Putting the spotlight on individual-specific psychosomatic processes:An introduction to the special issue on intensive longitudinal research methods in psychosomatic research

status: publishe

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time:A Tutorial Showcasing Potential Applications

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.</p

Sparse common and distinctive covariates regression

Having large sets of predictors from multiple sources concerning the same observation units and the same criterion is becoming increasingly common in chemometrics. When analyzing such data, chemometricians often have multiple objectives: prediction of the criterion, variable selection, and identification of underlying processes associated to individual predictor sources or to several sources jointly. Existing methods offer solutions regarding the first two aims of uncovering the predictive mechanisms and relevant variables therein for a single block of predictor variables, but the challenge of uncovering joint and distinctive predictive mechanisms and the relevant variables therein in the multisource setting still needs to be addressed. To this end, we present a multiblock extension of principal covariates regression that aims to find the complex mechanisms in which several or single sources may be involved; taken together, these mechanisms predict an outcome of interest. We call this method sparse common and distinctive covariates regression (SCD‐CovR). Through a simulation study, we demonstrate that SCD‐CovR provides competitive solutions when compared with related methods. The method is also illustrated via an application to a publicly available dataset

(In)variability of attachment in middle childhood: secure base script evidence in diary data

Secure attachment is characterised by a secure base script regarding the attachment figure as a source for support. Having such a cognitive script should affect the stability of state attachment. Specifically, incongruent attachment-related information should get assimilated to this secure base script, leading to state attachment scores that hardly fluctuate. For children without a script, state attachment should vary depending on the quality of attachment-related interactions. Two diary studies were carried out in 9- to 13-year-old children. Results suggested that with assimilation: (1) securely attached children fluctuated less in their daily attachment-related appraisals; (2) fluctuations were related to conflicts with mother; (3) this relation was stronger for less securely attached children. Consequently, these studies further support the secure base script hypothesis and provide insight into the interplay of trait and state components of attachment-related appraisals

The distinction of 'Psychosomatogenic family types' Based on parents' self reported questionnaire information: a cluster analysis

The theory of 'psychosomatogenic family types' is often used in treatment of somatizing adolescents. This study investigated the validity of distinguishing 'psychosomatogenic family types' based on parents' self-reported family features. The study included a Flemish general population sample of 12-year olds (n = 1428). We performed cluster analysis on 3 variables concerning parents' self-reported problems in family functioning. The distinguished clusters were examined for differences in marital problems, parental emotional problems, professional help for family members, demographics, and adolescents' somatization. Results showed the existence of 5 family types: 'chaotic family functioning,' 'average amount of family functioning problems,' 'few family functioning problems,' 'high amount of support and communication problems,' and ' high amount of sense of security problems' clusters. Membership of the 'chaotic family functioning' and 'average amount of family functioning problems' cluster was significantly associated with higher levels of somatization, compared with 'few family functioning problems' cluster membership. Among additional variables, only marital and parental emotional problems distinguished somatization relevant from non relevant clusters: parents in 'average amount of family functioning problems' and 'chaotic family functioning' clusters reported higher problems. The data showed that 'apparently perfect' or 'enmeshed' patterns of family functioning may not be assessed by means of parent report as adopted in this study. In addition, not only adolescents from 'extreme' types of family functioning may suffer from somatization. Further, professionals should be careful assuming that families in which parents report average to high amounts of family functioning problems also show different demographic characteristics

Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups

Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement invariance). Otherwise, one would be comparing apples and oranges. Nowadays, measurement invariance is often tested across a large number of groups by means of multigroup factor analysis. When the assumption is untenable, one may compare group-specific measurement models to pinpoint sources of noninvariance, but the number of pairwise comparisons exponentially increases with the number of groups. This makes it hard to unravel invariances from noninvariances and for which groups they apply, and it elevates the chances of falsely detecting noninvariance. An intuitive solution is clustering the groups into a few clusters based on the measurement model parameters. Therefore, we present mixture multigroup factor analysis (MMG-FA) which clusters the groups according to a specific level of measurement invariance. Specifically, in this article, clusters of groups with metric invariance (i.e., equal factor loadings) are obtained by making the loadings cluster-specific, whereas other parameters (i.e., intercepts, factor (co)variances, residual variances) are still allowed to differ between groups within a cluster. MMG-FA was found to perform well in an extensive simulation study, but a larger sample size within groups is required for recovering more subtle loading differences. Its empirical value is illustrated for data on the social value of emotions and data on emotional acculturation

Older adults’ affective experiences across 100 days are less variable and less complex than younger adults’.

Older adults are often described as being more emotionally competent than younger adults, and higher levels of affect complexity are seen as an indicator of this competence. We argue, however, that once age differences in affect variability are taken into account, older adults' everyday affective experiences will be characterized by lower affect complexity when compared with younger adults'. In addition, reduced affect complexity seems more likely from a theoretical point of view. We tested this hypothesis with a study in which younger and older adults reported their momentary affect on 100 days. Affect complexity was examined using clusterwise simultaneous component analysis based on covariance matrices to take into account differences in affect variability. We found that in the majority of older adults (55%), structures of affect were comparatively simpler than those of younger adults because they were reduced to a positive affect component. Most remaining older adults (35%) were characterized by differentiated rather than undifferentiated affective responding, as were a considerable number of younger adults (43%). When affect variability was made comparable across age groups, affect complexity also became comparable across age groups. It is interesting that individuals with the least complex structures had the highest levels of well-being. We conclude that affective experiences are not only less variable in the majority of older adults, but also less complex. Implications for understanding emotions across the life span are discussed

Exploring parental behavior and child interactive engagement : a study on children with a significant cognitive and motor developmental delay

Background and aims: Parenting factors are one of the most striking gaps in the current scientific literature on the development of young children with significant cognitive and motor disabilities. We aim to explore the characteristics of, and the association between, parental behavior and children's interactive engagement within this target group. Methods and procedures: Twenty-five parent-child dyads (with children aged 6-59 months) were video-taped during a 15-min unstructured play situation. Parents were also asked to complete the Parental Behavior Scale for toddlers. The video-taped observations were scored using the Child and Maternal Behavior Rating Scales. Outcomes and results: Low levels of parental discipline and child initiation were found. Parental responsivity was positively related to child attention and initiation. Conclusions and implications: Compared to children with no or other levels of disabilities, this target group exhibits large differences in frequency levels and, to a lesser extent, the concrete operationalization of parenting domains Further, this study confirms the importance of sensitive responsivity as the primary variable in parenting research

Scattering amplitudes of massive Nambu-Goldstone bosons

Massive Nambu-Goldstone (mNG) bosons are quasiparticles whose gap is determined exactly by symmetry. They appear whenever a symmetry is broken spontaneously in the ground state of a quantum many-body system, and at the same time explicitly by the system's chemical potential. In this paper, we revisit mNG bosons and show that apart from their gap, symmetry also protects their scattering amplitudes. Just like for ordinary gapless NG bosons, the scattering amplitudes of mNG bosons vanish in the long-wavelength limit. Unlike for gapless NG bosons, this statement holds for any scattering process involving one or more external mNG states; there are no kinematic singularities associated with the radiation of a soft mNG boson from an on-shell initial or final state.Comment: 12 pages; v2: added discussion of the double-soft limit in response to the referee report; matches version published in PR

ConNEcT:A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups