Models of Care Delivery for Patients With Parkinson Disease Living in Rural Areas

Patterns and determinants of health care utilization among people with Parkinson's disease: A population-based analysis in Ontario, Canada

In Ontario, despite the increasing prevalence of Parkinson's disease (PD), barriers to access-to-care for people with Parkinson's disease (PwP) and their caregivers are not well understood. The objective of this study is to examine spatial patterns of health care utilization among PwP and identify factors associated with PD-related health care utilization of individuals in Ontario. We employed a retrospective, population-based study design involving administrative health data to identify PwP as of March 31, 2018 (N = 35,482) using a previously validated case definition. An enhanced 2-step floating catchment area method was used to measure spatial accessibility to PD care and a descriptive spatial analysis was conducted to describe health service utilization by geographic area and specialty type. Negative binomial regression models were then conducted to identify associated geographic, socioeconomic, comorbidity and demographic factors. There was marked spatial variability in PD-related service utilization, with neurology and all provider visits being significantly higher in urban areas (CMF>1.20; p<0.05) and family physician visits being significantly higher (CMF >1.20; p<0.05) in more rural areas and remote areas. More frequent visits to family physicians were associated with living in rural areas, while less frequent visitation was associated with living in areas of low spatial accessibility with high ethnic concentration. Visits to neurologists were positively associated with living in areas of high spatial accessibility and with high ethnic concentration. Visits to all providers were also positively associated with areas of high spatial accessibility. For all outcomes, less frequent visits were found in women, older people, and those living in more deprived areas as years living with PD increased. This study demonstrates the importance of geographic, socioeconomic and individual factors in determining PwP's likelihood of accessing care and type of care provided. Our results can be expected to inform the development of policies and patient care models aimed at improving accessibility among diverse populations of PwP.

Towards improved access to device-aided therapies in advanced PD: the importance of cooperation of different disciplines

Managing the many issues in advanced Parkinson's disease (PD) requires education, continuous support, and specialized outpatient care involving a variety of allied healthcare professionals. It would be greatly appreciated if general neurologists and professionals from various disciplines who work with people diagnosed with Parkinson's disease (PwP) could remain knowledgeable about the existing therapies and their respective roles within the treatment continuum. The movement disorders specialist and the PD nurse are key actors in the coordination of a targeted and patient-empowering multidisciplinary approach for advanced PD. Affordable and timely access to these therapies for the PwP who may need them is presently a challenge for health systems. Education, training, and support for all the involved stakeholders in the process of PD care may improve quality of life both for PwP and caregivers, and reduce inadequate, expensive, time-consuming, and unsuccessful prolongation of standard medical therapies.

Extension of community healthcare outcomes in Parkinson disease (Parkinson ECHO): A feasibility study

Background

Parkinson's disease is the second most common neurodegenerative disorder and presents with a heterogeneous group of symptoms. Managing these symptoms requires coordinated care from a neurology specialist and a primary care provider. Access to neurology care is limited for those patients with Parkinson's disease who reside in rural areas given financial and mobility constraints along with the rarity of specialty providers.

Methods

To close this gap, we developed and implemented a telehealth-based Project ECHO® (Extension for Community Healthcare Outcomes) program, "Parkinson ECHO," to provide education and support for rural clinicians and allied health members. The sessions focused on a topic within Parkinson's disease diagnosis or management followed by case discussions. We assessed the feasibility of this tele-mentoring educational offering, the favorability of this approach, and the effect it had on clinician confidence in diagnosing and treating Parkinson's disease using Likert-based surveys.

Results

Thirty-three unique participants from 13 Oregon counties and one county in the state of Washington, of whom 70 % served rural and/or medically underserved communities, participated in Parkinson ECHO. There was a 52 % dropout rate based on survey response, though session attendance was higher. Participants were overall satisfied with the format and content of Parkinson ECHO. There were improvements in knowledge and confidence in diagnosing and treating Parkinson's disease which persisted 6 months following the conclusion of the program. Unexpectedly, two participants reported convening a multidisciplinary group to discuss improvements to PD care.

Conclusion

The COVID-19 pandemic was an unexpected obstacle, but the teleconference nature permitted us to complete the program to positive effect. We found Parkinson ECHO did significantly increase participant confidence levels in diagnosing and managing Parkinson's disease.

Co-Designing an Integrated Care Network with People Living with Parkinson’s Disease: A Heterogeneous Social Network of People, Resources and Technologies

As part of the iCARE-PD project, a multinational and multidisciplinary research endeavour to address complex care in Parkinson’s disease, a Canadian case study focused on gaining a better understanding of people living with Parkinson’s disease (PwP) experiences with health and medical services, particularly their vision for a sustainable, tailored and integrated care delivery network. The multifaceted nature of the condition means that PwP must continuously adapt and adjust to every aspect of their lives, and progressively rely on support from care partners (CP) and various health care professionals (HCP). To envision the integrated care delivery network from the perspective of PwP, the study consisted of designing scenarios for an integrated care delivery network with patients, their CP and their HCP, as well as identifying key requirements for designing an integrated care delivery network. The results demonstrate that numerous networks interact, representing specific inscriptions, actors and mediators who meet at specific crossing points. This resulted in the creation of a roadmap and toolkit that takes into consideration the unique challenges faced by PwP, and the necessity for an integrated care delivery network that can be personalized and malleable so as to adapt to evolving and changing needs over time.

Analysis of Gait Sub-Movements to Estimate Ataxia Severity using Ankle Inertial Data

Objective: Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia. Methods: A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features. Results: Estimated total BARS showed strong agreement with clinician-evaluated scores with r² = 0.72 and a root mean square error of 2.6 BARS points. Movement element-based models significantly outperformed conventional, spatiotemporal gait feature-based models. Conclusion: The proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task. Significance: Our work could support fine-grained monitoring of disease progression and patients' responses to medical/clinical interventions.

Barriers to Accessing Healthcare Services for People with Parkinson's Disease: A Scoping Review

Parkinson's disease is a complex condition that affects many different aspects of a person's health. Because of its complexity, people with Parkinson's disease require access to a variety of healthcare services. The aim of the present study was to identify the barriers to access healthcare services for people with Parkinson's disease. We conducted a scoping review according to guidelines posed by Arksey & O'Malley (2005). A search of MEDLINE, Embase, CINHAL, and PsycINFO databases was conducted, and 38 articles were selected based on the inclusion criteria. The review findings identified person-level and system-level barriers. The person-level barriers included skills required to seek healthcare services, ability to engage in healthcare and cost for services. The system-level barriers included the availability of appropriate healthcare resources. Based on the existing barriers elucidated in the scope review, we have discussed potential areas in healthcare that require improvement for people with Parkinson's disease to manage their healthcare needs more equitably.

Estimating Ground Reaction Force and Center of Pressure using Low-Cost Wearable Devices

OBJECTIVE

Ambulatory monitoring of ground reaction force (GRF) and center of pressure (CoP) could improve management of health conditions that impair mobility. Insoles instrumented with force-sensitive resistors (FSRs) are an unobtrusive, low-cost, and low-power technology for sampling GRF and CoP in real-world environments. However, FSRs have variable response characteristics that complicate estimation of GRF and CoP. This study introduces a unique data analytic pipeline that enables accurate estimation of GRF and CoP despite relatively inaccurate FSR responses. This paper also investigates whether inclusion of a complementary knee angle sensor improves estimation accuracy.

METHODS

Seventeen healthy subjects were equipped with an insole instrumented with six FSRs and a string-based knee angle sensor. Subjects walked in a straight line at self-selected slow, preferred, and fast speeds over an in-ground force platform. Twenty repetitions were performed for each speed. Supervised machine learning models estimated weight-normalized GRF and shoe size-normalized CoP, which were re-scaled to obtain GRF and CoP.

RESULTS

Anteroposterior GRF, Vertical GRF, and Anteroposterior CoP were estimated with a normalized root mean square error (NRMSE) of less than 5%. Mediolateral GRF and CoP were estimated with an NRMSE of 8.1% and 6.4%$ respectively. Knee angle-related features slightly improved GRF estimates.

CONCLUSION

Normalized models accurately estimated GRF and CoP despite deficiencies in FSR data.

SIGNIFICANCE

Ambulatory use of the proposed system could enable objective, longitudinal monitoring of severity and progression for a variety of health conditions.

Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants' decomposed wrist velocity time-series. A machine learning regression model was trained to estimate overall ataxia severity, as measured by the Brief Ataxia Rating Scale (BARS). Classification models were trained to distinguish between ataxia participants and controls and between ataxia and parkinsonism phenotypes. Movement decomposition revealed expected and novel characteristics of the ataxia phenotype. The distance, speed, duration, morphology, and temporal relationships of decomposed movements exhibited strong relationships with disease severity. The regression model estimated BARS with a root mean square error of 3.6 points, r² = 0.69, and moderate-to-excellent reliability. Classification models distinguished between ataxia participants and controls and ataxia and parkinsonism phenotypes with areas under the receiver-operating curve of 0.96 and 0.89, respectively. Movement decomposition captures core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments.

Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning

Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and response to therapies. Assessments are limited as they require an in-person evaluation by a neurology subspecialist or specialized and expensive equipment. We tested the hypothesis that important eye movement abnormalities in cerebellar disorders (i.e., ataxias) could be captured from iPhone video. Videos of the face were collected from individuals with ataxia (n = 102) and from a comparative population (Parkinson's disease or healthy participants, n = 61). Computer vision algorithms were used to track the position of the eye which was transformed into high temporal resolution spectral features. Machine learning models trained on eye movement features were able to identify abnormalities in smooth pursuit (a key eye behavior) and accurately distinguish individuals with abnormal pursuit from controls (sensitivity = 0.84, specificity = 0.77). A novel machine learning approach generated severity estimates that correlated well with the clinician scores. We demonstrate the feasibility of capturing eye movement information using an inexpensive and widely accessible technology. This may be a useful approach for disease screening and for measuring severity in clinical trials.

Factors related to sleep disturbances for individuals with Parkinson's disease: a regional perspective

OBJECTIVES

Sleep disturbances negatively impact the quality of life of patients with Parkinson's disease (PD). While persons living in regional areas are at higher risk of PD, PD is poorly managed in regional communities. This study examined factors associated with sleep problems in PD in a regional context.

DESIGN

A mixed-methods cross-sectional design was used.

PARTICIPANTS

Patients with PD were recruited from the Queensland Parkinson's Project database.

MEASUREMENTS

Those who agreed to participate were sent a questionnaire assessing aspects of sleep, depression, anxiety, quality of life, and PD severity. Qualitative information was also gathered. Correlations between variables were examined; thematic analyses were performed for qualitative data.

RESULTS

All participants (n = 49) reported sleep disturbances, with 73% (n = 36) reporting sleep disturbance to be problematic. Global sleep dysfunction positively correlated with daytime napping (r = .34, p = .01), watching the clock when unable to sleep (r = .38, p = <.01), staying in bed when unable to sleep (r = .43, p = <.01), and going to bed hungry (r = .31, p = .03) and negatively correlated with daytime exercise (r = -.32, p = .02). Positive correlations were observed between global sleep dysfunction and depression (r = .55, p = <.01), anxiety (r = .31, p = .04), and dysfunctional sleep beliefs (r = .39, p = <.01).

CONCLUSION

There is a clear need for identifying factors related to sleep disturbances in PD for effective management.