This study evaluated the performance of the web-delivered BRISC (full and mini versions) in identifying emotional dysregulation, a hallmark of clinical status in patients with a range of psychiatric and neurological conditions. The study results were consistent across the full- and mini-BRISC versions. For the three BRISC scores combined, the full 45-question BRISC had a high overall accuracy of 0.93 (). The best classification of clinical status was at the threshold of z = −1.57, substantially below the population average of 0. The mini 15-question BRISC showed a similarly high accuracy of 0.92 (). These results support the effectiveness of the BRISC for identifying risk for a clinical disorder, manifested as loss of emotion regulation.
Receiver operating curve results for the 45-item BRISC, for negativity bias (a), emotional resilience (b), social skills (c), and all three scores combined (d).
Receiver operating curve results for the 15-item BRISC, for negativity bias (a), emotional resilience (b), social skills (c), and all three scores combined (d).
Negativity bias scores made the main contribution to the determination of clinical versus healthy status. For the full 45-question BRISC, the negativity bias score on its own detected clinical status best at a z-score of −1.14, consistent with a threshold of clinical meaningfulness. At this threshold, negativity bias scores showed high accuracy for detecting outpatients with a clinical condition. Across diagnostic categories, negativity bias scores showed the highest detection for major depressive disorder, posttraumatic stress disorder, and panic disorder. This profile of accuracy was duplicated for the mini version's negativity bias scores.
Emotional resilience and social skills separated clinical from healthy status at a higher z-score threshold than did negativity bias. Both emotional resilience and social skills scores showed high specificity. These scores are consistent with the view that a higher-than-average coping capacity may offset risk for a clinical condition and thus support screening and triaging decisions. Results were duplicated for the full and mini version of these scores.
These findings suggest that the BRISC functions to effectively assess the spectrum of poor through to effective emotion regulation. It provides a quick and accurate screen for identifying risk of a clinical disorder across multiple diagnostic categories that takes into account both susceptibility and coping factors. These findings support the use of the BRISC as an objective pan-diagnostic screen for multiple populations, from general through specialty. It expands on the current tools that screen for a particular diagnosis such as major depressive disorder (Mulrow et al. 1995
; Rush et al. 2003
). The sensitivity of the BRISC was highest in participants with diagnoses of depressive and anxiety disorders, consistent with the concept of negativity bias, but also retained a good level of classification across the other diagnostic categories. It also accomplishes the consideration of coping factors, and how they may offset risk factors, which has not been a part of previous instruments.
Strengths of the study include the large sample size, and coverage of multiple diagnostic groups. Future research is needed to extend the findings and address its limitations. The range of clinical participants included in the study was defined by the types of clinics being operated in participating sites. Future studies are needed to extend the evaluation to other diagnostic groups. Validation work with the BRISC has shown it correlates with real-world capacities such as quality of life and work productivity. Here, the cross-sectional design means there was no opportunity to follow up participants to assess the BRISC in relation to real-world functional outcomes over time. A controlled design would be of value, in which the BRISC is evaluated pretreatment and posttreatment. Future research is also needed to evaluate the replicability of the current findings, and their generalizability to additional populations. A prospective study might address this study's limitations involving the range of clinical participants and the lack of participant follow-up in relation to outcomes. Another valuable area for future studies would be to compare the sensitivity/specificity of the BRISC against multiple disorder-specific measures.
The BRISC offers a web-based tool to support the efficient management of mental and neurological health across populations. Its accuracy enables nonspecialist physicians and physician assistants to confidently screen for emotion dysregulation, as a core feature of mental health issues. The mini-BRISC offers an even briefer screen of emotional health that retains high levels of accuracy and may be especially suitable when a heavy patient load constrains the clinician's time. BRISC scores, especially negativity bias, capture maladaptive emotional reactivity to daily events and could be used to identify this feature of risk for depressive and anxiety disorders within other chronic conditions. The coping scores of emotional resilience and social skills may help to determine which patients are best able to cope with clinical issues and engage social support. Using this tool may help support early management of emotional mental health issues and limit the disproportionate flow on effects to disability and loss of productivity.