# Development of a New Patient-Reported Outcome to Measure Fatigue in Patients with Multiple Sclerosis

## TL;DR

Researchers developed a new tool to measure fatigue in multiple sclerosis patients, capturing physical, cognitive, and psychosocial aspects with high reliability.

## Contribution

A novel patient-reported outcome instrument for multidimensional fatigue assessment in MS with validated subdomains and strong internal consistency.

## Key findings

- A 14-item instrument covering psychosocial, physical, and cognitive fatigue domains was developed.
- Exploratory factor analysis confirmed a three-factor structure explaining 75% of total variance.
- High internal consistency was observed across all subdomains (Cronbach’s alpha ≥ 0.844).

## Abstract

Background: Fatigue is a multidimensional and subjective experience, and it is one of the most common symptoms of multiple sclerosis (MS), affecting up to 80% of patients and acting as a major driver of work disability. Despite its clinical significance, existing assessment tools often lack conceptual clarity or remain too lengthy for routine clinical use. Objective: To develop and evaluate a new patient-reported outcome instrument designed to assess multidimensional fatigue domains in patients with multiple sclerosis (MS) for use in clinical practice. Methods: This study was carried out in three research stages. Stage 1 (Concept Elicitation) involved qualitative interviews (n = 19) to identify core fatigue domains based on patient experience. Stage 2 (Cognitive Interviews) consisted of interviews with 50 patients to ensure the relevance and clarity of the items. Stage 3 (Exploratory Factor Analysis) and internal consistency testing (Cronbach’s alpha) were performed on the same sample of 50 patients to examine the preliminary factor structure and reliability. Results: Concept elicitation identified lack of energy and persistent exhaustion as core symptoms. The resulting 14-item instrument covers three subdomains: Psychosocial, Physical, and Cognitive. Exploratory factor analysis supported a three-factor solution explaining 75% of the total variance (Factor 1: 28%; Factor 2: 27%; Factor 3: 20%). Internal consistency was high across all factors: Psychosocial (α = 0.923), Physical (α = 0.895), and Cognitive (α = 0.844). Conclusions: This new instrument is a conceptually robust tool that captures the interconnected nature of fatigue in multiple sclerosis (MS). These initial findings support its internal structure and conceptual foundation, providing a practical tool for symptom monitoring in neurological consultations.