Factors contributing to falls in people with multiple sclerosis: The exploration of the moderation and mediation effects

Walking performance differs between people with multiple sclerosis who perform distinct types of exercise

Aim: To determine whether walking performance differed between people with multiple sclerosis (MS) who performed distinct types, volumes and intensities of exercise.Materials & methods: Forty-five people with relapsing-remitting MS performed two trials of the 2-min walk test, one at a preferred speed and another at a fast speed. Gait metrics were measured by wireless inertial sensors. Participants reported the type (aerobic, resistance), volume and intensity of exercise performed.Results: Walking speed reserve and gait variability were better in participants who performed combined aerobic and resistance exercises compared with those who performed aerobic-only exercise.Conclusion: Walking performance differs in people with mild MS disability based on the type and volume of exercise performed.

Exploring nurses' clinical judgment concerning the relative importance of fall risk factors: A mixed method approach using the Q Methodology

BACKGROUND

Nurses are pivotal in averting patient falls through their assessment of cues presented by patients and their environments, rendering clinical judgments regarding the risk of falling, and implementing tailored interventions. Despite the intricate cognitive processes entailed in nurses' judgment, no prior studies have explored their approach to assessing the risk of falling.

OBJECTIVE

This study aimed to examine how nurses judge the risk of falling among patients with different conditions, whether there are differences in the importance of risk factors as judged by nurses, how they justify their judgments, and what attributes of the nurses influence their judgments.

DESIGN

A mixed method approach using the Q Methodology was employed.

SETTING(S)

Three public and private hospitals in Japan.

PARTICIPANTS

Eighteen nurses participated in the study.

METHODS

Participants were tasked with ranking 36 patient scenarios, each featuring a distinct set of fall risk factors. Subsequently, post-sorting interviews were conducted to gather insights into their typical approach to assessing fall risk and the rationale behind their ranking decisions. A by-person principal component factor extraction was employed to examine differences in the rankings of the scenarios. The interview data were analyzed descriptively to elucidate the reasons behind these discrepancies.

RESULTS

Nurses engage in complex cognitive manipulations when evaluating the risk of patient falls, drawing extensively from their wealth of experience while utilizing assessment tools to support their judgments. In essence, nurses identify patients' tendency to act alone without calling a nurse, impaired gait and cognition, sedative use, drains, and limited information sharing among healthcare professionals as key fall risks. In addition, nurses vary in the importance they attribute to certain risk factors, leading to the discrimination of three distinct judgment profiles. One group of nurses judges patients with cognitive impairment and acting alone as high risk. Another group of nurses considers patients with unstable gait and acting alone as high risk. The last group of nurses sees patients wearing slippers as high risk. The post-sorting interviews revealed that their judgments are closely related to the healthcare context and patient population.

CONCLUSIONS

Nurses operate within diverse contexts, wherein they interact with patients of varying characteristics, collaborate with professionals from diverse disciplines, and have access to varying levels of human and physical resources. This nuanced understanding empowers the formulation of judgments that are finely attuned to the specific context at hand.

STUDY REGISTRATION

Not registered.

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

BACKGROUND

Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.

OBJECTIVE

The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.

METHODS

A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.

RESULTS

We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.

CONCLUSIONS

The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.

TRIAL REGISTRATION

PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.

Examination of Risk Factors Associated With Falls and Injurious Falls in People With Multiple Sclerosis: An Updated Nationwide Study

OBJECTIVE

To provide an update on risk factors associated with falls and injurious falls among people with multiple sclerosis (PwMS) in the United States.

DESIGN

Nationwide cross-sectional web-based survey.

SETTING

Community setting.

PARTICIPANTS

Adult PwMS (n=965).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Participants completed self-report surveys of demographics, clinical data, concerns about falling, occurrence of falls, factors associated with falls, and injurious falls in the past 6 months. Participants also completed Patient-Reported Outcomes Measurement Information System (PROMIS) measures of depression, pain interference, and physical function, and the Fatigue Severity Scale.

RESULTS

The most common self-reported factors associated with falls included personal factors such as poor balance (75%), muscle weakness (54%), and/or fatigue (35%), environmental factors such as general surface conditions (37%) and/or distraction (15%), and activities-related factors such as urgency to complete a task (35%) and/or multitasking (27%). Logistic regression analyses indicated that higher fatigue severity (OR=1.19, P<.01) and higher pain interference (OR=1.02, P<.01) were associated with higher odds of experiencing at least 1 fall. Any level of concern, even minimal concern about falling was also significantly associated with a higher odd of experiencing at least 1 fall (ORs range 2.78 - 3.95, all P<.01). Fair to very high concerns about falling compared with no concern about falling (ORs range=5.17 - 10.26, all P<.05) was significantly associated with higher odds of sustaining an injurious fall.

CONCLUSIONS

Findings suggest falls prevention approaches in PwMS should be multifactorial and include personal, environmental, and activities-related factors. Particular attention on fatigue, pain, and concern about falling may be needed to reduce incidence of falls and injurious falls in this population.