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Parkinson AND smartphone

Kostas M Tsiouris, Dimitrios Gatsios, George Rigas, Dragana Miljkovic, Barbara Koroušić Seljak, Marko Bohanec, Maria T Arredondo, Angelo Antonini, Spyros Konitsiotis, Dimitrios D Koutsouris, Dimitrios I Fotiadis
PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease...
June 2017: Healthcare Technology Letters
Will Lee, Andrew Evans, David R Williams
INTRODUCTION: The phenomenon of sleep benefit (SB) in Parkinson's disease (PD), whereby waking motor function is improved despite no dopaminergic treatment overnight, is controversial. Previous studies suggested a significant discrepancy between subjective functional and objective motor improvement. The aim of this study was to determine how well subjective reporting of SB correlates with objective measures and if true motor improvement can be predicted by a standardized questionnaire...
June 30, 2017: Parkinsonism & related Disorders
Simon Mezgec, Barbara Koroušić Seljak
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet...
June 27, 2017: Nutrients
Rashmi Lakshminarayana, Duolao Wang, David Burn, K Ray Chaudhuri, Clare Galtrey, Natalie Valle Guzman, Bruce Hellman, Ben James, Suvankar Pal, Jon Stamford, Malcolm Steiger, R W Stott, James Teo, Roger A Barker, Emma Wang, Bastiaan R Bloem, Martijn van der Eijk, Lynn Rochester, Adrian Williams
The progressive nature of Parkinson's disease, its complex treatment regimens and the high rates of comorbid conditions make self-management and treatment adherence a challenge. Clinicians have limited face-to-face consultation time with Parkinson's disease patients, making it difficult to comprehensively address non-adherence. Here we share the results from a multi-centre (seven centres) randomised controlled trial conducted in England and Scotland to assess the impact of using a smartphone-based Parkinson's tracker app to promote patient self-management, enhance treatment adherence and quality of clinical consultation...
2017: NPJ Parkinson's Disease
M Linares-Del Rey, L Vela-Desojo, R Cano-de la Cuerda
INTRODUCTION: Parkinson's disease (PD) is the second most common neurodegenerative disease. However, diagnosing, assessing, and treating these patients is a complex process requiring continuous monitoring. In this context, smartphones may be useful in the management of patients with PD. OBJECTIVE: The purpose of this study is to perform a systematic review of the literature addressing the use of mobile phone applications (apps) in PD. MATERIALS AND METHODS: We conducted a literature search of articles published in English or Spanish between 2011 and 2016 analysing or validating apps specifically designed for or useful in PD...
May 23, 2017: Neurología: Publicación Oficial de la Sociedad Española de Neurología
Teresa Arroyo-Gallego, Maria Jesus Ledesma-Carbayo, Alvaro Sanchez-Ferro, Ian Butterworth, Carlos Sanchez-Mendoza, Michele Matarazzo, Paloma Montero, Roberto Lopez-Blanco, Veronica Puertas-Martin, Rocio Trincado, Luca Giancardo
Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease...
February 20, 2017: IEEE Transactions on Bio-medical Engineering
Megan Doerr, Amy Maguire Truong, Brian M Bot, John Wilbanks, Christine Suver, Lara M Mangravite
BACKGROUND: To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent) processes. As we do so, we have an ethical obligation to ensure that core tenants of informed consent-informedness, comprehension, and voluntariness-are upheld. Furthermore, we should be wary of recapitulating the pitfalls of "traditional" informed consent processes. OBJECTIVE: Our objective was to describe the essential qualities of participant experience, including delineation of common and novel themes relating to informed consent, with a self-administered, smartphone-based eConsent process...
February 16, 2017: JMIR MHealth and UHealth
R Lakshminarayana, D Wang, D Burn, K R Chaudhuri, B Hellman, I Smart-Pd Investigators
No abstract text is available yet for this article.
November 2016: Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research
Marianna Capecci, Lucia Pepa, Federica Verdini, Maria Gabriella Ceravolo
INTRODUCTION: The freezing of gait (FOG) is a common and highly distressing motor symptom in patients with Parkinson's Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. METHODS: In order to verify the acceptance of a smartphone-based architecture and its reliability at detecting FOG in real-time, we studied 20 patients suffering from PD-related FOG. They were asked to perform video-recorded Timed Up and Go (TUG) test with and without dual-tasks while wearing the smartphone...
August 21, 2016: Gait & Posture
Ana Lígia Silva de Lima, Tim Hahn, Nienke M de Vries, Eli Cohen, Lauren Bataille, Max A Little, Heribert Baldus, Bastiaan R Bloem, Marjan J Faber
BACKGROUND: Long-term management of Parkinson's disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients' own home environment) might fill this knowledge gap. OBJECTIVE: The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data...
August 26, 2016: JMIR Research Protocols
Chae Young Lee, Seong Jun Kang, Sang-Kyoon Hong, Hyeo-Il Ma, Unjoo Lee, Yun Joong Kim
BACKGROUND: Most studies of smartphone-based assessments of motor symptoms in Parkinson's disease (PD) focused on gait, tremor or speech. Studies evaluating bradykinesia using wearable sensors are limited by a small cohort size and study design. We developed an application named smartphone tapper (SmT) to determine its applicability for clinical purposes and compared SmT parameters to current standard methods in a larger cohort. METHODS: A total of 57 PD patients and 87 controls examined with motor UPDRS underwent timed tapping tests (TT) using SmT and mechanical tappers (MeT) according to CAPSIT-PD...
2016: PloS One
David Ireland, Christina Atay, Jacki Liddle, Dana Bradford, Helen Lee, Olivia Rushin, Thomas Mullins, Dan Angus, Janet Wiles, Simon McBride, Adam Vogel
People with neurological conditions such as Parkinson's disease and dementia are known to have difficulties in language and communication. This paper presents initial testing of an artificial conversational agent, called Harlie. Harlie runs on a smartphone and is able to converse with the user on a variety of topics. A description of the application and a sample dialog are provided to illustrate the various roles chat-bots can play in the management of neurological conditions. Harlie can be used for measuring voice and communication outcomes during the daily life of the user, and for gaining information about challenges encountered...
2016: Studies in Health Technology and Informatics
E Ray Dorsey, Yu-Feng Yvonne Chan, Michael V McConnell, Stanley Y Shaw, Andrew D Trister, Stephen H Friend
Because of their growing popularity and functionality, smartphones are increasingly valuable potential tools for health and medical research. Using ResearchKit, Apple's open-source platform to build applications ("apps") for smartphone research, collaborators have developed apps for researching asthma, breast cancer, cardiovascular disease, type 2 diabetes, and Parkinson disease. These research apps enhance widespread participation by removing geographical barriers to participation, provide novel ways to motivate healthy behaviors, facilitate high-frequency assessments, and enable more objective data collection...
February 2017: Academic Medicine: Journal of the Association of American Medical Colleges
Will Lee, Andrew Evans, David R Williams
BACKGROUND: Measurement of motor function is critical to the assessment and management of Parkinson's disease. Ambulatory motor assessment has the potential to provide a glimpse of the patient's clinical state beyond the consultation. We custom-designed a smartphone application that quantitatively measures hand dexterity and hypothesized that this can give an indication of a patient's overall motor function. OBJECTIVE: The aims of this study were to (i) validate this smartphone application against MDS-UPDRS motor assessment (MDS-UPDRS-III) and the two-target tapping test; (ii) generate a prediction model for MDS-UPDRS-III; (iii) assess repeatability of our smartphone application and (iv) examine compliance and user-satisfaction of this application...
April 2, 2016: Journal of Parkinson's Disease
Carlos Medrano, Inmaculada Plaza, Raúl Igual, Ángel Sánchez, Manuel Castro
The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms--Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)--and a traditional supervised algorithm, Support Vector Machine (SVM)...
2016: Sensors
Pieter Ginis, Alice Nieuwboer, Moran Dorfman, Alberto Ferrari, Eran Gazit, Colleen G Canning, Laura Rocchi, Lorenzo Chiari, Jeffrey M Hausdorff, Anat Mirelman
BACKGROUND: Inertial measurement units combined with a smartphone application (CuPiD-system) were developed to provide people with Parkinson's disease (PD) real-time feedback on gait performance. This study investigated the CuPiD-system's feasibility and effectiveness compared with conventional gait training when applied in the home environment. METHODS: Forty persons with PD undertook gait training for 30 min, three times per week for six weeks. Participants were randomly assigned to i) CuPiD, in which a smartphone application offered positive and corrective feedback on gait, or ii) an active control, in which personalized gait advice was provided...
January 2016: Parkinsonism & related Disorders
Elias Chaibub Neto, Brian M Bot, Thanneer Perumal, Larsson Omberg, Justin Guinney, Mike Kellen, Arno Klein, Stephen H Friend, Andrew D Trister
We propose hypothesis tests for detecting dopaminergic medication response in Parkinson disease patients, using longitudinal sensor data collected by smartphones. The processed data is composed of multiple features extracted from active tapping tasks performed by the participant on a daily basis, before and after medication, over several months. Each extracted feature corresponds to a time series of measurements annotated according to whether the measurement was taken before or after the patient has taken his/her medication...
2016: Pacific Symposium on Biocomputing
Hanbyul Kim, Hong Ji Lee, Woongwoo Lee, Sungjun Kwon, Sang Kyong Kim, Hyo Seon Jeon, Hyeyoung Park, Chae Won Shin, Won Jin Yi, Beom S Jeon, Kwang S Park
Freezing of gait (FOG) is a common motor impairment to suffer an inability to walk, experienced by Parkinson's disease (PD) patients. FOG interferes with daily activities and increases fall risk, which can cause severe health problems. We propose a novel smartphone-based system to detect FOG symptoms in an unconstrained way. The feasibility of single device to sense gait characteristic was tested on the various body positions such as ankle, trouser pocket, waist and chest pocket. Using measured data from accelerometer and gyroscope in the smartphone, machine learning algorithm was applied to classify freezing episodes from normal walking...
August 2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
N Kostikis, D Hristu-Varsakelis, M Arnaoutoglou, C Kotsavasiloglou
The aim of this study is to propose a practical smartphone-based tool to accurately assess upper limb tremor in Parkinson's disease (PD) patients. The tool uses signals from the phone's accelerometer and gyroscope (as the phone is held or mounted on a subject's hand) to compute a set of metrics which can be used to quantify a patient's tremor symptoms. In a small-scale clinical study with 25 PD patients and 20 age-matched healthy volunteers, we combined our metrics with machine learning techniques to correctly classify 82% of the patients and 90% of the healthy volunteers, which is high compared to similar studies...
November 2015: IEEE Journal of Biomedical and Health Informatics
Paweeya Raknim, Kun-Chan Lan
BACKGROUND: Diagnosing brain disorders, such as Parkinson's disease (PD) or Alzheimer's disease, is often difficult, especially in the early stages. Moreover, it has been estimated that nearly 40% of people with PD may not be diagnosed. Traditionally, the diagnosis of neurological disorders, such as PD, often required a doctor to observe the patient over time to recognize signs of rigidity in movement. MATERIALS AND METHODS: The pedestrian dead reckoning (PDR) system is a self-contained technique that has been widely used for indoor localization...
January 2016: Telemedicine Journal and E-health: the Official Journal of the American Telemedicine Association
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