Use of Mobile Device Accelerometry to Enhance Evaluation of Postural Instability in Parkinson Disease

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

Background

With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.

Objective

We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.

Methods

A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers.

Results

A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles).

Conclusions

Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

Many studies have reported the use of wearable devices to acquire biological data for the diagnosis and treatment of various diseases. Balance dysfunction, however, is difficult to evaluate in real time because the equilibrium function is conventionally examined using a stabilometer installed on the ground. Here, we used a wearable accelerometer that measures head motion to evaluate balance and examined whether it performs comparably to a conventional stabilometer. We constructed a simplified physical head-feet model that simultaneously records “head” motion measured using an attached wearable accelerometer and center-of-gravity motion at the “feet”, which is measured using an attached stabilometer. Total trajectory length (r = 0.818, p -false discovery rate [FDR] = 0.004) and outer peripheral area (r = 0.691, p -FDR = 0.026) values measured using the wearable device and stabilometer were significantly positively correlated. Root mean square area values were not significantly correlated with wearable device stabilometry but were comparable. These results indicate that wearable, widely available, non-medical devices may be used to assess balance outside the hospital setting, and new approaches for testing balance function should be considered.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Effectiveness of a Long-Term, Home-Based Aerobic Exercise Intervention on Slowing the Progression of Parkinson Disease: Design of the Cyclical Lower Extremity Exercise for Parkinson Disease II (CYCLE-II) Study

OBJECTIVE

Previous short duration studies have demonstrated that high-intensity aerobic exercise improves aspects of motor and non-motor function in people with Parkinson disease (PwPD); however, the effectiveness of a long-term exercise intervention on slowing disease progression is unknown. The primary aim of this study is to determine the disease-altering effects of high-intensity aerobic exercise, administered on an upright stationary cycle, on the progression of PD. A secondary aim is to develop a prognostic model for 12-month changes in the Movement Disorder Society Unified Parkinson's Disease Rating Scale III (MDS-UPDRS III) of PwPD undergoing an aerobic exercise intervention.

METHODS

This pragmatic, multisite, single-rater blinded, randomized controlled trial will recruit PwPD from 2 large, urban, academic medical centers. Participants (N = 250 PwPD) will be randomized to (1) home-based aerobic exercise or (2) usual and customary care. Those in the aerobic exercise arm will be asked to complete in-home aerobic exercise sessions at 60% to 80% of heart rate reserve 3 times per week for 12 months utilizing a commercially available upright exercise cycle. The usual and customary care group will continue normal activity levels. Daily activity will be monitored for both groups throughout the 12-month study period. The primary outcome, both to assess disease-modifying response to aerobic exercise and for prognostic modeling in the aerobic exercise arm, is 12-month rate of change in the MDS-UPDRS III. Clinical and biomechanical measures will also be used to assess upper and lower extremity motor function as well as non-motor functions.

IMPACT

Should long-term aerobic exercise demonstrate disease-modifying capability, this study will provide evidence that "Exercise is Medicine" for PwPD. Further, the derived prognostic model will inform a patient-specific exercise prescription for PwPD and expected effects on PD progression.

Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review

Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.

Quantification of Dual-task Performance in Healthy Young Adults Suitable for Military Use

INTRODUCTION

Dual-task performance, in which an individual performs two tasks simultaneously, is compromised following mild traumatic brain injury (mTBI). Proficient dual-task performance is essential in a military setting for both military member safety and execution of skilled tasks. To address the unique needs of military members, a portable dual-task assessment was developed incorporating an auditory dual-task task as a novel assessment module utilizing mobile-device technology. The aim of this study was to develop and validate a dual-task mobile device-based application that accurately quantifies cognitive and motor function.

MATERIALS AND METHODS

Fifty, healthy, military-age civilians completed three cognitive tasks in single- and dual-task conditions with eyes open and closed: visual Stroop, auditory Stroop at 1.5- and 2.5-second stimulus presentation, and number discrimination. All dual-task conditions required the maintenance of postural stability while simultaneously completing a cognitive task.

RESULTS

There were no differences between single- and dual-task conditions for cognitive performance on any of the tests, and a ceiling effect was observed for the visual Stroop and auditory Stroop 1.5-second stimulus presentation (P > .05). Significant differences in postural stability were observed between the eyes-open and eyes-closed conditions in all single- and dual-task conditions (P < .01). Significant differences in postural stability were observed between the eyes-open single-task condition and all dual-task conditions (P < .01).

CONCLUSIONS

Based on the performance of healthy young adults, the number discrimination task may be optimal for detecting subtle changes in dual-task performance. The detected differences found between the eyes-open and eyes-closed conditions provide discriminatory value and insight into the reliance of vision of postural stability performance. While dual-task cognitive performance was not observed in this healthy population, individuals with mTBI may exhibit decreased dual-task performance. The independent evaluation of cognitive and motor function under dual-task conditions has the potential to transform the management and treatment of mTBI.

Use of a Smartphone to Gather Parkinson's Disease Neurological Vital Signs during the COVID-19 Pandemic

Introduction

To overcome travel restrictions during the COVID-19 pandemic, consumer-based technology was rapidly deployed to the smartphones of individuals with Parkinson's disease (PD) participating in a 12-month exercise trial. The aim of the project was to determine the feasibility of utilizing a combined synchronous and asynchronous self-administered smartphone application to characterize PD symptoms.

Methods

A synchronous video virtual visit was completed for the administration of virtual Movement Disorder Society-Unified Parkinson's Disease Rating Scale III (vMDS-UPDRS III). Participants asynchronously completed a mobile application consisting of a measure of upper extremity bradykinesia (Finger Tapping Test) and information processing.

Results

Twenty-three individuals completed the assessments. The mean vMDS-UPDRS III was 23.65 ± 8.56 points. On average, the number of taps was significantly greater for the less affected limb, 97.96 ± 17.77 taps, compared to the more affected, 89.33 ± 18.66 taps (p = 0.025) with a significantly greater number of freezing episodes for the more affected limb (p < 0.05). Correlation analyses indicated the number of errors and the number of freezing episodes were significantly related to clinical ratings of vMDS-UPDRS III bradykinesia (Rho = 0.44, p < 0.01; R = 0.43, p < 0.01, resp.) and finger tapping performance (Rho = 0.31, p = 0.03; Rho = 0.32, p = 0.03, resp.). Discussion. The objective characterization of bradykinesia, akinesia, and nonmotor function and their relationship with clinical disease metrics indicate smartphone technology provides a remote method of characterizing important aspects of PD performance. While theoretical and position papers have been published on the potential of telemedicine to aid in the management of PD, this report translates the theory into a viable reality.

Integrating Technology Into Clinical Practice for the Assessment of Balance and Mobility: Perspectives of Exercise Professionals Practicing in Retirement and Long-term Care

Objective

To explore exercise professionals’ perspectives on technology integration for balance and mobility assessment practices in retirement and long-term care.

Setting

A private residential care organization in Ontario, Canada, with 18 sites providing accommodation and services for older adults.

Design

A qualitative descriptive approach was used including semistructured focus group interviews. Open-ended questions explored perceptions of technology integration along with factors influencing its adoption. Analysis involved preliminary coding based on research questions, review and discussion of emerging themes, and final, resultant coding for each category.

Participants

Exercise professionals (kinesiologists and exercise therapists) (N=18).

Interventions

Not applicable.

Main Outcome Measures

Not applicable.

Results

All participants felt that technology could enhance their practice by supporting programming, communication, and/or information management. Potential barriers to technology integration related primarily to the need to accommodate the broad range of complex health conditions present among clients, which would impact (1) their ability to engage with the technology and (2) relevance of technology-derived outcomes. Specific concerns related to individuals with significant cognitive and/or functional impairment. Solutions to these barriers emphasized the need for flexible technology and appropriate normative data to maximize the potential for uptake.

Conclusions

The participating exercise professionals working in a retirement and long-term care setting saw technology as a potentially effective addition to current clinical practice. To increase the likelihood for clinical uptake, technology must be maximize flexibility in order to accommodate a wide range of physical and cognitive abilities and meet specific needs related to setting and job responsibilities. The findings emphasize the need for continuous dialogue between technology producers and end users for successful development and implementation.

Video-Based Analyses of Parkinson's Disease Severity: A Brief Review

Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

Measures of balance and falls risk prediction in people with Parkinson's disease: a systematic review of psychometric properties

OBJECTIVE

To investigate the psychometric properties of measures of balance and falls risk prediction in people with Parkinson's disease (PD).

DATA SOURCES

PubMed, Embase, CINAHL, Ovid Medline, Scopus, and Web of Science were searched from inception to August 2019.

REVIEW METHOD

Studies testing psychometric properties of measures of balance and falls risk prediction in PD were included. The four-point COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) assessed quality.

RESULTS

Eighty studies testing 68 outcome measures were reviewed; 43 measures assessed balance, 9 assessed falls risk prediction, and 16 assessed both. The measures with robust psychometric estimation with acceptable properties were the (1) Mini-Balance Evaluation Systems Test (Mini-BEST), (2) Berg Balance Scale, (3) Timed Up and Go test, (4) Falls Efficacy Scale International, and (5) Activities-Specific Balance Confidence scale. These measures assess balance and falls risk prediction at the body, structure and function level, falls risk and balance, and falls risk at the activity level. The motor examination of the Unified Parkinson's Disease Rating Scale (UPDRS-ME) with robust psychometric analysis is a condition-specific measure with acceptable properties. Except the UPDRS-ME and Mini-BESTest, the responsiveness of the other four measures has yet to be established.

CONCLUSION

Six of the 68 outcome measures have strong psychometric properties for the assessment of balance and falls risk prediction in PD. Measures assessing balance and falls risk prediction at the participatory level are limited in number with a lack of psychometric validation.

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen's kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., "active aging", biofeedback-based rehabilitation for fall prevention, and the management of Parkinson's disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

The Use of Smart Technology in an Online Community of Patients With Degenerative Cervical Myelopathy

Background

Degenerative cervical myelopathy (DCM) is a prevalent and progressively disabling neurological condition. Treatment is currently limited to surgery, the timing of which is not without controversy. New international guidelines recommend that all patients should undergo lifelong surveillance and those with moderate-to-severe or progressive disease should be offered surgery. Long-term surveillance will place substantial burden on health services and short clinic assessments may risk misrepresenting disease severity. The use of smart technology to monitor disease progression could provide an invaluable opportunity to lessen this burden and improve patient care. However, given the older demographic of DCM, the feasibility of smart technology use is unclear.

Objective

The aim of this study was to investigate current usage of smart technology in patients with self-reported DCM to inform design of smart technology apps targeted at monitoring DCM disease progression.

Methods

Google Analytics from the patient section of Myelopathy.org, an international DCM charity with a large online patient community, was analyzed over a 1-year period. A total of 15,761 sessions were analyzed.

Results

In total, 39.6% (295/744) of visitors accessed the website using a desktop computer, 35.1% (261/744) using mobile, and 25.3% (188/744) using a tablet. Of the mobile and tablet visitors, 98.2% (441/449) utilized a touchscreen device. A total of 51.3% (141/275) of mobile and tablet visitors used iPhone Operating System (iOS) and 45.8% (126/275) used an Android operating system. Apple and Samsung were the most popular smart devices, utilized by 53.6% (241/449) and 25.8% (116/449) of visitors, respectively. The overall visitor age was representative of DCM trials. Smart technology was widely used by older visitors: 58.8% (113/192) of mobile visitors and 84.2% (96/114) of tablet visitors were aged 45 years or older.

Conclusions

Smart technology is commonly used by DCM patients. DCM apps need to be iOS and Android compatible to be accessible to all patients.

A Mobile Device Dual-Task Paradigm for the Assessment of mTBI

Research Objective

Dual-task performance, in which individuals complete two or more activities simultaneously, is impaired following mild traumatic brain injury. The aim of this project was to develop a dual-task paradigm that may be conducive to military utilization in evaluating cognitive-motor function in a standardized and scalable manner by leveraging mobile device technology.

Methods

Fifty healthy young adult civilians (18–24 years) completed four balance stances and a number discrimination task under single- and dual-task conditions. Postural stability was quantified using data gathered from iPad’s native accelerometer and gyroscope. Cognitive task difficulty was manipulated by presenting stimuli at 30, 60, or 90 per minute. Performance of cognitive and balance tasks was compared between single- and dual-task trials.

Results

Cognitive performance from single- to dual-task paradigms showed no significant main effect of balance condition or the interaction of condition by frequency. From single- to dual-task conditions, a significant difference in postural control was revealed in only one stance: tandem with eyes closed, in which a slight improvement in postural stability was observed under dual-task conditions.

Conclusion

The optimal dual-task paradigm to evaluate cognitive-motor performance with minimal floor and ceiling effects consists of tandem stance with eyes closed while stimuli are presented at a rate of one per second.

Mobility improves after high intensity aerobic exercise in individuals with Parkinson's disease

Emerging literature indicates aerobic exercise improves the motor symptoms associated with Parkinson's disease (PD). However, the impact of aerobic exercise on functional locomotor performance has not been evaluated systematically. The aim of this project was to determine the impact of an 8-week high intensity aerobic exercise intervention on Timed Up and Go (TUG) performance in PD. Fifty-nine participants with idiopathic PD completed 24 aerobic exercise sessions over 8 weeks. Two modes of exercise were utilized: forced (FE) and voluntary (VE). A mobile application was used to gather biomechanical data for the characterization of the TUG subtasks: Sit-Stand, Gait, Turning, and Stand-Sit. Participants were assessed in an off medication state at: 1) baseline, prior to any exercise intervention, and 2) after completion of exercise treatment. At baseline, the VE group completed the TUG in 9.41 s, while the FE group completed the TUG significantly faster in 8.0 s. Following the exercise intervention, the VE group decreased TUG time to 8.9 s (p < .01). Both exercise groups demonstrated significant improvements in Turning Velocity, time of Gait phase and Stand-Sit duration. Overall mobility in participants with PD was significantly improved after high intensity aerobic exercise training. Improvements in turning and gait speed, and in Stand-Sit times indicate exercise is effective in improving functional aspects of mobility that are often associated with falls and quality of life measures. These results support the use of high intensity aerobic exercise for improvements in functional lower extremity performance in a PD population.

A low-cost quantitative continuous measurement of movements in the extremities of people with Parkinson's disease

The assessment of Parkinson's disease currently relies on the history of the present illness, the clinical interview, the physical examination, and structured instruments. Drawbacks to the use of clinical ratings include the reliance on real-time human vision to quantify small differences in motion and significant inter-rater variability due to inherent subjectivity in scoring the procedures. Rating tools are semi-quantitative by design, however, in addition to significant inter-rater variability, there is inherent subjectivity in administering these tools, which are not blinded in clinical settings. Sophisticated systems to quantify movements are too costly to be used by some providers with limited resources. A simple procedure is described to obtain continuous quantitative measurements of movements of people with Parkinson's disease for objective analysis and correlation with visual observation of the movements. •Inexpensive accelerometers are attached to the upper and lower extremities of patients with Parkinson's disease and related conditions to generate a continuous, three-dimensional recorded representation of movements occurring while performing tasks to characterize the deficits of Parkinson's disease.•Movements of the procedure are rated by trained examiners live in real-time and later by videotapes.•The output of the instrumentation can be conveyed to experts for interpretation for diagnostic and therapeutic purposes.

Quantifying turning behavior and gait in Parkinson's disease using mobile technology

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Improvements in mobility were detected from meds using a mobile device IMU in PD.

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Algorithms using mobile device IMU data can segment the TUG into subtasks.

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The Cleveland Clinic Mobility App can provide an objective assessment of mobility.

Gait and balance impairments associated with Parkinson’s disease (PD) are often refractory to traditional treatments. Objective, quantitative analysis of gait patterns is crucial in successful management of these symptoms. This project aimed to 1) determine if biomechanical metrics from a mobile device inertial measurement unit were sensitive enough to characterize the effects of anti-parkinsonian medication during the Timed Up and Go (TUG) Test, and 2) develop the Cleveland Clinic Mobility and Balance application (CC-MB) to provide clinicians with objective report following completion of the TUG. The CC-MB captured 3-dimensional acceleration and rotational data from people with PD (pwPD) to characterize center of mass movement while performing the TUG. Trials were segmented into four components: Sit-to-Walk, Gait, Turning, and Stand-to-Sit. Thirty pwPD were tested On and Off (12 h) anti-PD medication. Significant improvements (p < 0.05) between On versus Off conditions included: reduction in MDS-UPDRS III motor scores (10.7%), faster trial times (9.3%), more dynamic walking as evident by increased normalized jerk scores (vertical: 17.3%, medial-lateral: 12.3%), shorter turn durations (10.4%), and faster turn velocities (8%). Measures in Sit-to-Walk and Stand-to-Sit did not show significant changes. Trial time and turn velocity showed excellent test-retest reliability (ICC range: 0.83-0.96) across both medication states. A mobile device platform provided quantitative measures of gait and turning during the TUG that detected significant improvements from anti-parkinsonian medications. This platform is a low-cost, easy-to-use tool that can provide objective reports immediately following the clinical assessments, making it ideal for use in and outside the clinical setting.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.