Philip Santangelo

ECOLOGICAL MOMENTARY ASSESSMENT IN BORDERLINE PERSONALITY DISORDER: A REVIEW OF RECENT FINDINGS AND METHODOLOGICAL CHALLENGES

The use of Ecological Momentary Assessment (EMA) has led to increased insight into borderline personality disorder (BPD) symptoms, especially regarding affective instability. EMA is characterized by a series of repeated assessments of current affective, behavioral, and contextual experiences or physiological processes while participants engage in normal daily activities. EMA has several advantages. It enables researchers to avoid biased recollection, to investigate within-person processes, and to enhance real-life generalizability. This review is dedicated to four main objectives: (1) to discuss the characteristics of EMA in studying BPD symptomatology; (2) to provide an extensive overview of EMA findings in BPD structured into findings regarding DSM-IV criteria and findings regarding emotional dysregulation as stated in the biosocial theory of Linehan; (3) to discuss challenges of EMA and to give recommendations for the proper use of it; and (4) to highlight prospects and promising applications that should be addressed.

Specificity of affective instability in patients with borderline personality disorder compared to posttraumatic stress disorder, bulimia nervosa, and healthy controls.

Affective instability is a core feature of borderline personality disorder (BPD). The use of advanced assessment methodologies and appropriate statistical analyses has led to consistent findings that indicate a heightened instability in patients with BPD compared with healthy controls. However, few studies have investigated the specificity of affective instability among patients with BPD with regard to relevant clinical control groups. In this study, 43 patients with BPD, 28 patients with posttraumatic stress disorder (PTSD), 20 patients with bulimia nervosa (BN), and 28 healthy controls carried e-diaries for 24 hours and were prompted to rate their momentary affective states approximately every 15 minutes while awake. To quantify instability, we used 3 state-of-the-art indices: multilevel models for squared successive differences (SSDs), multilevel models for probability of acute changes (PACs), and aggregated point-by-point changes (APPCs). Patients with BPD displayed heightened affective instability for emotional valence and distress compared with healthy controls, regardless of the specific instability indices. These results directly replicate earlier studies. However, affective instability did not seem to be specific to patients with BPD. With regard to SSDs, PACs, and APPCs, patients with PTSD or BN showed a similar heightened instability of affect (emotional valence and distress) to that of patients with BPD. Our results give raise to the discussion if affective instability is a transdiagnostic or a disorder-specific mechanism. Current evidence cannot answer this question, but investigating psychopathological mechanisms in everyday life across disorders is a promising approach to enhance validity and specificity of mental health diagnoses.

Unraveling Affective Dysregulation in Borderline Personality Disorder: A Theoretical Model and Empirical Evidence

Although emotion dysregulation has consistently been conceptualized as a core problem of borderline personality disorder (BPD), a comprehensive, and empirically and ecologically validated model that captures the exact types of dysregulation remains absent. In the present article, we combine insights from basic affective science and the biosocial theory of BPD to present a theoretical model that captures the most fundamental affective dynamical processes that underlie BPD and stipulates that individuals with BPD are characterized by more negative affective homebases, higher levels of affective variability, and lower levels of attractor strength or return to baseline. Next, we empirically validate this proposal by statistically modeling data from three electronic diary studies on emotional responses to personally relevant stimuli in personally relevant environments that were collected both from patients with BPD (N = 50, 42, and 43) and from healthy subjects (N = 50, 24, and 28). The results regarding negative affective homebases and heightened affective variabilities consistently confirmed our hypotheses across all three datasets. The findings regarding attractor strengths (i.e., return to baseline) were less consistent and of smaller magnitude. The transdiagnostic nature of our approach may help to elucidate the common and distinctive mechanisms that underlie several different disorders that are characterized by affective dysregulation.

How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

We present MoA2, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a formative study, we evaluated the usability and unobtrusiveness of our mood assessment. A median SUS score of 90 shows a high usability. Subjects reported an easy, fast and intuitive use. The mood recognition was evaluated in terms of classification accuracy. First, we analyzed which features are best for the recognition. Spatio-temporal attributes, i.e. daytime, day of week and location, correlate most with the monitored mood. Based on the identified attributes, we trained personalized classifiers using Naïve Bayes and applied ten-fold-cross validation. The average recognition accuracy was 0.76 which is comparable to related work.

The specificity of emotional switching in borderline personality disorder in comparison to other clinical groups

In an attempt to better understand the nature of emotion dysregulation in the daily lives of persons with a borderline personality disorder (BPD), Houben et al. (2016) recently identified emotional switching, which refers to the tendency to make large changes between positive and negative emotional states over time, as a possible defining characteristic of the emotion dynamics observed in BPD. The goal of this study was to examine the specificity of these previous findings in 2 samples by comparing BPD patients (N = 43 in sample 1; N = 81 in sample 2) to patients with bulimia nervosa (N = 20), posttraumatic stress disorder (N = 28), or healthy controls (N = 28) in sample 1, and to patients with depressive disorder (N = 50) in sample 2, with respect to measures of emotional switching. Analyses of these 2 experience sampling datasets revealed that contrary to expectations, BPD patients did not differ from the clinical groups regarding their mere tendency to switch between positive and negative emotional states on consecutive moments over time and regarding the magnitude of such changes between positive and negative emotional states over time. However, all clinical groups did differ from healthy controls regarding all switch measures in dataset 1. These results indicate that emotional switching, similar to other more traditional indicators of overall changes in emotional intensity in daily life, might reflect a feature of emotional responding characterizing a range of disorders with mood disturbances.

Leveraging smartwatches for unobtrusive mobile ambulatory mood assessment

In ambulatory assessment, subjects are monitored in everyday life. Though, it is difficult to unobtrusively assess information -- e.g. about their context and affective state -- which results in an increased burden for the subjects. This burden is caused by a complex self-report that they need to provide or by additional wearables that need to be carried. Newest technology can solve this issue by assessing a variety of information automatically. We propose to use smartwatches in combination with smartphones to assess physiological and smartphone data from which the affective state of a user can be inferred. We present the principle idea of our app and how we intend on evaluating it. A review of state of the art approaches and available Android Wear smartwatches in terms of sensors is given. We present a number of smartphone sensors and a selection of smartwatches whose combination should be evaluated regarding usefulness for mood assessment and recognition.

The temporal interplay of self-esteem instability and affective instability in borderline personality disorder patients’ everyday lives

Borderline personality disorder (BPD) is defined by a pervasive pattern of instability. Although there is ample empirical evidence that unstable self-esteem is associated with a myriad of BPD-like symptoms, self-esteem instability and its temporal dynamics have received little empirical attention in patients with BPD. Even worse, the temporal interplay of affective instability and self-esteem instability has been neglected completely, although it has been hypothesized recently that the lack of specificity of affective instability in association with BPD might be explained by the highly intertwined temporal relationship between affective and self-esteem instability. To investigate self-esteem instability, its temporal interplay with affective instability, and its association with psychopathology, 60 patients with BPD and 60 healthy controls (HCs) completed electronic diaries for 4 consecutive days during their everyday lives. Participants reported their current self-esteem, valence, and tense arousal levels 12 times a day in approximately one-hr intervals. We used multiple state-of-the-art statistical techniques and graphical approaches to reveal patterns of instability, clarify group differences, and examine the temporal interplay of self-esteem instability and affective instability. As hypothesized, instability in both self-esteem and affect was clearly elevated in the patients with BPD. In addition, self-esteem instability and affective instability were highly correlated. Both types of instability were related to general psychopathology. Because self-esteem instability could not fully explain affective instability and vice versa and neither affective instability nor self-esteem instability was able to explain psychopathology completely, our findings suggest that these types of instability represent unique facets of BPD.

On the definition of latent state-trait models with autoregressive effects: insights from LST-R theory

In longitudinal studies with short time lags, classical models of latent state-trait (LST) theory that assume no carry-over effects between neighboring occasions of measurement are often inappropriate, and have to be extended by including autoregressive effects. The way in which autoregressive effects should be defined in LST models is still an open question. In a recently published revision of LST theory (LST-R theory), Steyer, Mayer, Geiser, and Cole (2015) stated that the trait-state-occasion (TSO) model (Cole, Martin, & Steiger, 2005), one of the most widely applied LST models with autoregressive effects, is not an LST-R model, implying that proponents of LST-R theory might recommend not to apply the TSO model. In the present article, we show that a version of the TSO model can be defined on the basis of LST-R theory and that some of its restrictions can be reasonably relaxed. Our model is based on the idea that situational effects can change time-specific dispositions, and it makes full use of the basic idea of LST-R theory that dispositions to react to situational influences are dynamic and malleable. The latent variables of the model have a clear meaning that is explained in detail.

Identification of relevant sensor sources for context-aware ESM apps in ambulatory assessment

The experience sampling method (ESM) is applied in ambulatory assessment to prompt subject self-reporting. Existing mobile apps provide time-triggered prompts but lack event-triggers. Hence, the sampling might not occur in moments that are of interest for a psychologist. To identify relevant sensor sources and contexts we conducted an online survey with ambulatory assessment experts. Most relevant for these experts are time, date and user activity, followed by location, notifications and accelerometer. A feasibility test proved that all relevant sources are accessible on Android phones. We also assessed the desired granularity of the data gathered from each sensor source. Our results are a first step towards an ESM platform to create context-aware Android apps for ambulatory assessment.

Investigating Binge Eating Using Ecological Momentary Assessment: The Importance of an Appropriate Sampling Frequency

With great interest, we read the recently published review on emotion regulation in binge eating disorder (BED) by Dingemans et al. [1].[...].