The Cascade of Stress: A Network Approach to Explore Differential Dynamics in Populations Varying in Risk for Psychosis

Klippel, Annelie; Viechtbauer, Wolfgang; Reininghaus, Ulrich; Wigman, Johanna T. W.; Borkulo, Claudia van; Myin-Germeys, Inez; Wichers, Marieke

doi:10.1093/schbul/sbx037

Cited by 81 publications

(74 citation statements)

References 35 publications

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“…The “off” state was patient rated and defined as the state in which he experienced no effect of anti‐Parkinson medication. Because regression coefficients of linear and logistic regression cannot be combined in 1 network graph, the analysis with “on/off” as the dependent variable was repeated with linear regression as has been done in a previous study . As the results, in terms of statistical significance, were very similar between the 2 analyses, the linear regression results were used to generate the network.…”

Section: Methodsmentioning

confidence: 99%

“…Because regression coefficients of linear and logistic regression cannot be combined in 1 network graph, the analysis with "on/off" as the dependent variable was repeated with linear regression as has been done in a previous study. 16 As the results, in terms of statistical significance, were very similar between the 2 analyses, the linear regression results were used to generate the network. Independent variables were the lag (t−1) of the same 7 variables added to the regression model simultaneously.…”

Section: Statistical Analysesmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Statistical Analysesmentioning

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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“…Los nodos pueden ser todo tipo de variables como, por ejemplo, síntomas psicopatológicos, rasgos de personalidad, o estímulos ambientales (p. ej., experiencias traumáticas, consumo de cannabis) (Isvoranu et al, 2017;Klippel et al, 2017). También podría ser algún otro tipo de variable procedente de niveles de análisis no observables al ojo humano (p.…”

Section: Conceptos Básicos En El Análisis De Redes Psicológicasunclassified

Análisis De Redes en Psicología

Fonseca‐Pedrero¹

2018

Pap. Psicol.

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a psicología, desde sus orígenes, no ha cesado en su empeño por mejorar la comprensión de la conducta humana. Este continuo acicate ha impulsado el desarrollo de diferentes modelos psicológicos que, en esencia, tratan de avanzar en el conocimiento del comportamiento y los procesos psicológicos (en su sentido amplio). Los nuevos modelos teóricos y psicomé-tricos tal vez permitan incorporar un prisma alternativo con el que conceptualizar y repensar los fenómenos psicológicos. El modelo de redes, la teoría del caos o la teoría de los sistemas dinámicos son solo algunos ejemplos que, aunque son temas clásicos en algunas disciplinas científicas, se están incorporando en la ciencia del comportamiento humano (Nelson, McGorry, Wichers, Wigman, y Hartmann, 2017). Especialmente interesante son las aportaciones del modelo de redes para el análisis de variables psico(pato)lógicas (Borsboom, 2017;. Esta nueva forma de comprender e intervenir en la conducta tiene enormes posibilidades ya que puede, entre otros aspectos, motivar formas alternativas de analizar datos, sugerir maneras diferentes de modelar y analizar las relaciones entre variables (p.ej., síntomas, signos, procesos psicológicos, rasgos de personalidad, desencadenantes ambientales, consumo de sustancias, etc.), diseñar nuevas formas de prevención e intervención y/o incluso mejorar la búsqueda de mecanismos etiológicos.Dentro de este contexto el objetivo de este trabajo es realizar una introducción al análisis de redes en psicología. Se trata de presentar el modelo de redes, de forma breve, amena, sencilla y, en la medida de lo posible, alejada de ciertos tecnicismos y el complejo aparataje estadístico. La meta es que sirva de tutorial

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“…PE are often functionally impairing in this age group, both in the context of mental health disorders and in children without diagnoses (Jeppesen et al., ; Kelleher et al., ). An affective pathway to psychosis has been studied extensively, supported by studies that report a strong effect of affective disturbances on the formation of PE (Klippel et al., ; Myin‐Germeys & van Os, ). PE often co‐occur with anxiety and depression in clinical populations (Achim et al., ; Upthegrove, Marwaha, & Birchwood, ; Wigman et al., ) and in nonclinical populations of children (Jeppesen et al., ; Kelleher et al., ).…”

Section: Introductionmentioning

confidence: 97%

“…PE often co‐occur with anxiety and depression in clinical populations (Achim et al., ; Upthegrove, Marwaha, & Birchwood, ; Wigman et al., ) and in nonclinical populations of children (Jeppesen et al., ; Kelleher et al., ). Furthermore, affective disturbances may have a causal impact on the formation of PE (Klippel et al., ; Van Rossum, Dominguez, Lieb, Wittchen, & Van Os, ).…”

Section: Introductionmentioning

confidence: 99%

Psychotic experiences are associated with health anxiety and functional somatic symptoms in preadolescence

Rimvall

Jespersen

Clemmensen

et al. 2018

Child Psychology Psychiatry

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Background Health anxiety (HA) is an increasing public health problem related to increased health service costs, and associated with functional somatic symptoms (FSS) and considerable personal suffering. Abnormal bodily experiences which may resemble HA and FSS are common in psychotic disorders, but a potential link between HA and psychosis vulnerability in childhood is largely unexplored. The current study estimates the association between subclinical psychotic experiences (PE) and HA and FSS in a general population cohort of preadolescents. Methods The study population consisted of 1,572 11–12‐year‐old children from the Copenhagen Child Cohort 2000. PE were comprehensibly assessed as either present or not present using the Kiddie Schedule of Affective Disorders and Schizophrenia psychosis section. HA and FSS were assessed by self‐report on validated questionnaires. Additional variables on general psychopathology, puberty, and chronic somatic illness were also obtained. Results Psychotic experiences were associated with the top 10% high scores of HA (Odds Ratio (OR) 3.2; 95% CI: 2.1–4.8) and FSS (OR 4.6; 95% CI: 3.1–6.9) in univariate analyses. After mutual adjustment, the association was reduced to (HA: OR 2.3; 95% CI: 1.5–3.5; FSS: OR 3.7; 95% CI: 2.4–4.7), suggesting interdependence. Further adjustment for potential confounders and general psychopathology only reduced the associations slightly: HA OR 2.2 (95% CI: 1.4–3.4); FSS OR 3.3 (95% CI: 2.1–5.2). Secondary analyses of subdimensions of HA showed that PE were associated with fears (OR 3.0; 95% CI: 2.0–4.6) and daily impact of HA symptoms (OR 5.0; 95% CI: 3.4–7.5), but not help seeking (OR 1.2; 95% CI: 0.7–2.1). Conclusions This is the first study to investigate the associations between PE and HA and FSS, respectively. PE were significantly associated with HA and FSS over and above general psychopathology in preadolescence. Individuals with PE expressed high levels of health‐related fears and daily impact, but no corresponding help‐seeking behavior.

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The Cascade of Stress: A Network Approach to Explore Differential Dynamics in Populations Varying in Risk for Psychosis

Cited by 81 publications

References 35 publications

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

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

Análisis De Redes en Psicología

Psychotic experiences are associated with health anxiety and functional somatic symptoms in preadolescence

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