Clinician Opinions: Perceived Utility of and Barriers to Incorporating Pretreatment Ecological Momentary Assessment Into Clinical Care.

Increasingly, clinicians have the option of including technological components into clinical care. However, little research has assessed clinicians' interest in utilizing technology in their clinical work. Here, clinicians reported their opinions related to using a mobile assessment platform (MAP) to collect ecological data from clients before providing clinical care. Practicing and training mental health clinicians (N = 221) reported demographics, characteristics of their clinical work, and confidence in their clinical skill. Participants then read a description of MAP and responded to questions about their perceived benefits of and barriers to its use. Last, participants rated their interest in using MAP in their clinical work. These perceptions were then factor-analyzed and the resulting factor scores were regressed onto clinician characteristics. Interest in using MAP was significantly lower for the group that endorsed a psychodynamic/psychoanalytic orientation and those with greater confidence in their clinical skills. Across scales, we found a pattern that participants who did not identify as male, those with a psychodynamic/psychoanalytic orientation, and those with greater confidence in their clinical skills tended to have lower ratings of the benefits of and higher ratings for the barriers to using MAP. Results revealed that significant differences in opinions about incorporating technology into clinical work exist between different groups of clinicians. This information may be useful in future work that attempts to implement technological tools into clinical settings.

The Hierarchical Taxonomy of Psychopathology (HiTOP) Is Not an Improvement Over the DSM.

In their response to our article (both in this issue), DeYoung and colleagues did not sufficiently address three fundamental flaws with the Hierarchical Taxonomy of Psychopathology (HiTOP). First, HiTOP was created using a simple-structure factor-analytic approach, which does not adequately represent the dimensional space of the symptoms of psychopathology. Consequently, HiTOP is not the empirical structure of psychopathology. Second, factor analysis and dimensional ratings do not fix the problems inherent to descriptive (folk) classification; self-reported symptoms are still the basis on which clinical judgments about people are made. Finally, HiTOP is not ready to use in real-world clinical settings. There is currently no empirical evidence demonstrating that clinicians who use HiTOP have better clinical outcomes than those who use the Diagnostic and Statistical Manual of Mental Disorders (DSM). In sum, HiTOP is a factor-analytic variation of the DSM that does not get the field closer to a more valid and useful taxonomy.

Folk Classification and Factor Rotations: Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP).

The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group people with similar self-reported symptoms (i.e., like-goes-with-like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory-building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the DSM is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system.

Pooled and person-specific machine learning models for predicting future alcohol consumption, craving, and wanting to drink: A demonstration of parallel utility.

BACKGROUND AND AIMS: The specific factors driving alcohol consumption, craving, and wanting to drink, are likely different for different people. The present study sought to apply statistical classification methods to idiographic time series data in order to identify person-specific predictors of future drinking-relevant behavior, affect, and cognitions in a college student sample. DESIGN: Participants were sent 8 mobile phone surveys per day for 15 days. Each survey assessed the number of drinks consumed since the previous survey, as well as positive affect, negative affect, alcohol craving, drinking expectancies, perceived alcohol consumption norms, impulsivity, and social and situational context. Each individual's data were split into training and testing sets, so that trained models could be validated using person-specific out-of-sample data. Elastic net regularization was used to select a subset of a set of 40 variables to be used to predict either alcohol consumption, craving, or wanting to drink, forward in time. SETTING: A west-coast university. PARTICIPANTS: Thirty-three university students who had consumed alcohol in their lifetime. MEASUREMENTS: Mobile phone surveys. FINDINGS: Averaging across participants, accurate out-of-sample predictions of future drinking were made 76% of the time. For craving, the mean out-of-sample R² value was .27. For wanting to drink, the mean out-of-sample R² value was .27. CONCLUSION: Using a person-specific constellation of psychosocial and temporal variables, it may be possible to accurately predict drinking behavior, affect, and cognitions before they occur. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Involving stakeholders in the design of ecological momentary assessment research: An example from smoking cessation.

Ecological momentary assessment (EMA) is a data collection method that involves repeated sampling of participants' real-time experience and behavior as they unfold in context. A primary challenge in EMA research is to design surveys that adequately assess constructs of interest while minimizing participant burden. To achieve this balance, researchers must make decisions regarding which constructs should be included and how those constructs should be assessed. To date, a dearth of direction exists for how to best design and carry out EMA studies. The lack of guidelines renders it difficult to systematically compare findings across EMA studies. Study design decisions may be improved by including input from potential research participants (stakeholders). The goal of the present paper is to introduce a general approach for including stakeholders in the development of EMA research design. Rather than suggesting rigid prescriptive guidelines (e.g., the correct number of survey items), we present a systematic and reproducible process through which extant research and stakeholder experience can be leveraged to make design decisions. To that end, we report methods and results for a series of focus group discussions with current tobacco users that were conducted to inform the design of an EMA study aimed at identifying person-specific mechanisms driving tobacco use. We conclude by providing recommendations for item-selection procedures in EMA studies.

Neurodegenerative Disease Caregivers' 5-HTTLPR Genotype Moderates the Effect of Patients' Empathic Accuracy Deficits on Caregivers' Well-Being.

OBJECTIVE: To test the hypothesis that a functional polymorphism of the serotonin transporter gene (serotonin-transporter-linked polymorphic region [5-HTTLPR]), which is thought to be associated with differential environmental sensitivity, moderates the association between low levels of empathic accuracy (i.e., ability to recognize emotions in others) in patients with neurodegenerative disease and caregivers' well-being. METHODS: Participants were 54 patients with neurodegenerative disease and their caregivers. Patients' empathic accuracy was measured using a dynamic tracking task in which they continuously rated the emotions of a character in a film; accuracy was determined by comparing patient ratings with those made by an expert panel. Caregivers provided a saliva sample for genotyping. Caregivers' well-being was measured as a latent construct indicated by validated measures of depression, anxiety, and negative affect. RESULTS: Lower levels of patients' empathic accuracy were associated with lower levels of caregivers' well-being. Importantly, caregivers' 5-HTTLPR genotype moderated this association such that lower empathic accuracy in patients predicted lower well-being for caregivers with the short/short genotype (standardized ß = 0.66), but not for caregivers with the short/long (standardized ß = 0.05) or long/long genotypes (standardized ß = -0.21). CONCLUSION: Consistent with previous findings that the short/short variant of 5-HTTLPR is associated with greater sensitivity to environmental influences, caregivers with the short/short variant manifest lower well-being when caring for a patient with low levels of empathic accuracy than caregivers with the other variants. This finding contributes to the authors' understanding of biological factors associated with individual differences in caregiver vulnerability and resilience.

Open trial of a personalized modular treatment for mood and anxiety.

Psychosocial treatments for mood and anxiety disorders are generally effective, however, a number of treated individuals fail to demonstrate clinically-significant change. Consistent with the decades-old aim to identify 'what works for whom,' personalized and precision treatments have become a recent area of interest in medicine and psychology. The present study followed the recommendations of Fisher (2015) to employ a personalized modular model of cognitive-behavioral therapy. Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analysis and dynamic factor modeling. The results of these analyses were then used to generated personalized modular treatment plans on a person-by-person basis. Thirty-two participants completed therapy. The average number of sessions was 10.38. Hedges g's for the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) were 2.33 and 1.62, respectively. The change per unit time was g = .24/session for the HRSD and g = 0.17/session for the HARS. The current open trial provides promising data in support of personalization, modularization, and idiographic research paradigms.

Evidence for the Feasibility of Person-Specific Ecological Momentary Assessment Across Diverse Populations and Study Designs.

Clinical psychological science has seen an exciting shift toward the use of person-specific (idiographic) approaches to studying psychopathology and change in treatment at the level of the individual. One commonly used method in idiographic research is ecological momentary assessment (EMA). EMA offers a way to sample individuals intensively - often multiple times per day - as they go about their lives. While these methods offer benefits such as greater ecological validity and streamlined data collection, many share concerns about their feasibility across diverse clinical populations. To investigate the feasibility of using EMA to study psychological processes idiographically both in- and out of the context of therapy, the present study aggregated participants across seven studies spanning diverse clinical and community populations (N = 496), all of which utilized an idiographic EMA approach to study symptoms of psychopathology (e.g., PTSD, mood and anxiety, substance abuse). In a series of linear regression models, participant and study design characteristics were used to predict compliance with EMA surveys. Across study designs, we found that (1) participants were willing to report on symptoms and mechanisms relating to a wide range of psychopathological domains; (2) on average, participants completed 82.21% (SD = 16.34%) of all EMA surveys; and (3) compliance with EMA surveys was not significantly related to participant demographics, psychological diagnosis, personality characteristics, or most study characteristics (e.g., number of surveys per day). These findings suggest feasibility of idiographic EMA for collecting the data needed to understand psychopathology and change in treatment at the level of the individual.

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity.

Studies of affect dynamics in psychopathology often focus on the prediction of broad constructs like subjective well-being and psychological health. Less is known about how fluctuation in affect over time relates to specific symptom measures (e.g., anxiety or depression), or how these domains change in treatment. A clinical sample of 32 adults with mood and anxiety disorders (13 generalized anxiety, 5 major depression, 14 comorbid) completed four daily assessments of positive (PA) and negative affect (NA) for 30 days prior to receiving cognitive behavioral treatment. Anxiety and depression symptom severity were assessed pre- and post-treatment. We calculated three metrics of affect dynamics for each person's PA and NA time series: (1) variability (experiencing emotional extremes, the standard deviation of a person's PA or NA vector); (2) instability (magnitude of point-to-point change in emotion, the vector's mean squared successive difference); and (3) inertia (the extent to which emotions self-perpetuate over time, the lag-1 autocorrelation of the vector). Multiple regression models were run to test dynamics of positive and negative affect as between-subjects predictors of symptom severity and pre-to-posttreatment change in symptoms. Findings suggest NA dynamics are unrelated to depression symptom severity or treatment response, but we observed a specific effect of NA instability (MSSD) on both severity and response of anxiety symptoms. All PA dynamics were unrelated to anxiety or depression symptom severity. However, variability, instability, and inertia of PA were all found to relate to treatment response for both anxiety and depression symptoms. Taken together, our results suggest that affect dynamics have some specificity in their relationship to clinically relevant phenomena such as symptom severity and treatment outcomes at the between-subjects level of analysis.