Pilot Study of Real-Time Longitudinal Fatigue Monitoring in Patients With MS

Tuesday, 31 October 2017: 9:20 AM

Pamela Newland, PhD, RN, CMSRN
School of Nursing, Goldfarb School of Nursing at Barnes Jewish College, St Louis, MO, USA

Background: Fatigue is rated as one of the most common and disabling symptoms in adults with multiple sclerosis (MS). Further, fatigue is subjective and occurs at different times of day in and across patients groups (Heine et al., 2016; Kim et al, 2010). Likewise, the time-dependence of fatigue warrants investigation in the form of analyses for personalized patient characteristics. Convenient and reliable monitoring methods need to be created to capture longitudinal real time data for these measures.

Objective: To assess self-reported MS-related fatigue severity and chronology in real time using a mobile/online application and to correlated fatigue measures with self-report measures of cognition, depression, disease duration, functional limitations, perceived biopsychosocial disability, and reported medication adherence.

Methods: Using the Symptom Management Model (SMM) by Dodd et al. (2001), we used monitor fatigue severity in real time and to gather self-report measures. A convenience sample of (how many?) adults with MS used web-based surveys to report fatigue, medication adherence, and site injection reaction (ISR) daily for 7 days at baseline and again 30 days later. Fatigue was evaluated using the NIH PROMIS MS Fatigue Scale Short Form, cognition with the NIH Promis Cognitive Abilities scale, depression severity with the CES-D short form, pain with the VAS scale, MS related functional impairment with the EDSS-R, and perceived biopsychosocial disability with the WHO-DAS-II. Medication adherence was measured with the Morisky Scale.

Results: Thirty-two participants aged 33 to 67 participated. Fatigue and pain scores did not significantly differ between time 1 and time 2. Fatigue correlated significantly with pain (p<0.01), and WHO-DAS (p<0.02). Pain correlated positively with EDSS (p<0.01) and WHO-DAS (p=0.01) and negatively with disease duration (p<0.05). Age and EDSS were also significantly correlated (p<0.02) and cognitive impairment and depression severity were correlated at Time 2 (p<0.02). Medication adherence indicated a significant difference between time 1 (p = .009) and time 2 (p = .03) for forgetting DMT associated with higher fatigue.

Conclusion: This pilot study of longitudinal fatigue monitoring proved feasible and demonstrated correlations between measures of fatigue and pain with other self-report measures. Further study using this application is expected to increase knowledge within clinical practice for both health are delivery and the MS population.