ArticleJMIR rehabilitation and assistive technologies2022
The Association Between Actigraphy-Derived Behavioral Clusters and Self-Reported Fatigue in Persons With Multiple Sclerosis: Cross-sectional Study.
Article in JMIR rehabilitation and assistive technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study.JMIR mHealth and uHealth · 2025Article
- The association of upper limb sensorimotor capacity, everyday inpatient behavior, and the effects of neurorehabilitation in persons with multiple sclerosis and stroke: a mixed-design study.Journal of neuroengineering and rehabilitation · 2025Article
- Detecting fatigue in multiple sclerosis through automatic speech analysis.Frontiers in human neuroscience · 2024Article
- Going beyond PA: Assessing sensorimotor capacity with wearables in multiple sclerosis-a cross-sectional study.Journal of neuroengineering and rehabilitation · 2023Article
- The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults - a multicentric cross-sectional study.BMC geriatrics · 2023Article
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2 authors.
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Abstract
backgroundPersons with multiple sclerosis frequently report increased levels of fatigue and fatigability. However, behavioral surrogates that are strongly associated with self-reports are lacking, which limits research and treatment.
objectiveThe aim of this study was to derive distinct behavioral syndromes that are reflected by self-reports concerning fatigue and fatigability.
methodsWe collected actigraphic data of 30 persons with multiple sclerosis over a period of 1 week during an inpatient stay at a neurorehabilitation facility. Further, participants completed the German fatigue severity scale. A principal component analysis of actigraphic parameters was performed to extract the latent component levels of behaviors that reflect fatigue (quantity of activity) and fatigability (fragmentation of activity). The resulting components were used in a cluster analysis.
resultsAnalyses suggested 3 clusters, one with high activity (d=0.65-1.57) and low clinical disability levels (d=0.91-1.39), one with high levels of sedentary behavior (d=1.06-1.58), and one with strong activity fragmentation (d=1.39-1.94). The cluster with high levels of sedentary behavior further revealed strong differences from the other clusters concerning participants' reported levels of fatigue (d=0.99-1.28).
conclusionsCluster analysis data proved to be feasible to meaningfully differentiate between different behavioral syndromes. Self-reports reflected the different behavioral syndromes strongly. Testing of additional domains (eg, volition or processing speed) and assessments during everyday life seem warranted to better understand the origins of reported fatigue symptomatology.
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