Parkinson AND smartphone

INTRODUCTION: In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments...

INTRODUCTION: Deep brain stimulation (DBS) is an effective and standard-of-care therapy for Parkinson's Disease and other movement disorders when symptoms are inadequately controlled with conventional medications. It requires expert care for patient selection, surgical targeting, and therapy titration. Despite the known benefits, racial/ethnic disparities in access have been reported. Technological advancements with smartphone-enabled devices may influence racial disparities. Real-world evidence investigations can shed further light on barriers to access and demographic disparities for DBS patients...

BACKGROUND: Mobile healthcare apps have transformed the healthcare industry and these apps can now be used for educational and preventive purposes, as well as providing valuable information for self-care. Apps related to Parkinson's disease can help from diagnoses to treatment, however the purpose of the app in this study is to inform and to educate. OBJECTIVE: To describe user appraisal of the refinement of a Parkinson's Disease app through a technical evaluation by researchers in the technological area of Human-computer Interaction HCI and usability from the perspective of the end user...

BACKGROUND: Parkinson's disease (PD) patients experience deterioration in mobility with consequent inactivity and worsened health and social status. Physical activity and physiotherapy can improve motor impairments, but several barriers dishearten PD patients to exercise regularly. Home-based approaches (e.g., via mobile apps) and remote monitoring, could help in facing this issue. OBJECTIVE: This study aimed at testing the feasibility, usability and training effects of a home-based exercise program using a customized version of Parkinson Rehab® application...

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus ( n = 48), Parkinson's disease ( n = 21), and other neuromuscular diseases ( n = 45) comprised the pathological gait group ( n = 114), and the control group consisted of 160 healthy volunteers...

Parkinson's disease (PD) is one of the leading neurological disorders negatively impacting health on a global scale. Patients diagnosed with PD require frequent monitoring, prescribed medications, and therapy for extended periods as symptom severity worsens. The primary pharmaceutical treatment for PD patients is levodopa (L-Dopa) which reduces many symptoms experienced by PD patients (e.g., tremors, cognitive ability, motor dysfunction, etc.) through the regulation of dopamine levels in the body. Herein, the first detection of L-Dopa in human sweat using a low-cost 3D printed sensor with a simple and rapid fabrication protocol combined with a portable potentiostat wirelessly connected to a smartphone via Bluetooth is reported...

Gait impairments are among the most common hallmarks of Parkinson's disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms...

INTRODUCTION: The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm...

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase...

OBJECTIVE: The purpose of this study was to coproduce a smart-phone application for digital falls reporting in people with Parkinson disease (PD) and to determine usability using an explanatory mixed-methods approach. METHODS: This study was undertaken in 3 phases. Phase 1 was the development phase, in which people with PD were recruited as co-researchers to the project. The researchers, alongside a project advisory group, coproduced the app over 6 months. Phase 2 was the implementation phase, in which 15 people with PD were invited to test the usability of the app...

INTRODUCTION: Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test...

Many patients with Parkinson's disease (PD) experiencing l-DOPA-induced dyskinesia (LID) receive adjunct treatment with dopamine agonists, whose functional impact on LID is unknown. We set out to compare temporal and topographic profiles of abnormal involuntary movements (AIMs) after l-DOPA dose challenges including or not the dopamine agonist ropinirole. Twenty-five patients with PD and a history of dyskinesias were sequentially administered either l-DOPA alone (150% of usual morning dose) or an equipotent combination of l-DOPA and ropinirole in random order...

BACKGROUND: Parkinson disease (PD) is the second most prevalent neurodegenerative disease, with around 10 million people with PD worldwide. Current assessments of PD symptoms are conducted by questionnaires and clinician assessments and have many limitations, including unreliable reporting of symptoms, little autonomy for patients over their disease management, and standard clinical review intervals regardless of disease status or clinical need. To address these limitations, digital technologies including wearable sensors, smartphone apps, and artificial intelligence (AI) methods have been implemented for this population...

Tapping tasks have the potential to distinguish between ON-OFF fluctuations in Parkinson's disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON-OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h...

BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why...

BACKGROUND: Markerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical measurement-identification and measurement of movement kinematics in a clinical population, its actual application is still in its preliminary stages. The benefits of MMC technology are also inconclusive with regard to its use in assessing patients' conditions...

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic...

In the quantification of symptoms of Parkinson's disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed...

Circadian rhythm impairment may play a role in Parkinson's disease (PD) pathophysiology. Recent literature associated circadian rhythm features to the risk of developing Parkinson and to its progression through stages. The association between the chronotype and the phenotype should be verified on a clinical and biological point of view. Herein we investigate the chronotype of a sample of 50 PD patients with the Morningness Eveningness Questionnaire and monitor their daily activity with a motion sensor embedded in a smartphone...

INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP)...