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Recent studies showed that attachment security can change within persons, suggesting that there might be an interplay between a rather stable (trait) and rather variable (state) part of attachment. The study's first aim was to investigate whether attachment priming could influence the level of state attachment. The second aim was to explore possible moderators explaining individual differences in the relation between state attachment responses to attachment versus neutral primes. This can shed light on individual differences in attachment prime-induced state attachment. We conducted a within-subjects priming study in which children (9-13 years) were allocated to three priming procedures (neutral, secure, avoidant). Results showed an increase in secure state attachment after secure attachment priming. Individual differences in the strength of this effect were moderated by trait attachment and state attachment volatility. First findings cautiously suggest that state attachment security can change in response to environmental cues under certain circumstances.

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.

Students with dyslexia face particular challenges in higher education. Individuals with disabilities have a right to accommodations to guarantee equal opportunities and participation in education. The present study addresses (1) the frequency of functioning and participation problems of students with dyslexia in higher education, (2) the teaching and evaluation methods during which the functioning and participation problems emerge, and (3) the effectiveness of accommodations perceived by students with dyslexia. Results showed that students with dyslexia primarily have problems with reading comprehension, reading speed, spelling and text writing. Furthermore, most students with dyslexia experience more problems during classical teaching (e.g. lecture) or classical evaluation methods (e.g. written exam with open questions) than during dynamic or activating teaching methods (e.g. excursion or internship) or alternative evaluation methods (e.g. peer evaluation). Finally, perception of the effectiveness of accommodations depends strongly on the individual functioning and participation problems students with dyslexia experienced.

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. What is the public health significance of this article?: Changes in the mean levels of self-reported emotions precede recurrent depressive episodes and are therefore considered warning signals. This study shows that the early detection of recurrent depression improves when warning signals are not only tracked in the mean but also in the variability of emotions. Thus, monitoring for both changes in emotional intensity and variability may aid the early detection, and possibly the prevention, of depression. [ABSTRACT FROM AUTHOR]

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. What is the public health significance of this article?: This study suggests that happy mood, sad mood, and negative ways of thinking often start to improve around the same time during treatment of depression. Fewer improvements were found in activities and interactions, which explains why activities and interactions often improved after individuals started to feel better. [ABSTRACT FROM AUTHOR]

Are intimate partners similar in how accurately they infer each other's feelings and thoughts, and if so, does this similarity predict their relationship well-being? To answer this question, we analyzed data from two laboratory-based studies (n = 155 and n = 172 couples) in which couples participated in a conflict interaction task and afterwards reported on their own feelings and thoughts and inferred those of their partner. Relationship well-being was measured on both a global (i.e., relationship satisfaction) and a situational level (i.e., post-interaction closeness and satisfaction with the outcome of the interaction). We found that intimate partners were more similar in their empathic accuracy than randomly-paired individuals. This similarity predicted the extent to which partners reported that the conflict interaction had led to a positive outcome for their relationship, but was not associated with partners' global relationship satisfaction or their post-interaction closeness. [ABSTRACT FROM AUTHOR]

Perceiving partner emotions accurately is relevant for relationship functioning and may relate to perceivers' attachment orientations. Here we tested whether attachment anxiety and avoidance affect the perception of a hard and a flat partner emotion which can signal a relationship threat, that is, irritation and indifference towards the partner, and of general affective valence. We assessed whether individuals (1) over‐ or underestimate (directional bias) and (2) track changes (tracking accuracy) in their partner's emotions. Ninety‐four couples reported on their own emotions and their perception of their partner's emotions multiple times per day during 1 week. Results revealed that more avoidantly attached men less accurately tracked changes in partner irritation and more anxiously attached men less accurately tracked changes in partner affective valence. No other significant associations of attachment with tracking accuracy nor with directional bias were found. Overall, the findings suggest no robust relation between attachment insecurity and perception of partner irritation, indifference and affective valence in everyday life. [ABSTRACT FROM AUTHOR]

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T2, EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T2 procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. Detecting early warning signals of developing mood disorders in continuously collected data, would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. We focus on data collected in experience sampling (ESM) studies, where individuals report on their momentary affect at a number of occasions throughout the day, for multiple days. To detect such early warning signals, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this paper, we didactically introduce three univariate and three multivariate SPC procedures. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the assumption violations, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that certain SPC procedures clearly outperform others, and support using day averages rather than the original data. Based on these results we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. [ABSTRACT FROM AUTHOR]