Predicting Non-Suicidal Self-Injury in Young Adults with and without Borderline Personality Disorder: a Multilevel Approach Combining Ecological Momentary Assessment and Self-Report Measures

Shame, suicidal ideation, and urges for non-suicidal self-injury among individuals with borderline personality disorder receiving dialectical behavior therapy: The mediating role of anger

INTRODUCTION

Borderline personality disorder (BPD) is a disorder of pervasive emotion dysregulation associated with high rates of self-injurious thoughts and behaviors (SITB). Understanding specific emotion states in relation to SITB is important for effective intervention.

METHODS

The current study examined whether, and how, the specific emotion of shame contributes to suicide ideation and urges to engage in non-suicidal self-injury (NSSI) both directly, and indirectly via anger, among individuals with BPD. Participants (N = 100) were enrolled in a 6-month comprehensive dialectical behavior therapy (DBT) program and provided daily ratings of shame, anger, suicide ideation, and urges for NSSI.

RESULTS

We found that higher daily ratings of shame and anger were directly associated with higher same-day ratings of both suicidal ideation and urges for NSSI. Furthermore, anger partially mediated the relationships between shame and both suicidal ideation and urges for NSSI.

CONCLUSION

These findings highlight shame and anger as potential antecedents of SITB among individuals with BPD. Clinical approaches, such as DBT, that include personalized, ongoing, clinical assessment of these specific affective states may be particularly important for treatment of SITB.

Decentering from Emotions in Daily Life: Dynamic Associations with Affect, Symptoms, and Wellbeing

Decentering is thought to be protective against a range of psychological symptoms, but little is known about the outcomes of decentering as a momentary state in daily life. We used ecological momentary assessment (42 reports across one week) to examine the temporal ordering of the associations of decentering with affect, dysphoria, participant-specific idiographic symptoms, and wellbeing. We also hypothesized that greater decentering predicts less inertia (persistence) of each variable, and weakens the association of affect with dysphoria, idiographic symptoms, and wellbeing. Results in 345 community participants indicated that decentering and these variables were mutually reinforcing over time, and that greater decentering was associated with less inertia of negative affect and dysphoria. Decentering generally predicted reduced impact of positive and negative affect on dysphoria symptoms, but results were mixed when predicting idiographic symptoms or wellbeing. Clinical implications and refinements for theory on decentering are discussed.

Study Features and Response Compliance in Ecological Momentary Assessment Research in Borderline Personality Disorder: Systematic Review and Meta-analysis

BACKGROUND

Borderline personality disorder (BPD) is characterized by frequent and intense moment-to-moment changes in affect, behavior, identity, and interpersonal relationships, which typically result in significant and negative deterioration of the person's overall functioning and well-being. Measuring and characterizing the rapidly changing patterns of instability in BPD dysfunction as they occur in a person's daily life can be challenging. Ecological momentary assessment (EMA) is a method that can capture highly dynamic processes in psychopathology research and, thus, is well suited to study intense variability patterns across areas of dysfunction in BPD. EMA studies are characterized by frequent repeated assessments that are delivered to participants in real-life, real-time settings using handheld devices capable of registering responses to short self-report questions in daily life. Compliance in EMA research is defined as the proportion of prompts answered by the participant, considering all planned prompts sent. Low compliance with prompt schedules can compromise the relative advantages of using this method. Despite the growing EMA literature on BPD in recent years, findings regarding study design features that affect compliance with EMA protocols have not been compiled, aggregated, and estimated.

OBJECTIVE

This systematic meta-analytic review aimed to investigate the relationship between study design features and participant compliance in EMA research of BPD.

METHODS

A systematic review was conducted on November 12, 2021, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and MOOSE (Meta-analyses of Observational Studies in Epidemiology) guidelines to search for articles featuring EMA studies of BPD that reported compliance rates and included sufficient data to extract relevant design features. For studies with complete data, random-effect models were used to estimate the overall compliance rate and explore its association with design features.

RESULTS

In total, 28 peer-reviewed EMA studies comprising 2052 participants were included in the study. Design features (sampling strategy, average prompting frequency, number of items, response window, sampling device, financial incentive, and dropout rate) showed a large variability across studies, and many studies did not report design features. The meta-analytic synthesis was restricted to 64% (18/28) of articles and revealed a pooled compliance rate of 79% across studies. We did not find any significant relationship between design features and compliance rates.

CONCLUSIONS

Our results show wide variability in the design and reporting of EMA studies assessing BPD. Compliance rates appear to be stable across varying setups, and it is likely that standard design features are not directly responsible for improving or diminishing compliance. We discuss possible nonspecific factors of study design that may have an impact on compliance. Given the promise of EMA research in BPD, we also discuss the importance of unifying standards for EMA reporting so that data stemming from this rich literature can be aggregated and interpreted jointly.

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.