery' but also as 'psychological recovery' considering the snow sports-specific setting with specific social norms. Ultimately, this may improve the rehabilitation success of snow sports athletes

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones have opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modelling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy=82% and sensitivity=82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future

Ecological Momentary Assessment and Other Digital Technologies for Capturing Daily Life in Mental Health

Improved mental health care requires innovative strategies that are person centered, embedded in context, and capable of capturing meaningful symptom occurrence and nonoccurrence in daily life. Vulnerability and resilience (strength) require equal attention. Traditional static assessments are insufficient to capture the dynamic variation required to understand how to develop well-being. mHealth digital technologies, such as Ecological Momentary Assessment strategies and passive sensor tracking provide attractive solutions. This chapter develops a personalized medicine using mHealth technology and evaluates its merit for mental health care. In the future, ecological momentary assessment and passive monitoring using sensors may blend to form more powerful care methods for use in mental health.</p

Use of the experience sampling method in the context of clinical trials

Objective The experience sampling method (ESM) is a structured diary technique to appraise subjective experiences in daily life. It is applied in psychiatric patients, as well as in patients with somatic illness. Despite the potential of ESM assessment, the improved logistics and its increased administration in research, its use in clinical trials remains limited. This paper introduces ESM for clinical trials in psychiatry and beyond. Methods ESM is an ecologically valid method that yields a comprehensive view of an individual's daily life. It allows the assessment of various constructs (eg, quality of life, psychopathology) and psychological mechanisms (eg, stress-sensitivity, coping). These constructs are difficult to assess using cross-sectional questionnaires. ESM can be applied in treatment monitoring, as an ecological momentary intervention, in clinical trials, or in single case clinical trials. Technological advances (eg, smartphone applications) make its implementation easier. Results Advantages of ESM are highlighted and disadvantages are discussed. Furthermore, the ecological nature of ESM data and its consequences are explored, including the potential pitfalls of ambiguously formulated research questions and the specificities of ESM in statistical analyses. The last section focuses on ESM in relation to clinical trials and discusses its future use in optimising clinical decision-making. Conclusions ESM can be a valuable asset in clinical trial research and should be used more often to study the benefits of treatment in psychiatry and somatic health

Ecological momentary assessment and other digital technologies for capturing daily life in mental health

Improved mental health care requires innovative strategies that are person centered, embedded in context, and capable of capturing meaningful symptom occurrence and nonoccurrence in daily life. Vulnerability and resilience (strength) require equal attention. Traditional static assessments are insufficient to capture the dynamic variation required to understand how to develop well-being. mHealth digital technologies, such as Ecological Momentary Assessment strategies and passive sensor tracking provide attractive solutions. This chapter develops a personalized medicine using mHealth technology and evaluates its merit for mental health care. In the future, ecological momentary assessment and passive monitoring using sensors may blend to form more powerful care methods for use in mental health

Biotransformation of the Major Fungal Metabolite 3,5-Dichloro- p-Anisyl Alcohol under Anaerobic Conditions and Its Role in Formation of Bis(3,5-Dichloro-4-Hydroxyphenyl)methane

Higher fungi have a widespread capacity for biosynthesis of organohalogens. Commonly occurring chloroaromatic fungal metabolites can end up in anaerobic microniches at the boundary of fungal colonies and wetland soils. The aim of this study was to investigate the environmental fate of a major fungal metabolite, 3,5-dichloro-p-anisyl alcohol, under anaerobic conditions. This compound was incubated with methanogenic sludge to study its biotransformation reactions. Initially, 3,5-dichloro-p-anisyl alcohol was readily demethylated in stoichiometric quantities to 3,5-dichloro-4-hydroxybenzyl alcohol. The demethylated product was converted further via two routes: a biotic route leading to the formation of 3,5-dichloro-4-hydroxybenzoate and 2,6-dichlorophenol, as well as an abiotic route leading to the formation of bis(3,5-dichloro-4-hydroxyphenyl)methane. In the first route, the benzyl alcohol moiety on the aromatic ring was oxidized, giving 3,5-dichloro-4-hydroxybenzoate as a transient or accumulating product, depending on the type of methanogenic sludge used. In sludge previously adapted to low-molecular-weight lignin from straw, a part of the 3,5-dichloro-4-hydroxybenzoate was decarboxylated, yielding detectable levels of 2,6-dichlorophenol. In the second route, 3,5-dichloro-4-hydroxybenzyl alcohol dimerized, leading to the formation of a tetrachlorinated bisphenolic compound, which was identified as bis(3,5-dichloro-4-hydroxyphenyl)methane. Since formation of this dimer was also observed in incubations with autoclaved sludge spiked with 3,5-dichloro-4-hydroxybenzyl alcohol, it was concluded that its formation was due to an abiotic process. However, demethylation of the fungal metabolite by biological processes was a prerequisite for dimerization. The most probable reaction mechanism leading to the formation of the tetrachlorinated dimer in the absence of oxygen is presented, and the possible environmental implications of its natural occurrence are discussed