the standard of care. In the management of PD, these targets are starting to be defined by expert consensus   though further studies are needed to demonstrate that treating PWP to particular targets will impact their clinical outcomes.
Given the exploratory nature of this study, some limitations deserve mention. A key limitation of the study was that patients were not followed through medication optimization; therefore, the two visits captured in this study offered a brief snapshot in the care continuum of these patients. As such, while the clinical outcomes observed here are encouraging, overall clinical outcomes achieved when COM is used to optimize medical management of PD patients could not be fully assessed. Additionally, this study did not have a control group; therefore, we cannot directly attribute results seen here to the PKG System. However, the study aimed to isolate the impact of the new information provided by the PKG System by reviewing the PKG after completion of routine clinical care activities and at that time the study MDS determined whether the new information would change the established clinical plan. While clinical assessments completed in this project are the clinical acumen of one MDS and patients were not always able to be evaluated in the ON state, the project reflects real-world clinical practice of a patient population that is typically encountered in a tertiary MDS clinic.
Based on the data collected in this study, we found the PKG System to be a valuable tool in augmenting clinical management planning and decisions, and when utilized along with a clinical assessment. The device was well received by both physicians and patients, scoring high in survey results as a tool to assess impact of therapy and indicating the device had an overall positive impact on patient care and outcomes. Further research is needed to continue the important work of creating evidence-based guidance for the role of COM in the clinical management of PWP.
The authors wish to thank Karen Krygier for her assistance.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.
International Parkinson and Movement Disorder Society Parkinson’s Disease & Parkinsonism.
|||Marras, C., Beck, J.C., Bower, J.H., Roberts, E., Ritz, B. and Ross, G.W. (2018) Prevalence of Parkinson’s Disease across North America. NPJ Parkinson’s Disease, 4, Article No. 21. https://doi.org/10.1038/s41531-018-0058-0|
|||Hermanowicz, N. and Edwards, K. (2015) Parkinson’s Disease Psychosis: Symptoms, Management, and Economic Burden. American Journal of Managed Care, 21, s199-s206.|
Mantri, S., Fullard, M., Beck, J. and Willis, A. (2019) State-Level Prevalence, Health Service Use, and Spending Vary Widely among Medicare Beneficiaries with Parkinson Disease. NPJ Parkinson’s Disease, 5, Article No. 1.
|||Michael J. Fox Foundation (2019) Parkinson’s Disease Economic Burden on Patients, Families and the Federal Government Is $52 Billion, Doubling Previous Estimates. https://www.michaeljfox.org/publication/parkinsons-disease-economic-burden-patients-families-and-federal-government-52-billion|
|||U.S. Food and Drug Administration (FDA) (2016) The Voice of the Patient, Parkinson’s Disease. https://www.fda.gov/media/124392/download|
|||Bergquist, F. and Horne, M. (2014) Can Objective Measurements Improve Treatment Outcomes in Parkinson’s Disease? European Neurological Review, 9, 27-30. https://doi.org/10.17925/ENR.2014.09.01.27|
Espay, A., Bonato, P., Nahab, F.B., et al. (2016) Technology in Parkinson’s Disease: Challenges and Opportunities. Movement Disorders, 31, 1272-1282.
|||Sánchez-Ferro, A., Elshehabi, M., Godinho, C., et al. (2015) New Methods for the Assessment of Parkinson’s Disease (2005 to 2015): A Systematic Review. Movement Disorders, 31, 1283-1292. https://doi.org/10.1002/mds.26723|
|||Ossig, C., Antonini, A., Buhmann, C., et al. (2015) Wearable Sensor-Based Objective Assessment of Motor Symptoms in Parkinson’s Disease. Journal of Neural Transmission, 123, 57-64. https://doi.org/10.1007/s00702-015-1439-8|
|||Ossig, C., Gandor, F., Bosredon, C., Fauser, M., Reichmann, H., Horne, M.K., et al. (2016) Correlation of Objective Measurement of Motor States Using a Kinetograph and Patient Diaries in Advanced Parkinson’s Disease. PLoS ONE, 11, e0161559. https://doi.org/10.1371/journal.pone.0161559|
|||Horne, M., Kotschet, K. and McGregor, S. (2016) The Clinical Validation of Objective Measurement of Movement in Parkinson’s Disease. CNS, 1, 15-22.|
Klingelhoefer, L., Rizos, A., Sauerbier, A., et al. (2016) Night-Time Sleep in Parkinson’s Disease—The Potential Use of Parkinson’s KinetiGraph: A Prospective Comparative Study. European Journal of Neurology, 23, 1275-1288.
|||Griffiths, R.I., Kotschet, K., Arfon, S., et al. (2012) Automated Assessment of Bradykinesia and Dyskinesia in Parkinson’s Disease. Journal of Parkinson’s Disease, 2, 47-55.|
Kotschet, K., Johnson, W., McGregor, S., Kettlewell, J., Kyoong, A., O’Driscoll, D.M., et al. (2014) Daytime Sleep in Parkinson’s Disease Measured by Episodes of Immobility. Parkinsonism & Related Disorders, 20, 578-583.
Braybrook, M., O’Connor, S., Churchward, P., et al. (2016) An Ambulatory Tremor Score for Parkinson’s Disease. Journal of Parkinson’s Disease, 6, 723-731.
Horne, M.K., McGregor, S. and Bergquist, F. (2015) An Objective Fluctuation Score for Parkinson’s Disease. PLoS ONE, 10, e0124522.
Evans, A.H., Kettlewell, J., McGregor, S., Kotschet, K., Griffiths, R.I. and Horne, M. (2014) A Conditioned Response as a Measure of Impulsive-Compulsive Behaviours in Parkinson’s Disease. PLoS ONE, 9, e89319.
|||Price, J., Martin, H., Ebenezer, L., Cotton, L., Shuri, J., Martin, A. and Sauerbier, A. (2016) A Service Evaluation by Parkinson’s Disease Nurse Specialists, of Parkinson’s KinetiGraph (PKG) Movement Recording System Use in Routine Clinical Care of Patients with Parkinson’s Disease. 4th World Parkinson’s Congress, Portland, 20-23 September 2016. http://content.iospress.com/articles/journal-of-parkinsons-disease/jpd169900|
Spengler, D., Velez-Aldahondo, V.A., Singer, C. and Luca, C. (2016) Initial Deep Brain Stimulation Programming Optimization Using the Personal Kineti Graph (PKG) Movement Recording System. AAN Annual Meeting Abstract.
Farzanehfar, P., Woodrow, H., Braybrook, M., McGregor, S., Evans, A., Nicklason, F., et al. (2018) Objective Measurement in Routine Care of People with Parkinson’s Disease Improves Outcomes. NPJ Journal of Parkinson’s Disease, 4, Article No. 10.
Berghuis, E., Van Harten, B., Van Kesteren-Biegstraaten, M., Rutgers, W. and Verwey, N. (2018) Parkinson Kinetic Graph: Are Motor Fluctuations in Parkinson Disease Related with Disease Duration? Advances in Parkinson’s Disease, 7, 1-6.
Santiago, A., Langston, J.W., Gandhy, R., Dhall, R., Brillman, S., Rees, L. and Barlow, C. (2019) Qualitative Evaluation of the Personal KinetiGraphTM Movement Recording System in a Parkinson’s Clinic. Journal of Parkinson’s Disease, 9, 207-219.
Khodakarami, H., Farzanehfar, P. and Horne, M. (2019) The Use of Data from the Parkinson’s KinetiGraph to Identify Potential Candidates for Device Assisted Therapies. Sensors (Basel, Switzerland), 19, 2241.
Hughes, A.J., Daniel, S.E., Kilford, L. and Lees, A.J. (1992) Accuracy of Clinical Diagnosis of Idiopathic Parkinson’s Disease. A Clinic-Pathological Study of 100 Cases. Journal of Neurology, Neurosurgery, and Psychiatry, 55, 181-184.
Goetz, C.G., Tilley, B.C., Shaftman, S.R., et al. (2008) Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results. Movement Disorders, 23, 2129-2170.
Martínez-Martín, P., Rodríguez-Blázquez, C., Alvarez, M., et al. (2015) Parkinson’s Disease Severity Levels and MDS-Unified Parkinson’s Disease Rating Scale. Parkinsonism & Related Disorders, 21, 50-54.
|||Busner, J. and Targum, S. (2007) The Clinical Global Impressions Scale Applying a Research Tool in Clinical Practice. Psychiatry, 4, 28-37.|
|||Harris, P.A., Taylor, R., Thielke, R., et al. (2009) Research Electronic Data Capture (REDCap)—A Metadata-Driven Methodology and Workflow Process for Providing Translational Research Informatics Support. Journal of Biomedical Informatics, 42, 377-381. https://doi.org/10.1016/j.jbi.2008.08.010|
Odin, P., Chaudhuri, K.R., Volkmann, J., et al. (2018) Viewpoint and Practical Recommendations from a Movement Disorder Specialist Panel on Objective Measurement in the Clinical Management of Parkinson’s Disease. NP Journal of Parkinson’s Disease, 4, 14.
|||Pahwa, R., Isaacson, S.H., Torres-Russotto, D., Nahab, F.B., Lynch, P.M. and Kotschet, K.E. (2018) Role of the Personal KinetiGraph in the Routine Clinical Assessment of Parkinson’s Disease: Recommendations from an Expert Panel. Expert Review of Neurotherapeutics, 18, 669-680. https://doi.org/10.1080/14737175.2018.1503948|
Goetz, C., Poewe, W., Rascol, O., Sampaio, C., Stebbins, G.T., et al. (2004) Movement Disorder Society Task Force Report on the Hoehn and Yahr Staging Scale: Status and Recommendations. Movement Disorders, 19, 1020-1028.
|||Tomlinson, C., Stowe, R., Patel, S., et al. (2010) Systematic Review of Levodopa Dose Equivalency Reporting in Parkinson’s Disease. Movement Disorders, 25, 2649-2685. https://doi.org/10.1002/mds.23429|
|||Shulman, L.M., Gruber-Baldini, A.L., Anderson, K.E., et al. (2010) The Clinically Important Difference on the Unified Parkinson’s Disease Rating Scale. Archives of Neurology, 67, 64-70. https://doi.org/10.1001/archneurol.2009.295|
Makkos, A., Kovács, M., Pintér, D., Janszky, J. and Kovacs, N. (2019) Minimal Clinically Important Difference for the Historic Parts of the Unified Dyskinesia Rating Scale. Parkinsonism & Related Disorders, 58, 79-82.
Atluri, V., Rao, S., Rajah, T., et al. (2015) Unlocking Digital Health: Opportunities for the Mobile Value Chain.
|||Al-Eidan, R., Al-Khalifa, H. and Al-Salman, A. (2018) A Review of Wrist-Worn Wearable: Sensors, Models, and Challenges. Journal of Sensors, 2018, Article ID: 5853917. https://doi.org/10.1155/2018/5853917|
|||Cho, J. (2019) Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices. Journal of Healthcare Engineering, 2019, Article ID: 3924508. https://doi.org/10.1155/2019/3924508|
Wan, W., Skandari, M.R., Minc, A., et al. (2018) Cost-Effectiveness of Continuous Glucose Monitoring for Adults with Type 1 Diabetes Compared with Self-Monitoring of Blood Glucose: The DIAMOND Randomized Trial. Diabetes Care, 41, 1227-1234.
|||Ong, M.K., et al. (2016) Effectiveness of Remote Patient Monitoring after Discharge of Hospitalized Patients with Heart Failure: The Better Effectiveness after Transition Heart Failure (BEAT-HF) Randomized Clinical Trial. JAMA Internal Medicine, 176, 310-318. https://doi.org/10.1001/jamainternmed.2015.7712|
Lee, Y.H., et al. (2013) Impact of Home-Based Exercise Training with Wireless Monitoring on Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. Journal of Korean Medical Science, 28, 564-568.
|||Ryan, D., et al. (2012) Clinical and Cost Effectiveness of Mobile Phone Supported Self-Monitoring of Asthma: Multi-Center Randomized Controlled Trial. BMJ, 344, e1756. https://doi.org/10.1136/bmj.e1756|
Monje, M., Foffani, G., Obeso, J. and Sanchez-Ferro, A. (2019) New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson’s Disease. Annual Review of Biomedical Engineering, 21, 111-143.
Rovini, E., Maremmani, C. and Cavallo, F. (2018) Automated Systems Based on Wearable Sensors for the Management of Parkinson’s Disease at Home: A Systematic Review. Telemedicine and e-Health, 25, 167-183.
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