Journal of psychopathology and clinical science最新文献

Passive sensing technology shows promise in capturing elements of adolescent mental health. Research testing if there is a signal between adolescents' self-reports of same-day mental health indicators and passively sensed variables can be inconsistent, particularly with metrics pertaining to digital behaviors. Moreover, little is known if adolescent participation in passive sensing research is biased with respect to demographics and general metrics of mental health. The current research tested these aims among adolescents recruited from a large and diverse sample participating in an ongoing longitudinal study. Adolescents (aged 15-20, N = 131) participated in a 90-day passive sensing study, which collected data on both digital (keystroke and app usage) and offline (sleep and physical activity) behaviors. Although correlations indicated a small signal between same-day mental health indicators and several passively sensed variables (e.g., proportion of typed negative words and call behaviors), associations typically disappeared when disaggregating between- from within-person associations. Additionally, participation uptake was low, but there was little evidence of bias in participation or data coverage based on mental health risk or demographics. Results demonstrate the feasibility of collecting passive sensing data with a diverse sample of adolescents, but barriers remain on adolescent willingness to engage in this research and the strength of signal between passively sensed variables and self-report constructs. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Reports an error in "Understanding types of transitions in clinical change: An introduction from the complex dynamic systems perspective" by Jingmeng Cui, Fred Hasselman, Merlijn Olthof and Anna Lichtwarck-Aschoff (Journal of Psychopathology and Clinical Science, 2025[May], Vol 134[4], 469-482; see record 2025-93753-001). In the article, in Figure 8 the final steps in the decision tree were inadvertently reversed: A "Yes" response should lead to B-tipping, and a "No" response should lead to R-tipping. The corrected figure is present in the erratum. (The following abstract of the original article appeared in record 2025-93753-001). Sudden changes are common in clinical trajectories. While theoretical work in complex dynamic systems has provided mathematical theories for various types and mechanisms of change, a concrete application for the field of psychopathology is still lacking. We aim to bridge this gap by outlining an applied theoretical framework using theoretical concepts of the natural sciences for the field of clinical psychopathology, also devoting attention to issues and providing recommendations that are specific to the psychopathology domain. First, the mechanisms and features of four distinct types of transitions are introduced: bifurcation-induced tipping (B-tipping), noise-induced tipping (N-tipping), rate-induced tipping (R-tipping), and noise-induced diffusion (N-diffusion). Those types of transitions differ in the main cause of the change and data characteristics. To illustrate their application to clinical phenomena, we present two real-life scenarios using simulated time series. These examples demonstrate how theoretical types of change may connect to clinical phenomena and highlight how different types of transitions can co-occur in various subsystems. In the first example, we show that the mood system and the momentary affect system of a patient with sudden loss may show B-tipping and N-diffusion at the same time; in the second example, we show that increasing the stimulus strengthening speed in exposure therapy may lead to R-tipping, while the therapeutic decision in this context may be caused by N-tipping. Finally, we lay out possible pathways for determining the appropriate type of transition for future empirical research, highlighting methods both from dynamic system research and special opportunities for research in clinical psychology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Early detection of emerging affective episodes is crucial in managing bipolar disorders (BD). Passive sensing-passive data collection via smartphone or wearable-offers a promising solution by potentially capturing altered activity, communication, and sleep patterns, indicative of manic and depressive episodes. Recently, statistical process control (SPC) has been introduced to psychopathology as a novel approach to identifying out-of-bounds processes. However, its application to mobile sensing data and to BD remains unexplored. To investigate SPC's potential in detecting emerging affective episodes, we utilized the BipoSense study, which monitored patients with BD. The BipoSense data cover 12 months of continuously collected passive sensing data via smartphone app, daily e-diary data, and biweekly expert interviews, that is, 26 in a row, to assess the psychopathological status. Compliance was excellent. A total of 26 depressive and 20 (hypo)manic emerging episodes in 28 patients were included in the analyses. SPC charts and multilevel analyses revealed heterogeneous results. Passive sensing, despite its potential as a low-burden, continuous measurement tool, did not demonstrate robust detection of affective episodes or preepisode weeks. Self-rated current bipolar mood, assessed via e-diary, outperformed passive sensing parameters in predicting current episodes, whereas predicting preepisode weeks was also limited. Notably, SPC with personalized control limits did not surpass established clinical cutoff scores. Even after systematic optimization of SPC settings, the combination of detected emerging episodes in relation to false alarms was insufficient for clinical use. Future studies warrant mobile sensing parameters closer aligned to psychopathology, thereby increasing validity, sensitivity, and specificity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).