Predictive Factors of Concerns about Falling in People with Parkinson's Disease : A 3-Year Longitudinal Study

Introduction: Fear of falling (FOF) is more common in people with Parkinson's disease (PD) than in healthy controls. It can lead to several negative consequences such as restrictions in everyday life. Moreover, FOF is a risk factor for future falls. Aim: This study aimed to identify predictive factors of FOF (conceptualized as concerns about falling) after three years, with and without adjusting for concerns about falling at baseline, in people with PD. Methods: This study included 151 participants (35% women) with PD. At baseline, their mean (SD) age and PD duration were 68 (±9.0) and 9 (±6.1) years, respectively. The Falls Efficacy Scale-International (FES-I) was used as the dependent variable in multivariable linear regression analyses. Results: The mean (SD) FES-I score increased from 28.1 (11.9) to 33.1 (14.0) three years later (p<0.001). The strongest (according to the standardized regression coefficient, β) predictor of concerns about falling was walking difficulties (β = 0.378), followed by age (0.227), problems maintaining balance while dual tasking (0.172), and needing help in daily activities (0.171). When adjusting for baseline FES-I scores, the strongest predictive factor was problems maintaining balance while dual tasking (β = 0.161), which was followed by age (0.131) and female sex (0.105). Conclusions: This study pinpoints several predictive factors of concerns about falling that are modifiable and which could be addressed in rehabilitation: perceived walking difficulties, having problems maintaining balance while dual tasking, and dependence on others in daily activities. The importance of dual tasking is a novel finding, which future studies need to confirm or refute. One should be aware of the fact that an increased age predicts concerns about falling with and without adjusting for baseline FES-I scores, whereas female sex predicts concerns about falling only when adjusting for baseline FES-I scores

Perceived walking difficulties in Parkinson’s disease – predictors and changes over time

Background: People with Parkinson’s disease (PD) have described their walking difficulties as linked to activity avoidance, social isolation, reduced independence and quality of life. There is a knowledge gap regarding predictive factors of perceived walking difficulties in people with PD. Such knowledge could be useful when designing intervention studies. This study aimed to investigate how perceived walking difficulties evolve over a 3-year period in people with PD. A specific aim was to identify predictive factors of perceived walking difficulties. Methods: One hundred forty-eight people with PD (mean age 67.9 years) completed the Generic Walk-12 (Walk-12G) questionnaire (which assesses perceived walking difficulties) at both baseline and the 3-year follow-up. Paired samples t-test was used for comparing baseline and follow-up mean scores. Multivariable linear regression analyses were used to identify predictive factors of perceived walking difficulties. Results: Perceived walking difficulties increased after 3 years: mean Walk-12G score 14.8 versus 18.7, p < 0.001. Concerns about falling was the strongest predictor (β = 0.445) of perceived walking difficulties, followed by perceived balance problems while dual tasking (β = 0.268) and pain (β = 0.153). Perceived balance problems while dual tasking was the strongest predictor (β = 0.180) of a change in perceived walking difficulties, followed by global cognitive functioning (β = − 0.107). Conclusions: Perceived walking difficulties increase over time in people with PD. Both personal factors (i.e. concerns about falling) and motor aspects (i.e. balance problems while dual tasking) seem to have a predictive role. Importantly, our study indicates that also non-motor symptoms (e.g. pain and cognitive functioning) seem to be of importance for future perceived walking difficulties. Future intervention studies that address these factors need to confirm their preventative effect on perceived walking difficulties

Components of gait in people with and without mild cognitive impairment

Background: Several objective gait parameters are associated with cognitive impairment, but there is limitedknowledge of gait models in people with mild cognitive impairment (MCI).Research question: How can 18 objective gait characteristics be used to define different components of gait inpeople with MCI (with suspected incipient neurocognitive disorder) and cognitively unimpaired people (CU),respectively?Methods: Spatiotemporal gait data were collected by using an electronic walkway (GAITRite®), i.e. assessmentsin comfortable gait speed. Using cross-sectional gait data, two principal component analyses (PCA) were performed(varimax rotation) to define different components of gait in people with MCI (n = 114) and CU (n = 219),respectively, from the BioFINDER-2 study.Results: Both PCAs produced four components, here called Variability, Pace/Stability, Rhythm and Asymmetry.Total variance explained was 81.0% (MCI) versus 80.3% (CU). The Variability component explained the largestamount of variance (about 25%) in both groups. The highest loading gait parameter was the same for bothgroups in three out of four components, i.e. step velocity variability (Variability), mean step length (Pace/Stability)and mean step time (Rhythm). In the asymmetry component, stance time asymmetry (MCI) and swingtime asymmetry (CU) loaded the highest.Significance: The gait components seem similar in people with and without MCI, although there were somedifferences. This study may aid the identification of gait variables that represent different components of gait.Gait parameters such as step velocity variability, mean step length, mean step time as well as swing and stancetime asymmetry could serve as interesting core variables of different gait components in future research in peoplewith MCI (with suspected incipient neurocognitive disorder) and CU. However, the selection of gait variablesdepends on the purpose. It needs to be noted that assessment of variability measures requires more advancedtechnology than is usually used in the clinic