Exploring Factors Associated with Falls in Multiple Sclerosis: Insights from a Scoping Review.
Rachid KaddouraHanan FarajiMalek OthmanAmin Abu HijlehTom LoneyNandu GoswamiHani T S BenamerPublished in: Clinical interventions in aging (2024)
Multiple sclerosis (MS) is a chronic inflammatory condition that causes demyelination of the central nervous system accompanied by a wide range of symptoms. The high prevalence of falls among patients diagnosed with MS within the initial six months highlights the importance of this issue. The objective of this study is to identify factors associated with falls in MS patients in order to increase awareness and reduce the risk of falls. This scoping review used specific Mesh terms to formulate the literature search around falls and MS using Medline, Google Scholar, Scopus, and Embase search engines. English papers published between 2012 and 2022, studies with a clear definition of falls, McDonald's diagnostic criteria for MS, and those with Expanded Disability Status Scale (EDSS) or Patient Determined Disease Steps (PDDS) scores were included. Critical data from the selected articles were extracted and classified according to the different factors associated with falls in MS patients. Eighteen articles were included in this review. The most important factors associated with falls in MS patients identified were the severity and progression of the disease, mobility and balance problems, bladder dysfunction, fear of falling, fatigue, and cognitive dysfunction. In conclusion, this scoping review yielded the most common factors associated with falls in patients with MS. Study findings can be used to develop future interventions focusing on improving mobility, proprioception, and balance to decrease fall risk and injury amongst MS patients.
Keyphrases
- multiple sclerosis
- mass spectrometry
- end stage renal disease
- ms ms
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- systematic review
- randomized controlled trial
- oxidative stress
- patient reported outcomes
- machine learning
- electronic health record
- deep learning
- big data
- artificial intelligence
- drug induced