Towards a precision psychiatry approach to anxiety disorders with ecological momentary assessment: the example of panic disorder

Abstract

BackgroundTreatments for anxiety disorders are among the most effective in psychiatry. Yet, there is considerable room for improvement.AimIn this paper, we discuss the value of ecological momentary assessment as a research method and clinical tool.MethodsWe begin by describing ecological momentary assessment and its advantages, including the ability to collect ecologically valid information about mental disorders, in real time, in individual patients. We then illustrate the value of this approach for anxiety disorder treatment using two patients with panic disorder who completed ecological momentary assessments for 2 weeks before and after a cognitive-behavioural therapy intervention. We focus especially on two key pieces of information provided by ecological momentary assessment data: information about symptom dynamics and information about the relationships among symptoms as they unfold over time within individual patients.PerspectiveAlthough considerable work is needed to further develop this methodology in the context of anxiety disorder treatment, we believe that these pieces of information may ultimately inform our understanding of how anxiety disorder treatments have their effect and how those treatments can be tailored to individual patients.

Integrating a functional view on suicide risk into idiographic statistical models

“…However, the majority of existing interventions for suicide risk are not designed to be delivered at a frequency that matches abrupt changes in suicidal ideation and nor are they tailored to the heterogeneous nature of suicide (Cha et al, 2018;Coppersmith et al, 2021;Zalsman et al, 2016). The ability of network depictions to provide a nuanced overview of functional relationships across risk factors is compelling, and from an empirical point of view, personalized models have been celebrated for their potential clinical utility (Fisher & Boswell, 2016;Kroeze et al, 2017;Piccirillo & Rodebaugh, 2019;Robinaugh, Brown, et al, 2020;van der Krieke et al, 2015;Zimmermann et al, 2019). Because suicide risk is often uncertain and unpredictable, person-specific time series models of suicide risk may offer both the patient and therapist a quantitative tool that is useful to increment safety planning.…”

The psychophysiological response during post-traumatic stress disorder treatment with modular motion-assisted memory desensitisation and reconsolidation (3MDR)

“…Many studies exploring psychophysiological responses in people with stress, anxiety and/or panic disorders have been carried out at times where participants were exposed to an experimental stress stimulus or a standardized stress-provoking paradigm. For instance, differences in reactive vulnerability between patients and healthy individuals were explored using designed biological and/or cognitive challenges in a laboratory setting (Robinaugh et al, 2020 ; Siess, Blechert, & Schmitz, 2014 ). Studies exploring the response to psychological therapy for PTSD mostly involve pre- and post-measurement of hyperreactivity to trauma-relevant stimuli (Katz et al, 2020 ; Maples-Keller et al, 2019 ).…”

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls

“…If considering symptom motivations represents a theoretical shift that may enhance understanding of neuropsychological impairment in OCD, capitalizing on machine learning methods constitutes a methodological shift in kind. In recent years, novel data‐gathering techniques such as those used in mobile health have transformed the study of individual differences (Robinaugh et al, 2020), also presenting a need for analytic methods suited to handle the complexity and volume of data these paradigms produce (Bzdok & Meyer‐Lindenberg, 2018; Dwyer, Falkai, & Koutsouleris, 2018). With an increasing focus on modelling processes at the individual level to promote precision psychiatry, researchers are faced with high‐dimensional and interdependent data, often from small samples, that present challenges to traditional statistical paradigms (Dwyer et al, 2018).…”