Sensor-based applications in Parkinson’s disease

What, How, and When to measure?

Alberto J. Espay, MD, MSc, FAAN

Professor of Neurology

Director and Endowed Chair, James J. and Joan A. Gardner Family Center

for Parkinson's Disease and Movement Disorders

University of Cincinnati

Disclosures

• Research: NIH and Michael J Fox Foundation

• Consultant/scientific advisory board: Abbvie, Chelsea Therapeutics, TEVA, Impax, Merz, Pfizer, Acadia, Acorda, Cynapsus, Solstice Neurosciences, Eli Lilly, Lundbeck, and USWorldMeds

• Honoraria: Abbvie, UCB, USWorldMeds, Lundbeck, AmericanAcademy of Neurology, and Movement Disorders Society

• Royalties: Lippincott Williams & Wilkins, Cambridge University Press, and Springer

Advantages of wearable sensor technologies

1. Objective and reliable measurements˗ No concerns regarding inter- or intra-rater variability

2. Continuous data collection˗ Assessing of patients across time instead of a single snapshot

3. High resolution of sensors˗ Can detect smaller magnitudes of change compared to human observers

4. Unobtrusiveness of data collection˗ Passive data collection while patients are in their natural environment

5. Patient empowerment˗ Increase adherence to protocols and clinician’s directions

6. Minimal training required˗ Easier to train in the use of a technology than to train a master clinician

Adapted from Kubota KJ. Mov Disord 2016;31(9):1314-26.

Sensor-based measures are more sensitive to change

Why technology in clinical trials?2. Clinical measures by clinicians are not as sensitive as device’s

• More sensitive to small changes by Kinesia device

• Less variability in repetitive measures by Kinesia device

Heldman et al. Clinician  versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease. Parkinsonism Relat Disord.

2014 Jun;20(6):590-5

Heldman et al. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease. Parkinsonism Relat Disord. 2014 Jun;20(6):590-5

More sensitive measures: fewer patients needed for clinical trialsFeature Clinician

ICCKinesia ICC

Number of subjects

- clinician

Number of subjects

- Kinesia

Percent fewer subjects

Rest tremor 0.63 0.68 100 93 7.5%

Postural tremor 0.68 0.71 100 96 3.9%

Speed 0.62 0.94 100 65 34.6%

Amplitude 0.72 0.94 100 77 23.3%

Rhythm 0.45 0.63 100 72 28.3%

Heldman et al. Parkinsonism Relat Disord. 2014 Jun;20(6):590-5

•More sensitive measures that vary less allow greater precision in trials

Sensor-based applications in Parkinson’s disease

•What•How •When

Features of people with PDDiagnostic, monitoring needs

Unobtrusive sensorsArtificial intelligence

Episodic may suffice in some, continuous must be for others

WHAT to measure? • Diagnosis (present vs absent)

• A disease feature of interest

• Fluctuations• Daily• Hourly• Related to medications• Motor• Non-motor

• Monitoring of disease

• Monitoring of treatment response

Feasible ⚠️

☢️

☢️Promising

Promising

Feasible

Tricky ❌

✅

Addressed in separate lecture: “Adopting and integrating technology-based assessments in PD”

The binary nature of diagnosis

Symptom A

Diagnosis rejected

Diagnosis confirmed

Symptom B

Symptom C

The alluring attraction of technology

James Parkinson

UKPDSBB

Gelb

EFNS/MDS-ES

MDS 2015

MDS 2018

“Involuntary tremulous motion… in parts not in action… with a propensity to bend the trunk forwards...”

+ clinico-pathology

+ olfactory test+ neuroimaging+/- genetic test

+ olfactory test+ neuroimaging…TO RULE OUT

OTHER CONDITIONS

“Red Flags”

What is Parkinson’s disease 200 years after James Parkinson’s description?

…in the absence of sensitive, objective measures such as viral load, serum cholesterol level……Why do our efforts continue to insist in validating the clinical diagnosis?

We face two main limitations:

1. Our current “gold standard” is the clinical definition of PD – which is imperfect

2. Even if our current “gold standard” were ideal, technology is not being conceived to outperform it (e.g., “gold standard” paradox).

Richman PB. Acad Emerg Med 2002;9(7):710-2

Limitation #1: Uncertain “Gold Standard” –PD as single disease

Adapted from Berg et al, Lancet Neurol. 2013 May;12(5):514-24

Espay, Brundin, Lang, Nat Rev Neurol. 2017 Feb;13(2):119-126

Meets Sensor-based

threshold for PD

Courtesy, Dr. Francesca Morgante Bhambhani et al. Mov Disord 2013 Rodriguez-Porcel, et al, J Clin MovDisord 2017

PKAN (NBIA) Chediak-Higashi Gaucher

LRRK2

Wider et al, Neurodegener Dis. 2010;7(1-3):175-9

Dementia

Lewy BodyTauopathy

TDP-43Pure nigral

degeneration

Even the “PD gene” is problematic: it can lead to non-PD pathologies

Limitation #2: “Gold Standard” Paradox

Diseased Non-diseased

“Gold Standard”

Wearable technology

False positives = 1False negative = 2

What if our “index test” is, in fact, better than the “Gold Standard”?

Diseased Non-diseased

What if technology is better than the clinical diagnosis?

“Gold Standard”CT scan

“Index test”MRI scan

Kinematic but not clinical measures

predicted falls in PD patients with OH

• A cohort of 26 consecutive PD patients with

OH had a fall prevalence of 53.8% over six

months.

• Gait and postural stability tests failed to

predict the falls

• Kinematic data predicted the risk of falls

with high sensitivity and specificity (> 80%;

AUC 0.81).

• There was a trend for higher risk of falls in patients with

orthostatic mean arterial pressure 75 mmHg.

Continuous non-invasive

BP monitor

Waist Sway - Kinematic

Analysis

Sturchio et al, Journal of Neurology (2020, in press)

How to measure? Parkinson’s Apps Landscape Overview

DISEASE MANAGEMENT AND TRACKING

THERAPY AND SUPPORT

EDUCATION

RESEARCH AND DIAGNOSIS

myHealthPal OneRing

PD Me

Parkinson’s Diary Toozon tremor

9zest Parkinson’s

therapy ($)

Parkinson

Home Exercises ($)

Daily Dose ($) DAF Professional

Parkinson’s

Speech AidParkinson’s

EasyCall

Parkinson’s

Central*

Parkinson’s

Toolkit

mPower uMotif*

ParkEDx Fox Wearable

Companion App

Lift Pulse

*Has pharma sponsorship. Parkinson’s Central – Teva, Ipsen, UCB; uMotif – UCB; Hopkins PD – Roche Pharma Research;

Hopkins

PD*

?

Analysis courtesy of Acorda researchers

KEY FUNCTIONALITY OF TOP SYMPTOM TRACKING APPS

• The top Parkinson’s symptom tracking apps provide a wide range of tracking and testing capabilities

• None of the multi-symptom tracking apps measure tremor (only dedicated tremor apps do)

• All apps require significant active/manual entry

APPPRIMARY FOCUS

TREMOR TRACKING

RIGIDITY/ DEXTERITY

TEST

COGNITIVE TEST

SPEECH TEST

MOOD TRACKING

EXERCISE TRACKING

SLEEP TRACKING

DIET TRACKING

PHYSICAL EXERCISE

S

MEDICATION

TRACKER

HCP OR CAREGIVER COMM

Medication Tracker

Active Active Active HealthKit Active Active Active Active

Physical Therapy

Active Active Active

Physical Therapy

Active Active Active Active HealthKit Active Active

Research Active Active Active HealthKit

Research Active Active Active Wearables Active Active Active

myHealthPal

9zest Parkinson’sTherapy

Daily Dose

Parkinson mPower

uMotif

Analysis courtesy of Acorda researchers

21

In the Personalized Parkinson Project 650 Parkinson’s patients, the adherence to the smartwatch has been as high as 95% (per Data shared at MDS Congress)

The Verily Study Watch (an Investigational Device), along with syncing/charging cradle and Study Hub.

Bloem et al. BMC Neurology (2019) 19:160

Project BlueSky: 60 healthy volunteers and 95 PD patients (42–80 years; 1–24 years of disease) were monitored in either a laboratory, a simulated apartment, or at home and in the community

• 38% of PD patients

missed ~25% of

entries in an electronic

motor diary and had an

average delay of >4 h.

• Self-reports of

dyskinesia: ~35% false

negatives and 15%

false positives.

⚠

️

But the at-home function is often anchored on clinic-based measures

Objective Sensor Mobile App Web Portal and Reports

Daytime Cycle From a Patient’s Perspective

• The lines between “OFF” and “ON” are blurry, representing the continuum of the

fluctuating phenomenon

OFF OFFON OFFON

DK DKDK

DT

OFF OFFON OFFON

DK DKDK

Dyskinesia Dystonia

WHEN to measure?

In-clinic function may differ from at-home function

•All changes can be accounted for a

day in a summary•OFF-ON changes

•Stress, fatigue during visit

Addressed in Prof. Maetzler’s lecture

Conclusions• Device measures are more sensitive, accurate, and reliable: fewer subjects,

lower cost of trials

• Technology can serve as markers of treatment response and monitoring

• Wearable technologies can quantify features of Parkinsonism better than clinicians but should not be relied upon to diagnose “PD”

• Device measures are more ecologically valid (reflecting at home function)

• Patient-centric approach (empowers patients to take action in their care by establishing a reciprocal interaction with clinician)

• Improve selection of PD subgroups targeted for future testing of symptomatic treatments (not sufficient for disease-modifying trials) [beyond scope]

Thank you!

Email: Alberto.Espay@uc.eduTwitter: @AlbertoEspay