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Bridges B, Taylor J, Weber JT. Evaluation of the Parkinson's Remote Interactive Monitoring System in a Clinical Setting: Usability Study. JMIR Hum Factors 2024; 11:e54145. [PMID: 38787603 PMCID: PMC11161713 DOI: 10.2196/54145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The fastest-growing neurological disorder is Parkinson disease (PD), a progressive neurodegenerative disease that affects 10 million people worldwide. PD is typically treated with levodopa, an oral pill taken to increase dopamine levels, and other dopaminergic agonists. As the disease advances, the efficacy of the drug diminishes, necessitating adjustments in treatment dosage according to the patient's symptoms and disease progression. Therefore, remote monitoring systems that can provide more detailed and accurate information on a patient's condition regularly are a valuable tool for clinicians and patients to manage their medication. The Parkinson's Remote Interactive Monitoring System (PRIMS), developed by PragmaClin Research Inc, was designed on the premise that it will be an easy-to-use digital system that can accurately capture motor and nonmotor symptoms of PD remotely. OBJECTIVE We performed a usability evaluation in a simulated clinical environment to assess the ease of use of the PRIMS and determine whether the product offers suitable functionality for users in a clinical setting. METHODS Participants were recruited from a user sign-up web-based database owned by PragmaClin Research Inc. A total of 11 participants were included in the study based on the following criteria: (1) being diagnosed with PD and (2) not being diagnosed with dementia or any other comorbidities that would make it difficult to complete the PRIMS assessment safely and independently. Patient users completed a questionnaire that is based on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. Interviews and field notes were analyzed for underlying themes and topics. RESULTS In total, 11 people with PD participated in the study (female individuals: n=5, 45%; male individuals: n=6, 55%; age: mean 66.7, SD 7.77 years). Thematic analysis of the observer's notes revealed 6 central usability issues associated with the PRIMS. These were the following: (1) the automated voice prompts are confusing, (2) the small camera is problematic, (3) the motor test exhibits excessive sensitivity to the participant's orientation and position in relation to the cameras, (4) the system poses mobility challenges, (5) navigating the system is difficult, and (6) the motor test exhibits inconsistencies and technical issues. Thematic analysis of qualitative interview responses revealed four central themes associated with participants' perspectives and opinions on the PRIMS, which were (1) admiration of purpose, (2) excessive system sensitivity, (3) video instructions preferred, and (4) written instructions disliked. The average system usability score was calculated to be 69.2 (SD 4.92), which failed to meet the acceptable system usability score of 70. CONCLUSIONS Although multiple areas of improvement were identified, most of the participants showed an affinity for the overarching objective of the PRIMS. This feedback is being used to upgrade the current PRIMS so that it aligns more with patients' needs.
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Affiliation(s)
- Bronwyn Bridges
- School of Pharmacy, Memorial University, St. John's, NL, Canada
| | - Jake Taylor
- School of Exercise Science, Physical & Health Education, University of Victoria, Victoria, BC, Canada
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Lützow L, Teckenburg I, Koch V, Marxreiter F, Jukic J, Stallforth S, Regensburger M, Winkler J, Klucken J, Gaßner H. The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson's disease: a long-term feasibility study. J Patient Rep Outcomes 2023; 7:106. [PMID: 37902922 PMCID: PMC10616049 DOI: 10.1186/s41687-023-00631-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/28/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Exercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson's disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients. Training effects were assessed using patient-reported outcome measures combined with sensor-based and clinical scores. METHODS 16 PD patients (smartphone group, SG) completed a home-based, individualized training program over 6-8 months using a smartphone app, remotely supervised by a therapist, and tailored to the patient's motor impairments and capacity. A control group (CG, n = 16) received medical treatment without participating in digital exercise training. The usability of the app was validated using System Usability Scale (SUS) and User Version of the Mobile Application Rating Scale (uMARS). Outcome measures included among others Unified Parkinson Disease Rating Scale, part III (UPDRS-III), sensor-based gait parameters derived from standardized gait tests, Parkinson's Disease Questionnaire (PDQ-39), and patient-defined motor activities of daily life (M-ADL). RESULTS Exercise frequency of 74.5% demonstrated high adherence in this cohort. The application obtained 84% in SUS and more than 3.5/5 points in each subcategory of uMARS, indicating excellent usability. The individually assessed additional benefit showed at least 6 out of 10 points (Mean = 8.2 ± 1.3). From a clinical perspective, patient-defined M-ADL improved for 10 out of 16 patients by 15.5% after the training period. The results of the UPDRS-III remained stable in the SG while worsening in the CG by 3.1 points (24%). The PDQ-39 score worsened over 6-8 months by 83% (SG) and 59% (CG) but the subsection mobility showed a smaller decline in the SG (3%) compared to the CG (77%) without reaching significance level for all outcomes. Sensor-based gait parameters remained constant in both groups. CONCLUSIONS Long-term training over 6-8 months with the app is considered feasible and acceptable, representing a cost-effective, individualized approach to complement dopaminergic treatment. This study indicates that personalized, digital, high-frequency training leads to benefits in motor sections of ADL and Quality of Life.
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Affiliation(s)
- Lisa Lützow
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Isabelle Teckenburg
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Veronika Koch
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Center for Movement Disorders, Passauer Wolf, Bad Gögging, Neustadt an der Donau, Germany
| | - Jelena Jukic
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Medical Valley - Digital Health Application Center GmbH, Bamberg, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Medical Valley - Digital Health Application Center GmbH, Bamberg, Germany
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany.
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
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Tam W, Alajlani M, Abd-Alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. J Med Internet Res 2023; 25:e42950. [PMID: 37594791 PMCID: PMC10474516 DOI: 10.2196/42950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
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Affiliation(s)
- William Tam
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | - Mohannad Alajlani
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
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Gaßner H, Friedrich J, Masuch A, Jukic J, Stallforth S, Regensburger M, Marxreiter F, Winkler J, Klucken J. The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study. JMIR Rehabil Assist Technol 2022; 9:e38994. [PMID: 36378510 PMCID: PMC9709672 DOI: 10.2196/38994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/10/2022] [Accepted: 09/07/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Bradykinesia and rigidity are prototypical motor impairments of Parkinson disease (PD) highly influencing everyday life. Exercise training is an effective treatment alternative for motor symptoms, complementing dopaminergic medication. High frequency training is necessary to yield clinically relevant improvements. Exercise programs need to be tailored to individual symptoms and integrated in patients' everyday life. Due to the COVID-19 pandemic, exercise groups in outpatient setting were largely reduced. Developing remotely supervised solutions is therefore of significant importance. OBJECTIVE This pilot study aimed to evaluate the feasibility of a digital, home-based, high-frequency exercise program for patients with PD. METHODS In this pilot interventional study, patients diagnosed with PD received 4 weeks of personalized exercise at home using a smartphone app, remotely supervised by specialized therapists. Exercises were chosen based on the patient-defined motor impairment and depending on the patients' individual capacity (therapists defined 3-5 short training sequences for each participant). In a first education session, the tailored exercise program was explained and demonstrated to each participant and they were thoroughly introduced to the smartphone app. Intervention effects were evaluated using the Unified Parkinson Disease Rating Scale, part III; standardized sensor-based gait analysis; Timed Up and Go Test; 2-minute walk test; quality of life assessed by the Parkinson Disease Questionnaire; and patient-defined motor tasks of daily living. Usability of the smartphone app was assessed by the System Usability Scale. All participants gave written informed consent before initiation of the study. RESULTS In total, 15 individuals with PD completed the intervention phase without any withdrawals or dropouts. The System Usability Scale reached an average score of 72.2 (SD 6.5) indicating good usability of the smartphone app. Patient-defined motor tasks of daily living significantly improved by 40% on average in 87% (13/15) of the patients. There was no significant impact on the quality of life as assessed by the Parkinson Disease Questionnaire (but the subsections regarding mobility and social support improved by 14% from 25 to 21 and 19% from 15 to 13, respectively). Motor symptoms rated by Unified Parkinson Disease Rating Scale, part III, did not improve significantly but a descriptive improvement of 14% from 18 to 16 could be observed. Clinically relevant changes in Timed Up and Go test, 2-minute walk test, and sensor-based gait parameters or functional gait tests were not observed. CONCLUSIONS This pilot interventional study presented that a tailored, digital, home-based, and high-frequency exercise program over 4 weeks was feasible and improved patient-defined motor activities of daily life based on a self-developed patient-defined impairment score indicating that digital exercise concepts may have the potential to beneficially impact motor symptoms of daily living. Future studies should investigate sustainability effects in controlled study designs conducted over a longer period.
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Affiliation(s)
- Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Digital Health Systems, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
| | - Jana Friedrich
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Alisa Masuch
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jelena Jukic
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Medical Valley, Digital Health Application Center GmbH, Bamberg, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Digital Health Systems, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
- Medical Valley, Digital Health Application Center GmbH, Bamberg, Germany
- Digital Medicine Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Digital Medicine Group, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
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Tam W, Alajlani M, Abd-alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.
OBJECTIVE
In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.
METHODS
A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.
RESULTS
Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.
CONCLUSIONS
This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
CLINICALTRIAL
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A deep learning approach for parkinson’s disease severity assessment. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00698-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Abstract
Purpose
Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved.
Methods
We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest.
Results
Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity.
Conclusion
This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.
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Liu WY, Tung TH, Zhang C, Shi L. Systematic review for the prevention and management of falls and fear of falling in patients with Parkinson's disease. Brain Behav 2022; 12:e2690. [PMID: 35837986 PMCID: PMC9392538 DOI: 10.1002/brb3.2690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/17/2022] [Accepted: 04/24/2022] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVE To synthesize recent empirical evidence for the prevention and management of falls and fear of falling in patients with Parkinson's disease (PD). DATA SOURCE Database from PubMed, Cochrane Library, and EMBASE. STUDY DESIGN Systematic review. DATA COLLECTION We searched the PubMed, Cochrane Library, and EMBASE databases for studies published from inception to February 27, 2021. Inclusion criteria were nonreview articles on prevention and management measures related to falls and fall prevention in Parkinson's disease patients. PRINCIPAL FINDINGS We selected 45 articles and conducted in-depth research and discussion. According to the causes of falls in PD patients, they were divided into five directions, namely physical status, pre-existing conditions, environment, medical care, and cognition. In the cognitive domain, we focused on the fear of falling. On the above basis, we constructed a fall prevention model, which is a tertiary prevention health care network, based on The Johns Hopkins Fall Risk Assessment Tool to provide ideas for the prevention and management of falling and fear of falling in PD patients in clinical practice CONCLUSIONS: Falls and fear of falls in patients with Parkinson's disease can be reduced by effective clinical prevention and management. Future studies are needed to explore the efficacy of treatment and prevention of falls and fear of falls.
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Affiliation(s)
- Wen-Yi Liu
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.,Shanghai Bluecross Medical Science Institute, Shanghai, China.,Institute for Hospital Management, Tsing Hua University, Shenzhen Campus, China
| | - Tao-Hsin Tung
- Evidence-based Medicine Center, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Linhai, Zhejiang, China
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Leiyu Shi
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
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Riggare S, Stamford J, Hägglund M. A Long Way to Go: Patient Perspectives on Digital Health for Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 11:S5-S10. [PMID: 33682728 PMCID: PMC8385497 DOI: 10.3233/jpd-202408] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson’s disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis shows that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.
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Affiliation(s)
- Sara Riggare
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Jon Stamford
- Gentleman Neuroscientist and Independent Parkinson's Patient Advocate, UK
| | - Maria Hägglund
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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Eggers C, Wellach I, Groppa S, Strothjohann M, Klucken J. [Care of patients with Parkinson's disease in Germany: status quo and perspectives as reflected in the digital transition]. DER NERVENARZT 2021; 92:602-610. [PMID: 33196867 PMCID: PMC7667482 DOI: 10.1007/s00115-020-01027-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 01/04/2023]
Abstract
As a chronic neurodegenerative disease, Parkinson's disease requires a close cooperation between different specialist disciplines in order to ensure the best possible quality of life for patients. A problem that has been identified is the inadequate communication between the protagonists (e.g. caregivers, physicians and therapists), especially at the sectoral interfaces. Due to structural hurdles, the current process and supply chains for Parkinson's disease do not reflect successful cross-sectoral care. Against the background of the new Digital Care Act in Germany that refunds patient-centered digital healthcare applications (DiGA), innovative, digital care and communication structures can now be established and thus comprehensively revolutionize the care of chronic diseases, such as Parkinson's disease. In this review examples and case application scenarios are presented and critically discussed.
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Affiliation(s)
- Carsten Eggers
- Neurologie, Philipps-Universität Marburg, Baldingerstr., 35033, Marburg, Deutschland.
| | - Ingmar Wellach
- Praxis für Neurologie & Psychiatrie Hamburg Walddörfer, Wiesenkamp 22c, 22359, Hamburg, Deutschland
- Evangelisches Amalie Sieveking Krankenhaus, Haselkamp 33, 22359, Hamburg, Deutschland
| | - Sergiu Groppa
- Bewegungsstörungen und Neurostimulation, Klinik und Poliklinik für Neurologie, Forschungszentrum Translationale Neurowissenschaften (FTN) Rhein-Main-Neuro-Zentrum (rmn2), Universitätsmedizin der Johannes Gutenberg-Universität, Langenbeckstr. 1, 55131, Mainz, Deutschland
| | - Martin Strothjohann
- Medical Park Bad Camberg, Obertorstraße 100-102, 65520, Bad Camberg, Deutschland
| | - Jochen Klucken
- Molekulare Neurologie, Universitätsklinikum Erlangen, Schwabachanlage 6, 91054, Erlangen, Deutschland
- Fraunhofer IIS, Am Wolfsmantel 33, 91058, Erlangen, Deutschland
- Medical Valley Digital Health Application Center GmbH, Promenadestr. 6a, 96047, Bamberg, Deutschland
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11
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Dockendorf MF, Hansen BJ, Bateman KP, Moyer M, Shah JK, Shipley LA. Digitally Enabled, Patient-Centric Clinical Trials: Shifting the Drug Development Paradigm. Clin Transl Sci 2021; 14:445-459. [PMID: 33048475 PMCID: PMC7993267 DOI: 10.1111/cts.12910] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/23/2020] [Indexed: 12/29/2022] Open
Abstract
The rapidly advancing field of digital health technologies provides a great opportunity to radically transform the way clinical trials are conducted and to shift the clinical trial paradigm from a site-centric to a patient-centric model. Merck's (Kenilworth, NJ) digitally enabled clinical trial initiative is focused on introduction of digital technologies into the clinical trial paradigm to reduce patient burden, improve drug adherence, provide a means of more closely engaging with the patient, and enable higher quality, faster, and more frequent data collection. This paper will describe the following four key areas of focus from Merck's digitally enabled clinical trials initiative, along with corresponding enabling technologies: (i) use of technologies that can monitor and improve drug adherence (smart dosing), (ii) collection of pharmacokinetic (PK), pharmacodynamic (PD), and biomarker samples in an outpatient setting (patient-centric sampling), (iii) use of digital devices to collect and measure physiological and behavioral data (digital biomarkers), and (iv) use of data platforms that integrate digital data streams, visualize data in real-time, and provide a means of greater patient engagement during the trial (digital platform). Furthermore, this paper will discuss the synergistic power in implementation of these approaches jointly within a trial to enable better understanding of adherence, safety, efficacy, PK, PD, and corresponding exposure-response relationships of investigational therapies as well as reduced patient burden for clinical trial participation. Obstacle and challenges to adoption and full realization of the vision of patient-centric, digitally enabled trials will also be discussed.
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AlMahadin G, Lotfi A, Zysk E, Siena FL, Carthy MM, Breedon P. Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective. BMC Neurol 2020; 20:419. [PMID: 33208135 PMCID: PMC7677815 DOI: 10.1186/s12883-020-01996-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/09/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices. METHODS This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach. RESULTS It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor. CONCLUSION The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
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Affiliation(s)
- Ghayth AlMahadin
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Ahmad Lotfi
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
| | - Eva Zysk
- Department of Psychology, University of British Columbia in Vancouver, West Mall, Vancouver, V6T 1Z4 Canada
| | - Francesco Luke Siena
- School Of Architecture Design & Built Environment, Nottingham Trent University, Goldsmith Street, Nottingham, NG1 4FQ UK
| | | | - Philip Breedon
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS UK
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Ahamed F, Shahrestani S, Cheung H. Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia. SENSORS 2020; 20:s20216031. [PMID: 33114070 PMCID: PMC7660294 DOI: 10.3390/s20216031] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/26/2020] [Accepted: 10/22/2020] [Indexed: 11/16/2022]
Abstract
Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, and selecting distinctive features based on dynamic ranking, a classification model is built. We compare and contrast several machine learning approaches for developing a reliable and efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.
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Zhang A, De la Torre F, Hodgins J. Comparing laboratory and in-the-wild data for continuous Parkinson's Disease tremor detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5436-5441. [PMID: 33019210 DOI: 10.1109/embc44109.2020.9176255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Passive, continuous monitoring of Parkinson's Disease (PD) symptoms in the wild (i.e., in home environments) could improve disease management, thereby improving a patient's quality of life. We envision a system that uses machine learning to automatically detect PD symptoms from accelerometer data collected in the wild. Building such systems, however, is challenging because it is difficult to obtain labels of symptom occurrences in the wild. Many researchers therefore train machine learning algorithms on laboratory data with the assumption that findings will translate to the wild. This paper assesses how well laboratory data represents wild data by comparing PD symptom (tremor) detection performance of three models on both lab and wild data. Findings indicate that, for this application, laboratory data is not a good representation of wild data. Results also show that training on wild data, even though labels are less precise, leads to better performance on wild data than training on accurate labels from laboratory data.
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Zajki-Zechmeister T, Kögl M, Kalsberger K, Franthal S, Homayoon N, Katschnig-Winter P, Wenzel K, Zajki-Zechmeister L, Schwingenschuh P. Quantification of tremor severity with a mobile tremor pen. Heliyon 2020; 6:e04702. [PMID: 32904326 PMCID: PMC7452531 DOI: 10.1016/j.heliyon.2020.e04702] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/26/2020] [Accepted: 08/11/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND An objective evaluation of tremor severity is necessary to document the course of disease, the efficacy of treatment, or interventions in clinical trials. Most available objective quantification devices are complex, immobile, or not validated. NEW METHOD We used the TREMITAS-System that comprises a pen-shaped sensor for tremor quantification. The Power of Main Peak and the Total Power were used as surrogate markers for tremor amplitude. Tremor severity was assessed by the TREMITAS-System and relevant subscores of the MDS-UPDRS and TETRAS rating scales in 14 patients with Parkinson's disease (PD) and 16 patients with Essential tremor (ET) off and on therapy. We compared tremor amplitudes assessed during wearable and hand-held constellations. RESULTS We found significant correlations between tremor amplitudes captured by TREM and tremor severity assessed by the MDS-UPDRS in PD (r = 0.638-0.779) and the TETRAS in ET (r = 0.597-0. 704) off and on therapy. The TREMITAS-System captured the L-Dopa-induced improvement of tremor in PD patients (p = 0.027). Tremor amplitudes did not differ between the handheld and wearable constellation (p > 0.05). COMPARISON WITH EXISTING METHODS We confirm the results of previous studies using inertial based sensors that tremor severity and drug-induced changes of tremor severity can be quantified using inertial based sensors. The assessment of tremor amplitudes was not influenced by using a handheld or wearable constellation. CONCLUSIONS The TREMITAS-System can be used to quantify rest tremor in PD and postural tremor in ET and is capable of detecting clinically relevant changes in tremor in clinical and research settings.
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Affiliation(s)
| | - Mariella Kögl
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Kerstin Kalsberger
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Sebastian Franthal
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Nina Homayoon
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Petra Katschnig-Winter
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Karoline Wenzel
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | | | - Petra Schwingenschuh
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
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Taylor KI, Staunton H, Lipsmeier F, Nobbs D, Lindemann M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. NPJ Digit Med 2020; 3:97. [PMID: 32715091 PMCID: PMC7378210 DOI: 10.1038/s41746-020-0305-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/26/2020] [Indexed: 11/08/2022] Open
Abstract
Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients' functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson's disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.
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Affiliation(s)
- Kirsten I. Taylor
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
- Faculty of Psychology, University of Basel, Missionsstrasse 60/62, 4055 Basel, Switzerland
| | - Hannah Staunton
- Patient-Centered Outcomes Research, Biometrics, Product Development, Roche Products Limited, Hexagon Place, 6 Falcon Way, Shire Park, Welwyn Garden City, AL7 1TW UK
| | - Florian Lipsmeier
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - David Nobbs
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michael Lindemann
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
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Zeissler ML, Li V, Parmar MK, Carroll CB. Is It Possible to Conduct a Multi-Arm Multi-Stage Platform Trial in Parkinson's Disease: Lessons Learned from Other Neurodegenerative Disorders and Cancer. JOURNAL OF PARKINSON'S DISEASE 2020; 10:413-428. [PMID: 32116263 PMCID: PMC7242843 DOI: 10.3233/jpd-191856] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/07/2020] [Indexed: 12/12/2022]
Abstract
Many potential disease modifying therapies have been identified as suitable for clinical evaluation in Parkinson's disease (PD). Currently, the evaluation of compounds in phase II and phase III clinical trials in PD are set up in isolation, a process that is lengthy, costly and lacks efficiency. This review will introduce the concept of a multi-arm, multi-stage (MAMS) trial platform which allows for the assessment of several potential therapies at once, transitioning seamlessly from a phase II safety and efficacy study to a phase III trial by means of an interim analysis. At the interim checkpoint, ineffective arms are dropped and replaced by new treatment arms, thereby allowing for the continuous evaluation of interventions. MAMS trial platforms already exist for prostate, renal and oropharyngeal cancer and are currently being developed for progressive multiple sclerosis (PMS) and motor neuron disease (MND) within the UK. As a MAMS trial will evaluate many potential treatments it is of critical importance that a widely endorsed core protocol is developed which will investigate outcomes and objectives meaningful to patients. This review will discuss the challenges of drug selection, trial design, stratification and outcome measures and will share strategies implemented in the planned MAMS trials for MND and PMS that may be of interest to the PD field.
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Affiliation(s)
- Marie-Louise Zeissler
- Applied Parkinson’s Research Group, University of Plymouth, Faculty of Health: Medicine, Dentistry and Human Sciences, Plymouth, United Kingdom
| | - Vivien Li
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
- Department of Uro-Neurology, National Hospital for Neurology and Neurosurgery, UCL Institute of Neurology, Queen Square, London, United Kingdom
- MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom
| | - Mahesh K.B. Parmar
- MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom
| | - Camille Buchholz Carroll
- Applied Parkinson’s Research Group, University of Plymouth, Faculty of Health: Medicine, Dentistry and Human Sciences, Plymouth, United Kingdom
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Elm JJ, Daeschler M, Bataille L, Schneider R, Amara A, Espay AJ, Afek M, Admati C, Teklehaimanot A, Simuni T. Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson's disease data. NPJ Digit Med 2019; 2:95. [PMID: 31583283 PMCID: PMC6761168 DOI: 10.1038/s41746-019-0169-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 08/20/2019] [Indexed: 11/29/2022] Open
Abstract
Mobile and wearable device-captured data have the potential to inform Parkinson’s disease (PD) care. The objective of the Clinician Input Study was to assess the feasibility and clinical utility of data obtained using a mobile health technology from PD patients. In this observational, exploratory study, PD participants wore a smartwatch and used the Fox Wearable Companion mobile phone app to stream movement data and report symptom severity and medication intake for 6 months. Data were analyzed using the Intel® Pharma Analytics Platform. Clinicians reviewed participants’ data in a dashboard during in-office visits at 2 weeks, 1, 3, and 6 months. Clinicians provided feedback in focus groups leading to dashboard updates. Between June and August 2017, 51 PD patients were recruited at four US sites, and 39 (76%) completed the 6-month study. Patients streamed 83,432 h of movement data from their smartwatches (91% of expected). Reporting of symptoms and medication intake using the app was lower than expected, 44% and 60%, respectively, but did not differ according to baseline characteristics. Clinicians’ feedback resulted in ten updates to the dashboard during the study period. Clinicians reported that medications and patient reported outcomes were generally discernable in the dashboard and complementary to clinical assessments. Movement, symptoms, and medication intake data were feasibly translated from the app into a clinician dashboard but there was substantial attrition rate over 6 months. Further enhancements are needed to ensure long-term patient adherence to portable technologies and optimal digital data transfer to clinicians caring for PD patients.
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Affiliation(s)
- Jordan J Elm
- 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425 USA
| | - Margaret Daeschler
- 2The Michael J. Fox Foundation for Parkinson's Research, New York, NY USA
| | - Lauren Bataille
- 2The Michael J. Fox Foundation for Parkinson's Research, New York, NY USA
| | | | - Amy Amara
- 4University of Alabama at Birmingham, Birmingham, AL USA
| | - Alberto J Espay
- 5University of Cincinnati Medical Center, Cincinnati, OH USA
| | - Michal Afek
- 6Advanced Analytics Department, Intel, Petach Tikva, Israel
| | - Chen Admati
- 6Advanced Analytics Department, Intel, Petach Tikva, Israel
| | - Abeba Teklehaimanot
- 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425 USA
| | - Tanya Simuni
- 7Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Abstract
This review elaborates on multidisciplinary care for persons living with Parkinson disease by using gait and balance impairments as an example of a treatable target that typically necessitates an integrated approach by a range of different and complementary professional disciplines. Using the International Classification of Functioning, Disability, and Health model as a framework, the authors discuss the assessment and multidisciplinary management of reduced functional mobility due to gait and balance impairments. By doing so, they highlight the complex interplay between motor and nonmotor symptoms, and their influence on rehabilitation. They outline how multidisciplinary care for Parkinson disease can be organized.
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20
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Iannone LF, Preda A, Blottière HM, Clarke G, Albani D, Belcastro V, Carotenuto M, Cattaneo A, Citraro R, Ferraris C, Ronchi F, Luongo G, Santocchi E, Guiducci L, Baldelli P, Iannetti P, Pedersen S, Petretto A, Provasi S, Selmer K, Spalice A, Tagliabue A, Verrotti A, Segata N, Zimmermann J, Minetti C, Mainardi P, Giordano C, Sisodiya S, Zara F, Russo E, Striano P. Microbiota-gut brain axis involvement in neuropsychiatric disorders. Expert Rev Neurother 2019; 19:1037-1050. [PMID: 31260640 DOI: 10.1080/14737175.2019.1638763] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: The microbiota-gut brain (MGB) axis is the bidirectional communication between the intestinal microbiota and the brain. An increasing body of preclinical and clinical evidence has revealed that the gut microbial ecosystem can affect neuropsychiatric health. However, there is still a need of further studies to elucidate the complex gene-environment interactions and the role of the MGB axis in neuropsychiatric diseases, with the aim of identifying biomarkers and new therapeutic targets, to allow early diagnosis and improving treatments. Areas covered: To review the role of MGB axis in neuropsychiatric disorders, prediction and prevention of disease through exploitation, integration, and combination of data from existing gut microbiome/microbiota projects and appropriate other International '-Omics' studies. The authors also evaluated the new technological advances to investigate and modulate, through nutritional and other interventions, the gut microbiota. Expert opinion: The clinical studies have documented an association between alterations in gut microbiota composition and/or function, whereas the preclinical studies support a role for the gut microbiota in impacting behaviors which are of relevance to psychiatry and other central nervous system (CNS) disorders. Targeting MGB axis could be an additional approach for treating CNS disorders and all conditions in which alterations of the gut microbiota are involved.
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Affiliation(s)
- Luigi Francesco Iannone
- Science of Health Department, School of Medicine, University of Catanzaro , Catanzaro , Italy
| | - Alberto Preda
- Paediatric Neurology and Muscular Diseases Unit, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, "G. Gaslini" Institute , Genova , Italy
| | - Hervé M Blottière
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, JouyenJosas&MetaGenoPolis, INRA, Université Paris-Saclay , Jouyen Josas , France
| | - Gerard Clarke
- Department of Psychiatry and Neurobehavioural Science, School of Medicine, College of Medicine & Health, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork , Cork , Ireland
| | - Diego Albani
- Department of Neuroscience, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | | | - Marco Carotenuto
- Clinic of Child and Adolescent Neuropsychiatry, Department of Mental Health, Physical and Preventive Medicine, Università degli Studi della Campania 'Luigi Vanvitelli' , Napoli , Italy
| | - Annamaria Cattaneo
- Biological Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli , Brescia , Italy.,Stress, Psychiatry and Immunology Laboratory, Department of Psychological Medicine, Institute of Psychiatry , King's College , London
| | - Rita Citraro
- Science of Health Department, School of Medicine, University of Catanzaro , Catanzaro , Italy
| | - Cinzia Ferraris
- Human Nutrition and Eating Disorder Research Center, Department of Public Health, Experimental and Forensic Medicine University of Pavia , Pavia , Italy
| | - Francesca Ronchi
- Department forBiomedical Research, University of Bern , Bern , Switzerland
| | - Gaia Luongo
- Ordine dei Tecnologi Alimentari Campania e Lazio , Napoli , Italy
| | | | - Letizia Guiducci
- National Research Council, Institute of Clinical Physiology , Pisa , Italy
| | - Pietro Baldelli
- Department of Experimental Medicine, Section of Physiology, University of Genova , Genova , Italy
| | - Paola Iannetti
- Department of Pediatrics`, "Sapienza" University of Rome , Rome , Italy
| | - Sigrid Pedersen
- Department of Refractory Epilepsy, Division of Clinical Neuroscience, Oslo University Hospital , Oslo , Norway
| | - Andrea Petretto
- Laboratory of Mass Spectrometry - Core Facilities, Istituto Giannina Gaslini , Genova , Italy
| | - Stefania Provasi
- Biological Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli , Brescia , Italy
| | - Kaja Selmer
- Department of Research and Development, Division of Clinical Neuroscience, Oslo University Hospital, Osla, Norway and Department of Refractory Epilepsy, Division of Clinical Neuroscience, Oslo University Hospital , Osla , Norway
| | - Alberto Spalice
- Department of Experimental Medicine, Section of Physiology, University of Genova , Genova , Italy
| | - Anna Tagliabue
- Stress, Psychiatry and Immunology Laboratory, Department of Psychological Medicine, Institute of Psychiatry , King's College , London
| | - Alberto Verrotti
- Department of Pediatrics, University of L'Aquila , L'Aquila , Italy
| | - Nicola Segata
- Centre for Integrative Biology, University of Trento , Trento , Italy
| | - Jakob Zimmermann
- Human Nutrition and Eating Disorder Research Center, Department of Public Health, Experimental and Forensic Medicine University of Pavia , Pavia , Italy
| | - Carlo Minetti
- Paediatric Neurology and Muscular Diseases Unit, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, "G. Gaslini" Institute , Genova , Italy
| | | | - Carmen Giordano
- Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano , Milano , Italy
| | - Sanjay Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology , Queen Square, London , UK
| | - Federico Zara
- Laboratory of Neurogenetics, Istituto Giannina Gaslini , Genova , Italy
| | - Emilio Russo
- Science of Health Department, School of Medicine, University of Catanzaro , Catanzaro , Italy
| | - Pasquale Striano
- Paediatric Neurology and Muscular Diseases Unit, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, "G. Gaslini" Institute , Genova , Italy
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Kononova A, Li L, Kamp K, Bowen M, Rikard RV, Cotten S, Peng W. The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change. JMIR Mhealth Uhealth 2019; 7:e9832. [PMID: 30950807 PMCID: PMC6473213 DOI: 10.2196/mhealth.9832] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 07/09/2018] [Accepted: 11/13/2018] [Indexed: 01/17/2023] Open
Abstract
Background Wearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse. Objective The aim of this study was to investigate older adults’ perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage. Methods Data for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type). Results The results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior. Conclusions The results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology’s limitations and gaining awareness of one’s physical activity without changing the physical activity level itself.
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Affiliation(s)
- Anastasia Kononova
- Department of Advertising and Public Relations, Michigan State University, East Lansing, MI, United States
| | - Lin Li
- Department of Media and Information, Michigan State University, East Lansing, MI, United States
| | - Kendra Kamp
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States
| | - Marie Bowen
- Center for Innovation and Research, Michigan State University, East Lansing, MI, United States
| | - R V Rikard
- Department of Media and Information, Michigan State University, East Lansing, MI, United States
| | - Shelia Cotten
- Department of Media and Information, Michigan State University, East Lansing, MI, United States
| | - Wei Peng
- Department of Media and Information, Michigan State University, East Lansing, MI, United States
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22
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Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H. Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study. JMIR Res Protoc 2019; 8:e12808. [PMID: 30916665 PMCID: PMC6456828 DOI: 10.2196/12808] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/20/2019] [Accepted: 02/23/2019] [Indexed: 11/15/2022] Open
Abstract
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. Trial Registration ClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558 International Registered Report Identifier (IRRID) DERR1-10.2196/12808
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Affiliation(s)
- Milla Jauhiainen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Juha Puustinen
- Unit of Neurology, Satakunta Central Hospital, Satakunta Hospital District, Pori, Finland.,Clinical Pharmacy Group, Division of Pharmacology and Pharmacotherapy, University of Helsinki, Helsinki, Finland.,Hospital Services, Social Security Center of Pori, Pori, Finland
| | - Saeed Mehrang
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jari Ruokolainen
- Faculty of Management and Business, Tampere University, Tampere, Finland
| | - Anu Holm
- Faculty of Medicine, University of Turku, Turku, Finland.,Department of Clinical Neurophysiology, Satakunta Central Hospital, Satakunta Hospital District, Pori, Finland.,Faculty of Health and Welfare, Satakunta University of Applied Sciences, Pori, Finland
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Hannu Nieminen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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23
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Cohen S, Waks Z, Elm JJ, Gordon MF, Grachev ID, Navon-Perry L, Fine S, Grossman I, Papapetropoulos S, Savola JM. Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease. BMC Med Inform Decis Mak 2018; 18:138. [PMID: 30572891 PMCID: PMC6302308 DOI: 10.1186/s12911-018-0714-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 11/23/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). METHODS Patient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home. RESULTS While compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home. CONCLUSIONS Our findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies. TRIAL REGISTRATION Trials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .
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Affiliation(s)
- Shani Cohen
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel
| | - Zeev Waks
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel.
| | - Jordan J Elm
- Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Suite 303, PO Box 250835, Charleston, SC, 29425, USA
| | - Mark Forrest Gordon
- Teva Branded Pharmaceutical Products R&D, Inc, 41 Moores Rd., Frazer, Petah Tikva, PA, 19355, USA
| | - Igor D Grachev
- Guide Pharmaceutical Consulting, LLC, Millstone Township, NJ, 08535, USA
| | - Leehee Navon-Perry
- Teva Pharmaceutical Industries Ltd, 12 Hatrufa St, 4250483, Netanya, Israel
| | - Shai Fine
- Data Science Institute, Interdisciplinary Center, 1 Kanfei Nesharim St, 4610101, Herzliya, Israel
| | - Iris Grossman
- CAMP4 Therapeutics, One Kendall Square, Bldg 1400 West, 3rd Floor, Cambridge, MA, 02139, USA
| | | | - Juha-Matti Savola
- Teva Pharmaceuticals International GmbH, Elisabethenstrasse 15, 4051, Basel, Switzerland
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24
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Antonini A, Gentile G, Giglio M, Marcante A, Gage H, Touray MML, Fotiadis DI, Gatsios D, Konitsiotis S, Timotijevic L, Egan B, Hodgkins C, Biundo R, Pellicano C. Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson's disease (PD_Manager): study protocol for a pilot randomised controlled trial. Trials 2018; 19:492. [PMID: 30217235 PMCID: PMC6138904 DOI: 10.1186/s13063-018-2767-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 06/25/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Parkinson's disease is a degenerative neurological condition causing multiple motor and non-motor symptoms that have a serious adverse effect on quality of life. Management is problematic due to the variable and fluctuating nature of symptoms, often hourly and daily. The PD_Manager mHealth platform aims to provide a continuous feed of data on symptoms to improve clinical understanding of the status of any individual patient and inform care planning. The objectives of this trial are to (1) assess patient (and family carer) perspectives of PD_Manager regarding comfort, acceptability and ease of use; (2) assess clinician views about the utility of the data generated by PD_Manager for clinical decision making and the acceptability of the system in clinical practice. METHODS/DESIGN This trial is an unblinded, parallel, two-group, randomised controlled pilot study. A total of 200 persons with Parkinson's disease (Hoehn and Yahr stage 3, experiencing motor fluctuations at least 2 h per day), with primary family carers, in three countries (110 Rome, 50 Venice, Italy; 20 each in Ioannina, Greece and Surrey, England) will be recruited. Following informed consent, baseline information will be gathered, including the following: age, gender, education, attitudes to technology (patient and carer); time since Parkinson's diagnosis, symptom status and comorbidities (patient only). Randomisation will assign participants (1:1 in each country), to PD_Manager vs control, stratifying by age (1 ≤ 70 : 1 > 70) and gender (60% M: 40% F). The PD_Manager system captures continuous data on motor symptoms, sleep, activity, speech quality and emotional state using wearable devices (wristband, insoles) and a smartphone (with apps) for storing and transmitting the information. Control group participants will be asked to keep a symptom diary covering the same elements as PD_Manager records. After a minimum of two weeks, each participant will attend a consultation with a specialist doctor for review of the data gathered (by either means), and changes to management will be initiated as indicated. Patients, carers and clinicians will be asked for feedback on the acceptability and utility of the data collection methods. The PD_Manager intervention, compared to a symptom diary, will be evaluated in a cost-consequences framework. DISCUSSION Information gathered will inform further development of the PD_Manager system and a larger effectiveness trial. TRIAL REGISTRATION ISRCTN Registry, ISRCTN17396879 . Registered on 15 March 2017.
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Affiliation(s)
- Angelo Antonini
- Department of Neuroscience, University of Padua, Padua, Italy.,IRCCS San Camillo Hospital, Venice, Italy
| | | | | | - Andrea Marcante
- Department of Neuroscience, University of Padua, Padua, Italy.,IRCCS San Camillo Hospital, Venice, Italy
| | - Heather Gage
- Surrey Health Economics Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Morro M L Touray
- Surrey Health Economics Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
| | - Dimitrios I Fotiadis
- Department of Materials Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Dimitris Gatsios
- Department of Materials Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | - Spyridon Konitsiotis
- Department of Neurology, Medical School, University of Ioannina, Ioannina, Greece
| | - Lada Timotijevic
- Department of Psychology, University of Surrey, Guildford, England
| | - Bernadette Egan
- Department of Psychology, University of Surrey, Guildford, England
| | - Charo Hodgkins
- Department of Psychology, University of Surrey, Guildford, England
| | | | - Clelia Pellicano
- Fondazione Santa Lucia IRCCS, Via Ardeatina 306, 00179, Rome, Italy.,Department of Neuriscience, Mental Health and Sensory Organs, Sapienza University, Via di Grottarossa 1035, 00189, Rome, Italy
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25
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Prashanth R, Dutta Roy S. Early detection of Parkinson's disease through patient questionnaire and predictive modelling. Int J Med Inform 2018; 119:75-87. [PMID: 30342689 DOI: 10.1016/j.ijmedinf.2018.09.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Revised: 07/01/2018] [Accepted: 09/06/2018] [Indexed: 01/10/2023]
Abstract
Early detection of Parkinson's disease (PD) is important which can enable early initiation of therapeutic interventions and management strategies. However, methods for early detection still remain an unmet clinical need in PD. In this study, we use the Patient Questionnaire (PQ) portion from the widely used Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to develop prediction models that can classify early PD from healthy normal using machine learning techniques that are becoming popular in biomedicine: logistic regression, random forests, boosted trees and support vector machine (SVM). We carried out both subject-wise and record-wise validation for evaluating the machine learning techniques. We observe that these techniques perform with high accuracy and high area under the ROC curve (both >95%) in classifying early PD from healthy normal. The logistic model demonstrated statistically significant fit to the data indicating its usefulness as a predictive model. It is inferred that these prediction models have the potential to aid clinicians in the diagnostic process by joining the items of a questionnaire through machine learning.
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Affiliation(s)
- R Prashanth
- Department of Electrical Engineering, Indian Institute of Technology Delhi, India.
| | - Sumantra Dutta Roy
- Department of Electrical Engineering, Indian Institute of Technology Delhi, India
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26
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Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt‐Eriksen J, Cheng W, Fernandez‐Garcia I, Siebourg‐Polster J, Jin L, Soto J, Verselis L, Boess F, Koller M, Grundman M, Monsch AU, Postuma RB, Ghosh A, Kremer T, Czech C, Gossens C, Lindemann M. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord 2018; 33:1287-1297. [PMID: 29701258 PMCID: PMC6175318 DOI: 10.1002/mds.27376] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. OBJECTIVES The present study assessed the feasibility, reliability, and validity of smartphone-based digital biomarkers of PD in a clinical trial setting. METHODS During a 6-month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45-day study in 35 age-matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger-tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit-to-stand transitions by gyroscopic and accelerometer data. RESULTS Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor-based features showed moderate-to-excellent test-retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS items at the site visit. CONCLUSIONS These findings demonstrate the feasibility of smartphone-based digital biomarkers and indicate that smartphone-sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Kirsten I. Taylor
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Timothy Kilchenmann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Alf Scotland
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Jens Schjodt‐Eriksen
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Wei‐Yi Cheng
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Ignacio Fernandez‐Garcia
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Juliane Siebourg‐Polster
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Liping Jin
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Jay Soto
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
| | - Lynne Verselis
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Frank Boess
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Martin Koller
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
| | - Michael Grundman
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
- Global R&D Partners, LLCSan DiegoCaliforniaUSA
| | - Andreas U. Monsch
- Felix Platter Hospital, University Center for Medicine of Aging, Memory Clinic, Basel, Switzerland; University of Basel, Faculty of PsychologyBaselSwitzerland
| | - Ronald B. Postuma
- Department of NeurologyMcGill University, Montreal General HospitalMontrealQuebecCanada
| | - Anirvan Ghosh
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Thomas Kremer
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Christian Czech
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
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27
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Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemed J E Health 2018; 25:167-183. [PMID: 29969384 DOI: 10.1089/tmj.2018.0035] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy. OBJECTIVES This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL. DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between January 2008 and September 2017. STUDY ELIGIBILITY CRITERIA Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found. RESULTS Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture. CONCLUSION This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.
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Affiliation(s)
- Erika Rovini
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
| | - Carlo Maremmani
- 2 U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa (MS), Italy
| | - Filippo Cavallo
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
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28
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Izmailova ES, Wagner JA, Perakslis ED. Wearable Devices in Clinical Trials: Hype and Hypothesis. Clin Pharmacol Ther 2018; 104:42-52. [PMID: 29205294 PMCID: PMC6032822 DOI: 10.1002/cpt.966] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/14/2017] [Accepted: 11/29/2017] [Indexed: 12/18/2022]
Abstract
The development of innovative wearable technologies has raised great interest in new means of data collection in healthcare and biopharmaceutical research and development. Multiple applications for wearables have been identified in a number of therapeutic areas; however, researchers face many challenges in the clinic, including scientific methodology as well as regulatory, legal, and operational hurdles. To facilitate further evaluation and adoption of these technologies, we highlight methodological and logistical considerations for implementation in clinical trials, including key elements of analytical and clinical validation in the specific context of use (COU). Additionally, we provide an assessment of the maturity of the field and successful examples of recent clinical experiments.
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Affiliation(s)
| | - John A. Wagner
- Takeda Pharmaceuticals International Co.CambridgeMassachusettsUSA
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29
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López-Blanco R, Velasco MA, Méndez-Guerrero A, Romero JP, Del Castillo MD, Serrano JI, Benito-León J, Bermejo-Pareja F, Rocon E. Essential tremor quantification based on the combined use of a smartphone and a smartwatch: The NetMD study. J Neurosci Methods 2018; 303:95-102. [PMID: 29481820 DOI: 10.1016/j.jneumeth.2018.02.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 02/13/2018] [Accepted: 02/20/2018] [Indexed: 12/01/2022]
Abstract
BACKGROUND The use of wearable technology is an emerging field of research in movement disorders. This paper introduces a clinical study to evaluate the feasibility, clinical correlation and reliability of using a system based in smartwatches to quantify tremor in essential tremor (ET) patients and check its acceptance as clinical monitoring tool. NEW METHOD The system is based on a commercial smartwatch and an Android smartphone. An investigational Android application controls the process of recording raw data from the smartwatch three-dimensional gyroscopes. Thirty-four ET patients were consecutively enrolled in the experiments and assessed along one year. Arm tremor was videofilmed and scored using the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS). Tremor intensity was quantified with the root mean square of angular velocity measured in the patients' wrists. RESULTS Eighty-two assessments with smartwatches were performed. Spearman's correlation coefficients (ρ) between clinical tremor (FTM-TRS) scores and smartwatch measures for tremor intensity were 0.590 at rest; ρ = 0.738 in steady posture; ρ = 0.189 in finger-to-nose maneuvers; and ρ = 0.652 in pouring water task. Smartwatch reliability was checked by intraclass realiability coefficients: 0.85, 0.95, 0.91, 0.95 respectively. Most of patients showed good acceptance of the system. COMPARISON WITH EXISTING METHOD(S) This commodity hardware contributes to quantify tremor objectively in a consulting-room by customized Android smart devices as clinical monitoring tool. CONCLUSIONS The NetMD system for tremor analysis is feasible, well-correlated with clinical scores, reliable and well-accepted by patients to tremor follow-up. Therefore, it could be an option to objectively quantify tremor in ET patients during their regular follow-up.
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Affiliation(s)
- Roberto López-Blanco
- Healthcare Research Institute (i+12), Hospital Universitario 12 de Octubre, Madrid, Spain; Neurology Department, Hospital Universitario Príncipe de Asturias, Alcalá de Henares Madrid, Spain.
| | | | | | - Juan Pablo Romero
- Faculty of Biosanitary Sciences, Francisco de Vitoria University, Pozuelo de Alarcón, Madrid, Spain; Brain Damage Service, Hospital Beata Maria Ana, Madrid, Spain
| | | | | | - Julián Benito-León
- Healthcare Research Institute (i+12), Hospital Universitario 12 de Octubre, Madrid, Spain; Neurology Department, Hospital Universitario 12 de Octubre, Madrid, Spain; Center of Biomedical Network Research on Neurodegenerative Dseases (CIBERNED), Spain; Medicine Department, Faculty of Medicine, Universidad Complutense Madrid (UCM), Spain
| | - Félix Bermejo-Pareja
- Medicine Department, Faculty of Medicine, Universidad Complutense Madrid (UCM), Spain; Clinical Research Unit, University Hospital, "12 de Octubre", Madrid, Spain
| | - Eduardo Rocon
- Centro de Automática y Robótica (CAR), CSIC-UPM, Madrid, Spain
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Aşuroğlu T, Açıcı K, Berke Erdaş Ç, Kılınç Toprak M, Erdem H, Oğul H. Parkinson's disease monitoring from gait analysis via foot-worn sensors. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.06.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Historical perspective: The pros and cons of conventional outcome measures in Parkinson's disease. Parkinsonism Relat Disord 2018; 46 Suppl 1:S47-S52. [DOI: 10.1016/j.parkreldis.2017.07.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 07/29/2017] [Indexed: 11/18/2022]
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Klucken J, Krüger R, Schmidt P, Bloem BR. Management of Parkinson's Disease 20 Years from Now: Towards Digital Health Pathways. JOURNAL OF PARKINSON'S DISEASE 2018; 8:S85-S94. [PMID: 30584171 PMCID: PMC6311358 DOI: 10.3233/jpd-181519] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/29/2018] [Indexed: 01/19/2023]
Abstract
Current best medical treatment for patients with Parkinson's disease (PD) involves a medical professional who applies state-of-the-art knowledge of diagnostics and treatment- as derived from cohort studies and clinical trials- to the healthcare process of individual patients. Thus, the much-needed personalization of medicine depends on the abilities, experience and intuition of medical professionals to adjust group-based knowledge to individual decision making. Within 20 years from now, such personal clinical decisions will be largely supported by digital means, also defining a new ecosystem of healthcare often referred to as "digital medicine". We expect that the next phase of digitalization will include new "digital health pathways": data-driven personalized decision support that is based on a combination of multimodal data sources, including evidence-based medical knowledge (e.g., clinical guidelines), personal disease profiles (including genetic determinants of disease progression and treatment response), insights into individual disease trajectories (thereby defining subgroups of patients) and individual patients' needs. Here, we illustrate the potential of this development by sketching the contours of a digitally supported care pathway for gait disability and falls. Such digital health pathways will support the introduction of personalized medicine for PD patients, allowing patients to benefit optimally from individually tailored treatments. This should result in a better quality of life for patients and lower costs for society.
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Affiliation(s)
- Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Germany
- Research Group Digital Health Pathways, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Luxembourg
| | - Peter Schmidt
- Brody School of Medicine, East Carolina University, Greenville, NC, USA
| | - Bastiaan R. Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
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Silva de Lima AL, Hahn T, Evers LJW, de Vries NM, Cohen E, Afek M, Bataille L, Daeschler M, Claes K, Boroojerdi B, Terricabras D, Little MA, Baldus H, Bloem BR, Faber MJ. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS One 2017; 12:e0189161. [PMID: 29261709 PMCID: PMC5738046 DOI: 10.1371/journal.pone.0189161] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 11/20/2017] [Indexed: 02/02/2023] Open
Abstract
Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.
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Affiliation(s)
- Ana Lígia Silva de Lima
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- CAPES Foundation, Ministry of Education of Brazil, Brasília/DF, Brazil
| | - Tim Hahn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luc J. W. Evers
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nienke M. de Vries
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eli Cohen
- Intel, Advanced Analytics, Tel Aviv, Israel
| | | | - Lauren Bataille
- The Michael J Fox Foundation for Parkinson’s Research, New York, United States of America
| | - Margaret Daeschler
- The Michael J Fox Foundation for Parkinson’s Research, New York, United States of America
| | | | | | | | - Max A. Little
- Aston University, Birmingham, United Kingdom
- Media Lab, Massachusetts Institute of Technology, Cambridge, United States of America
| | - Heribert Baldus
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Bastiaan R. Bloem
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marjan J. Faber
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, the Netherlands
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Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dunton G, Hoppin JA, Koutrakis P, Jerrett M. Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations. Annu Rev Public Health 2017; 38:215-239. [PMID: 28384083 PMCID: PMC7161939 DOI: 10.1146/annurev-publhealth-082516-012802] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.
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Affiliation(s)
- Michelle C Turner
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; , .,Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain.,McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario K1G 3Z7, Canada
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health (ISGlobal), Barcelona 08003, Spain; , .,Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Kim Anderson
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon 97331;
| | - David Balshaw
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709; ,
| | - Yuxia Cui
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709; ,
| | - Genevieve Dunton
- Department of Preventive Medicine and Department of Psychology, University of Southern California, Los Angeles, California 90033;
| | - Jane A Hoppin
- Center for Human Health and the Environment, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695;
| | - Petros Koutrakis
- Department of Environmental Health, Harvard University, Boston, Massachusetts 02115;
| | - Michael Jerrett
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California 94704; .,Department of Environmental Health Science, Fielding School of Public Health, University of California, Los Angeles, California 90095-1772;
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Kovalchick C, Sirkar R, Regele OB, Kourtis LC, Schiller M, Wolpert H, Alden RG, Jones GB, Wright JM. Can composite digital monitoring biomarkers come of age? A framework for utilization. J Clin Transl Sci 2017; 1:373-380. [PMID: 29707260 PMCID: PMC5916505 DOI: 10.1017/cts.2018.4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 01/16/2018] [Accepted: 01/19/2018] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard. METHODS A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare. RESULTS The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated. CONCLUSIONS The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.
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Affiliation(s)
| | - Rhea Sirkar
- Eli Lilly Innovation Center, Cambridge, MA, USA
| | | | | | | | | | | | - Graham B. Jones
- Clinical & Translational Science Institute, Tufts University Medical Center, Boston, MA, USA
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Radder DL, Sturkenboom IH, van Nimwegen M, Keus SH, Bloem BR, de Vries NM. Physical therapy and occupational therapy in Parkinson's disease. Int J Neurosci 2017; 127:930-943. [DOI: 10.1080/00207454.2016.1275617] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Danique L.M. Radder
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ingrid H. Sturkenboom
- Department of Rehabilitation-Occupational Therapy, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marlies van Nimwegen
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Samyra H. Keus
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bastiaan R. Bloem
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nienke M. de Vries
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
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