New methods for the assessment of Parkinson’s Disease (2005 to 2015): a systematic review

"BACKGROUND: The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated. METHODS: A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD. RESULTS: Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period. CONCLUSIONS: This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society."Funding agency: The research leading to these results has received funding from “Consejería de Educación, Juventud y Deporte of Comunidad de Madrid” and the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA Grant 291820.info:eu-repo/semantics/acceptedVersio

Effect of Fear of Falling on Turning Performance in Parkinson's Disease in the Lab and at Home

Background: Parkinson's disease (PD) is a neurodegenerative movement disorder associated with gait and balance problems and a substantially increased risk of falling. Falls occur often during complex movements, such as turns. Both fear of falling (FOF) and previous falls are relevant risk factors for future falls. Based on recent studies indicating that lab-based and home assessment of similar movements show different results, we hypothesized that FOF and a positive fall history would influence the quantitative turning parameters differently in the laboratory and home. Methods: Fifty-five PD patients (43 underwent a standardized lab assessment; 40 were assessed over a mean of 12 days at home with approximately 10,000 turns per participant; and 28 contributed to both assessments) were classified regarding FOF and previous falls as "vigorous" (no FOF, negative fall history), "anxious" (FOF, negative fall history), "stoic" (no FOF, positive fall history) and "aware" (FOF, positive fall history). During the assessments, each participant wore a sensor on the lower back. Results: In the lab assessment, FOF was associated with a longer turning duration and lowered maximum and middle angular velocities of turns. In the home evaluations, a lack of FOF was associated with lowered maximum and average angular velocities of turns. Positive falls history was not significantly associated with turning parameters, neither in the lab nor in the home. Conclusion: FOF but not a positive fall history influences turning metrics in PD patients in both supervised and unsupervised environments, and this association is different between lab and home assessments. Our findings underline the relevance of comprehensive assessments including home-based data collection strategies for fall risk evaluation

Future Opportunities for IoT to Support People with Parkinson’s

Recent years have seen an explosion of internet of things (IoT) technologies being released to the market. There has also been an emerging interest in the potentials of IoT devices to support people with chronic health conditions. In this paper, we describe the results of engagements to scope the future potentials of IoT for supporting people with Parkinson’s. We ran a 2-day multi-disciplinary event with professionals with expertise in Parkinson’s and IoT, to explore the opportunities, challenges and benefits. We then ran 4 workshops, engaging 13 people with Parkinson’s and caregivers, to scope out the needs, values and desires that the community has for utilizing IoT to monitor their symptoms. This work contributes a set of considerations for future IoT solutions that might support people with Parkinson’s in better understanding their condition, through the provision of objective measurements that correspond to their, currently unmeasured, subjective experiences

No relevant association of kinematic gait parameters with Health-related Quality of Life in Parkinson's disease.

Health-related Quality of Life (HrQoL) is probably the most important outcome parameter for the evaluation and management of chronic diseases. As this parameter is subjective and prone to bias, there is an urgent need to identify objective surrogate markers. Gait velocity has been shown to be associated with HrQoL in numerous chronic diseases, such as Parkinson's disease (PD). With the development and wide availability of simple-to-use wearable sensors and sophisticated gait algorithms, kinematic gait parameters may soon be implemented in clinical routine management. However, the association of such kinematic gait parameters with HrQoL in PD has not been assessed to date.Kinematic gait parameters from a 20-meter walk from 43 PD patients were extracted using a validated wearable sensor system. They were compared with the Visual Analogue Scale of the Euro-Qol-5D (EQ-5D VAS) by performing a multiple regression analysis, with the International Classification of Functioning, Disability and Health (ICF) model as a framework.Use of assistive gait equipment, but no kinematic gait parameter, was significantly associated with HrQoL.The widely accepted concept of a positive association between gait velocity and HrQoL may, at least in PD, be driven by relatively independent parameters, such as assistive gait equipment

Effect of Fear of Falling on Turning Performance in Parkinson’s Disease in the Lab and at Home

Background: Parkinson’s disease (PD) is a neurodegenerative movement disorder associated with gait and balance problems and a substantially increased risk of falling. Falls occur often during complex movements, such as turns. Both fear of falling (FOF) and previous falls are relevant risk factors for future falls. Based on recent studies indicating that lab-based and home assessment of similar movements show different results, we hypothesized that FOF and a positive fall history would influence the quantitative turning parameters differently in the laboratory and home.Methods: Fifty-five PD patients (43 underwent a standardized lab assessment; 40 were assessed over a mean of 12 days at home with approximately 10,000 turns per participant; and 28 contributed to both assessments) were classified regarding FOF and previous falls as “vigorous” (no FOF, negative fall history), “anxious” (FOF, negative fall history), “stoic” (no FOF, positive fall history) and “aware” (FOF, positive fall history). During the assessments, each participant wore a sensor on the lower back.Results: In the lab assessment, FOF was associated with a longer turning duration and lowered maximum and middle angular velocities of turns. In the home evaluations, a lack of FOF was associated with lowered maximum and average angular velocities of turns. Positive falls history was not significantly associated with turning parameters, neither in the lab nor in the home.Conclusion: FOF but not a positive fall history influences turning metrics in PD patients in both supervised and unsupervised environments, and this association is different between lab and home assessments. Our findings underline the relevance of comprehensive assessments including home-based data collection strategies for fall risk evaluation

Quantitative home-based assessment of Parkinson’s symptoms: The SENSE-PARK feasibility and usability study

Published version, also available at http://dx.doi.org/10.1186/s12883-015-0343-zBackground: Currently, assessment of symptoms associated with Parkinson’s disease is mainly performed in the clinic. However, these assessments have limitations because they provide only a snapshot of the condition. Methods: The feasibility and usability of an objective, continuous and relatively unobtrusive system (SENSE-PARK System), which consists of wearable sensors (three worn during the day and one worn at night), a smartphone-based App, a balance board and computer software, was tested 24/7 over 12 weeks in a study including 22 PD patients. During the first four weeks of the study, patients did not get feedback about their performance, during the last eight weeks they did. The study included seven clinical visits with standardized interviews, and regular phone contact. The primary outcome was the number of drop-outs during the study. As secondary outcomes, the Post-Study System Usability Questionnaire (PSSUQ), score and information obtained from the standardized interviews were used to evaluate the usability of the system. Results: All patients completed the study. The participants rated the usability of the SENSE-PARK System with a mean score of 2.67 (±0.49) on the PSSUQ. The interviews revealed that most participants liked using the system and appreciated that it signaled changes in their health condition. Conclusions: This 12 week controlled study demonstrates that the acceptance level of PD patients using the SENSE-PARK System as a home-based 24/7 assessment is very good. Particular emphasis should be given to a user-friendly design. Motivation to wear such a system can be increased by providing direct feedback about the individual health condition

Block wise multivariate regression analysis for HrQoL using the ICF model as framework.

<p>Block wise multivariate regression analysis for Health-related Quality of Life (HrQoL) assessment in Parkinson’s disease with consideration of International Classification of Functioning, disability and health (ICF)-relevant parameters, including kinematic gait parameters. Significant β-values are presented in bold. EQ-5D VAS, Visual Analogue Scale of the Euro-QoL-5D; adj. r², adjusted regression coefficient for the entire model.</p