Personalized feedback on symptom dynamics of psychopathology: A proof-of-principle study

Kroeze, Renske; Veen, Date C. van der; Servaas, Michelle N.; Bastiaansen, Jojanneke A.; Voshaar, Richard C. Oude; Borsboom, Denny; Ruhé, Henricus G.; Schoevers, Robert A.; Riese, Harriëtte

doi:10.17505/jpor.2017.01

Cited by 83 publications

(103 citation statements)

References 24 publications

(30 reference statements)

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“…This allows the field to move from modelling cross-sectional group-level data to modelling the temporal dynamics of causal systems across time, and might bring us closer to developing novel recommendations for intervention or prevention strategies (Bos et al, 2017). Third, more attention to modelling the dynamics of causal systems also allows a renewed focus on personalized medicine, seeing that time-series network models are not limited to modelling the symptom dynamics of groups of patients, but can also be used to obtain idiographic network structures for individual patients (Epskamp, van Borkulo et al, 2017; Fisher & Boswell, 2016; Kroeze et al, 2017). Fourth, there is evidence that biological markers are differentially related to specific psychopathology symptoms (e.g.…”

Section: Discussionmentioning

confidence: 99%

PTSD symptomics: network analyses in the field of psychotraumatology

Armour¹,

Fried

Olff

2017

European Journal of Psychotraumatology

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Recent years have seen increasing attention on posttraumatic stress disorder (PTSD) research. While research has largely focused on the dichotomy between patients diagnosed with mental disorders and healthy controls — in other words, investigations at the level of diagnoses — recent work has focused on psychopathology symptoms. Symptomics research in the area of PTSD has been scarce so far, although several studies have focused on investigating the network structures of PTSD symptoms. The present special issue of EJPT adds to the literature by curating additional PTSD network studies, each looking at a different aspect of PTSD. We hope that this special issue encourages researchers to conceptualize and model PTSD data from a network perspective, which arguably has the potential to inform and improve the efficacy of therapeutic interventions.

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Section: Discussionmentioning

confidence: 99%

PTSD symptomics: network analyses in the field of psychotraumatology

Armour¹,

Fried

Olff

2017

European Journal of Psychotraumatology

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“…In line with a call for more intraindividual and personbased research (Molenaar, 2004), an increasingly popular form of data pertains to n = 1 time series, in which a single individual is measured repeatedly over a period of time. One such situation is in clinical practice (Kroeze et al, 2017;Epskamp et al, 2018), where a patient can be measured several times per day over a period of a few weeks. We will limit our discussion to data obtained in a relatively short time-frame so that we can reasonably assume the model will remain stable over time.…”

Section: Temporally Ordered Data Of a Single Subjectmentioning

confidence: 99%

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Epskamp

Waldorp

Mõttus

et al. 2018

Multivariate Behavioral Research

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673

929

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We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

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“…ESM can be used for this purpose by generating treatment‐relevant information specific for that patient that cannot be obtained by commonly used cross‐sectional assessment methods and rating scales. Previous studies using network analysis for different diseases have demonstrated that ESM data can be incorporated into a personalized treatment plan and facilitate decision‐making in a collaborative process . For PD patients, ESM data can provide insight into the relationship between different variables and symptom severity.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies using network analysis for different diseases have demonstrated that ESM data can be incorporated into a personalized treatment plan and facilitate decision-making in a collaborative process. 15,17,21 For PD patients, ESM data can provide insight into the relationship between different variables and symptom severity. This particular network analysis focused on mood variables, but based on the specific situation of the patient, other relevant variables can be included in the network.…”

Section: Discussionmentioning

confidence: 99%

Network analysis of symptoms in a Parkinson patient using experience sampling data: An n = 1 study

et al. 2018

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Background Around 50% of Parkinson's disease patients experience motor fluctuations after long‐term treatment with levodopa. These fluctuations may be accompanied by mood fluctuations. Routine cross‐sectional assessments cannot capture the extent of these motor and mood fluctuations and their possible associations. Experience sampling techniques that use frequently repeated measurements of symptoms over time are able to capture such fluctuations. Based on such data, longitudinal associations between symptoms can be studied using network analysis. Aim The purpose of this study is to identify longitudinal associations between motor symptoms and mood states in a patient with Parkinson's disease. Methods A 53‐year‐old man with Parkinson's disease and motor fluctuations collected experience sampling data during 34 consecutive days. A set of dependent variables included tremor, rigidity, balance problems, and “on/off” state, and the mood variables anxiety, cheerful, and “down.” Independent variables were the same variables assessed at the preceding measurement. Regression coefficients were calculated and presented in a network graph. Results In this patient, anxiety and cheerfulness had a central position within the symptom network. Higher anxiety was prospectively associated with increased rigidity and tremor and with feeling “down.” Cheerfulness was associated with less tremor. Balance problems were not influenced by cheerfulness nor anxiety, but increased balance problems were associated with reduced cheerfulness at the next assessment. Feeling “down” did not influence self‐reported motor symptom severity at the next assessment. Conclusion This n = 1 study shows that network analysis of experience sampling data may reveal longitudinal associations of self‐reported motor symptoms and mood states that may have relevance for treatment strategies. © 2018 International Parkinson and Movement Disorder Society

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Personalized feedback on symptom dynamics of psychopathology: A proof-of-principle study

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Cited by 83 publications

References 24 publications

PTSD symptomics: network analyses in the field of psychotraumatology

PTSD symptomics: network analyses in the field of psychotraumatology

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Network analysis of symptoms in a Parkinson patient using experience sampling data: An n = 1 study

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