Interplay between self-compassion and affect during Mindfulness-Based Compassionate Living for recurrent depression: An Autoregressive Latent Trajectory analysis.

BACKGROUND: The current study aimed to investigate the possible interplay between self-compassion and affect during Mindfulness-Based Compassionate Living (MBCL) in recurrently depressed individuals. METHODS: Data was used from a subsample of a parallel-group randomized controlled trial investigating the efficacy of MBCL in recurrently depressed adults (n = 104). Self-reports of self-compassion and positive/negative affect were obtained at the start of each of the eight MBCL sessions. RESULTS: Bivariate Autoregressive Latent Trajectory (ALT) modeling showed that, when looking at the interplay between self-compassion and positive/negative affect on a session-to-session basis, no significant reciprocal cross-lagged effects between self-compassion and positive affect were found. Although there were no cross-lagged effects from negative affect to self-compassion, higher levels of self-compassion at each session did predict lower levels of negative affect at the subsequent session (bSC(t-1),NA(t) = -0.182, s.e. = 0.076, p = .017). CONCLUSIONS: The current study shows that increases in self-compassion are followed by decreases in negative affect in MBCL for depression.

Revealing interdyad differences in naturally occurring staff reactions to challenging behaviour of clients with severe or profound intellectual disabilities by means of Clusterwise Hierarchical Classes Analysis (HICLAS).

BACKGROUND: Investigating interdyad (i.e. couples of a client and their usual caregiver) differences in naturally occurring patterns of staff reactions to challenging behaviour (e.g. self-injurious, stereotyped and aggressive/destructive behaviour) of clients with severe or profound intellectual disabilities is important to optimise client-staff interactions. Most studies, however, fail to combine a naturalistic setup with a person-level analysis, in that they do not involve a careful inspection of the interdyad differences and similarities. METHOD: In this study, the recently proposed Clusterwise Hierarchical Classes Analysis (HICLAS) method is adopted and applied to data of in which video fragments (recorded in a naturalistic setting) of a client showing challenging behaviour and the staff reacting to it were analysed. In a Clusterwise HICLAS analysis, the staff-client dyads are grouped into a number of clusters and the prototypical behaviour-reaction patterns that are specific for each cluster (i.e. interdyad differences and similarities) are revealed. RESULTS: Clusterwise HICLAS discloses clear interdyad differences (and similarities) in the prototypical patterns of clients' challenging behaviour and the associated staff reactions, complementing and qualifying the results of earlier studies in which only general patterns were disclosed. CONCLUSIONS: The usefulness and clinical relevance of Clusterwise HICLAS is demonstrated. In particular, Clusterwise HICLAS may capture idiosyncratic aspects of staff-client interactions, which may stimulate direct support workers to adopt person-centred support practices that take the specific abilities of the client into account.

Clusterwise HICLAS: a generic modeling strategy to trace similarities and differences in multiblock binary data.

In many areas of the behavioral sciences, different groups of objects are measured on the same set of binary variables, resulting in coupled binary object × variable data blocks. Take, as an example, success/failure scores for different samples of testees, with each sample belonging to a different country, regarding a set of test items. When dealing with such data, a key challenge consists of uncovering the differences and similarities between the structural mechanisms that underlie the different blocks. To tackle this challenge for the case of a single data block, one may rely on HICLAS, in which the variables are reduced to a limited set of binary bundles that represent the underlying structural mechanisms, and the objects are given scores for these bundles. In the case of multiple binary data blocks, one may perform HICLAS on each data block separately. However, such an analysis strategy obscures the similarities and, in the case of many data blocks, also the differences between the blocks. To resolve this problem, we proposed the new Clusterwise HICLAS generic modeling strategy. In this strategy, the different data blocks are assumed to form a set of mutually exclusive clusters. For each cluster, different bundles are derived. As such, blocks belonging to the same cluster have the same bundles, whereas blocks of different clusters are modeled with different bundles. Furthermore, we evaluated the performance of Clusterwise HICLAS by means of an extensive simulation study and by applying the strategy to coupled binary data regarding emotion differentiation and regulation.