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Group-level studies showed associations between depressive symptoms and circadian rhythm elements, though whether these associations replicate at the within-person level remains unclear. We investigated whether changes in circadian rhythm elements (namely, rest-activity rhythm, physical activity, and sleep) occur close to depressive symptom transitions and whether there are differences in the amount and direction of circadian rhythm changes in individuals with and without transitions. We used 4 months of actigraphy data from 34 remitted individuals tapering antidepressants (20 with and 14 without depressive symptom transitions) to assess circadian rhythm variables. Within-person kernel change point analyses were used to detect change points (CPs) and their timing in circadian rhythm variables. In 69% of individuals experiencing transitions, CPs were detected near the time of the transition. No-transition participants had an average of 0.64 CPs per individual, which could not be attributed to other known events, compared to those with transitions, who averaged 1 CP per individual. The direction of change varied between individuals, although some variables showed clear patterns in one direction. Results supported the hypothesis that CPs in circadian rhythm occurred more frequently close to transitions in depression. However, a larger sample is needed to understand which circadian rhythm variables change for whom, and more single-subject research to untangle the meaning of the large individual differences.

Attachment theory states that children learn to trust in their parent's availability and support if they repeatedly experience that their parents respond sensitively to their needs during distress. Attachment is thus developed and shaped by day-to-day interactions, while at the same time, each interaction is a momentary expression of the attachment relation. How attachment-related behaviors of mother and child follow upon each other during interactions in middle childhood, and how these sequences differ in function of attachment quality, has hardly been studied up to now. To fill this gap, we analyzed the micro-coded interaction of 55 mother-child dyads (27 girls, 28 boys, mean age: 10.3) after a standardized stress-induction. Results reveal that all mother-child dyads show a loop between positive mother and child behaviors. This pattern is complemented with a loop of negative mother and child behaviors in low-trust and more avoidantly attached children: these children tend to handle negative mother behavior less well as they show more negative behavior and less positive behavior in response to negative maternal behavior. More anxiously attached children also show less positive behavior, but react positively on collaborative interactions. The micro-coded interactions thus reveal important insights that inform practitioners and advance attachment theory.

Background Data analysis methods are usually subdivided in two distinct classes: There are methods for prediction and there are methods for exploration. In practice, however, there often is a need to learn from the data in both ways. For example, when predicting the antibody titers a few weeks after vaccination on the basis of genomewide mRNA transcription rates, also mechanistic insights about the effect of vaccinations on the immune system are sought. Principal covariates regression (PCovR) is a method that combines both purposes. Yet, it misses insightful representations of the data as these include all the variables. Results Here, we propose a sparse extension of principal covariates regression such that the resulting solutions are based on an automatically selected subset of the variables. Our method is shown to outperform competing methods like sparse principal components regression and sparse partial least squares in a simulation study. Furthermore good performance of the method is illustrated on publicly available data including antibody titers and genomewide transcription rates for subjects vaccinated against the flu: the selected genes by sparse PCovR are higly enriched for immune related terms and the method predicts the titers for an independent test sample well. In comparison, no significantly enriched terms were found for the genes selected by sparse partial least squares and out-of-sample prediction was worse. Conclusions Sparse principal covariates regression is a promising and competitive tool for obtaining insights from high-dimensional data. Availability The source code implementing our proposed method is available from GitHub, together with all scripts used to extract, pre-process, analyze, and post-process the data: https://github.com/katrijnvandeun/SPCovR .

The spread of COVID-19 and the implementation of various containment strategies across the world have seriously disrupted people’s everyday life, and it is especially uncertain what the psychological impact of this pandemic will be for vulnerable individuals, such as psychiatric (ex-)patients. Governments fear that this virus outbreak may prelude a major mental health crisis, and psychiatrists launch critical calls to flatten an upcoming mental ill-health surge. Here, we aim to add nuance to the idea that we are heading towards a mental health pandemic and that psychiatric populations will uvoidably (re)develop psychopathology. Despite being subjected to the same challenges posed by COVID-19, we argue that people with a history of psychiatric illness will psychologically deal with this adversity in different ways. To showcase the short-term differential impact of COVID-19 on patients’ mental health, we present the day-to-day emotion and symptom trajectories of different psychiatric patients that took part in an experience sampling study before, during, and after the start of the first wave of the COVID-19 pandemic in March 2020 and associated lockdown measures in Belgium. Piecewise regression models show that not all patients’ psychological well-being is affected to a similar degree. As such, we argue that emphasizing human resilience, also among the more vulnerable in society, may be opportune in these unsettling times.

Profile similarity measures are used to quantify the similarity of two sets of ratings on multiple variables. Yet, it remains unclear how different measures are distinct or overlap and what type of information they precisely convey, making it unclear what measures are best applied under varying circumstances. With this study, we aim to provide clarity with respect to how existing measures interrelate and provide recommendations for their use by comparing a wide range of profile similarity measures. We have taken four steps. First, we reviewed 88 similarity measures by applying them to multiple cross-sectional and intensive longitudinal data sets on emotional experience and retained 43 useful profile similarity measures after eliminating duplicates, complements, or measures that were unsuitable for the intended purpose. Second, we have clustered these 43 measures into similarly behaving groups, and found three general clusters: one cluster with difference measures, one cluster with product measures that could be split into four more nuanced groups and one miscellaneous cluster that could be split into two more nuanced groups. Third, we have interpreted what unifies these groups and their subgroups and what information they convey based on theory and formulas. Last, based on our findings, we discuss recommendations with respect to the choice of measure, propose to avoid using the Pearson correlation, and suggest to center profile items when stereotypical patterns threaten to confound the computation of similarity.

To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis and factor analysis strategies at their disposal. In this article, we focus on three types of multiblock component methods—namely, principal component analysis on each data block separately, simultaneous component analysis, and the recently proposed clusterwise simultaneous component analysis, which is a generic and flexible approach that has no counterpart in the factor analysis tradition. We describe the steps to take when applying those methods in practice. Whereas plenty of software is available for fitting factor analysis solutions, up to now no easy-to-use software has existed for fitting these multiblock component analysis methods. Therefore, this article presents the MultiBlock Component Analysis program, which also includes procedures for missing data imputation and model selection.

Obsessions – recurrent unwanted intrusive thoughts – are one of the two pillars of the Obsessive Compulsive Disorder (OCD). Although OCD has been reported across many different cultures, research on these cultural variations is hampered by the lack of cross-culturally sound instruments to assess intrusive thoughts. The aim of the current study is to investigate the psychometric properties of the recently developed Leuven Obsessiol Intrusions Instrument (LOII) in two different cultural contexts. Turkish (N = 663) and Belgian (N = 496) participants were sampled from non-clinical student populations. Results from confirmatory factor alyses yielded a shortened version of the LOII (i.e., LOII-R) with a four-factor solution – aggressive, sexual, and contamition intrusions, and ‘just-right’ doubts – as the best fitting model across both cultures. The model met most criteria for strong measurement invariance, and proved to be both valid and reliable. The results of this study suggest that the LOII-R is a good candidate for cross-cultural studies on obsessiol intrusions.

IntroductionCluster analysis, a machine learning-based and data-driven technique for identifying groups in data, has demonstrated its potential in a wide range of contexts. However, critical appraisal and reproducibility are often limited by insufficient reporting, ultimately hampering the interpretation and trust of key stakeholders. The present paper describes the protocol that will guide the development of a reporting guideline and checklist for studies incorporating cluster analyses—Transparent Reporting of Cluster Analyses.Methods and analysisFollowing the recommended steps for developing reporting guidelines outlined by the Enhancing the QUAlity and Transparency Of health Research Network, the work will be divided into six stages. Stage 1: literature review to guide development of initial checklist. Stage 2: drafting of the initial checklist. Stage 3: internal revision of checklist. Stage 4: Delphi study in a global sample of researchers from varying fields (n=≈) to derive consensus regarding items in the checklist and piloting of the checklist. Stage 5: consensus meeting to consolidate checklist. Stage 6: production of statement paper and explanation and elaboration paper. Stage 7: dissemination via journals, conferences, social media and a dedicated web platform.Ethics and disseminationDue to local regulations, the planned study is exempt from the requirement of ethical review. The findings will be disseminated through peer-reviewed publications. The checklist with explanations will also be made available freely on a dedicated web platform (troca-statement.org) and in a repository.

Dynamic networks are valuable tools to depict and investigate the concurrent and temporal interdependencies of various variables across time. Although several software packages for computing and drawing dynamic networks have been developed, software that allows investigating the pairwise associations between a set of binary intensive longitudinal variables is still missing. To fill this gap, this paper introduces an R package that yields contingency measure-based networks (ConNEcT). ConNEcT implements different contingency measures: proportion of agreement, corrected and classic Jaccard index, phi correlation coefficient, Cohen’s kappa, odds ratio, and log odds ratio. Moreover, users can easily add alternative measures, if needed. Importantly, ConNEcT also allows conducting non-parametric significance tests on the obtained contingency values that correct for the inherent serial dependence in the time series, through a permutation approach or model-based simulation. In this paper, we provide an overview of all available ConNEcT features and showcase their usage. Addressing a major question that users are likely to have, we also discuss similarities and differences of the included contingency measures.

Major depressive disorder (MDD) and borderline personality disorder (BPD) are characterized by disturbed patterns of emotional and interpersonal functioning, which might imply altered use of emotion regulation in interpersonal contexts. In the current study, we examined how individuals with MDD and/or BPD differ from healthy controls in (1) their overall daily life use of expressive suppression and social sharing and (2) their tendency to adjust the use of these strategies to the emotional context (i.e., preceding negative and positive affect). Thirty‐four individuals with MDD, 20 individuals with BPD, 19 individuals with comorbid MDD and BPD, and 40 healthy controls participated in a week of experience sampling during which they reported their use of expressive suppression, social sharing, and experienced negative and positive affect. The results indicated that all clinical groups reported more expressive suppression and social sharing in their daily lives than healthy controls. Group differences remained when controlling for differences in mean experienced affect, except for increased suppression for MDD and increased sharing for BPD and comorbid MDD and BPD, which seemed related to these participants' overall higher levels of negative affect. Additionally, associations between within‐person fluctuations in negative or positive affect and subsequent strategy use were equally strong for clinical and control participants, indicating that clinical groups did not differentially adjust the use of suppression and sharing to the emotional context. In conclusion, individuals with MDD and/or BPD showed increased use of suppression and sharing in daily life, which might contribute to, or follow from their emotional and interpersonal difficulties.