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Kraiss J, Glaesmer H, Forkmann T, Spangenberg L, Hallensleben N, Schreiber D, Höller I. Beyond one-size-fits-all suicide prediction: Studying idiographic associations of risk factors for suicide in a psychiatric sample using ecological momentary assessment. J Psychiatr Res 2024; 178:130-138. [PMID: 39141992 DOI: 10.1016/j.jpsychires.2024.07.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 06/28/2024] [Accepted: 07/31/2024] [Indexed: 08/16/2024]
Abstract
The Interpersonal Psychological Theory of Suicide (IPTS) states that thwarted belongingness (TB), perceived burdensomeness (PB), and hopelessness are risk factors for suicidal ideation. This ecological momentary assessment (EMA) study aimed to (1) demonstrate that there is substantial between-person variability in the association between IPTS predictors and suicidal ideation, (2) identify clusters of patients for which the predictors differently predict suicidal ideation, and (3) examine whether identified clusters are characterized by specific patient characteristics. EMA data were collected ten times per day for six days in 74 psychiatric inpatients and was analyzed with dynamic structural equation modelling. Idiographic associations were obtained and clustered using k-means clustering. We found substantial between-person variability in associations between IPTS predictors and suicidal ideation. Four distinct clusters were identified and different risk factors were relevant for different clusters. In the largest cluster (n = 36), none of the IPTS predictors predicted suicidal ideation. Clusters in which associations between IPTS variables and suicidal ideation were stronger showed higher suicidal ideation, depression, and lower positive affect. These findings suggest that a one-size-fits-all model may not adequately reflect idiosyncratic processes leading to suicidal ideation. A promising avenue might be to use idiographic approaches to personalize prediction and interventions.
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Affiliation(s)
- Jannis Kraiss
- Department of Psychology, Health and Technology, University of Twente, Enschede, the Netherlands.
| | - Heide Glaesmer
- Department of Medical Psychology and Medical Sociology, University Leipzig, Leipzig, Germany
| | - Thomas Forkmann
- Department of Clinical Psychology and Psychotherapy, University of Duisburg-Essen, Essen, Germany
| | - Lena Spangenberg
- Department of Medical Psychology and Medical Sociology, University Leipzig, Leipzig, Germany
| | - Nina Hallensleben
- Department of Medical Psychology and Medical Sociology, University Leipzig, Leipzig, Germany
| | - Dajana Schreiber
- Department of Clinical Psychology and Psychotherapy, University of Duisburg-Essen, Essen, Germany
| | - Inken Höller
- Department of Clinical Psychology and Psychotherapy, University of Duisburg-Essen, Essen, Germany
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2
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Peng J, Yuan S, Wei Z, Liu C, Li K, Wei X, Yuan S, Guo Z, Wu L, Feng T, Zhou Y, Li J, Yang Q, Liu X, Wu S, Ren L. Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder. BMC Psychiatry 2024; 24:241. [PMID: 38553683 PMCID: PMC10981297 DOI: 10.1186/s12888-024-05698-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 03/18/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom. METHODS A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network. RESULTS In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality. CONCLUSIONS This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.
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Affiliation(s)
- Jiaxi Peng
- Mental Health Education Center, Chengdu University, 610106, Chengdu, China
| | - Shuai Yuan
- University of Amsterdam, 1018WB, Amsterdam, the Netherlands
| | - Zihan Wei
- Xijing Hospital, Air Force Medical University, 710032, Xi'an, China
| | - Chang Liu
- Brain Park, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, 3800, Clayton, VIC, Australia
| | - Kuiliang Li
- Department of Psychology, Army Medical University, 400038, Chongqing, China
| | - Xinyi Wei
- Department of Psychology, Renmin University of China, 100000, Beijing, China
| | - Shangqing Yuan
- School of Psychology, Capital Normal University, 100089, Beijing, China
| | - Zhihua Guo
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China
| | - Lin Wu
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China
| | - Tingwei Feng
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China
| | - Yu Zhou
- Military Psychology Section, Logistics University of PAP, 300309, Tianjin, China
- Military Mental Health Services & Research Center, 300309, Tianjin, China
| | - Jiayi Li
- Military Psychology Section, Logistics University of PAP, 300309, Tianjin, China
- Military Mental Health Services & Research Center, 300309, Tianjin, China
| | - Qun Yang
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China
| | - Xufeng Liu
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China
| | - Shengjun Wu
- Department of Military Medical Psychology, Air Force Medical University, 710032, Xi'an, China.
| | - Lei Ren
- Military Psychology Section, Logistics University of PAP, 300309, Tianjin, China.
- Military Mental Health Services & Research Center, 300309, Tianjin, China.
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Zilcha-Mano S. Individual-Specific Animated Profiles of Mental Health. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024:17456916231226308. [PMID: 38377015 DOI: 10.1177/17456916231226308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.
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4
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Holtmann J, Eid M, Santangelo PS, Kockler TD, Ebner-Priemer UW. Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:148-170. [PMID: 37130226 DOI: 10.1080/00273171.2023.2201824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
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.
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Affiliation(s)
- Jana Holtmann
- Wilhelm Wundt Institute of Psychology, Leipzig University, Leipzig, Germany
| | - Michael Eid
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | | | - Tobias D Kockler
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg
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5
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Fisher ZF, Parsons J, Gates KM, Hopfinger JB. Blind Subgrouping of Task-based fMRI. PSYCHOMETRIKA 2023; 88:434-455. [PMID: 36892726 DOI: 10.1007/s11336-023-09907-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Indexed: 05/17/2023]
Abstract
Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.
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Affiliation(s)
- Zachary F Fisher
- Quantitative Developmental Systems Methodology Core, Department of Human Development and Family Studies, The Pennsylvania State University, Health and Human Development Building, University Park, PA, 16802, USA.
| | | | - Kathleen M Gates
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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6
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van Loo HM, Booij SH, Jeronimus BF. Testing the mood brightening hypothesis: Hedonic benefits of physical, outdoor, and social activities in people with anxiety, depression or both. J Affect Disord 2023; 325:215-223. [PMID: 36632849 DOI: 10.1016/j.jad.2023.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 12/16/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023]
Abstract
BACKGROUND The mood brightening hypothesis postulates that people with depressive symptoms report more positive affect (PA) and less negative affect (NA) than healthy controls after rewarding daily life activities. Whether mood brightening also occurs in people with anxiety symptoms remains unclear. This study examined effects of physical activity, being outdoors, and social activity on PA and NA across different levels of depression and anxiety symptoms in the general Dutch population. METHODS Participants completed an electronic diary on their smartphone, thrice daily over 30 days, to assess activities and affect (n = 430; 22,086 assessments). We compared five groups based on their scores on the Depression, Anxiety and Stress Scales: asymptomatic participants, participants with mild symptoms of depression and/or anxiety, depression symptoms, anxiety symptoms, and comorbid depression and anxiety symptoms. Multilevel linear regression models with interaction terms were used to compare the association between activities and affect in these five groups. RESULTS All activities were associated with increased PA and reduced NA in all groups. We found a mood brightening effect in participants with depression, as physical activity and being outdoors were associated with reduced NA. Participants with depression had increased PA and reduced NA when in social company compared to asymptomatic participants. No mood brightening effects were observed in participants with anxiety or comorbid depression and anxiety. LIMITATIONS Our sample included mainly women and highly educated subjects, which may limit the generalizability of our findings. CONCLUSION Mood brightening is specific to depression, and typically stronger when in social company.
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Affiliation(s)
- Hanna M van Loo
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, Groningen, the Netherlands.
| | - Sanne H Booij
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, Groningen, the Netherlands; University of Groningen, Department of Developmental Psychology, Faculty of Behavioural and Social Sciences, Groningen University, 9712 TS Groningen, the Netherlands; Center for Integrative Psychiatry, Lentis, Groningen, the Netherlands
| | - Bertus F Jeronimus
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, Groningen, the Netherlands; University of Groningen, Department of Developmental Psychology, Faculty of Behavioural and Social Sciences, Groningen University, 9712 TS Groningen, the Netherlands
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7
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Ruissen GR, Zumbo BD, Rhodes RE, Puterman E, Beauchamp MR. Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer. Health Psychol Rev 2022; 16:492-525. [PMID: 34643154 DOI: 10.1080/17437199.2021.1987953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
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.
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Affiliation(s)
- Geralyn R Ruissen
- School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Bruno D Zumbo
- Department of Educational and Counseling Psychology and Special Education, University of British Columbia, Vancouver, Canada
| | - Ryan E Rhodes
- School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, Canada
| | - Eli Puterman
- School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Mark R Beauchamp
- School of Kinesiology, University of British Columbia, Vancouver, Canada
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8
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Lafit G, Meers K, Ceulemans E. A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models. PSYCHOMETRIKA 2022; 87:432-476. [PMID: 34724142 DOI: 10.1007/s11336-021-09803-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 07/13/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
The use of multilevel VAR(1) models to unravel within-individual process dynamics is gaining momentum in psychological research. These models accommodate the structure of intensive longitudinal datasets in which repeated measurements are nested within individuals. They estimate within-individual auto- and cross-regressive relationships while incorporating and using information about the distributions of these effects across individuals. An important quality feature of the obtained estimates pertains to how well they generalize to unseen data. Bulteel and colleagues (Psychol Methods 23(4):740-756, 2018a) showed that this feature can be assessed through a cross-validation approach, yielding a predictive accuracy measure. In this article, we follow up on their results, by performing three simulation studies that allow to systematically study five factors that likely affect the predictive accuracy of multilevel VAR(1) models: (i) the number of measurement occasions per person, (ii) the number of persons, (iii) the number of variables, (iv) the contemporaneous collinearity between the variables, and (v) the distributional shape of the individual differences in the VAR(1) parameters (i.e., normal versus multimodal distributions). Simulation results show that pooling information across individuals and using multilevel techniques prevent overfitting. Also, we show that when variables are expected to show strong contemporaneous correlations, performing multilevel VAR(1) in a reduced variable space can be useful. Furthermore, results reveal that multilevel VAR(1) models with random effects have a better predictive performance than person-specific VAR(1) models when the sample includes groups of individuals that share similar dynamics.
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Affiliation(s)
- Ginette Lafit
- Research Group of Quantitative Psychology and Individual Differences, KU Leuven - University of Leuven, Leuven, Belgium.
| | - Kristof Meers
- Research Group of Quantitative Psychology and Individual Differences, KU Leuven - University of Leuven, Leuven, Belgium
| | - Eva Ceulemans
- Research Group of Quantitative Psychology and Individual Differences, KU Leuven - University of Leuven, Leuven, Belgium
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9
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Takano K, Stefanovic M, Rosenkranz T, Ehring T. Clustering Individuals on Limited Features of a Vector Autoregressive Model. MULTIVARIATE BEHAVIORAL RESEARCH 2021; 56:768-786. [PMID: 32431169 DOI: 10.1080/00273171.2020.1767532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Dynamical interplays in emotions have been investigated using vector autoregressive (VAR) models, whose estimates can be used to cluster participants into unknown groups. The present study evaluated a clustering algorithm, the alternating least square (ALS) algorithm, for accuracy in predicting individual group membership. We systematically manipulated (a) the number of variables in a model, (b) the size of group differences in regression coefficients, and (c) the number of regression coefficients that vary across the groups (i.e., effective features). The ALS algorithm works reliably when there are at least 5 effective features with very large group differences in a 5-variable model; and 9 effective features with very large group differences in a 10-variable model. These findings suggest that the ALS algorithm is sensitive to group differences that are present only in several coefficients of a VAR model, but that the group differences have to be large. We also found that the ALS algorithm outperforms another clustering method, Gaussian mixture modeling. The ALS algorithm was further evaluated with unbalanced sample sizes between groups and with a greater number of groups in data (Study 2). A real data application was provided to illustrate how to interpret the detected group differences (Study 3).
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10
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Den Teuling NGP, Pauws SC, van den Heuvel ER. A comparison of methods for clustering longitudinal data with slowly changing trends. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2020.1861464] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- N. G. P. Den Teuling
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands
- Philips Research, Eindhoven, the Netherlands
| | - S. C. Pauws
- Philips Research, Eindhoven, the Netherlands
- Department Communication and Cognition, Tilburg University, Tilburg, the Netherlands
| | - E. R. van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands
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11
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Ariens S, Ceulemans E, Adolf JK. Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview. J Psychosom Res 2020; 137:110191. [PMID: 32739633 DOI: 10.1016/j.jpsychores.2020.110191] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/13/2020] [Accepted: 07/05/2020] [Indexed: 12/17/2022]
Abstract
Time series analysis of intensive longitudinal data provides the psychological literature with a powerful tool for assessing how psychological processes evolve through time. Recent applications in the field of psychosomatic research have provided insights into the dynamical nature of the relationship between somatic symptoms, physiological measures, and emotional states. These promising results highlight the intrinsic value of employing time series analysis, although application comes with some important challenges. This paper aims to present an approachable, non-technical overview of the state of the art on these challenges and the solutions that have been proposed, with emphasis on application towards psychosomatic hypotheses. Specifically, we elaborate on issues related to measurement intervals, the number and nature of the variables used in the analysis, modeling stable and changing processes, concurrent relationships, and extending time series analysis to incorporate the data of multiple individuals. We also briefly discuss some general modeling issues, such as lag-specification, sample size and time series length, and the role of measurement errors. We hope to arm applied researchers with an overview from which to select appropriate techniques from the ever growing variety of time series analysis approaches.
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Affiliation(s)
- Sigert Ariens
- KU Leuven, Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Leuven 3000, Belgium.
| | - Eva Ceulemans
- KU Leuven, Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Leuven 3000, Belgium
| | - Janne K Adolf
- KU Leuven, Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Leuven 3000, Belgium
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Ernst AF, Albers CJ, Jeronimus BF, Timmerman ME. Inter-Individual Differences in Multivariate Time-Series. EUROPEAN JOURNAL OF PSYCHOLOGICAL ASSESSMENT 2020. [DOI: 10.1027/1015-5759/a000578] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Abstract. Theories of emotion regulation posit the existence of individual differences in emotion dynamics. Current multi-subject time-series models account for differences in dynamics across individuals only to a very limited extent. This results in an aggregation that may poorly apply at the individual level. We present the exploratory method of latent class vector-autoregressive modeling (LCVAR), which extends the time-series models to include clustering of individuals with similar dynamic processes. LCVAR can identify individuals with similar emotion dynamics in intensive time-series, which may be of unequal length. The method performs excellently under a range of simulated conditions. The value of identifying clusters in time-series is illustrated using affect measures of 410 individuals, assessed at over 70 time points per individual. LCVAR discerned six clusters of distinct emotion dynamics with regard to diurnal patterns and augmentation and blunting processes between eight emotions.
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Affiliation(s)
- Anja F. Ernst
- Department Psychometrics and Statistics, University of Groningen, The Netherlands
| | - Casper J. Albers
- Department Psychometrics and Statistics, University of Groningen, The Netherlands
| | - Bertus F. Jeronimus
- Department Developmental Psychology, University of Groningen, The Netherlands
| | - Marieke E. Timmerman
- Department Psychometrics and Statistics, University of Groningen, The Netherlands
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