Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data

Exploring person-specific associations of situational motivation and readiness with leisure-time physical activity effort and experience

Identifying determinants of leisure-time physical activity (LTPA) often relies on population-level (nomothetic) averages, potentially overlooking person-specific (idiographic) associations. This study uses an idiographic perspective to explore how subjective readiness and motives for LTPA relate to volitional effort (duration, intensity) and affective experience (pleasure, displeasure). We also highlight the potential for different interpretations when data are averaged within individuals and assessed using a variable-centered approach. Participants (N = 22, 25±8 years old, 54.5% women) were asked to continue their regular PA patterns for 10 weeks. Ecological momentary assessment procedures allowed participants to provide pre-activity reports (physical, cognitive, emotional readiness and situational motive for activity) and post-activity reports (activity type, duration, perceived exertion, ratings of affective valence). Spearman rank correlation was implemented to interpret within- and between-person associations. Data visualization approaches were used to showcase person-specific differences in associations. Participants provided 519 reports of LTPA (24±11 events/person), which displayed between- and within-person variety in type, duration, intensity, and affective experience. Exemplar cases highlight discrepancies in interpretation based on level of analysis, such that the nomothetic association (rho = .42, p = .05; 95% CI -.02, .72) between motive to replenish energy and LTPA duration was observed in only one within-person analysis (41% were weak-to-large inverse effects). Alternatively, the negligible nomothetic association (rho = .02, p = .93; 95% CI -.41, .44) between physical readiness and LTPA-related affect did not reflect the 59% of within-person analyses showing moderate-to-large, positive effects. Future research aiming to identify determinants of LTPA effort and experience should integrate contemporary, idiographic analyses in early-stage research for developing person-specific strategies for LTPA promotion.

A network analysis of mechanisms of change during exposures over the course of intensive OCD treatment

Exposure and response prevention (ERP) is an evidence-based treatment for obsessive-compulsive disorder (OCD). Theories for how it works vary in their emphasis on active mechanisms of change. The current study aimed to clarify mechanisms of change in ERP for OCD using network analysis, comparing ERP networks at the start and end of intensive treatment (partial hospital and residential). In our sample of 182 patients, the most central node in both networks was engagement with exposure, which was consistently related to greater understanding of ERP rationale, higher willingness, and less ritualization, accounting for all other variables in the network. There were no significant differences in networks between the start and end of treatment. These results suggest that nonspecific parameters like facilitating engagement in exposures without ritualizing and providing a clear rationale to clients may be key to effective treatment. As such, it may be useful for clinicians to spend adequate time underscoring the need to eliminate rituals to fully engage in exposure tasks and explaining the rationale for ERP prior to doing exposures, regardless of theoretical orientation. Nonetheless, findings represent group-level statistics and more fine-grained idiographic analyses may reveal individual-level differences with respect to central mechanisms of change. Other limitations include demographic homogeneity of our sample.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Individual differences in the long-term impact of the pandemic: moderators of COVID-related hardship, worry, and social support

PURPOSE

Understanding people's response to the pandemic needs to consider individual differences in priorities and concerns. The present study sought to understand how individual differences in cognitive-appraisal processes might moderate the impact of three COVID-specific factors-hardship, worry, and social support-on reported depression.

METHODS

This longitudinal study of the psychosocial impact of the COVID-19 pandemic included 771 people with data at three timepoints over 15.5 months. Participants were recruited from panels of chronically ill or general population samples. Depression was measured by an item response theory validated depression index created using items from existing measures that reflected similar content to the Patient Health Questionnaire-8. COVID-specific factors of hardship, worry, and social support were assessed with items compiled by the National Institutes of Health. The Quality of Life Appraisal Profilev2 Short-Form assessed cognitive appraisal processes. A series of random effects models examined whether appraisal moderated the effects of hardship, worry, and social support on depression over time.

RESULTS

Over time the association between low social support and depression was greater (p = 0.0181). Emphasizing the negative was associated with exacerbated depression, in particular for those with low social support (p = 0.0007). Focusing on demands and habituation was associated with exacerbated depression unless one experienced greater hardship (p = 0.0074). There was a stronger positive connection between recent changes and depression for those people with higher worry scores early in the pandemic as compared to later, but a stronger positive correlation for those with lower worry scores later in the pandemic (p = 0.0015). Increased endorsement of standards of comparison, emphasizing the negative, problem goals, and health goals was associated with worse depression scores (all p < 0.0001). People who were younger, disabled, or had greater difficulty paying bills also reported worse depression (p < 0.0001, 0.0001, and 0.002, respectively).

CONCLUSION

At the aggregate level, COVID-specific stressors changed over the course of the pandemic, whereas depression and social-support resources seemed stable. However, deeper analysis revealed substantial individual differences. Cognitive-appraisal processes showed considerable variability across individuals and moderated the impact of COVID-specific stressors and resources over time. Future work is needed to investigate whether coaching individuals away from maladaptive cognitive-appraisal processes can reduce depression and lead to better overall well-being.

A socioemotional network perspective on momentary experiences of family conflict in young adults

Family conflict is an established predictor of psychopathology in youth. Traditional approaches focus on between-family differences in conflict. Daily fluctuations in conflict within families might also impact psychopathology, but more research is needed to understand how and why. Using 21 days of daily diary data and 6-times a day experience-sampling data (N = 77 participants; mean age = 21.18, SD = 1.75; 63 women, 14 men), we captured day-to-day and within-day fluctuations in family conflict, anger, anxiety, and sadness. Using multilevel models, we find that days of higher-than-usual anger are also days of higher-than-usual family conflict. Examining associations between family conflict and emotions within days, we find that moments of higher-than-usual anger predict higher-than-usual family conflict later in the day. We observe substantial between-family differences in these patterns with implications for psychopathology; youth showing the substantial interplay between family conflict and emotions across time had a more perseverative family conflict and greater trait anxiety. Overall, findings indicate the importance of increases in youth anger for experiences of family conflict during young adulthood and demonstrate how intensive repeated measures coupled with network analytic approaches can capture long-theorized notions of reciprocal processes in daily family life.

Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.

Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances

Researchers and clinicians working within the Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition, Text Rev (DSM-5-TR) framework face a difficult question: what does it mean to have an evidence-based assessment of a nonevidence-based diagnostic construct? Alternative nosological approaches conceptualize psychopathology as (a) hierarchical, allowing researchers to move between levels of description and (b) dimensional, eliminating artificial dichotomies between disorders and the dichotomy between mental illness and mental well-being. In this article, we provide an overview of ongoing efforts to develop validated measures of transdiagnostic nosologies (i.e., the Hierarchical Taxonomy of Psychopathology; HiTOP) with applications for measurement-based care. However, descriptive models like HiTOP, which summarize patterns of covariation among psychopathology symptoms, do not address dynamic processes underlying the problems associated with psychopathology. Ambulatory assessment, well-suited to examine such dynamic processes, has also developed rapidly in recent decades. Thus, the goal of the current article is twofold. First, we provide a brief overview of developments in constructing valid measures of the HiTOP model as well as developments in ambulatory assessment practices. Second, we outline how these parallel developments can be integrated to advance measurement-based treatment. We end with a discussion of some major challenges for future research to address to integrate advances more fully in transdiagnostic and ambulatory assessment practices.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

A smartphone-based intervention for young people who self-harm ('PRIMARY'): study protocol for a multicenter randomized controlled trial

BACKGROUND

Self-harm in young people is a public health concern connected with severe mental health problems, such as personality pathology. Currently, there are no specific evidence-based interventions available for young people who self-harm. Therefore, we developed PRe-Intervention Monitoring of Affect and Relationships in Youth (PRIMARY), a smartphone-based intervention, co-designed by clinicians and young people with lived experience of mental ill-health. PRIMARY combines the Experience Sampling Method (ESM) with weekly report sessions. The study aims to examine the effectiveness of PRIMARY with regard to reducing self-harm, and improving emotion regulation and quality of relationships.

METHODS

This study is a multicenter, parallel groups, randomized controlled trial (RCT) comparing the PRIMARY intervention to a waiting list control group. PRIMARY comprises 28 consecutive days of questionnaires five times each day (i.e., ESM) and four weekly report sessions. Participants will comprise 180 young people referred for treatment to the participating Dutch mental healthcare institutions and (1) are aged 12 to 25 years, and (2) engaged in ≥ 1 act of self-harm in the past year. Participants are randomly allocated to a study group after screening in a 1:1 ratio by an independent researcher using computer-generated randomization sequences with stratified block randomization by age (12 to 15 years / 16 to 25 years). Staff will conduct assessments with all participants at baseline (Wave 1), after 28 days (Wave 2), and in a subsample after 10 weeks of subsequent specialized treatment (Wave 3). The primary outcomes are self-harm, emotion regulation, and quality of relationships. Secondary outcomes include patient and clinician satisfaction. Exploratory analyses of ESM data will examine the relationship between emotions, social relationships, and self-harm.

DISCUSSION

The results of this trial will clarify whether an innovative smartphone-based intervention is effective for reducing self harm and improving emotion regulation and the quality of social relationships. It has the potential to fill a treatment gap of interventions specifically targeting self-harm. If proven effective, it would provide an accessible, easy-to-implement, low-cost intervention for young people. Furthermore, the ESM-data will allow detailed analyses into the processes underlying self-harm, which will contribute to theoretical knowledge regarding the behavior.

TRIAL REGISTRATION

ISRCTN42088538 ( https://doi.org/10.1186/ISRCTN42088538 ), retrospectively registered on the 26th of October 2022.

m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice

In this paper, we present m-Path (www.m-Path.io), an online platform that provides an easy-to-use and highly tailorable framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people's everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., retrospective surveys or lab-based experiments), EMA and EMI have become popular in recent years. Although a surge in the use of these methods has led to a myriad of EMA and EMI applications, many existing platforms only focus on a single aspect of daily life data collection (e.g., assessment vs. intervention, active self-report vs. passive mobile sensing, research-dedicated vs. clinically-oriented tools). With m-Path, we aim to integrate all of these facets into a single platform, as it is exactly this all-in-one approach that fosters the clinical utility of accumulated scientific knowledge. To this end, we offer a comprehensive platform to set up complex and highly adjustable EMA and EMI designs with advanced functionalities, using an intuitive point-and click web interface that is accessible for researchers and clinicians with limited programming skills. We discuss the strengths of daily life data collection and intervention in general and m-Path in particular. We describe the regular workflow to set up an EMA or EMI design within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software.

The direction of effects between parenting and adolescent affective well-being in everyday life is family specific

Numerous theories and empirical studies have suggested that parents and their adolescent children reciprocally influence each other. As most studies have focused on group-level patterns, however, it remained unclear whether this was true for every family. To investigate potential heterogeneity in directionality, we applied a novel idiographic approach to examine the effects between parenting and adolescent well-being in each family separately. For 100 days, 159 Dutch adolescents (Mage = 13.31, 62% female) reported on affective well-being and four parenting dimensions. The family-specific effects of pre-registered ( https://osf.io/7n2jx/ ) dynamic structural equation models indeed revealed that a reciprocal day-to-day association between parenting and adolescent affective well-being was present only in some families, with the proportion of families displaying a reciprocal association varying across the four parenting dimensions (11-55%). In other families, either parenting predicted the adolescent's affective well-being (8-43%) or vice versa (10-27%), or no day-to-day associations were found (16-60%). Adolescents with higher trait levels of environmental sensitivity and neuroticism were more strongly affected by parenting. Thus, findings suggest that the ways in which parents and adolescents influence each other in everyday life are unique, stressing the need to move towards an idiographic parenting science.

Network analysis of physical activity and depressive and affective symptoms during COVID-19 home confinement

INTRODUCTION

The aim of this study was to analyze the network structure of physical activity, frequency, depressive, and affective symptoms in people under home isolation due to COVID-19.

METHOD

A longitudinal study was conducted in two phases (beginning (March 19, 2020) and end of home confinement (April 8, 2020)). The sample consisted of 579 participants from Spain (250 men and 329 women) aged 16 to 92 years (overall sample: M = 47.06, SD = 14.52). A network analysis was performed.

RESULTS

Four clusters emerged (PA, depressive symptoms, positive affect, and negative affect). A higher frequency of physical activity was related to better-sustained attention, increased alertness, and enthusiasm. In addition, feelings of guilt and shame were mitigated, and confinement distress and irritability were reduced. Physical activity also mitigated fatigue in women, whereas feelings of unhappiness were reduced in men.

CONCLUSION

Physical activity seems to be an effective option for mitigating the negative effects of the COVID-19 pandemic. Public health policymakers should develop programs to promote physical activity in order to be able to cope with confinement or similar scenarios in the future.

Researching what we practice-The paradigm of systemic family research: Part 2

This is the second of two articles focusing on ideological and philosophical preferences for relating to and conducting research in the field of systemic couple and family therapy (CFT). To emphasize the need for the field of systemic CFT to be based on the best available knowledge, in the first article, we argue the benefits of applying the principles of evidence-based practice, and in the current article, we present the rationale behind the contents of a program for systemic family therapy research that safeguards methodological multiplicity. The need for multi-methodological systemic research is also recognized on the basis of the authors' self-reflexive accounts of overcoming barriers to learning skills and deepening their understanding of quantitative methods. We thus argue that trans-methodological reflexivity is necessary and we argue a preference for methodological multiplicity that includes statistical competency as regards the interdependence of observations (i.e., nonindependence), and we further argue that these are crucial components of a systemic research program.

Measuring what matters: Why and how to include patient reported outcomes in clinical care and research on inborn errors of metabolism

Patient reported outcomes (PROs) are generally defined as 'any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else'. A broader definition of PRO also includes 'any information on the outcomes of health care obtained directly from patients without modification by clinicians or other health care professionals'. Following this approach, PROs encompass subjective perceptions of patients on how they function or feel not only in relation to a health condition but also to its treatment as well as concepts such as health-related quality of life (HrQoL), information on the functional status of a patient, signs and symptoms and symptom burden. PRO measurement instruments (PROMs) are mostly questionnaires and inform about what patients can do and how they feel. PROs and PROMs have not yet found unconditional acceptance and wide use in the field of inborn errors of metabolism. This review summarises the importance and usefulness of PROs in research, drug legislation and clinical care and informs about quality standards, development, and potential methodological shortfalls of PROMs. Inclusion of PROs measured with high-quality, well-selected PROMs into clinical care, drug legislation, and research helps to identify unmet needs, improve quality of care, and define outcomes that are meaningful to patients. The field of IEM should open to new methodological approaches such as the definition of core sets of variables including PROs to be systematically assessed in specific metabolic conditions and new collaborations with PRO experts, such as psychologists to facilitate the systematic collection of meaningful data.

Determining menstrual cycle phase: An empirical examination of methodologies and recommendations for improvement in behavioral and brain sciences

The recent decade has brought an exciting proliferation of behavioral, psychological and neuroscientific research involving the menstrual cycle. However, the reliability and validity of many popular methodologies for determining menstrual cycle phase lack empirical examination. These under-investigated methods include: (1) predicting menstrual cycle phase using self-report information only (e.g., "count" methods), (2) utilizing ovarian hormone ranges to determine menstrual cycle phase, and (3) using ovarian hormone changes from limited measurements (e.g., two time points) to determine menstrual cycle phase. In the current study, we examine the accuracy of these methods for menstrual cycle phase determination using 35-day within-person assessments of circulating ovarian hormones from 96 females across the menstrual cycle. Findings indicate that all three common methods are error-prone, resulting in phases being incorrectly determined for many participants, with Cohen's kappa estimates ranging from -0.13 to 0.53 indicating disagreement to only moderate agreement depending on the comparison. Such methodological challenges are surmountable through careful study design, more frequent hormone assays (when possible), and utilization of sophisticated statistical methods. With increased methodological rigor in behavioral, psychological and neuroscientific research, the field will be poised to detect biobehavioral correlates of ovarian hormone fluctuations for the betterment of the mental health and wellbeing of millions of females.

The future is dynamic: A call for intensive longitudinal data in immunopsychiatry

The long-term value of immunopsychiatry will be based on its ability to translate basic science into effective clinical interventions. In this article, we discuss a key obstacle to achieving this important translational goal-namely, the preponderance of studies that are cross-sectional, or that have months-to-years long follow-up periods. Immunopsychiatric processes such as stress, inflammation, and depression symptoms are inherently dynamic and fluctuate over hours, days, and weeks. This fact suggests that higher-density data collection with only days between measurements is necessary to capture-with adequate resolution-the actual dynamics of these systems, determine optimal time lags with which to observe associations between variables of interest, and maximize the translational potential of these data. To illustrate these points, we use pilot data from our own intensive longitudinal immunopsychiatric study. We then conclude by making several recommendations for future research. By learning how to better use existing data for dynamically informative studies as well as collecting intensive longitudinal data, we believe immunopsychiatry will be much better positioned to advance our causal understanding of the interplay between the immune system and health.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

Distress and Resilience Narratives within Pain Memories of Adolescents and Young Adults with Complex Regional Pain Syndrome: A Multi-Method Study

A comprehensive understanding of pain memories requires consideration of risk and resilience factors across biopsychosocial domains. Previous research has typically focused on pain-related outcomes, largely ignoring the nature and context of pain memories. Using a multiple-method approach, this study explores the content and context of pain memories in adolescents and young adults with complex regional pain syndrome (CRPS). Recruited via social media and pain-related organizations, participants completed an autobiographical pain memory task. Two-step cluster analysis was conducted on the pain memory narratives of adolescents and young adults with CRPS (n=50) using a modified version of the Pain Narrative Coding Scheme. Narrative profiles generated from the cluster analysis subsequently guided a deductive thematic analysis. Cluster analysis identified two narrative profiles of Distress and Resilience, with the role of coping and positive affect emerging as important profile predictors across pain memories. Subsequent deductive thematic analysis, utilizing Distress and Resilience codes, demonstrated the complex interplay between affect, social, and coping domains. Findings highlight the importance of applying a biopsychosocial framework to pain memory research, accounting for both risk and resilience perspectives and encourage the use of multiple method approaches to improve understanding of autobiographic pain memories. Clinical implications of reframing and recontextualizing pain memories and narratives are discussed, and the importance of exploring the origins of pain and possible application to developing resilience-based, preventative interventions is highlighted. PERSPECTIVE: Using multiple methods, this paper presents a comprehensive account of pain memories in adolescents and young adults with CRPS. Study findings promote the importance of adopting a biopsychosocial approach to examining both risk and resilience factors in understanding autobiographical pain memories in the context of pediatric pain.

Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations

OBJECTIVE

Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents.

METHOD

Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences.

RESULTS

A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035.

CONCLUSIONS

Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prenatal androgen influences on the brain: A review, critique, and illustration of research on congenital adrenal hyperplasia

Sex hormones, especially androgens, contribute to sex and gender differences in the brain and behavior. Organizational effects are particularly important because they are thought to be permanent, reflecting hormone exposure during sensitive periods of development. In human beings, they are often studied with natural experiments in which sex hormones are dissociated from other biopsychosocial aspects of development, such as genes and experiences. Indeed, the greatest evidence for organizational effects on sex differences in human behavior comes from studies of females with congenital adrenal hyperplasia (CAH), who have heightened prenatal androgen exposure, female-typical rearing, and masculinized toy play, activity and career interests, spatial skills, and some personal characteristics. Interestingly, however, neuroimaging studies of females with CAH have revealed few neural mechanisms underlying these hormone-behavior links, with the exception of emotion processing; studies have instead shown reduced gray matter volumes and reduced white matter integrity most consistent with other disease-related processes. The goals of this narrative review are to: (a) describe methods for studying prenatal androgen influences, while offering a brief overview of behavioral outcomes; (b) provide a critical methodological review of neuroimaging research on females with CAH; (c) present an illustrative analysis that overcomes methodological limitations of previous work, focusing on person-specific neural reward networks (and their associations with sensation seeking) in women with CAH and their unaffected sisters in order to inform future research questions and approaches that are most likely to reveal organizational hormone effects on brain structure and function.

Sources of Heterogeneity in Functional Connectivity During English Word Processing in Bilingual and Monolingual Children

Diversity and variation in language experiences, such as bilingualism, contribute to heterogeneity in children's neural organization for language and brain development. To uncover sources of such heterogeneity in children's neural language networks, the present study examined the effects of bilingual proficiency on children's neural organization for language function. To do so, we took an innovative person-specific analytical approach to investigate young Chinese-English and Spanish-English bilingual learners of structurally distinct languages. Bilingual and English monolingual children (N = 152, M(SD)_age = 7.71(1.32)) completed an English word recognition task during functional near-infrared spectroscopy neuroimaging, along with language and literacy tasks in each of their languages. Two key findings emerged. First, bilinguals' heritage language proficiency (Chinese or Spanish) made a unique contribution to children's language network density. Second, the findings reveal common and unique patterns in children's patterns of task-related functional connectivity. Common across all participants were short-distance neural connections within left hemisphere regions associated with semantic processes (within middle temporal and frontal regions). Unique to more proficient language users were additional long-distance connections between frontal, temporal, and bilateral regions within the broader language network. The study informs neurodevelopmental theories of language by revealing the effects of heterogeneity in language proficiency and experiences on the structure and quality of emerging language neural networks in linguistically diverse learners.

The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation

Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best-and were roughly equivalent in performance-when autoregressive relations were strong (e.g., β = .60). When they were weak (β ≤ .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personalized Neural Networks Underlie Individual Differences in Ethnic Identity Exploration and Resolution

This study examined how ethnic identity relates to large-scale brain networks implicated in social interactions, social cognition, self-definition, and cognitive control. Group Iterative Multiple Model Estimation (GIMME) was used to create sparse, person-specific networks among the default mode and frontoparietal resting-state networks in a diverse sample of 104 youths aged 17-21. Links between neural density (i.e., number of connections within and between these networks) and ethnic identity exploration and resolution were evaluated in the full sample. Ethnic identity resolution was positively related to frontoparietal network density, suggesting that having clarity about one's ethnic group membership is associated with brain network organization reflecting cognitive control. These findings help fill a critical knowledge gap about the neural underpinnings of ethnic identity.

Idiographic patient reported outcome measures (I-PROMs) for routine outcome monitoring in psychological therapies: Position paper

Idiographic patient-reported outcome measures (I-PROMs) are a growing set of individualized tools for use in routine outcome monitoring (ROM) in psychological therapies. This paper presents a position statement on their conceptualization, use, and analysis, based on contemporary evidence and clinical practice. Four problem-based, and seven goal-based, I-PROMs, with some evidence of psychometric evaluation and use in psychotherapy, were identified. I-PROMs may be particularly valuable to the evaluation of psychological therapies because of their clinical utility and their alignment with a patient-centered approach. However, there are several challenges for I-PROMs: how to generate items in a robust manner, their measurement model, methods for establishing their reliability and validity, and the meaning of an aggregated I-PROM score. Based on the current state of the literature, we recommend that I-PROMs are used to complement nomothetic measures. Research recommendations are also made regarding the most appropriate methods for analyzing I-PROM data.

Toward Individualized Prediction of Binge-Eating Episodes Based on Ecological Momentary Assessment Data: Item Development and Pilot Study in Patients With Bulimia Nervosa and Binge-Eating Disorder

BACKGROUND

Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail.

OBJECTIVE

We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data.

METHODS

We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors).

RESULTS

On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of r_D 0.40, SD 0.13; and mean r_CV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models.

CONCLUSIONS

Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

What about "space" is important for episodic memory?

Early cognitive neuroscientific research revealed that the hippocampus is crucial for spatial navigation in rodents, and for autobiographical episodic memory in humans. Researchers quickly linked these streams to propose that the human hippocampus supports memory through its role in representing space, and research on the link between spatial cognition and episodic memory in humans has proliferated over the past several decades. Different researchers apply the term "spatial" in a variety of contexts, however, and it remains unclear what aspect of space may be critical to memory. Similarly, "episodic" has been defined and tested in different ways. Naturalistic assessment of spatial memory and episodic memory (i.e., episodic autobiographical memory) is required to unify the scale and biological relevance in comparisons of spatial and mnemonic processing. Limitations regarding the translation of rodent to human research, human ontogeny, and inter-individual variability require greater consideration in the interpretation of this literature. In this review, we outline the aspects of space that are (and are not) commonly linked to episodic memory, and then we discuss these dimensions through the lens of individual differences in naturalistic autobiographical memory. Future studies should carefully consider which aspect(s) of space are being linked to memory within the context of naturalistic human cognition. This article is categorized under: Psychology > Memory.

Conceptualizing and Examining Change in Communication Research

Communication research often focuses on processes of communication, such as how messages impact individuals over time or how interpersonal relationships develop and change. Despite their importance, these change processes are often implicit in much theoretical and empirical work in communication. Intensive longitudinal data are becoming increasingly feasible to collect and, when coupled with appropriate analytic frameworks, enable researchers to better explore and articulate the types of change underlying communication processes. To facilitate the study of change processes, we (a) describe advances in data collection and analytic methods that allow researchers to articulate complex change processes of phenomena in communication research, (b) provide an overview of change processes and how they may be captured with intensive longitudinal methods, and (c) discuss considerations of capturing change when designing and implementing studies. We are excited about the future of studying processes of change in communication research, and we look forward to the iterations between empirical tests and theory revision that will occur as researchers delve into studying change within communication processes.

Cognitive trajectories during and after electroconvulsive therapy in patients with MDE: Taking different perspectives

Cognitive function during an ECT care pathway is mainly investigated at the group level by analyzing mean cognitive test scores over time. However, there are important inter-individual differences, with some patients experiencing residual invalidating cognitive deficits. This study provides a nuanced examination of cognitive functioning during and after ECT by combining three approaches for data analysis. A cognitive test battery was assessed in seventy-three ECT-treated patients with a Major Depressive Episode (MDE) at up to five time points (baseline, immediately prior to the third session and 1 week, 3 months and 6 months after completion of the index course). Group-level changes in cognitive function were investigated using linear mixed models and individual-level changes were examined using Reliable Change Indices (RCI). The presence of patient subgroups with similar cognitive trajectories was explored using Latent Class Growth Analysis (LCGA). At the group level, there was a temporary deterioration in processing speed, verbal memory and retrograde amnesia during and after index course of ECT. Individual-level analyses revealed considerable variability in cognitive effects of ECT. Three patient classes with a similar cognitive trajectory could be identified, all with a rather parallel courses over time, thus mainly differing in terms of pre-ECT cognitive functioning.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Mindfulness Training for Depressed Older Adults Using Smartphone Technology: Protocol for a Fully Remote Precision Clinical Trial

BACKGROUND

Precision medicine, optimized interventions, and access to care are catchphrases for the future of behavioral treatments. Progress has been slow due to the dearth of clinical trials that optimize interventions' benefits, individually tailor interventions to meet individual needs and preferences, and lead to rapid implementation after effectiveness is demonstrated. Two innovations have emerged to meet these challenges: fully remote trials and precision clinical trials.

OBJECTIVE

This paper provides a detailed description of Mindful MyWay, a study designed to test online mindfulness training in older adults with depression. Consistent with the concept of fully remote trials using a smartphone app, the study requires no in-person contact and can be conducted with participants anywhere in the United States. Based upon the precision medicine framework, the study assesses participants using high-frequency assessments of symptoms, cognitive performance, and patient preferences to both understand the individualized nature of treatment response and help individually tailor the intervention.

METHODS

Mindful MyWay is an open-label early-phase clinical trial for individuals 65 years and older with current depression. A smartphone app was developed to help coordinate the study, deliver the intervention, and evaluate the acceptability of the intervention, as well as predictors and outcomes of it. The curriculum for the fully remote intervention parallels the mindfulness-based stress reduction curriculum, a protocolized group-based mindfulness training that is typically provided in person. After consent and screening, participants download The Healthy Mind Lab mobile health smartphone app from the Apple App Store, allowing them to complete brief smartphone-based assessments of depressive symptoms and cognitive performance 4 times each day for 4 weeks prior to and after completing the intervention. The intervention consists of an introduction video and 10 weekly mindfulness training sessions, with the expectation to practice mindfulness at home daily. The app collects participant preference data throughout the 10-week intervention period; these high-frequency assessments identify participants' individually dynamic preferences toward the goal of optimizing the intervention in future iterations.

RESULTS

Participant recruitment and data collection began in March 2019. Final end point assessments will be collected in May 2022. The paper describes lessons learned regarding the critical role of early-phase testing prior to moving to a randomized trial.

CONCLUSIONS

The Mindful MyWay study is an exemplar of innovative clinical trial designs that use smartphone technology in behavioral and neuropsychiatric conditions. These include fully remote studies that can recruit throughout the United States, including hard-to-access areas, and collect high-frequency data, which is ideal for idiographic assessment and individualized intervention optimization. Our findings will be used to modify our methods and inform future randomized controlled trials within a precision medicine framework.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03922217; https://clinicaltrials.gov/ct2/show/NCT03922217.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39233.

Hormonal contraceptive influences on cognition and psychopathology: Past methods, present inferences, and future directions

In the last decade, there has been a remarkable surge in research on the neural and behavioral correlates of hormonal contraceptive use, particularly oral contraceptive use. Questions have evolved swiftly and notably, with studies no longer revealing if hormonal contraceptives matter for the brain and behavior, but rather how, when, and for whom they matter most. Paralleling this shift, the goal of this review is to move beyond an average synthesis of hormonal contraceptive influences on human cognition and psychopathology (and their neural substrates) in order to consider the nature and specificity of effects. Accompanied by an evaluation of study methods and informed by findings from animal models, this consideration uncovers promising areas of research in the next ten years, including potential activational and organizational effects of hormonal contraceptive use, individual differences in effects that matter for the wellbeing of unique individuals, and correlates of intrauterine device use.

Treated together-changed together: The application of dyadic analyses to understand the reciprocal nature of alliances and couple satisfaction over time

In a Norwegian study of 73 couples attending a residential couple therapy program lasting between 6 and 12 weeks, weekly self-report data on therapy alliance and couple satisfaction were collected using routine outcome monitoring (ROM). The aim was to show how dyadic analyses could be applied to examine the predictive association between alliances and couple satisfaction. Results showed that improved alliance between dyad members and their couple therapist predicted their spouses' couple satisfaction. Furthermore, improved couple satisfaction predicted improvement in spouse's alliance. The clinical implication of these findings should heighten awareness to the importance of establishing and maintaining the alliance of male partners in couple therapy, something that predicts their spouses' couple satisfaction. These findings help nuance the already existing literature on the working alliance. Furthermore, we propose that dyadic analyses should be widely used in any psychotherapeutic research that aims to understand the reciprocal effects of dyads.

Network Physiology of Exercise: Beyond Molecular and Omics Perspectives

Molecular Exercise Physiology and Omics approaches represent an important step toward synthesis and integration, the original essence of Physiology. Despite the significant progress they have introduced in Exercise Physiology (EP), some of their theoretical and methodological assumptions are still limiting the understanding of the complexity of sport-related phenomena. Based on general principles of biological evolution and supported by complex network science, this paper aims to contrast theoretical and methodological aspects of molecular and network-based approaches to EP. After explaining the main EP challenges and why sport-related phenomena cannot be understood if reduced to the molecular level, the paper proposes some methodological research advances related to the type of studied variables and measures, the data acquisition techniques, the type of data analysis and the assumed relations among physiological levels. Inspired by Network Physiology, Network Physiology of Exercise provides a new paradigm and formalism to quantify cross-communication among diverse systems across levels and time scales to improve our understanding of exercise-related phenomena and opens new horizons for exercise testing in health and disease.

Does the network structure of obsessive-compulsive symptoms at treatment admission identify patients at risk for non-response?

Exposure and response prevention is the gold-standard treatment for obsessive compulsive disorder (OCD), yet up to half of patients do not adequately respond. Thus, different approaches to identifying and intervening with non-responders are badly needed. One approach would be to better understand the functional connections among aspects of OCD symptoms and, ultimately, how to target those associations in treatment. In a large sample of patients who completed intensive treatment for OCD and related disorders (N = 1343), we examined whether differences in network structure of OCD symptom aspects existed at baseline between treatment responders versus non-responders. A network comparison test indicated a significant difference between OCD network structure for responders versus non-responders (M = 0.19, p = .02). Consistent differences emerged between responders and non-responders in how they responded to emotional distress. This pattern of associations suggests that non-responders may have been more reactive to their distress by performing compulsions, thereby worsening their functioning. By examining the association between baseline distress intolerance with other symptom aspects that presumably maintain the disorder (e.g., ritualizing), clinicians can more effectively target those associations in treatment.

Trauma Recovery Rubric: A Mixed-Method Analysis of Trauma Recovery Pathways in Four Countries

Research is beginning to examine gender-based violence (GBV) survivors' recovery, but little is known about diverse recovery trajectories or their relationships with other distress and recovery variables. This interdisciplinary, international multisite mixed-method study developed and used the TRR to identify and classify survivors' trauma pathways. This study describes the phases of the initial development of the preliminary TRR (Phase 1), refines and calibrates the TRR (Phase 2), and then integrates the TRR into quantitative data from four countries (Phase 3). Seven recovery pathways with six domains emerged: normalizing, minimizing, consumed/trapped; shutdown or frozen, surviving, seeking and fighting for integration; finding integration/equanimity. Depression scores were related to most recovery domains, and TRR scores had large effect sizes. At the same time, PTSD was not statistically related to TRR scores, but TRR had a medium effect size. Our study found that the TRR can be implemented in diverse cultural settings and promises a reliable cross-cultural tool. The TRR is a survivor-centered, trauma-informed way to understand different survivorship pathways and how different pathways impact health outcomes. Overall, this rubric provides a foundation for future study on differences in survivor healing and the drivers of these differences. This tool can potentially improve survivor care delivery and our understanding of how to meet best the needs of the survivor populations we intend to serve.

Verbal interaction pattern analysis in clinical psychology

Recent developments in pattern analysis research have made this methodology suitable for the study of the processes that are set in motion in psychological interventions. Outcome research, based on the comparison between clinical results from treatment and control groups, has leveraged our empirical knowledge about the efficacy of psychological interventions. However, these methods of research are not precise enough for the analysis of these processes. On the contrary, pattern analysis could be a powerful tool to study moment-to-moment interactions typical of psychological interventions. This is methodology is relevant because clinical psychology is experiencing a paradigm shift from a protocol for syndrome perspective to a principle-based and person-centered intervention. This evidence-based, theory-grounded, and process-oriented paradigm of clinical intervention needs new research methods to thrive (i.e., pattern analysis). The analysis of the therapeutic relationship built into the verbal interaction between the clinician and the client is one of the cornerstones of this new era of research. So, the purpose of this article is three-fold: (1) to discuss the role of the verbal interaction pattern analysis in the clinical context to the development of the principle-based clinical psychology, (2) to analyze the patterns of verbal interaction in a clinical case, and (3) to compare the results using two different methods. To reach these purposes, using the observational methodology, we have coded the verbal interaction of 16 clinical sessions with a person diagnosed with a borderline personality disorder. We have analyzed the data using sequential analysis (GSEQ) and pattern recognition algorithms (i.e., T-Pattern detection). We have been able to detect typical patterns during different phases of psychological intervention (i.e., evaluation, explanation, treatment, and consolidation). Finally, the conceptual, methodological, and empirical implications of this study will be discussed within the realms of pattern analysis research and principle-based clinical psychology.

[Growth trajectories of intrinsic value beliefs in mathematics and French: Relations with career orientations]

This longitudinal study investigated different trajectories in the development of intrinsic value beliefs in the subjects Mathematics and French in Grades 9 to 11 and their correlations with career aspirations. Using data from 850 students from German-Swiss high schools (54% female, age T1: 15.6 years), five distinct growth classes were identified in a bivariate growth model. Two of these classes showed clear differentiation between intrinsic value beliefs regarding the two subjects and stable growth in the preferred subject. The other three classes were characterized by mean differences (high, medium, low intrinsic value beliefs) and moderate decline in both subjects. The five growth classes were associated with different career orientations at the end of the 11th grade, with students exhibiting particularly high career orientations in one subject when intrinsic value regarding the other subject was low. Gender differences in career orientations could be fully explained by gender membership in the five growth classes.

Directional Ordering of Self-Concept, School Grades, and Standardized Tests Over Five Years: New Tripartite Models Juxtaposing Within- and Between-Person Perspectives

Much research shows academic self-concept and achievement are reciprocally related over time, based on traditional longitudinal data cross-lag-panel models (CLPM) supporting a reciprocal effects model (REM). However, recent research has challenged CLPM's appropriateness, arguing that CLPMs with random intercepts (RI-CLPMs) provide a more robust (within-person) perspective and better control for unmeasured covariates. However, there is much confusion in educational-psychology research concerning appropriate research questions and interpretations of RI-CLPMs and CLPMs. To clarify this confusion, we juxtapose CLPMs and RI-CLPMs relating math self-concept (MSCs), school grades, and achievement tests over the five years of compulsory secondary schooling (N = 3,425). We extend basic models to evaluate: directional ordering among three rather than only two constructs; longitudinal invariance over time (multiple school years) and multiple groups (school tracks); lag-2 paths between non-adjacent waves; and covariates (gender, primary-school math and verbal achievement). Across all basic and extended RI-CLPMs and CLPMs, there was consistent support for the REM bidirectional-ordering hypothesis that self-concept and achievement are each a cause and an effect of the other. Consistent with the logic of these models, extensions of the basic models had more effect on CLPMs, but the direction and statistical significance of cross-lagged paths were largely unaffected for both RI-CLPMs and CLPMs. This substantive-methodological synergy has important implications for theory, methodology, and policy/practice; we support the importance of MSC as a predictor of subsequent achievement and demonstrate a more robust methodological framework for evaluating longitudinal-panel models.

Integrating a functional view on suicide risk into idiographic statistical models

Acute risk of death by suicide manifests in heightened suicidal ideation in certain contexts and time periods. These increases are thought to emerge from complex and mutually reinforcing relationships between dispositional vulnerability factors and individually suicidogenic short-term stressors. Together, these processes inform clinical safety planning and our therapeutic tools accommodate a reasonable degree of idiosyncrasy when we individualize interventions. Unraveling these multifaceted factors and processes on a quantitative level, however, requires estimation frameworks capable of representing idiosyncrasies relevant to intervention and psychotherapy. Using, data from a 21-day ambulatory assessment protocol that included six random prompts per day, we developed personalized (i.e., idiographic) models of interacting risk factors and suicidal ideation via Group Iterative Multiple Model Estimation (GIMME) in a sample of people diagnosed with borderline personality disorder (N = 95) stratified for a history of high lethality suicide attempts. Our models revealed high levels of heterogeneity in state risk factors related to suicidal ideation, with no features shared among the majority of participants or even among relatively homogenous clusters of participants (i.e., empirically derived subgroups). We discuss steps toward clinical implementation of personalized models, which can eventually capture suicidogenic changes in proximal risk factors and inform safety planning and interventions.

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results

Background

Ecological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual’s behaviors and states. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated.

Objective

This paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study.

Methods

The TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non–EMA burst days, participants answer μEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study.

Results

Changes made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%).

Conclusions

The TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives.

Averting the Next Credibility Crisis in Psychological Science: Within-Person Methods for Personalized Diagnostics and Intervention

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between-person methods that we have long relied upon to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods are based on a misunderstanding of what these methods can tell us and what they cannot. An imminent methodological revolution will likely lead to a change of even well-established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and undergo rapid development, although they are not yet embraced in all areas of psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap, consisting of theories about within-person patterns being studied with between-person methods in psychology, and the potential loss of trust that might follow from this theory-method gap. Second, this article addresses advantages and limitations of available within-person methods. Third, this article discusses how within-person methods may help improving the individual descriptions and predictions that are needed in many applied fields that aim for tailored individual solutions, including personalized learning and personalized medicine.

Using temporal network methods to reveal the idiographic nature of development

Averages dominate developmental science: There are representative groups, mean trajectories, and generalizations to typical children. Nearly all parents and teachers, however, eagerly proclaim that few youth are average; each child, adolescent, and young adult is unique. Indeed, individual youth are the focus of many eminent developmental theories, yet there is a shocking paucity of developmental methods-including study designs and analysis techniques-that truly afford individual-level inferences. Thus, the goal of this chapter is to explicate the advantages of an idiographic approach to developmental science, that is, an approach that provides insight into individual youth, often by studying within-person variation in intensive longitudinal data, such as densely coded observations, repeated daily or momentary assessments, and functional neuroimages. In three domains across development, the chapter illustrates the benefits of an idiographic approach by comparing empirical conclusions offered by traditional mean-based analysis techniques versus techniques that leverage the temporal and individualized nature of intensive longitudinal data. The chapter then concentrates on group iterative multiple model estimation (GIMME), which is an analysis technique that uses intensive longitudinal data to create youth-specific temporal networks, detailing how brain regions or behaviors are directionally related across time. The promise of GIMME is exemplified by applications to three different domains across development. The chapter closes by encouraging future idiographic developmental science to consider how research questions, study designs, and data analyses can be formed, implemented, and conducted in ways that optimize inferences about individual-not average-youth.

Psychopathological networks: Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Assessing the need of young people using online counselling services: how useful are standardised measures?

BACKGROUND

Clinical assessments for children and young people entering a mental health service help to identify the prevalence of need within that population, support intervention recommendations, and enable service evaluation. Evidence related to the use of standardised measures in an ever-expanding online environment, for the purpose of identifying need, is limited.

METHODS

This study explores the reliability of using a standardised measure to detect clinical need in an online therapeutic environment, and the measures assessed are as follows: Strengths and Difficulties Questionnaire (SDQ), Young Person's CORE (YP-CORE) and the Short Warwick and Edinburgh Wellbeing Scale (SWEMWBS). A deep-dive approach is used to inform practitioner assessment of young people, followed by a Weighted Cohen's Kappa (Κw) to measure the interrater reliability between this and the individuals' self-rated outcome. Composite case studies represent the complexities of presentation among the sample population.

RESULTS

The interrater reliability between self-rated and practitioner rated assessment varied between Κw = .222 and Κw = 0.446 depending on the measure. High levels of need and low levels of well-being were found among the sample (YP-CORE Avg. = 26.9, SDQ Avg. = 19.56, SWEMWBS Avg. = 18.1).

CONCLUSIONS

The findings demonstrate a fair to moderate reliability when assessing concordance between service users and practitioners, which suggests standardised measures are a reliable indicator of need. Higher levels of need were present than those seen previously in general or face-to-face clinical populations, which suggests using such measures in an online therapeutic environment influences the way in which assessments are responded to.

Detecting Idiographic Personality Change

Personality changes across the lifespan, but strong evidence regarding the mechanisms responsible for personality change remains elusive. Studies of personality change and life events, for example, suggest that personality is difficult to change. But there are two key issues with assessing personality change. First, most change models optimize population-level, not individual-level, effects, which ignores heterogeneity in patterns of change. Second, optimizing change as mean-levels of self-reports fails to incorporate methods for assessing personality dynamics, such as using changes in variances of and correlations in multivariate time series data that often proceed changes in mean-levels, making variance change detection a promising technique for the study of change. Using a sample of N = 388 participants (total N = 21,790) assessed weekly over 60 weeks, we test a permutation-based approach for detecting individual-level personality changes in multivariate time series and compare the results to event-based methods for assessing change. We find that a non-trivial number of participants show change over the course of the year but that there was little association between these change points and life events they experienced. We conclude by highlighting the importance in idiographic and dynamic investigations of change.

The Synergistic but Troubled Relationship Between Psychotherapy Science and Practice: Moving Forward

How to foster the integration of science (especially empirical research) and practice has been a vexing problem since the beginnings of counseling psychology and other applied fields. We propose that the basis for this problem is the very different, even contradictory, demands of empirical research and practice, and the resulting ways of being research scientists and practitioners. Focusing on psychotherapy, we posit seven such demands/pulls and ways of being and seven tactics for strengthening integration. Clinically relevant research on the therapeutic relationship and therapist skills/interventions conducted at the Maryland Psychotherapy Clinic and Research Lab is summarized. We emphasize that for science and practice to be mutually facilitative, the field will need to pay close and ongoing attention to ways of strengthening integration.

All units are equal in humpback whale songs, but some are more equal than others

Flexible production and perception of vocalizations is linked to an impressive array of cognitive capacities including language acquisition by humans, song learning by birds, biosonar in bats, and vocal imitation by cetaceans. Here, we characterize a portion of the repertoire of one of the most impressive vocalizers in nature: the humpback whale. Qualitative and quantitative analyses of sounds (units) produced by humpback whales revealed that singers gradually morphed streams of units along multiple acoustic dimensions within songs, maintaining the continuity of spectral content across subjectively dissimilar unit "types." Singers consistently produced some unit forms more frequently and intensely than others, suggesting that units are functionally heterogeneous. The precision with which singing humpback whales continuously adjusted the acoustic characteristics of units shows that they possess exquisite vocal control mechanisms and vocal flexibility beyond what is seen in most animals other than humans. The gradual morphing of units within songs that we observed is inconsistent with past claims that humpback whales construct songs from a fixed repertoire of discrete unit types. These findings challenge the results of past studies based on fixed-unit classification methods and argue for the development of new metrics for characterizing the graded structure of units. The specific vocal variations that singers produced suggest that humpback whale songs are unlikely to provide detailed information about a singer's reproductive fitness, but can reveal the precise locations and movements of singers from long distances and may enhance the effectiveness of units as sonar signals.

Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning

Societal events - such as natural disasters, political shifts, or economic downturns - are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of this paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election - a period of heightened sociopolitical stress - and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19.