INTRODUCTION: Parkinson’s disease (PD) is a
disorder characterized by complex motor and non-motor symptoms that can be
difficult for patients to accurately communicate. Wearable technologies portend
improvements in assessment and monitoring of these symptoms, with their
clinical utility currently being evaluated in routine clinical care. OBJECTIVE:
To evaluate the clinical utility of the Personal
KinetiGraph? (PKG?) Movement Recording System in the routine
clinical care of persons with PD (PWP). METHODS: Clinically stable,
non-demented PWP presented for two routine clinic visits that included:
medication review, symptom review, neurological examination including the
Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)
III/IV, and completion of a clinical management plan by a movement disorder
specialist prior to review of the PKG report. After reviewing the PKG report,
the clinician completed a modified clinical management plan taking into
consideration the findings of the PKG. This was repeated at a second visit to evaluate
various outcome measures following PKG-enhanced management. RESULTS: The PKG
improved the assessment of PD symptoms and the response to treatment, while
increasing patient activity levels and compliance. Clinical management plans
enhanced by PKG led to different recommendations in 29.4% of cases compared
with standard of care due to higher rates of bradykinesia, dyskinesia, tremor,
and fluctuations identified by PKG. Using the PKG in the clinical management
plan led to a change in medications in 75% (21/28) of patients and both a
statistically significant difference and a clinically meaningful reduction in
MDS-UPDRS III score of 4.8 (p = 0.028). Additionally, positive changes in both
the clinician (17/28; 61%) and patient-reported (13/24; 54%) Global Impression
of Improvement were reported. CONCLUSION: The PKG is a valuable tool in
augmenting clinical management when utilized along with a clinical assessment.
References
[1]
International Parkinson and Movement Disorder Society Parkinson’s Disease & Parkinsonism.
http://www.movementdisorders.org/MDS/About/Movement-Disorder- Overviews/Parkinsons-Disease--Parkinsonism.htm
[2]
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
[3]
Hermanowicz, N. and Edwards, K. (2015) Parkinson’s Disease Psychosis: Symptoms, Management, and Economic Burden. American Journal of Managed Care, 21, s199-s206.
[4]
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.
https://doi.org/10.1038/s41531-019-0074-8
[5]
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
[6]
U.S. Food and Drug Administration (FDA) (2016) The Voice of the Patient, Parkinson’s Disease. https://www.fda.gov/media/124392/download
[7]
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
[8]
Espay, A., Bonato, P., Nahab, F.B., et al. (2016) Technology in Parkinson’s Disease: Challenges and Opportunities. Movement Disorders, 31, 1272-1282.
https://doi.org/10.1002/mds.26642
[9]
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
[10]
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
[11]
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
[12]
Horne, M., Kotschet, K. and McGregor, S. (2016) The Clinical Validation of Objective Measurement of Movement in Parkinson’s Disease. CNS, 1, 15-22.
[13]
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.
https://doi.org/10.1111/ene.13015
[14]
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.
[15]
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.
https://doi.org/10.1016/j.parkreldis.2014.02.011
[16]
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.
https://doi.org/10.3233/JPD-160898
[17]
Horne, M.K., McGregor, S. and Bergquist, F. (2015) An Objective Fluctuation Score for Parkinson’s Disease. PLoS ONE, 10, e0124522.
https://doi.org/10.1371/journal.pone.0124522
[18]
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.
https://doi.org/10.1371/journal.pone.0089319
[19]
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
[20]
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.
http://www.abstractsonline.com/pp8/#!/4046/presentation/8131
[21]
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.
https://doi.org/10.1038/s41531-018-0046-4
[22]
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.
https://doi.org/10.4236/apd.2018.71001
[23]
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. https://doi.org/10.3233/JPD-181373
[24]
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. https://doi.org/10.3390/s19102241
[25]
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.
https://doi.org/10.1136/jnnp.55.3.181
[26]
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. https://doi.org/10.1002/mds.22340
[27]
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.
https://doi.org/10.1016/j.parkreldis.2014.10.026
[28]
Busner, J. and Targum, S. (2007) The Clinical Global Impressions Scale Applying a Research Tool in Clinical Practice. Psychiatry, 4, 28-37.
[29]
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
[30]
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. https://doi.org/10.1038/s41531-018-0051-7
[31]
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
[32]
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.
https://doi.org/10.1002/mds.20213
[33]
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
[34]
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
[35]
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.
https://doi.org/10.1016/j.parkreldis.2018.08.018
[36]
Atluri, V., Rao, S., Rajah, T., et al. (2015) Unlocking Digital Health: Opportunities for the Mobile Value Chain.
https://healthcare.mckinsey.com/sites/default/files/Healthcare_ WhitePaper_screen_April17.pdf
[37]
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
[38]
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
[39]
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. https://doi.org/10.2337/dc17-1821
[40]
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
[41]
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.
https://doi.org/10.3346/jkms.2013.28.4.564
[42]
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
[43]
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.
https://doi.org/10.1146/annurev-bioeng-062117-121036
[44]
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.
https://doi.org/10.1089/tmj.2018.0035
