Identifying and Managing MS Fatigue Phenotypes by Developing an Artificially Intelligent Smartwatch App
Abstract
Rationale: Chronic fatigue is a common problem in people with multiple sclerosis (MS). MS fatigue differs from normal fatigue in that it can occur daily, often after a good night s sleep. It is a common reason why people with MS cannot engage in meaningful activities as desired and experience declines in quality of life. Unfortunately, there are currently no medications that have been approved by the Food and Drug Administration to treat fatigue. Fortunately, self-management interventions focused on activity pacing (i.e., integrating rest with physical activity) are more effective than medications. Research has shown that self-management education can help people with MS manage their fatigue and improve quality of life. The education involves learning strategies like taking rest breaks and knowing how and when to engage in physical activity. We believe that this education can be improved and made more effective by understanding how to personalize and tailor recommendations. By personalizing and tailoring recommendations, we mean providing detailed recommendations on how a person should manage fatigue based on his/her specific circumstances and underlying causes of fatigue. Conducting research to examine how people experience fatigue over time and identifying modifiable factors that influence fatigue may provide novel insights on how to tailor self-management recommendations for reducing MS fatigue. Objective: We propose to investigate patterns of MS fatigue over time and identify modifiable factors that influence fatigue. In doing so, we will be able to create better recommendations on how people with MS should self-manage their fatigue. To accomplish our objective, we will conduct a study in 300 people with MS who will complete online questionnaires at baseline, 6 weeks, and 3 months. A subset of 120 participants will be randomly assigned to also wear an activity tracking device called an accelerometer and complete questionnaires on a cell phone at random times throughout the day for 10 days at baseline and 3 months. What types of patients could it potentially help, and how? Fatigued patients with mild to moderate MS may potentially benefit from this study. Understanding how people experience fatigue over time and identifying modifiable factors that influence fatigue may lead to more effective recommendations for self-managing fatigue. We know people with MS can experience fatigue in very different ways. However, current recommendations for managing fatigue do not account for these differences. Thus, if we characterize the different ways in which people with MS experience fatigue, we may be able to make self-management recommendations that are personalized, thereby reducing negative influences of fatigue on daily activities. What are the potential clinical applications, benefits, and risks? Clinicians, such as nurses and occupational therapists, provide self-management education to their patients on how to manage fatigue. However, clinicians are often left wondering about how best to help patients manage fatigue; hence, they often use a trial-and-error process that results in only modest reductions in fatigue. The results of the proposed study will help clinicians prioritize which factors to address to reduce fatigue and help them make decisions on how to tailor self-management education. For example, the results of this proposed study may be used to develop evidence-based guidelines for activity pacing. Clinicians can use these guidelines to make personalized recommendations on optimal times for when to rest and when to engage in activity. The most significant risk involved in the project is the loss of confidentiality. However, safeguards are in place to decrease this risk. Thus, the potential benefits of designing more effective self-management education outweigh potential risks of the study. What is the projected time it may take to achieve a patient-related outcome? We expect to ac
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 07, 2017
- Source ID
- W81XWH1710089
Entities
People
- Matthew Plow
Organizations
- Case Western Reserve University
- United States Army