A novel approach for constructing personalized networks from longitudinal perceived causal relations

Area of Science:

• Psychology
• Psychiatry
• Network Science

Background:

• Personalized psychological networks aim to improve therapy by identifying patient-specific treatment targets.
• Current methods using statistical data or patient perceptions have limitations, including strong statistical assumptions and retrospective bias.

Purpose of the Study:

• To introduce and evaluate the Longitudinal Perceived Causal Problem Networks (L-PECAN) approach.
• To address limitations of existing methods for creating personalized psychological symptom networks.

Main Methods:

• Developed the L-PECAN approach for assessing perceived symptom interactions.
• Collected daily assessments from 20 participants with depression over 4 weeks.
• Introduced quality criteria for network stability and change over time.

Main Results:

• Approximately 40% of participants achieved stable networks.
• Few participants showed network structures that changed during the assessment period.
• The L-PECAN method was time-efficient (average 7.4 min/day) and well-received.

Conclusions:

• L-PECAN offers a clinically meaningful method for personalizing psychological symptom networks.
• The approach addresses prevailing issues in statistical network analysis, enhancing causal interpretation.
• L-PECAN provides a viable tool for identifying individualized treatment targets in mental health care.