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



• 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.

 Focus groups provided feedback on

hardware, software, and video instructions on four separate

• ccasions.

Patient training

Implications for Clinical Trials

 Patient Burden vs.

Sensitivity of Data

 Subject retention  Data reliability

Impact on Clinical Trial Data

Patient Compliance



• 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.

 97% of motor tasks completed as

instructed

 Compliance improved over time

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

• 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. Recently Published

08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 1 2 3 4

Tremor Score

Time of Day

1 2 3 4 25 50 75 100

Time (%) Algorithm Tremor Score

• Algorithms process data from

a single sensor to quantify tremor

• Complete temporal picture of

severity during daily life

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. Recently Published

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

 Two “stationary” tasks  In the absence of

voluntary motion, a single sensor on the hand can be used to quantify dyskinesia

 Currently integrated into

Kinesia HomeView

R = 0.81 RMSE = 0.55

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. Recently Published

Dyskinesia 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

Dyskinesias Quantification

1 2 3 4 1 2 3 4

Dressing

Clinician Combined Average Score Model Score

R = 0.89 RMSE = 0.39

1 2 3 4 1 2 3 4

Bagging Groceries

Clinician Combined Average Score Model Score

R = 0.91 RMSE = 0.37

1 2 3 4 1 2 3 4

Hair Brushing

Model Score Clinician Combined Average Score

R = 0.88 RMSE = 0.35

1 2 3 4 1 2 3 4

Cutting Food

Clinician Combined Average Score Model Score

R = 0.91 RMSE = 0.37

1 2 3 4 1 2 3 4

Clinician Combined Average Score Model Score

Drinking from a Cup R = 0.85 RMSE = 0.41

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
• Alberto Espay, Fredy Revilla
• Henry Ford Hospital
• Peter LeWitt
• Rush University Medical

Center

• Christopher Goetz
• Baylor College of Medicine
• Joseph Jankovic
• University of Rochester
• Michelle Burack
• National Institutes of Health
• 5R44NS065554-05
• 1R43NS074627-01A1
• 5R44MD004049-04
• 5R44AG034708-03
• 9R44AG044293-03

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