Does Executive Function Influence Walking in Acutely Hospitalized Patients With Advanced Parkinson's Disease: A Quantitative Analysis

Geritz, Johanna; Welzel, Julius; Hansen, Clint; Maetzler, Corina; Hobert, Markus A.; Elshehabi, Morad; Sobczak, Alexandra; Kudelka, Jennifer; Stiel, Christopher; Hieke, Johanne; Alpes, Annekathrin; Bunzeck, Nico; Maetzler, Walter

doi:10.3389/fneur.2022.852725

Cited by 4 publications

(2 citation statements)

References 66 publications

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“…Previous studies, indeed, demonstrated that, in PD patients, 24% of variation-in-difference in daily step-counting between waist-worn and wrist-worn devices may be explained by tremor, dyskinesias, and bradykinesia, with 19% explained by tremor and dyskinesias alone [30]. An effect of executive functions on ambulation has also been reported in the general population [15] and PD patients [48], even though a recent study questioned the extent of this influence in these latter patients [49]. The results from our study suggest that PD, at least with the disease severity included in our study, is more likely to affect the step-counting performance by its presence or absence in a more dichotomous way than on a continuous range of symptoms severities.…”

Section: Association Of Step-count Error Spatiotemporal Gait Paramete...mentioning

confidence: 98%

Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

Bianchini

Alborghetti

Rinaldi

et al. 2022

Sensors

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Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.

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Section: Association Of Step-count Error Spatiotemporal Gait Paramete...mentioning

confidence: 98%

Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

Bianchini

Alborghetti

Rinaldi

et al. 2022

Sensors

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“…Less is known about older and acutely hospitalized PD patients. It is important to close this gap, as the proportion of hospitalized PD patients is growing in Germany [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Gait Characteristics Associated with Fear of Falling in Hospitalized People with Parkinson’s Disease

Uhlig

Prell

2023

Sensors

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Background: Fear of falling (FOF) is common in Parkinson’s disease (PD) and associated with distinct gait changes. Here, we aimed to answer, how quantitative gait assessment can improve our understanding of FOF-related gait in hospitalized geriatric patients with PD. Methods: In this cross-sectional study of 79 patients with advanced PD, FOF was assessed with the Falls Efficacy Scale International (FES-I), and spatiotemporal gait parameters were recorded with a mobile gait analysis system with inertial measurement units at each foot while normal walking. In addition, demographic parameters, disease-specific motor (MDS-revised version of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr), and non-motor (Non-motor Symptoms Questionnaire, Montreal Cognitive Assessment) scores were assessed. Results: According to the FES-I, 22.5% reported low, 28.7% moderate, and 47.5% high concerns about falling. Most concerns were reported when walking on a slippery surface, on an uneven surface, or up or down a slope. In the final regression model, previous falls, more depressive symptoms, use of walking aids, presence of freezing of gait, and lower walking speed explained 42% of the FES-I variance. Conclusion: Our study suggests that FOF is closely related to gait changes in hospitalized PD patients. Therefore, FOF needs special attention in the rehabilitation of these patients, and targeting distinct gait parameters under varying walking conditions might be a promising part of a multimodal treatment program in PD patients with FOF. The effect of these targeted interventions should be investigated in future trials.

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Predicting executive functioning from walking features in Parkinson’s disease using machine learning

Piet,

Geritz,

Garcia

et al. 2024

Sci Rep

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Parkinson’s disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and executive functioning in patients with Parkinson’s disease using state-of-the-art machine learning approaches. A dataset of 103 geriatric Parkinson inpatients, who performed four walking conditions with varying difficulty levels depending on single task walking and additional motor and cognitive demands, was analyzed. Walking features were quantified using an inertial measurement unit (IMU) system positioned at the patient’s lower back. The analyses included five imputation methods and four regression approaches to predict executive functioning, as measured using the Trail-Making Test (TMT). Multiple imputation by chained equations (MICE) in combination with support vector regression (SVR) reduce the mean absolute error by about 4.95% compared to baseline. Importantly, predictions solely based on walking features obtained with support vector regression mildly but significantly correlated with Δ−TMT values. Specifically, this effect was primarily driven by step time variability, double limb support time variability, and gait speed in the dual task condition with cognitive demands. Taken together, our data provide direct evidence for a link between executive functioning and specific walking features in Parkinson’s disease.

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Does Executive Function Influence Walking in Acutely Hospitalized Patients With Advanced Parkinson's Disease: A Quantitative Analysis

Cited by 4 publications

References 66 publications

Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

Gait Characteristics Associated with Fear of Falling in Hospitalized People with Parkinson’s Disease

Predicting executive functioning from walking features in Parkinson’s disease using machine learning

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