Autogenic training is a relaxation technique that uses systematic exercises to induce a general disconnection of the organism. It is used in conjunction with conventional medical care as part of ...disease management to relieve symptoms associated with chronic health problems and to improve well-being. The purpose of this systematic review is to evaluate the efficacy of autogenic training on psychological well-being, quality of life, and adverse effects in people living with chronic physical health problems.
The methodology used follows the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. Studies, published up to December 31, 2019, will be identified through searches in the following databases: MEDLINE, Web of Science, EMBASE, SCOPUS, PsychINFO, CINAHL, EBM Reviews, Google Scholar, Dissertations & Theses Global, Open Access Theses and Dissertations, OpenGrey, E-Theses Online Service, Grey Literature Report, eScholarship@McGill, Papyrus, and CorpusUL. All studies of randomized controlled trials that assess autogenic training as an intervention to improve psychological well-being and quality of life in adults aged 18 and older living with one or more chronic physical health problem will be considered eligible. The study selection, the data collection, and the evaluation of the risk of bias will be conducted independently and in duplicate by two reviewers. RoB 2 tool will be used to assess the risk of bias. Discrepancies will be resolved through discussion. A tabular and narrative synthesis of data is planned, and a meta-analysis will be done according to the quality of data. The primary outcomes will be general psychological distress, depression, and anxiety, and the secondary outcomes will be quality of life and adverse effects. The present protocol of systematic review is reporting following MECIR standards for the reporting of protocols and the PRISMA-P recommendations.
Autogenic training appears to be a promising therapy to improve psychological well-being and quality of life in people living with chronic physical health problems, but no recent reports have synthesized the available evidence in this population. The results of this review will examine and synthesize the evidence on the benefits and harms of autogenic training on psychological well-being and quality of life in people living with chronic physical health problems, thus supporting the development of best practices for complementary approaches.
PROSPERO CRD42018105347.
The genomics era has led to an increase in the dimensionality of data collected in the investigation of biological questions. In this context, dimension-reduction techniques can be used to summarise ...high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. This paper revisits one such approach, previously known as principal component of heritability and renamed here as principal component of explained variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this method optimises power; however, due to its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach is illustrated using three examples taken from the fields of epigenetics and brain imaging.
To assess whether use of antidepressants with strong inhibition of serotonin reuptake is associated with a decreased incidence of ischemic stroke and myocardial infarction (MI).
We conducted a cohort ...study using the UK Clinical Practice Research Datalink and considering new users of selective serotonin reuptake inhibitors (SSRIs) or third-generation antidepressants who were ≥18 years of age between 1995 and 2014. Using a nested case-control approach, we matched each case of a first ischemic stroke or MI identified during follow-up with up to 30 controls on age, sex, calendar time, and duration of follow-up. We estimated incidence rate ratios (RRs) and 95% confidence intervals (CIs) of each outcome associated with current use of strong compared with weak inhibitors of serotonin reuptake using conditional logistic regression.
The cohort included 938,388 incident users of SSRIs (n = 868,755) or third-generation antidepressants (n = 69,633). Mean age at cohort entry was 46 years (64% women). During follow-up, 15,860 cases of ischemic stroke and 8,626 cases of MI were identified and matched to 473,712 and 258,022 controls, respectively. Compared with current use of weak inhibitors of serotonin reuptake, current use of strong inhibitors was associated with a decreased rate of ischemic stroke (RR 0.88, 95% CI 0.80-0.97), but the effect size was smaller in some sensitivity analyses. The rate of MI was similar between strong and weak inhibitors (RR 1.00, 95% CI 0.87-1.15).
Our large population-based study suggests that antidepressants strongly inhibiting serotonin reuptake may be associated with a small decrease in the rate of ischemic stroke.
Understanding the genetic basis of complex traits has been an ongoing quest for many researchers. Technological advances in data generation from multiple levels of biological systems, including DNA ...sequence data, RNA expression levels, methylation patterns, other epigenetic markers, proteomics, and metabolomics have driven the eld of translational bioinformatics for the past decade, producing ever-increasing amounts of data (Ritchie et al., (2015)). In the field of neuroscience, molecular and cellular data are also coupled with brain imaging data, which measures structural and functional information during mental and behavioral activity. As it becomes more commonplace for biomedical researchers to perform multiple assays on the same set of patient samples, methods for the integrative analysis of two or more high-dimensional data sets will become increasingly important (Witten et al., (2009)). In this thesis, our work is motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI), which combines information from brain imaging, genetics, and phenotypic data. A data integration strategy will need to be implemented to analyze all these data sets jointly. The framework we propose focuses on identifying the subsets of features in ADNI that possibly act through the same biological pathways leading to the phenotype of interest. In other words, our objective is to predict phenotypes jointly with the most highly shared information among the data sets. The methods we propose are based on a component-based data reduction strategy, such that components from different data sets are maximally correlated with each other. In order to achieve our objective, we rst propose nine statistical frameworks that are evaluated trough simulations under several scenarios. The framework with the best performance, according to this set of simulation is then evaluated in greater details in a second set of well-defined simulations. We also demonstrate the performance of the selected framework on the ADNI data. We also compare our results with a technique called Regularized Generalized Canonical Correlation Analysis (RGCCA) (Tenenhaus et al., (2011), Tenenhaus et al., (2014)).
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