The webinar will start at 12:00 PM EST Topics to be covered What - PowerPoint PPT Presentation

The webinar will start at 12:00 PM EST

Topics to be covered  What are patient considerations in technology?  How is patient-centered design executed?  What are implications for clinical trials?  How does patient centered technology impact data?

Patient Considerations

Patient Burden vs. Sensitivity

Mechanical Considerations  Ease of use  Donning, doffing  Comfort  Connectivity  Interference  Size  Weight  Wires  Cosmetics

Software Considerations  Computer literate?  Internet Access?  Cell phone / tablet user?

PD-Specific Considerations  Tremor  Bradykinesia  Elderly population  Cognitive impairment  Assistive devices

Patient Centered Design

Web-based reporting

Patient Design Considerations

Patient Interface

Symptom and Activity Rating

Instructional Videos

Patient Focus Group Feedback • Easy-to-don • Light-weight • Comfortable • Wireless “docking” station • Orientation independent

Focus Group Results  Focus groups provided feedback on hardware, software, and video instructions on four separate occasions.  D. E. Filipkowski, T. O. Mera, D. A. Heldman, and J. P. Giuffrida, “Ergonomic and Human Interface Design Factors for Home- Based Medical Devices in Movement Disorders,” 2011.

Patient training

Implications for Clinical Trials

 Patient Burden vs. Sensitivity of Data  Subject retention  Data reliability

Impact on Clinical Trial Data

Patient Compliance  97% of motor tasks completed as instructed  Compliance improved over time  T. O. Mera, D. A. Heldman, A. J. Espay, M. Payne, and J. P. Giuffrida, “Feasibility of home -based automated Parkinson’s disease motor assessment,” J. Neurosci. Methods , vol. 203, no. 1, pp. 152 – 156, Jan. 2012.  D. Filipkowski and D. A. Heldman, A. J. Espay, J. Mishra, T. O. Mera, And J. P. Giuffrida “Patient Compliance with Parkinson’s Disease Home Monitoring System (P02.244),” Neurology , vol. 78, no. Meeting Abstracts, p. P02.244, 2012.

Sample Reports  Medication titration – tremor  Bradykinesia titration – bradykinesia  No response

Tremor can be differentiated from voluntary motion by taking advantage of separation in the frequency spectrum

Continous Tremor Monitoring Recently Published D. A. Heldman, J. Jankovic, D. E. Vaillancourt, J. Prodoehl, R. J. Elble, and J. P. Giuffrida. Essential tremor quantification during activities of daily living. Parkinsonism & Related Disorders , 2011.

4 3 Tremor Score 2 1 0 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 Time of Day 100 • Algorithms process data from 75 a single sensor to quantify Time (%) tremor 50 • Complete temporal picture of 25 severity during daily life 0 0 1 2 3 4 Algorithm Tremor Score Recently Published Pulliam CL, Eichenseer SR, Goetz CG, Waln O, Hunter CB, Jankovic J, et al. Continuous in-home monitoring of essential tremor. Parkinsonism Relat Disord . 2013.

Isolating dyskinesia is significantly more challenging because it overlaps with voluntary movements in the frequency spectrum

Dyskinesia Quantification  Two “stationary” tasks R = 0.81  In the absence of RMSE = 0.55 voluntary motion, a single sensor on the hand can be used to quantify dyskinesia  Currently integrated into Kinesia HomeView Recently Published Mera TO, Burack MA, Giuffrida JP. Objective motion sensor assessment highly correlated with scores of global levodopa- induced dyskinesia in Parkinson’s disease. J Parkinsons Dis . 2013 Jan;3(3):399 – 407.

Dyskinesias Quantification  Series of representative activities of daily living  Use two sensors (hand, leg) and more sophisticated processing to predict an overall dyskinesia score  Upcoming study to evaluate continuous scoring

Hair Brushing Cutting Food Drinking from a Cup 4 4 4 R = 0.88 R = 0.91 R = 0.85 RMSE = 0.35 RMSE = 0.37 RMSE = 0.41 3 3 3 Model Score Model Score Model Score 2 2 2 1 1 1 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Clinician Combined Average Score Clinician Combined Average Score Clinician Combined Average Score Bagging Groceries Dressing 4 4 R = 0.91 R = 0.89 RMSE = 0.37 RMSE = 0.39 3 3 Model Score Model Score 2 2 1 1 0 0 0 1 2 3 4 0 1 2 3 4 Clinician Combined Average Score Clinician Combined Average Score

Conclusions  There is a trade-off between patient burden and sensitivity of data.  Keeping the patient in mind during the design process and throughout clinical use improves the user experience and increases the likelihood of patient acceptance.  Patient data demonstrates acceptance and clinical efficacy of Kinesia HomeView technology to assess Parkinson’s disease.

Acknowledgements  University of Cincinnati  University of Rochester  Alberto Espay, Fredy Revilla  Michelle Burack  Henry Ford Hospital  National Institutes of Health  Peter LeWitt  Rush University Medical  5R44NS065554-05 Center  1R43NS074627-01A1  Christopher Goetz  5R44MD004049-04  Baylor College of Medicine  5R44AG034708-03  Joseph Jankovic  9R44AG044293-03

For more information, please contact Dustin Heldman at dheldman@glneurotech.com