Bedtime regularity predicts positive affect among veterans with posttraumatic stress disorder: an ecological momentary assessment study.

BACKGROUND: Regularizing bedtime and out-of-bed times is a core component of behavioral treatments for sleep disturbances common among patients with posttraumatic stress disorder (PTSD). Although improvements in subjective sleep complaints often accompany improvements in PTSD symptoms, the underlying mechanism for this relationship remains unclear. Given that night-to-night sleep variability is a predictor of physical and mental well-being, the present study sought to evaluate the effects of bedtime and out-of-bed time variability on daytime affect and explore the optimal window lengths of over which variability is calculated. METHODS: For about 30 days, male U.S. military veterans with PTSD (N = 64) in a residential treatment program provided ecological momentary assessment data on their affect and slept on beds equipped with mattress actigraphy. We computed bedtime and out-of-bed time variability indices with varying windows of days. We then constructed multilevel models to account for the nested structure of our data and evaluate the impact of bedtime and out-of-bed time variability on daytime affect. RESULTS: More regular bedtime across 6-9 days was associated with greater subsequent positive affect. No similar effects were observed between out-of-bed time variability and affect. CONCLUSIONS: Multiple facets of sleep have been shown to differently predict daily affect, and bedtime regularity might represent one of such indices associated with positive, but not negative, affect. A better understanding of such differential effects of facets of sleep on affect will help further elucidate the complex and intertwined relationship between sleep and psychopathology. TRIAL REGISTRATION: The trial retrospectively was registered on the Defense Technical Information Center website: Award # W81XWH-15-2-0005.

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.

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.

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.

Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA).

Given the recent increase in transdiagnostic research, it is important to discern how dimensional models of psychopathology could be used to guide personalized, dynamic assessment and treatment of symptoms. Using the person-specific approach described by Fisher (2015), we devised an initial 4-step algorithm for devising a treatment plan based on modular cognitive behavioral therapy using results obtained from within-person factor analyses and dynamic factor models. Then, we describe the improvement and digitization of the algorithm, termed Dynamic Assessment Treatment Algorithm (DATA). The development, structure, and clinical implications of DATA are discussed.

Enhancing the Personalization of Psychotherapy With Dynamic Assessment and Modeling.

Clinicians have long recognized the importance of tailoring psychotherapy interventions to the needs and characteristics of the individual patient. However, traditional approaches to clinical assessment, service delivery, and intervention research have not been conducive to such personalization. Contrary to traditional nomothetic approaches, idiographic assessment and modeling of intraindividual dynamic processes holds tremendous promise for tailoring the implementation of psychotherapy to the individual patient. In this article, we (a) present an argument for assessing person-specific dynamics, (b) provide a detailed description of a method that harnesses person-specific dynamic assessment and modeling for use in routine psychotherapy,

Mood-Reactive Self-Esteem and Depression Vulnerability: Person-Specific Symptom Dynamics via Smart Phone Assessment.

Cognitive theories of depression suggest that mood-reactive self-esteem, a pattern of cognitive reactivity where low self-esteem is temporally dependent on levels of sadness, represents vulnerability for depression. Few studies have directly tested this hypothesis, particularly using intensive data collection methods (i.e., experience sampling) required to capture the temporal dynamics of sadness and self-esteem as they unfold naturally, over time. In this study we used participants' smartphones to collect multiple daily ratings of sadness and self-esteem over three weeks, in the real world. We then applied dynamic factor modeling to explore theoretically driven hypotheses about the temporal dependency of self-esteem on sadness (i.e., mood-reactive self-esteem) and its relationship to indices of depression vulnerability both contemporaneously (e.g., rumination, sad mood persistence) and prospectively (e.g., future symptomatology). In sum, individuals who demonstrated mood-reactive self-esteem reported higher levels of rumination at baseline, more persistent sad mood over three weeks, and increased depression symptoms at the end of three weeks above and beyond a trait-like index of self-esteem. The integration of smartphone assessment and person-specific analytics employed in this study offers an exiting new avenue to advance the study and treatment of depression.

Toward a dynamic model of psychological assessment: Implications for personalized care.

OBJECTIVE: The present article proposes a general framework and a set of specific methodological steps for conducting person-specific dynamic assessments, which yield information about syndrome structures and states that can be used to provide actionable information for the formulation of personalized interventions. It is proposed that researchers should (a) determine the relevant constituent inputs for a diagnostic system; (b) measure these inputs with as much detail as possible; (c) assess the correlational structure of system inputs via factor-analytic methods within individuals; and (d) subject the individual-level, latent dimension time series to dynamic analyses such as the dynamic factor model (Molenaar, 1985) to discern the time-dependent, dynamic relationships within and between system components. METHOD: An exemplar is provided wherein 10 individuals with clinically diagnosed generalized anxiety disorder completed surveys related to generalized anxiety disorder symptomatology for at least 60 consecutive days. These data were then subjected to person-specific exploratory and confirmatory factor analyses for the identification of latent symptom dimensions. Finally, dynamic factor models were used to model the dynamic interrelationships within and between symptom domains on a person-by-person basis. RESULTS: Person-specific factor analyses returned models with 3 (n = 8) or 4 (n = 2) latent factors, all with excellent fit. Dynamic factor modeling successfully revealed the contemporaneous correlations and time-lagged predictive relationships between factors, providing prescriptive information for the formulation of targeted interventions. CONCLUSIONS: The proposed approach has the potential to inform the construction and implementation of personalized treatments by delineating the idiosyncratic structure of psychopathology on a person-by-person basis.