Multidimensional pain patterns in Parkinson's disease: a longitudinal mobile health study

Area of Science:

• Neurology
• Digital Health
• Pain Medicine

Background:

• Parkinson's disease (PD) presents non-motor symptoms, including pain, often oversimplified in clinical settings.
• Current pain assessment in PD typically focuses on intensity, neglecting crucial aspects like duration and spatial distribution.
• Mobile health (mHealth) tools offer potential for detailed, longitudinal data collection on complex symptoms like pain.

Purpose of the Study:

• To investigate how pain intensity, duration, and spatial distribution differentiate patient profiles in Parkinson's disease.
• To utilize longitudinal mobile health data for a multidimensional analysis of pain in PD patients.
• To identify distinct patient subgroups based on comprehensive pain characteristics.

Main Methods:

• Analysis of 20,971 daily pain records from 68 Parkinson's disease patients over 14 months.
• Utilized unsupervised clustering (HDBSCAN) to identify patient profiles based on reported pain intensity, duration, and location.
• Assessed correlations between different pain dimensions (intensity, duration, spatial extent) using Spearman correlation.

Main Results:

• Two distinct patient profiles emerged: a high-burden group (n=17) and a low-burden group (n=50).
• Differences between groups were more pronounced in cumulative pain duration (6.7-fold) and spatial extent (9-11-fold) than in mean intensity (2.4-fold).
• Mean pain intensity showed moderate correlations with duration and spatial extent, while mean episode duration had weak associations.

Conclusions:

• Pain intensity alone is insufficient for fully characterizing the pain burden in Parkinson's disease.
• Incorporating temporal persistence (duration) and spatial distribution provides a more comprehensive understanding of the patient experience.
• A multidimensional approach to pain assessment, enabled by mHealth tools, is crucial for effective PD patient care.