Leveraging technology to meet

MDS-PAS Comprehensive Care in Parkinson’s Disease June 5, 2021

Leveraging technology to meet

comprehensive care needs

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, Neuroderm,

Neurocrine, Amneal, Adamas, Acadia, Acorda, Kyowa Kirin, Sunovion, Lundbeck, and USWorldMeds

• Honoraria: USWorldMeds, Acadia, and Sunovion

• 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

Held m an 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 trials

Feature Clinician

ICC

Kinesia 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

Portable Hardware / Software

• Plenty of digital health technologies

• Lots of data

• Any-time, continuous data

= Big data

Artusi et al, Neurotherapeutics 2020

Big data: the center of the health universe

https://iisc.talentsprint.com/digitalhealth/

Adapt patients to technologies or technologies to patients?

https://www.cbinsights.com/research/iot-healthcare-market-map-company-list/

Diagnosis

rejected

Diagnosis

confirmed ON No Dyskinesia OFF

Dyskinesia

The allure of making sense of Big Data

Non-fluctuator

Fluctuator

No Depression

Depression

No Anxiety

Anxiety

No OH

Labels we know OH

The allure of making sense of Big Data

Accelerometer

Gyroscope

Blood pressure

No OH

OH

ON

OFF

(Adapted from) Courtesy, Aristide Merola & Walter Maetzler

Active uses of mobile health technology

• All “channels” of information provided by sensors

require processing by algorithms

• The algorithms need to distinguish between

“background noise” and “clinically meaningful

signals”

• The validation process is anchored on the clinical

assessment by expert clinicians (“gold standard”)

From Big Data to Label = Validation

Input data Pre-process Feature extraction

Feature selection

Classification Patient with

suspected depression

“Depression”

Adapted from Bhat et all, Comput Biol Med. 2018: S0010-4825(18)30270-1

Diagnostic Concordance

Typical overflow for labeling procedures

Depression Scale

Validation

suspected depression

Patient with confirmed depression

The “analog” era

Perfect agreement

Beyond threshold

Is digital health an opportunity to

validate our clinical categories?

Or to revisit them entirely from the

patient’s perspective?

Digital Health for

Personalized and Integrated Care

Medicalized?

Validated with prior scales?

Validated in large populations?

For our medical records?

For regulatory agencies?

For understanding populations?

First problem: Information irrelevance

Little information generated is

directly useful to the users

Source: Endeavour Partners, September 2013

Major problem: Losing the individual

The information generated

must be applicable to

larger populations

Source: Endeavour Partners, September 2013

The information generated

reflects that from a

previously validated scale

Digital Health Pathway

Analog

The PD diary

example

An example of a patient-completed symptom diary (Adapted from Hauser et al. 2000)

(throwback to)

Category of Problems Examples

Artificial language OFF, ON, ON with non-troublesome dyskinesia, ON with troublesome dyskinesia

Recall biases Retrospectively rating a 30-minute epoch can be cognitively challenging

Absent non-motor fluctuations

Non-motor symptoms and non-motor fluctuations neglected

Motor reductionism Dyskinesia assumed to be a peak-dose only, OFF- associated dystonia not captured, etc.

No partial states All-or-none duality for fluctuating motor behaviors, without any gradations to ON and OFF states

Averaging of behaviors If dyskinesia is experienced < 50% of a 30-minute epoch, state is marked “ON without dyskinesia”

No medication tracking Lack of medication tracking affects interpretation of symptom fluctuations in relation with levodopa cycles

Adapted from Vizcarra et al, Mov Disord 2019 May;34(5):676-681

Selected shortcomings of the analog diary

A conceptual e-Diary development

? Or

Other examples

Digitalization of the UPDRS or a patient-centered

assessment of motor function?

Digitalization of the Spiral drawing or a patient-centered assessment of tremor?

Digitalization of the finger tapping or a patient-centered assessment of speed?

A true PD diary…

• Would not have to match the language of neurologists or dichotomize their lives into OFF and ON half-hour epochs

• Would summarize the real-life individualized experience of a patient through good and bad times during the day, and good and bad days with spectrum thereof –in a manner that is directly valuable to

patients themselves, without need for “translation” by a neurologist.

Passive and active data merged

• A future MDS e-Diary is being designed to provide a digital interface (e.g., smartphone and/or watch) for logging personalized information and tracking data

• Passive data collection: capture through one or more sensory channels, adapted to as many different needs as possible

• Active data entry: a finger sliding across a “Smiley meter” as often as desired (Less need over time).

• The latter provides context to the former so that the artificial intelligence algorithm “learns” the patterns associated with good and bad times –patterns in

background activity, speech, heart-rate variability, etc.

Researchgate, James Michael Fisher

Perception

Capacity

Performance

CAPACITY

PERFORMANCE PERCEPTION

Addressed in Prof. Maetzler’s lecture

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.

Requirements for translating promise into action

Relevant to patients:

long term adherence

Endorsed by regulators:

accepted outcomes Data accurate and interpretable

Metadata refers to data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and assist in data management, data sharing, and data analysis.

Badawi et al, Digit Biomark 2019;3:116–132

Enter Metadata

The Tension in The Translation

Relevant to patients:

long term adherence

Endorsed by regulators:

accepted outcomes Data accurate and

interpretable

Individualized, need not be generalizable

Generalizable, exchangeable across platforms

Validation for an individual

Validation for a population

1. Big data does not equal meaningful data; context is critical

2. Direct patient relevance is indispensable for success of

personalization of Digital Health Pathway and the

integration into care

3. “Validation” anchored on existent (analog) instruments

(e.g., prior scales) will continue to be inadequate to

harness the value of digital technology

4. Metadata (data interpretability, reproducibility, and

suitability for regulatory acceptance) allows validating

technology to individuals –and generalizing to populations

5. The MDS e-Diary development stands to become the first

demonstration that technology is not just a replacement of

the paper world but a veritable form of Digital Health

Concluding remarks

Thank you for your attention!

Email: [email protected] Twitter: @AlbertoEspay