Track Myself:a smartphone-based tool for monitoring Parkinson’s disease

Abstract. Parkinson disease (PD) is a fast-spreading neurological disorder that affects millions of people worldwide, it hinders its patients from performing daily activities with ease. Its symptoms may vary within hours and progress differently for each patient, and usually assessed clinically every six months. It requires customized treatment plan for each patient and demands adherence of patients to complex medication regimens. The goal of this thesis is to design, implement, and test a mobile app named “Track Myself” that can help people with Parkinson’s disease (PwP) resolve these issues. The app has two components that help PwP assess their symptoms level regularly, the first component is an accelerometer-based game that detects the patient’s hand movement and calculate a score for its accuracy, the second component is a self-report symptoms survey filled by the patient every day to rate their severity level. A medication journal is implemented in the app for the patients to log their medication intakes regularly, which are prescribed by their doctors using the app as well, this help keep track of the medication history and calculate the patient’s medication adherence. The app also contains a dashboard made of three charts, representing the medication time-adherence, symptom surveys, and game scores of the patient. The purpose of this dashboard is to help the doctors form relationships between the data in the charts and determine the best future treatment plan. The app was tested for two weeks by ten healthy participants, they were asked to act in the persona of a PD patient and perform certain tasks, where information about the disease and experiences of actual patients were provided for these participants. A questionnaire was sent to the participants after the study, it consists of open-ended questions, rating statements, as well as a validated mobile health app usability questionnaire (MAUQ). The participants rated the app as easy to use for PwP in most features with mean score of 6.04/7 and perceived the app as very useful in helping PwP with mean score of 6.18/7

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

Self-tracking in Parkinson’s: The lived efforts of self-management

People living with Parkinson's disease engage in self-tracking as part of their health self-management. Whilst health technologies designed for this group have primarily focused on improving the clinical assessments of the disease, less attention has been given to how people with Parkinson's use technology to track and manage their disease in their everyday experience. We report on a qualitative study in which we systematically analysed posts from an online health community (OHC) comprising people with Parkinson's (PwP). Our findings show that PwP track a diversity of information and use a wide range of digital and non-digital tools, informed by temporal and structured practices. Using an existing framework of sensemaking for chronic disease self-management, we also identify new ways in which PwP engage in sensemaking, alongside a set of new challenges that are particular to the character of this chronic disease. We relate our findings to technologies for self-tracking offering design implications

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

Speech function in persons with Parkinson\u27s disease: effects of environment, task and treatment

Parkinson’s Disease (PD) is a degenerative neurological disease affecting aspects of movement, including speech. Persons with PD are reported to have better speech functioning in the clinical setting than in the home setting, but this has not been quantified. New methodologies in ambulatory measures of speech are emerging that allow investigation of non-clinical settings. The following questions are addressed: Is speech different between environments in PD and in healthy controls? Can clinical tasks predict speech behaviors in the home? Is treatment proven effective by measures in the home? What can we glean from methods of measurement of speech function in the home? The experiment included 13 persons with PD and 12 healthy controls, studied in the clinical and home environments, and 7 of those 13 persons with PD participated in a treatment study. Major findings included: Spontaneous speech intelligibility, not intensity, was the differentiating factor between persons with PD and healthy controls. Intelligibility and intensity were not related. Both groups presented with higher sentence intensity in the home environment. Spontaneous speech intelligibility in the clinic was related to spontaneous speech intelligibility in the home. The Sentence Intelligibility Test emerged as the best predictor of spontaneous speech intelligibility in the home. Differences between pilot treatment groups measured in the home on intensity and intelligibility were not large enough to make a clinical trial feasible. Individual differences may account for many of these results, for example more severely impaired patients may have shown different data. Drawing conclusions regarding the home environment via measures outside the home should be carefully considered. Ambulatory measures of speech are a viable option for studying speech function in non-clinical settings, and technology is advancing. Further investigation is needed to develop methodologies and normative values for speech in the home

Smart Wearable Device for Reduction of Parkinson’s Disease Hand-Tremor

Parkinson\u27s disease is a neurodegenerative disorder that affects over 10 million people worldwide (Health Unlocked, 2017). People diagnosed with Parkinson\u27s Disease can experience tremors, muscular rigidity and slowness of movement. Tremor is the most common symptom and external agents like stress and anxiety can make it worse, which may cause complications to complete simple day-to-day tasks. Therefore Bio Protech proposes the development of a smart wearable device for reduction of the hand-tremors based on medical evidence that by applying vibration to the wrist may result in a reduction of the involuntary tremor. The device imitates the shape of a wristwatch and the vibration is supplied by motors placed around the wrist. The users will be given the possibility to regulate the frequency according to their needs using a mobile application connected via Bluetooth

Functional mobility in Parkinson’s disease

Introduction: Parkinson’s disease (PD) is the second most common neurodegenerative disease, affecting 1% of the world population over the age of 60. The presence of a large and heterogeneous spectrum of motor and non-motor symptoms, some resistant to levodopa therapy, is usually a major source of disability that affects patients’ daily activities and social participation. Functional mobility (FM) is an outcome that merges the concepts of function with mobility, autonomy, and the accomplishment of daily tasks in different environments. Its use in PD studies is common. However, several aspects associated with its application in PD remain to be defined, hampering a wider use of the concept in clinical practice and the comparison of clinical study results. Aim: This thesis aimed to provide evidence on the appropriateness of the concept of FM in the PD field. A two-fold approach was used to this end: 1) To investigate the clinical and research applicability of the concept of FM in PD; 2) To identify the most suitable clinical and technological outcome measures for evaluating the response of PD patients’ FM to a therapeutic intervention. Methods: A narrative review using the framework of the International Classification of Functioning, Disability, and Health (ICF) was performed to explore the concept of FM when applied to PD. This first study aimed to provide a better understanding of the interaction between PD symptoms, FM, and patients’ daily activities and social participation. To identify and recommend the most suitable outcome measures to assess FM in PD, a systematic review was conducted using the CENTRAL, MEDLINE, Embase, and PEDro databases, from their inception to January 2019. During this review, we also explored the different definitions of FM present in the literature, proposing the one we believed should be established as the definition of FM in the PD field. We then conducted a focus group to explore PD patients' and health professionals’ perspectives on the proposed definition. Part of the scope of the focus group was also to investigate the impact of FM problems on patients’ daily living and the strategies used to deal with this. The study included four focus groups, two with patients (early and advanced disease stages), and two with health professionals (neurologists and physiotherapists). A second systematic review using the CENTRAL, MEDLINE, Embase, and PEDro databases, from their inception to September 2019, was performed to summarize and critically appraise the published evidence on PD spatiotemporal gait parameters. Finally, a pragmatic clinical study was conducted to identify the clinical and technological outcome measures that better predict changes in FM, when patients are submitted to a specialized multidisciplinary program for PD. Results: All the definitions found in an open search of the literature on the FM concept included three key aspects: gait, balance, and transfers. All participants in the focus group study were able to present a spontaneous definition of FM that matched the one used by the authors. All also agreed that FM reflects the difficulties of PD patients in daily life activities. Early-stage PD patients mentioned needing more time to complete their usual tasks, while advanced-stage PD patients considered FM limitations as the main limiting factor of daily activities, especially in medication “OFF” periods. Physiotherapists maintained that the management of PD FM limitations should be a joint work of the multidisciplinary team. For neurologists, FM may better express patients’ perception of their overall health status and may help to adopt a more patient-centered approach. Of the 95 studies included in the systematic review aiming to appraise the outcome measures that have been used to assess FM in PD patients, only one defined the concept of FM. The most frequent terms used as synonyms of FM were mobility, mobility in association with functional activities/performance, motor function, gait-related activity, or balance. In the literature, the Timed Up and Go (TUG) test was the most frequently reported tool used as a single instrument to assess FM in PD. The changes from baseline in the TUG Cognitive test, step length, and free-living step time asymmetry were identified as the best predictors of TUG changes. Conclusion: The information generated by the different studies included in this thesis revealed FM as a useful concept to be adopted in the PD field. FM was shown to be a meaningful outcome (for patients and health professionals), easy to measure, and able to provide more global and ecological information on patients’ daily living performances. Our results support the use of FM for PD assessment and free-living monitoring, as a way to better understand and address patients’ needs. The changes in the TUG Cognitive test, the supervised step length, and the free-living step time asymmetry seem the most suitable outcomes to measure an effect in FM. Future research should focus on determining the severity cut-off for FM changes, the minimal clinical important difference (MCID) for each of these outcome measures and resolve the current obstacles to the widespread use of technological assessments in PD clinical practice and research