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Abstract: The evidence based assessment (EBA) movement stresses the importance of psychological measures with strong psychometric properties and clinical utility. The Functional Idiographic Assessment Template system (FIAT; Callaghan, 2006) is a functional analytic behavioral approach to the assessment of interpersonal functioning for use with therapies like Functional Analytic Psychotherapy (FAP; Kohlenberg & Tsai, 1991). While research has begun to demonstrate the clinical utility of the FIAT, its psychometric properties have not been explored. The present study examines the Functional Idiographic Assessment Template-Questionnaire (FIAT-Q), a self-report measure contained in the FIAT. Two different approaches are used to explore the psychometric properties and structure of the FIAT-Q, and test–retest reliability is examined. These methods are discussed along with the use of the FIAT-Q as an alternative to nosological assessment of client behaviors. [Copyright &y& Elsevier]

Abstract: Alexithymia is a term used to describe individuals who seem unable to experience or at least describe emotions. This paper offers a theoretical interpretation of alexithymia from a radical behaviorist perspective. While there have been attempts to explain the etiology of alexithymia, the current analysis is unique in that it provides direct treatment implications. The pragmatic analysis described focuses on the verbal behavior of individuals rather than looking “inside” for explanations. This is supported by a review of experimental research that has failed to find consistencies among alexithymic individuals׳ physiological responding. Descriptions of the various discriminative and consequential stimulus conditions involved in the complex learning histories of individuals that could result in an alexithymic presentation are provided. This analysis helps situate the alexithymia construct in a broader behavior analytic understanding of emotions. Finally this paper outlines implications for assessment and treatment, which involve influencing discriminative and consequential interpersonal stimulus conditions to shape verbal behavior about emotions. [Copyright &y& Elsevier]

Background: Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. Objective: In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. Methods: The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. Results: The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. Conclusions: Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. Trial Registration: ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599 International Registered Report Identifier (IRRID): DERR1-10.2196/23592 JMIR Res Protoc 2021;10(1):e23592 doi:10.2196/23592 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]