Forecasting the onset of depression with limited baseline data only: A comparison of a person-specific and a multilevel modeling based exponentially weighted moving average approach.

The onset of depressive episodes is preceded by changes in mean levels of affective experiences, which can be detected using the exponentially weighted moving average procedure on experience sampling method (ESM) data. Applying the exponentially weighted moving average procedure requires sufficient baseline data from the person under study in healthy times, which is needed to calculate a control limit for monitoring incoming ESM data. It is, however, not trivial to obtain sufficient baseline data from a single person. We therefore investigate whether historical ESM data from healthy individuals can help establish an adequate control limit for the person under study via multilevel modeling. Specifically, we focus on the case in which there is very little baseline data available of the person under study (i.e., up to 7 days). This multilevel approach is compared with the traditional, person-specific approach, where estimates are obtained using the person's available baseline data. Predictive performance in terms of Matthews correlation coefficient did not differ much between the approaches; however, the multilevel approach was more sensitive at detecting mean changes. This implies that for low-cost and nonharmful interventions, the multilevel approach may prove particularly beneficial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The temporal order of emotional, cognitive, and behavioral gains in daily life during treatment of depression.

OBJECTIVE: Despite the importance for understanding mechanisms of change, little is known about the order of change in daily life emotions, cognitions, and behaviors during treatment of depression. This study examined the within-person temporal order of emotional, cognitive, and behavioral improvements using ecological momentary assessment data. METHOD: Thirty-two individuals with diagnosed depression completed ecological momentary assessment questions on emotions (sad mood, happy mood), behaviors (social interaction, number of activities), and cognitive variables (worrying, negative self-thoughts) 5 times a day during a 4-month period in which they underwent psychotherapy for depression. Nonparametric change-point analyses were used to determine the timing of gains (i.e., improvements in the mean of each variable) for each individual. We then established whether the first (i.e., earliest) gains in emotions preceded, followed, or occurred in the same week as cognitive and behavioral gains for each individual. RESULTS: Contrary to our hypotheses, first gains in behaviors did not precede first emotional gains (3 times, 8%) more often than they followed them (26 times, 70%). Cognitive gains often occurred in the same week as first emotional gains (43 times, 58%) and less often preceded (13 times, 18%) or followed emotional gains (18 times, 24%). CONCLUSION: The first improvements in behaviors did not tend to precede the first improvements in emotions likely because fewer behavioral gains were found. The finding that cognitive variables tend to improve around the same time as sad mood may explain why many studies failed to find that cognitive change predicts later change in depressive symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis.

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial.

BACKGROUND: Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode. OBJECTIVES: We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states. METHOD: In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG. RESULTS: DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67. CONCLUSIONS: DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.

Monitoring emotional intensity and variability to forecast depression recurrence in real time in remitted adults.

OBJECTIVE: Recurrent depressive episodes are preceded by changing mean levels of repeatedly assessed emotions (e.g., feeling restless), which can be detected in real time using statistical process control (SPC). This study investigated whether monitoring changes in the standard deviation (SD) of emotions and negative thinking improves the early detection of recurrent depression. METHOD: Formerly depressed adults (N = 41) monitored their emotions five times a day for 4 consecutive months. During the study, 22 individuals experienced recurrent depression. We used SPC to detect warning signs (i.e., changing means and SDs) of four emotions (positive and negative affect with high or low arousal) and negative thinking. RESULTS: SD-based warning signs only preceded 23%-36% of recurrences, but almost never reflected a false alarm (0%-16%). Correspondingly, SD-based warnings had a high specificity (at the cost of sensitivity), while mean-based warnings had a higher sensitivity (but lower specificity). There was little overlap in mean- and SD-based warning signs. For the majority of emotions, monitoring for high SDs alongside monitoring changes in mean levels improved the detection of depression (p < .015) compared to when only monitoring for changing mean levels. CONCLUSIONS: Warning signs for depression manifest not only in changing mean levels of emotions and cognitions but also in increasing SDs. These warnings could eventually be used to detect not just who is at increased risk for depression but also when risk is rising. Further research is needed to evaluate the clinical utility of depression SPC. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individual-specific change points in circadian rest-activity rhythm and sleep in individuals tapering their antidepressant medication: an actigraphy study.

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.

Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches.

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e., s 2 , s , ln( s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA- S 2 , EWMA-ln( S 2 ), and EWMA- X ¯ - S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA- S 2 and EWMA-ln( S 2 ); the performance difference with EWMA- X ¯ - S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.

Evaluating dynamics in affect structure with latent Markov factor analysis.

In intensive longitudinal research, researchers typically consider the structure of affect to be stable across individuals and contexts. Based on an assumed theoretical structure (e.g., one bipolar or two separate positive and negative affect constructs), researchers create affect scores from items (e.g., sum or factor scores) and use them to examine the dynamics therein. However, researchers usually ignore that the affect structure itself is dynamic and varies across individuals and contexts. Understanding these dynamics provides valuable insights into individuals' affective experiences. This study uses latent Markov factor analysis (LMFA) to study what affect structures underlie individuals' responses, how individuals transition between structures, and whether their individual transition patterns differ. Moreover, we explore whether the intensity of negative events and the personality trait neuroticism relate to momentary transitions and individual differences in transition patterns, respectively. Applying LMFA to experience sampling data (N = 153; age: mean = 22; SD = 7.1; range = 17-66), we identified two affect structures-one with three and one with four dimensions. The main difference was the presence of negative emotionality, and the affect dimensions became more inversely related when the affect structure included negative emotionality. Moreover, we identified three latent subgroups that differed in their transition patterns. Higher negative event intensity increased the probability of adopting an affect structure with negative emotionality. However, neuroticism was unrelated to subgroup-membership. Summarized, we propose a way to incorporate contextual and individual differences in affect structure, contributing to advancing the theoretical basis of affect dynamics research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sensory Processing and Executive Functioning in Autistic Adults.

PURPOSE: One of the core features that can be experienced by adults on the autism spectrum is hyper- and hyporeactivity to sensory stimuli. Research suggests that executive functioning (EF) impairments are related to sensory issues. In this study the relationship between sensory processing issues and EF was investigated. We expected sensory processing issues to predict EF impairments. METHODS: Thirty men and 30 women on the autism spectrum, 20 men and 24 women without autism were included and matched on intelligence and age. Group comparisons were conducted to determine if groups differed regarding self-reported sensory processing issues (GSQ-NL) and self-reports on EF (BRIEF-A). Correlational and regression analyses were carried out to investigate the relationship between self-reports on GSQ-NL and BRIEF-A. RESULTS: We found significant differences between men and women on the spectrum with regard to sensory processing issues and EF. Hyporeactivity to sensory information explained most of the EF problems. CONCLUSION: Clinicians should be aware of differences in sensory experiences between adults on the spectrum and non-autistic adults and differences between men and women during assessment and subsequent counselling.

Recurrence of depression can be foreseen by monitoring mental states with statistical process control.

Detecting early signs of recurrence of psychopathology is key for prevention and treatment. Personalized risk assessment is especially relevant for formerly depressed patients, for whom recurrence is common. We aimed to examine whether recurrence of depression can be accurately foreseen by applying Exponentially Weighted Moving Average (EWMA) statistical process control charts to Ecological Momentary Assessment (EMA) data. Participants were formerly depressed patients (n = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based EMA questionnaires a day for 4 months. EWMA control charts were used to prospectively detect structural mean shifts in high and low arousal negative affect (NA), high and low arousal positive affect (PA), and repetitive negative thinking in each individual. A significant increase in repetitive negative thinking (worry, negative thoughts about the self) was the most sensitive early sign of recurrence: this was detected in 18 out of 22 patients (82%) before recurrence and in 8 out of 19 patients (42%) who stayed in remission. A significant increase in NA high arousal (stress, irritation, restlessness) was the most specific early sign of recurrence: this was detected in 10 out of 22 patients (45%) before recurrence and in 2 out of 19 patients (11%) who stayed in remission. These mean changes were detected at least a month before recurrence in the majority of the participants. The outcomes were robust across EWMA parameter choices, but not when using fewer observations per day. The findings demonstrate the value of monitoring EMA data with EWMA charts for detecting prodromal symptoms of depression in real-time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Exploring everyday state attachment dynamics in middle childhood.

The current study explored dynamics of secure state attachment expectations in everyday life in middle childhood, specifically state attachment carry-over and reactivity to experiences of caregiver support in the context of stress. In two independent samples (one community sample, N = 123; one adoption sample, N = 69), children (8-12 years) daily reported on their state attachment for respectively 14 and 7 consecutive days. Additionally, they reported daily on their experiences of distress and subsequent experiences of caregiver support. Results in both samples indicated that secure state attachment on a day-to-day basis is characterized by a significant positive carry-over effect, suggesting that state attachment fluctuations are (partially) self-predictive. In Study 1, experiencing no support following distress significantly related to intraindividual decreases in secure state attachment; in Study 2, experiencing effective support during distress related to intra-individual increases in secure state attachment. Taken together, the current studies provide novel and important insights into how state attachment temporally evolves on a day-to-day basis in middle childhood.

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.

ConNEcT: An R package to build contingency measure-based networks on binary time series.

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.

Motivation and empathic accuracy during conflict interactions in couples: it's complicated!

The aim of this study was to broadly investigate the role of relationship-, self-, and partner-serving motivation in empathic accuracy in couples' conflict interactions. To this end, a laboratory study was set up in which couples (n = 172) participated in a conflict interaction task, followed immediately by a video-review task during which they reported their own feelings and thoughts and inferred those of their partner to assess empathic accuracy. We used both trait and state measures of relationship-, self-, and partner-serving motivation, and we experimentally induced these three categories of motivation. Relationship-serving state motivation predicted greater empathic accuracy. In contrast, experimentally induced partner-serving motivation resulted in less empathic accuracy for men. Self-serving motivation was not found to be associated with empathic accuracy, nor were any of the trait measures. These findings underscore the complexity of the association between motivation and empathic accuracy in partners' conflict interactions. Supplementary Information: The online version contains supplementary material available at 10.1007/s11031-022-09982-x.

Development, validation, and comparison of self-report measures for positive and negative affect in intensive longitudinal research.

Affect is central to human functioning. Due to its dynamic nature, it is often studied with intensive longitudinal designs, yet the development and validation of measures for this purpose have received little systematic attention. In the present study, we review theoretical and methodological conceptualizations of affect that are relevant for repeated momentary positive and negative affect measurement. We developed a questionnaire including six dimensional affect and 22 discrete emotion items that allowed us to measure alternative momentary affect constructs with single- and multi-item scores. The items were operationalized into two bipolar, six positive, and six negative momentary affect measures. We compared the measures with three quantifiable criteria of construct validity: the amount of within-person variance, within-person sensitivity to emotional events, and between-person relations to depression and neuroticism. The criteria were empirically investigated with a preregistered experience sampling study (N = 153). We identify the measures with the strongest validity evidence across all criteria and evaluate their suitability for specific research questions, by looking at individual criteria. The overall findings provide strong evidence supporting the use of single-item measures of momentary affect. Furthermore, single items provide an efficient low-burden assessment tool that is comparable across studies. For multi-item scales, it is recommended to combine discrete emotion items of similar intensity, while simply selecting and averaging discrete emotion items is problematic concerning our validity criteria. In the future, we encourage the field to conduct systematic research on the use and interpretation of scores that aggregate different emotion items together. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Describing the communicative profiles of young children with a significant cognitive and motor developmental delay.

The communicative behavior of young children with significant cognitive and motor developmental delays is generally considered to be limited, idiosyncratic and non-intentional. At present, changes between and within children over time regarding their communicative behavior are hard to detect. This article describes an exploratory observational study that draws on data from the first data point of 38 children who are participating in a longitudinal project on the developmental trajectories of children with significant cognitive and motor developmental delays. The aims of this study were to (a) describe the participants' communicative behavior in detail with communication-related variables that reflect differences across individuals, (b) create summarizing variables and (c) explore whether subgroups of children can be detected. A self-developed coding scheme and descriptive statistics combined with correlational analyses were used, followed by a principal component analysis and visual inspection of the outcome of this analysis. The within-group differences related to communicative behavior was characterized using 16 variables. Based on these variables, three overarching components were formulated: communication proficiency, Expressions of Discomfort and Rejection and Differentiation According to Focus. All participating children were found to be unique in terms of their component scores and the relationship among their component scores.

Distinguishing dimensions of emotion dynamics across 12 emotions in adolescents' daily lives.

Research on emotion dynamics as indices of emotion functioning has become muddled by conceptual confusion, methodological heterogeneity, and seemingly conflicting results. One way to address this chaos is the study of profiles of emotion dynamics across 12 emotions and how they differ between 246 adolescents. The interpretation of these dynamic profiles was guided by auxiliary variables including age, personality, depressive symptoms, and social experiences. During 6 days, 246 adolescents (Mage = 14.20; 65% female) rated nine times daily the intensity of 12 emotions (cheerful, happy, energetic, joyful, content, relaxed, anxious, worried, irritable, insecure, down, and guilty) and their social experiences with family, friends, and classmates. Additional baseline measures included neuroticism, extraversion (Revised Junior Eysenck Personality Questionnaire Short Form), and depressive symptoms (Center for Epidemiological Studies Depression Scale). A three-mode principal component analysis (3MPCA Tucker3-based) model was estimated on the person-specific dynamic parameters of emotional intensity (mean), variability (standard deviation), instability (mean squared successive difference), and inertia (autocorrelation). The 3MPCA identified three emotion-mode components (positive affect, negative affect, and irritability) and three dynamic-mode components (emotional intensity, lability, and inertia). Five individual-mode components captured interactions between these modes, of which positive affect explained most variation in the data. These emotion dynamic profiles correlated differently with social experiences. Additional 3MPCA model structures based on imputed data (correcting missing autocorrelations) and affect scale composites (low- and high-arousal positive and negative affect) showed strong resemblance. The identified emotion dynamic profiles capture meaningful interpersonal differences in adolescents' emotional experiences and change. Future work should focus on irritability and positive affect as these were uniquely informative in adolescents' emotional experiences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Collinearity Issues in Autoregressive Models with Time-Varying Serially Dependent Covariates.

First-order autoregressive models are popular to assess the temporal dynamics of a univariate process. Researchers often extend these models to include time-varying covariates, such as contextual factors, to investigate how they moderate processes' dynamics. We demonstrate that doing so has implications for how well one can estimate the autoregressive and covariate effects, as serial dependence in the variables can imply predictor collinearity. This is a noteworthy contribution, since in current practice serial dependence in a time-varying covariate is rarely considered important. We first recapitulate the role of predictor collinearity for estimation precision in an ordinary least squares context, by discussing how it affects estimator variances, covariances and correlations. We then derive a general formula detailing how predictor collinearity in first-order autoregressive models is impacted by serial dependence in the covariate. We provide a simulation study to illustrate the implications of the formula for different types of covariates. The simulation results highlight when the collinearity issue becomes severe enough to hamper interpretation of the effects. We also show that the effect estimates can be biased in small samples (i.e., 50 time points). Implications for study design, the use of time as a predictor, and related model variants are discussed.

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

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.