Differentiating cognitive or motor dimensions associated with the perception of fall-related self-efficacy in Parkinson's disease

Fear of falling impairs spatiotemporal gait parameters, mobility, and quality of life in Parkinson's disease: a cross-sectional study

BACKGROUND

Fear of Falling (FOF) significantly affects Parkinson's Disease (PD) patients by limiting daily activities and reducing quality of life (QoL). Though common in PD, the relation between FOF, mobility, and QoL remains unclear. This study examines the connections between FOF, gait, daily motor activity, and QoL in PD patients.

METHODS

Fifty PD patients on stable levodopa therapy were enrolled, excluding those with dementia or walking impairments. Assessments included UPDRS-III, Hoehn and Yahr, MoCA, Berg Balance scale, Geriatric Depression Scale and Fall Efficacy Scale International (FES-I). QoL was evaluated using PDQ39. The Timed-Up-and-Go (TUG) test was performed at regular and fast paces, with data collected via smartphone apps for TUG gait parameters and for 24-h quantity of movement monitoring (Activity Index, AIX). A subgroup of 10 patients also underwent 24-h gait monitoring.

RESULTS

FOF was found in 38% of patients, correlating with worse motor scores and QoL (UPDRS-III, 26 vs 17, p < 0.0001; PDQ39 36 vs 14, p < 0.0001). FOF patients showed slower walking (0.73 m/s vs 1.13 m/s, p < 0.001), reduced step length (0.151 vs 0.220 m, p < 0.001), and poorer adaptation to fast walking and environment (being unable to vary their speed and frequency). FOF and sex were both associated with a reduced mobility QoL, with a significant contribution of AIX only in women (r -0.648, p = 0.012).

CONCLUSIONS

This study supports the existence of a significant correlation between FOF, motor activity and QoL in PD, especially in women, emphasize the need for targeted interventions, early rehabilitation and prospective studies focusing on gender.

Prevention of Falls in Parkinson's Disease: Guidelines and Gaps

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.

Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty

Background

Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics.

Methods

As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications.

Results

It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score.

Conclusion

This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

A theoretical framework for addressing fear of falling avoidance behavior in Parkinson's disease

Postural instability in Parkinson's disease (PD) is associated with several downstream consequences that ultimately lead to a greater risk of falling. Among the prominent downstream consequences is fear of falling (FOF), which is both common and problematic in PD. It can lead to a vicious cycle of FOF avoidance behavior that results in more sedentary behavior, physical deconditioning, and weakening of already impaired balance systems. This, in turn, may make the person with PD more susceptible to a future fall even with benign daily tasks. While FOF activity avoidance can be adaptive (appropriate), it can also be maladaptive (inappropriate or exaggerated). When this adaptive and maladaptive FOF avoidance behavior is contextualized to gait/balance performance, it provides a theoretical framework that can be used by clinicians to match patterns of behavior to a concordant treatment approach. In the theoretical framework proposed in this perspective, four different patterns related to FOF avoidance behavior and gait/balance performance are suggested: appropriate avoiders, appropriate non-avoiders, inappropriate avoiders, and inappropriate non-avoiders. For each of the four FOF avoidance behavior patterns, this paper also provides suggested treatment focuses, approaches and recommendations.

Factors predicting fear of falling avoidance behavior in parkinsonisms

BACKGROUND

Fear of falling avoidance behavior (FFAB) is common in parkinsonisms and results in potentially mitigable downstream consequences.

OBJECTIVE

Determine the characteristics of individuals with parkinsonisms most associated with FFAB.

METHODS

A retrospective, cross-sectional study was conducted from medical records data of 142 patients with parkinsonisms. These data included: demographics (age, sex), disease severity (Movement Disorders Society -Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III), years since diagnosis), fall history (number of fall injuries in previous year), and gait and balance function (five times sit to stand, MiniBESTest, Timed Up and Go (TUG), dual-task TUG, ten-meter walk test (10MWT), observed freezing of gait (FOG) (MDS-UPDRS III item 11)).

RESULTS

10MWT (p <  .001) and MDS-UPDRS III item 11 (p <  .014) were significantly associated with FFAB above and beyond disease severity, which also contributed significantly to the overall model (ps <  .046). Fall history was not associated with FFAB.

CONCLUSION

Our findings suggest that the largest portion of variability in FFAB is explained by gait velocity and FOG; however, disease severity also explains a significant portion of the variability of FFAB. Further investigation into factors predictive of FFAB and mitigation of downstream consequences, using more robust designs, is warranted.

Contra-Directional Expression of Plasma Superoxide Dismutase with Lipoprotein Cholesterol and High-Sensitivity C-reactive Protein as Important Markers of Parkinson's Disease Severity

Aim: Oxidative stress and inflammation play critical roles in the neuropathogenesis of PD. We aimed to evaluate oxidative stress and inflammation status by measuring serum superoxide dismutase (SOD) with lipoprotein cholesterol and high-sensitivity C-reactive protein (hsCRP) respectively in PD patients, and explore their correlation with the disease severity.

Methods: We performed a cross-sectional study that included 204 PD patients and 204 age-matched healthy controls (HCs). Plasma levels of SOD, hsCRP, total cholesterol, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were measured. A series of neuropsychological assessments were performed to rate the severity of PD.

Results: The plasma levels of SOD (135.7 ± 20.14 vs. 147.2 ± 24.34, P < 0.0001), total cholesterol, HDL-C and LDL-C in PD were significantly lower than those in HCs; the hsCRP level was remarkably increased in PD compared to HC (2.766 ± 3.242 vs. 1.637 ± 1.597, P < 0.0001). The plasma SOD was negatively correlated with the hsCRP, while positively correlated with total cholesterol, HDL-C, and LDL-C in PD patients. The plasma SOD were negatively correlated with H&Y, total UPDRS, UPDRS (I), UPDRS (II), and UPDRS (III) scores, but positively correlated with MoCA and MMSE scores. Besides, hsCRP was negatively correlated with MoCA; while total cholesterol, HDL-C and LDL-C were positively correlated with the MoCA, respectively.

Conclusion: Our findings suggest that lower SOD along with cholesterol, HDL-C and LDL-C, and higher hsCRP levels might be important markers to assess the PD severity. A better understanding of SOD and hsCRP may yield insights into the pathogenesis of PD.

A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson's disease

Objective measurement of walking speed and gait deficits are an important clinical tool in chronic illness management. We previously reported in Parkinson's disease that different types of gait tests can now be implemented and administered in the clinic or at home using Ambulosono smartphone-sensor technology, whereby movement sensing protocols can be standardized under voice instruction. However, a common challenge that remains for such wearable sensor systems is how meaningful data can be extracted from seemingly "noisy" raw sensor data, and do so with a high level of accuracy and efficiency. Here, we describe a novel pattern recognition algorithm for the automated detection of gait-cycle breakdown and freezing episodes. Ambulosono-gait-cycle-breakdown-and-freezing-detection (Free-D) integrates a nonlinear m-dimensional phase-space data extraction method with machine learning and Monte Carlo analysis for model building and pattern generalization. We first trained Free-D using a small number of data samples obtained from thirty participants during freezing of gait tests. We then tested the accuracy of Free-D via Monte Carlo cross-validation. We found Free-D to be remarkably effective at detecting gait-cycle breakdown, with mode error rates of 0% and mean error rates < 5%. We also demonstrate the utility of Free-D by applying it to continuous holdout traces not used for either training or testing, and found it was able to identify gait-cycle breakdown and freezing events of varying duration. These results suggest that advanced artificial intelligence and automation tools can be developed to enhance the quality, efficiency, and the expansion of wearable sensor data processing capabilities to meet market and industry demand.

Development and Validation of Ambulosono: A Wearable Sensor for Bio-Feedback Rehabilitation Training

Wearable technology-based measurement systems hold potential for the therapeutic and rehabilitation management of patients with various chronic diseases. The purpose of this study was to assess the accuracy and test–retest reliability of a new-generation wearable sensor-based system, dubbed Ambulosono, for bio-feedback training. The Ambulosono sensor system was cross-validated by comparing its functionality with the iPod touch (4th generation) sensor system. Fifteen participants underwent a gait test to measure various gait parameters while wearing both the iPod-based and Ambulosono sensors simultaneously. The physically measured values (i.e., the true values) of step length, distance traveled, velocity, and cadence were then compared to those obtained via the two-sensor systems using the same calculation algorithms. While the mean percentage error was <10% for all measured parameters, and the intra-class correlation coefficient revealed a high level of agreement between trials for both sensor systems, it was found that the Ambulosono sensor system outperformed the iPod-based system in some respects. The Ambulosono sensor system possessed both reliability and accuracy in obtaining gait parameter measurements, which suggests it can serve as an economical alternative to the iPod-based system that is currently used in various clinical rehabilitation programs.