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

A. Mulders, R. Velden, M. Drukker, M. Kuijf, A. Leentjens (Maastricht, Netherlands)

Meeting: 2018 International Congress

Abstract Number: 1021

Keywords: Scales

Session Information

Date: Sunday, October 7, 2018

Session Title: Phenomenology and Clinical Assessment Of Movement Disorders

Session Time: 1:45pm-3:15pm

Location: Hall 3FG

Objective: To identify longitudinal associations between motor symptoms and mood states in a patient with Parkinson’s disease.

Background: Experience Sampling consists of multiple repeated within-subject measurements of symptoms, experiences and context in daily life. Such measurements may reveal patterns and interconnections of symptoms and context that are not picked up by routine questionnaires or interviews. In addition, Experience Sampling data can be used to study longitudinal associations between symptoms using network analysis.

Methods: A 53-year old man with PD and motor fluctuations collected ESM data during 30 consecutive days. The patient completed 10 random assessments daily. An a priori selected set of dependent variables included the PD symptoms ‘tremor’, ‘rigidity’, ‘balance problems’ and ‘on/off’ state, and the mood variables ‘cheerful’ and ‘down’. Independent (predictor) variables were the same symptoms and mood states assessed at the preceding measurement. Regression coefficients were calculated and presented in a network graph.

Results: In this patient, ‘cheerfulness’ had a central position within the symptom network, and was prospectively associated with reduced rigidity and tremor. Balance problems were not influenced by cheerfulness, but instead better balance was associated with more cheerfulness at the next assessment. Feeling down did not influence self-reported motor symptom severity at the next assessment. ‘On’ and ‘off’ states could not be predicted by motor symptoms or mood states.

Conclusions: This n=1 study shows that network analysis of experience sampling data is able to reveal longitudinal associations of self-reported motor symptoms and mood states which may have relevance for treatment strategies.

References: This abstract has been presented at the 10 Annual Research Day of the School for Mental Health and Neuroscience (MHeNs), Maastricht University, Nov 29th, 2017.

To cite this abstract in AMA style:

A. Mulders, R. Velden, M. Drukker, M. Kuijf, A. Leentjens. Network analysis of symptoms in a Parkinson patient using experience sampling data: An n=1 study [abstract]. Mov Disord. 2018; 33 (suppl 2). https://www.mdsabstracts.org/abstract/network-analysis-of-symptoms-in-a-parkinson-patient-using-experience-sampling-data-an-n1-study/. Accessed May 13, 2025.
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