The mPower Study, Parkinson Disease Mobile Data Collected Using Researchkit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Analysis of Parkinson\u27s Disease Data

In this paper, we investigate the diagnostic data from patients suffering with Parkinson\u27s disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson\u27s research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson\u27s Disease diagnosis to certain extent. We use Parkinson\u27s Progression Markers Initiative (PPMI) dataset provided by Michael J. Fox Foundation to perform our analysis

Longitudinal falls data in Parkinson’s disease: feasibility of fall diaries and effect of attrition

Background: Identifying causes of falls for people with Parkinson’s disease has met with limited success. Prospective falls measurement using the “gold standard” approach is challenging. This paper examines the process and outcomes associated with longitudinal falls reporting in this population. Methods: Participants were recruited from ICICLE-GAIT (a collaborative study with ICICLE-PD; an incident cohort study). Monthly falls diaries were examined over 48 months for accuracy of data and rate of attrition. To further inform analysis, characteristics of participants with 36-month completed diaries were compared with those who did not complete diaries. Results: One hundred and twenty-one participants were included at baseline. By 12 months, falls diary data had reduced to 107 participants; to 81 participants by 36 months; and to 59 participants by 48 months. Key reasons for diary attrition were withdrawal from ICICLE-gait (n = 16) (13.2%), and noncompliance (n = 11) (9.1%). The only significant difference between the completed and non-completed diary groups was age at 36 months, with older participants being more likely to send in diaries. Conclusions: Prospective falls data is feasible to collect over the long term. Attrition rates are high; however, participants retained in the study are overall representative of the total falls diary cohort

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

\ua9 2023 The authors.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. Many reviews have explored the use of AI in the diagnosis of PD and management of specific symptoms; however, there is limited research on the application of AI to the monitoring and management of the range of PD symptoms. A comprehensive review of the application of AI methods is necessary to address the gap of high-quality reviews and highlight the developments of the use of AI within PD care. Objective: The purpose of this protocol is to guide a systematic review to identify and summarize the current applications of AI applied to the assessment, monitoring, and management of PD symptoms. Methods: This review protocol was structured using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) and the Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks. The following 5 databases will be systematically searched: PubMed, IEEE Xplore, Institute for Scientific Information’s Web of Science, Scopus, and the Cochrane Library. Title and abstract screening, full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a predetermined form, and any disagreements in screening or extraction will be discussed. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias 2 tool for randomized trials and the Mixed Methods Appraisal Tool for nonrandomized trials. Results: As of April 2023, this systematic review has not yet been started. It is expected to begin in May 2023, with the aim to complete by September 2023. Conclusions: The systematic review subsequently conducted as a product of this protocol will provide an overview of the AI methods being used for the assessment, monitoring, and management of PD symptoms. This will identify areas for further research in which AI methods can be applied to the assessment or management of PD symptoms and could support the future implementation of AI-based tools for the effective management of PD

Turning When Using Smartphone in Persons With and Those Without Neurologic Conditions: Observational Study

Background: Turning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions. Objective: This study aims to evaluate the effect of smartphone use on turning behavior in healthy individuals of different ages and those with various neurological diseases. Methods: Younger (aged 18-60 years) and older (aged >60 years) healthy individuals and those with Parkinson disease, multiple sclerosis, subacute stroke (<4 weeks), or lower-back pain performed turning-while-walking alone (single task [ST]) and while performing 2 different cognitive tasks of increasing complexity (dual task [DT]). The mobility task consisted of walking up and down a 5-m walkway at self-selected speed, thus including 180° turns. Cognitive tasks consisted of a simple reaction time test (simple DT [SDT]) and a numerical Stroop test (complex DT [CDT]). General (turn duration and the number of steps while turning), segmental (peak angular velocity), and intersegmental turning parameters (intersegmental turning onset latency and maximum intersegmental angle) were extracted for head, sternum, and pelvis using a motion capture system and a turning detection algorithm. Results: In total, 121 participants were enrolled. All participants, irrespective of age and neurologic disease, showed a reduced intersegmental turning onset latency and a reduced maximum intersegmental angle of both pelvis and sternum relative to head, thus indicating an en bloc turning behavior when using a smartphone. With regard to change from the ST to turning when using a smartphone, participants with Parkinson disease reduced their peak angular velocity the most, which was significantly different from lower-back pain relative to the head (P<.01). Participants with stroke showed en bloc turning already without smartphone use. Conclusions: Smartphone use during turning-while-walking may lead to en bloc turning and thus increase fall risk across age and neurologic disease groups. This behavior is probably particularly dangerous for those groups with the most pronounced changes in turning parameters during smartphone use and the highest fall risk, such as individuals with Parkinson disease. Moreover, the experimental paradigm presented here might be useful in differentiating individuals with lower-back pain without and those with early or prodromal Parkinson disease. In individuals with subacute stroke, en bloc turning could represent a compensative strategy to overcome the newly occurring mobility deficit. Considering the ubiquitous smartphone use in daily life, this study should stimulate future studies in the area of fall risk and neurological and orthopedic diseases

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Palliative care and Parkinson's disease : meeting summary and recommendations for clinical research

Introduction: Palliative care is an approach to caring for patients and families affected by serious illnesses that focuses on the relief of suffering through the management of medical symptoms, psychosocial issues, advance care planning and spiritual wellbeing. Over the past decade there has been an emerging clinical and research interest in the application of palliative care approaches to Parkinson’s disease (PD) and outpatient palliative care services are now offered by several movement disorders centers. Methods: An International Working Group Meeting on PD and Palliative Care supported by the Parkinson’s Disease Foundation was held in October 2015 to review the current state of the evidence and to make recommendations for clinical research and practice. Results: Topics included: 1) Defining palliative care for PD; 2) Lessons from palliative care for heart failure and other chronic illnesses; 3) Patient and caregiver Needs; 4) Needs assessment tools; 5) Intervention strategies; 6) Predicting prognosis and hospice referrals; 7) Choice of appropriate outcome measures; 8) Implementation, dissemination and education research; and 9) Need for research collaborations. We provide an overview of these discussions, summarize current evidence and practices, highlight gaps in our knowledge and make recommendations for future research. Conclusions: Palliative Care for PD is a rapidly growing area which holds great promise for improving outcomes for PD patients and their caregivers. While clinical research in this area can build from lessons learned in other diseases, there is a need for observational, methodological and interventional research to address the unique needs of PD patients and caregivers

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia?

Objective: The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Methods: Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results: DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of bradykinesia (Spearman coefficients): -0.74 speed, 0.66 amplitude, -0.65 rhythm for MBRS; -0.56 speed, 0.61 amplitude, -0.50 rhythm for MDS-UPDRS; -0.69 combined for MDS-UPDRS. All p Conclusion: New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Objective 'contactless' measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely 'contactless'. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements