Quantifying Fatigue Using Electrophysiological Techniques and Non-invasive Brain Stimulation in People With Multiple Sclerosis- A Review and Discussion.
Karlie HamiltonKaty Katherine SmithKaren WinnBrant OliverPamela K NewlandVerna Hendricks-FergusonPublished in: Biological research for nursing (2023)
Objective: The purpose of this literature review article is to provide a synthesis of recent research focused on the use of 3 techniques to evaluate MS-related fatigue: electroencephalography [EEG], transcranial direct-current stimulation (tDSC), and transcranial- magnetic stimulation (TMS). Method: We performed a literature search in the Cumulative Index to Nursing and Allied Health Literature (CINAHL, EBSCOhost), MEDLINE (OVID), APA PsycInfo (OVID), Scopus (Elsevier), and Web of Science (Clarivate) databases, limited to 2015 and after. Results: Our review revealed that fatigue in MS patients can be quantified and predicted using electrophysiological techniques. Such techniques, which yield objective data, are historically assessed in relation to subjective data, or perceived fatigue. We identified studies using EEG, TMS, and/or tDCS to study fatigue in people with MS. In total, 220 records were identified with 19 studies meeting inclusion criteria. Quality appraisal revealed that the level of evidence was generally graded "good". Conclusions: Despite the heterogenous nature of reviewed the studies and selected the varied self-report fatigue measures, our literature synthesis suggests promise for the use of EEG, TMS, and/or tDCS approaches in more accurately assessing fatigue in people with MS. Further research is needed in this arena.
Keyphrases
- transcranial magnetic stimulation
- multiple sclerosis
- transcranial direct current stimulation
- sleep quality
- working memory
- high frequency
- mass spectrometry
- resting state
- systematic review
- ms ms
- functional connectivity
- big data
- healthcare
- mental health
- public health
- end stage renal disease
- depressive symptoms
- white matter
- chronic kidney disease
- ejection fraction
- electronic health record
- case report
- prognostic factors
- physical activity
- machine learning
- climate change
- risk assessment
- deep learning
- health promotion