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BACKGROUND: Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF. METHODS: Forty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors. RESULTS: Both HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF. DISCUSSION: These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.
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Background: People living with Parkinson's disease (PD) have a high risk for falls. Objective: To examine gaps in falls prevention targeting people with PD as part of the Task Force on Global Guidelines for Falls in Older Adults. Methods: A Delphi consensus process was used to identify specific recommendations for falls in PD. The current narrative review was conducted as educational background with a view to identifying gaps in fall prevention. Results: A recent Cochrane review recommended exercises and structured physical activities for PD; however, the types of exercises and activities to recommend and PD subgroups likely to benefit require further consideration. Freezing of gait, reduced gait speed, and a prior history of falls are risk factors for falls in PD and should be incorporated in assessments to identify fall risk and target interventions. Multimodal and multi-domain fall prevention interventions may be beneficial. With advanced or complex PD, balance and strength training should be administered under supervision. Medications, particularly cholinesterase inhibitors, show promise for falls prevention. Identifying how to engage people with PD, their families, and health professionals in falls education and implementation remains a challenge. Barriers to the prevention of falls occur at individual, environmental, policy, and health system levels. Conclusion: Effective mitigation of fall risk requires specific targeting and strategies to reduce this debilitating and common problem in PD. While exercise is recommended, the types and modalities of exercise and how to combine them as interventions for different PD subgroups (cognitive impairment, freezing, advanced disease) need further study.
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BACKGROUND: Physical activity is lower in people with multiple sclerosis (pwMS) compared to healthy controls. Previous work focused on studying activity levels or activity volume, but studies of daily-living rest-activity fragmentation patterns, circadian rhythms, and fractal regulation in pwMS are limited. Based on findings in other cohorts, one could suggest that these aspects of daily-living physical activity will provide additional information about the health and well-being of pwMS. Therefore, here, we aimed to (1) identify which fragmentation, fractal, and circadian amplitude measures differ between pwMS and healthy controls, (2) evaluate the relationship between fragmentation, fractal, and circadian amplitude measures and disease severity, and (3) begin to evaluate the added value of those measures, as compared to more conventional measures of physical activity (e.g., mean signal vector magnitude (SVM). A global measure of the overall volume of physical activity). METHODS: 132 people with relapsing-remitting MS (47±11 yrs, 69.7% female, Expanded Disability Status Scale, EDSS, median (IQR): 3 (2-4)) and 90 healthy controls (46±11 yrs, 47.8% female) were asked to wear a 3D accelerometer on their lower back for 7 days. Rest-activity fragmentation, circadian amplitude, fractal regulation, and mean SVM metrics were extracted. PwMS and healthy controls were compared using independent samples t-tests and linear regression, including comparisons adjusted for mean SVM to control for the effect of physical activity volume. Spearman correlations between measures and logistic regressions were used to identify the clinical condition based on the measures that differed significantly after adjusting for SVM. All analyses included adjustments for demographic and clinical parameters (e.g., age, sex). RESULTS: Multiple measures of activity fragmentation significantly differed between pwMS and healthy controls, reflecting a more fragmented active behavior in pwMS. PwMS had a lower circadian rhythm amplitude, indicating a smaller amplitude in the circadian changes of daily activity, and weaker temporal correlations as based on the fractal analysis. When taking into account physical activity volume, one circadian amplitude measure and one fractal measure remained significantly different in pwMS and controls. Fragmentation measures and circadian amplitude measures were significantly associated with disability level as measured by the EDSS; the association with circadian amplitude remained significant, even after adjusting for the mean SVM. CONCLUSION: The physical activity patterns of pwMS differ from those of healthy individuals in rest-activity fragmentation, the amplitude of the circadian rhythm, and fractal regulation. Measures describing these aspects of activity provide information that is not captured in the total volume of physical activity and could, perhaps, augment the monitoring of disease progression and evaluation of the response to interventions.
