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Usability of a Mobile App for Real-Time Assessment of Fatigue and Related Symptoms in Patients With Multiple Sclerosis: Observational Study
- Source :
- JMIR mHealth and uHealth, Vol 9, Iss 4, p e19564 (2021), JMIR mHealth and uHealth
- Publication Year :
- 2021
- Publisher :
- JMIR Publications, 2021.
-
Abstract
- BackgroundAlthough fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited.ObjectiveThis study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity.MethodsAfter developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal.ResultsOf 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance.ConclusionsThe app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity.
- Subjects :
- medicine.medical_specialty
Multiple Sclerosis
Health Informatics
Information technology
03 medical and health sciences
0302 clinical medicine
Surveys and Questionnaires
Humans
Medicine
Prospective Studies
030212 general & internal medicine
Circadian rhythm
Prospective cohort study
Depression (differential diagnoses)
Original Paper
mobile phone
business.industry
Multiple sclerosis
real-time assessment
Usability
mobile application
medicine.disease
T58.5-58.64
Mobile Applications
Mood
depression
Physical therapy
Anxiety
fatigue
Observational study
medicine.symptom
Public aspects of medicine
RA1-1270
business
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 22915222
- Volume :
- 9
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- JMIR mHealth and uHealth
- Accession number :
- edsair.doi.dedup.....9c84f572226831e592098fe0e4b54800