Background: According to a growing body of literature, people are quite inaccurate in recalling past affective experiences. Nevertheless, the mechanism underlying this recall bias (i.e., the tendency to overestimate and/or underestimate positive or negative past emotional experiences) remains unclear, and its association with mental health has not been studied yet. Methods: We adopted a smartphone-based Ecological Momentary Assessment to monitor daily affect (n = 92) and investigate the association between affect recall bias, mental health and resilience. Results: While the tendency to overestimate negative affective experiences was observed in participants reporting mild depressive symptoms, positive affect (PA) overestimation as compared to PA underestimation was associated with better mental health (i.e. higher psychological well-being and lower depressive and anxiety symptoms) through the enhancement of resilience. Furthermore, positively biased participants (i.e. PA over estimators) benefited from greater well-being, even when compared to accurate individuals. Conclusions: While people appear to use retrospective PA overestimation as a strategy to enhance well-being and resilience, they are not likely to underestimate past negative experiences to feel better. Accordingly, owning an optimistic vision of the past may represent an adaptive “distortion” of reality that fosters people’s mental health. The clinical implications of cultivating PA and learning strategies to regulate both negative and positive emotions are discussed.

In recent decades, emotion regulation (ER) has been one of the most widely studied constructs within the psychological field. Nevertheless, laboratory experiments and retrospective assessments have been the 2 most common strands of ER research; thus, leaving open several crucial questions about ER antecedents and consequences in daily life. Beyond traditional methods, ecological momentary assessment (EMA) has the potential to capture ER dynamics during the flow of daily experiences, in real-life settings and through repeated measurements. Here, we discuss what we currently know about ER antecedents and consequences. We will compare findings from previous literature to findings from EMA studies, pointing out both similarities and differences, as well as questions that can be answered better with the EMA approach. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient’s response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words “psychotherapy”, “artificial intelligence”, and “emotional disorders”. From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients’ needs, more methodologically robust studies are needed.

This study aggregated data from three randomized control trials to explore the differential efficacy of three forms of exposure therapy, namely, in vivo (iVET), virtual reality (VRET), and augmented reality (ARET), in the treatment of small animal phobia. Additionally, baseline patient characteristics were used to detect subgroups of patients who showed a differential response to certain treatment modalities. Primary measures were distance covered, anxiety during the behavioral avoidance test (BAT), and overall fear of small animals. A repeated-measures analysis of variance was used to explore the overall treatment effect across the exposure modalities. A cluster analysis and an analysis of moderation were conducted to explore differential response to treatments. The main study finding was that the three treatment conditions were similarly efficacious in the treatment of small animal phobia for all study outcomes. Only for distance covered, our results revealed a tendency for iVET to be more effective than VRET and ARET in participants with worse performance on the BAT before treatment. The present study findings provide further evidence for the comparable efficacy of the three forms of exposure. Our results also suggest that, overall, treatments are likely to be similarly effective, regardless of the individual baseline characteristics (i.e., fear, anxiety, and age), whereas pretreatment scores on distance covered in the avoidance test might be used to personalize treatments (iVET may be preferable when participants perform worse at pretreatment).

Objectives: Ecological momentary assessment has been recommended in the management of chronic pain. Smartphone apps might be a useful tool for that purpose. This goal of this study was to develop and test a multidimensional smartphone app for adults with chronic pain. A multidisciplinary team developed the app content after a series of meetings, considering clinical guidelines for pain measurement. The content included pain intensity and interference, fatigue, mood, perceived health status, activity level, side effects of the medication, use of rescue medication, and pain-related sychological constructs (catastrophizing, acceptance, fear, and coping). Methods: Thirty-eight participants (21 to 59 y) used the app, called Pain Monitor, twice a day during 30 consecutive days. Patients completed a set of well-established measures at the beginning and end of the study via paper-and-pencil. Weekly phone assessments were also made for pain intensity, pain interference, fatigue, and mood. Results: Construct validity was revealed by moderate-to-strong correlations between app content and traditional measures. Feasibility was supported by high compliance (between 70% and 82%) and high acceptability and ease of use. Both side effects of the medication and use of rescue medication were found to be useful tools to guide treatment. Discussion: The new assessment protocol in the app allows for an easy and rapid multidimensional assessment of chronic pain patients.

Pain catastrophizing is known to contribute to physical and mental functioning, even when controlling for the effect of pain intensity. However, research has yet to explore whether the strength of the relationship between pain catastrophizing and pain-related outcomes varies across pain intensity levels (i.e., moderation). If this was the case, it would have important implications for existing models of pain and current interventions. The present investigation explored whether pain intensity moderates the relationship between pain catastrophizing and pain-related outcomes. Participants were 254 patients (62% women) with heterogeneous chronic pain. Patients completed a measure of pain intensity, pain interference, pain catastrophizing, and physical and mental health. Pain intensity moderated the relationship between pain catastrophizing and pain interference and between pain catastrophizing and physical health status. Specifically, the strength of the correlation between pain catastrophizing and these outcomes decreased considerably as pain intensity increased. In contrast, pain intensity did not moderate the relationship between pain catastrophizing and mental health. Study findings provide a new insight into the role of pain intensity (i.e., moderator) in the relationship between pain catastrophizing and various pain-related outcomes, which might help develop existent models of pain. Clinical implications are discussed in the context of personalized therapy.