Digital Phenotyping for Psychiatric Disorders: Ready for Primetime?

Gregory P. Strauss, Ph.D.

Assistant Professor

Department of Psychology

University of Georgia

Email: gstrauss@uga.edu

ACKNOWLEDGMENTS & DISCLOSURESDisclosures

▪ Receive royalties and consultation fees from ProPhase LLC in connection with commercial use of the BNSS and other professional activities; these fees are donated to the Brain and Behavior Research Foundation.

▪ Last 12 Months: Speaking/consultation with Minerva, Lundbeck, Acadia

▪ Grant support from NIH and Brain & Behavior Research Foundation

LIMITATIONS OF CLINICAL RATING SCALES

PRACTICAL & COST EFFECTIVE: Data collection and analysis can be automated, reducing cost of rater training, time and processing.

ASSESSMENT IN REAL TIME: Avoids influence of retrospective memory limitations, halo effects, biases, culture and other potential “noise”.

HIGH RESOLUTION/OPTICS: Allows for scalable resolution to view symptom dynamics over user-defined time and situation.

SPECIFICITY: Allows high “spectral” resolution for parsing symptoms from each other, and from global cognitive and other impairments.

FACILITATES DATA MINING FOR FUTURE ANALYSIS: Raw data is available for data mining, to fuel preliminary analyses, “proof of concept” studies and to uncover subtle medication effects and explain null findings.

ALL rating scales have certain limitations▪Cognitive impairment▪Rater biases▪Social desirability▪Halo effects that limit precision▪Arbitrariness of item anchors▪Limited scope of anchor scores▪Practicality (training, time, expense)

ADVANTAGES OF DIGITAL PHENOTYPING

DIGITAL PHENOTYPING METHODS

Patient Interface

Active Digital Phenotyping:

Subjective

Passive Digital Phenotyping:

Objective

DATA COLLECTION DATA TYPESANALYSIS/FEATURE

EXTRACTIONSPECIAL DATA

PROCESSING NEEDS

Self-Report Data(automated & “offline”)

Geolocation(automated & “offline”)

Accelerometry(automated & “offline”)

Ambient Acoustics(automated & “offline”)

None

DATA DISSEMINATION

None

UserInterface

Active Digital Phenotyping:

Objective

Vocal Acoustics(Requires simple, shareware processing

tools)

Requires minor human processing (e.g.,

transcribing, optimizing audio, video signal).

All analyses can be batched and automated and conducted “off-line”

(hence, “off the shelf” software solutions can

be used).

Semantic “Coherence” Analysis(Requires proprietary software and

“corpus”)

Video Analysis (facial, head, eye analysis)

Requires Proprietary software .

Lexical Analysis(Requires simple text-search tool). Proprietary “Dictionary” needed

ACTIVE DIGITAL PHENOTYPING: SURVEYS

▪ 7 Days of EMA▪ Scheduled within 90 minute epochs 9AM-9PM▪ 15min window▪ Takes ~5min per survey to complete

SURVEY ADHERENCE AND ACCEPTABILITY

Tolerability How Positive (0-10)

How Negative (0-10)

SZ 8.9 (1.0) 1.0 (1.4)

CN 8.5 (1.2) 1.4 (1.2)

Strauss et al, in prep: Acceptability

Summary:• Compliance is high• The method is highly tolerable• Minimally disruptive• Non-completion due to unavailability or missing prompt• High negative sx, older, less educated, lower IQ can complete surveys

Study Authors/Year # Surveys per Day/# Days

% Adherence

Brenner et al., 2014 6/7 98.1%

Visser et al., 2018 4/6 90.2%

Ben-Zeev et al., 2012 6/7 97.7%

Granholm et al., 2013 4/7 72.1%

Moran et al., 2016 4/7 80%

Sanchez et al., 2014 4/7 80.6%

Oorschot et al., 2013 10/6 80%

Granholm et al., 2019 7/7 85%

Strauss et al., 2019 4/6 90.2%

Palmier-Claus et al., 2012 6/7 82%

Edwards et al., 2018 7/6 71%

Summary 5.6 surveys, 6.6 days 84.3%

Moran, Culbreth, Barch, 2017

Correlation w/ # surveys: r = -.07; # days: r = -.12

ACTIVE DIGITAL PHENOTYPING: SURVEY VALIDITY

Moran, Culbreth, Barch, 2017

Associated with clinical interview based measures of the same construct and reward processing mechanisms underlying the symptoms

ACTIVE DIGITAL PHENOTYPING: DATA FROM VIDEO• Videos recorded concurrently with

each survey

• “Give a step by step description of what you did in the last hour. Please talk for a full 30 seconds"

• Submitted to automated facial and vocal analysis

Acoustic Analysis Lexical Analysis Semantic Analysis

Blunted AffectAlogia Emotional

Experience Motivated Behavior Mood Symptoms(Mania, Depression) Disorganization

Cognition

Facial Expression, Gesture, Eye movement Analysis

ACTIVE DIGITAL PHENOTYPING: DATA FROM VIDEOSignal is highly variable over time: But shows consistency for context/time:

Patients Versus Controls: Modelling Group Differences:

10-folds Training Validation:Predicting Group status

339 control cases271 patient cases

Average Accuracy = 74%

Cohen et al., under review)

Highly correlated with Disorganization Self-Harm

ACTIVE DIGITAL PHENOTYPING: DATA FROM VIDEOBNSS: Blunted Affect

222 control cases49 patient cases

R2 = .33Average Accuracy = 82%

BNSS: Alogia243 control cases28 patient cases

R2 = .36Average Accuracy = 91%

EMA Paranoid Delusions207 control cases15 patient cases

R2 = .18Average Accuracy = 92%

EMA Anxiety180 control cases42 patient cases

R2 = .12Average Accuracy = 82%

Cohen et al., in prep)

NLP: Speech to Text and Semantic Analysis can be automated (> 80% accuracy)

NLP-based measures of Cognition Measures of Cognitive State

ICC (3 sessions): Mean: 0.68

r with Neuropsych Measures 0.60

ACTIVE DIGITAL PHENOTYPING: COGNITION

Holmlund et al., 2019

PASSIVE DIGITAL PHENOTYPING: ACCELEROMETRY

▪ Change in ACL was passively measured on the phone.

▪ Participants wore an Empatica wristband, which passively collected ACL as a constant force in g, which was later converted to m/s2.

▪ Both measures of ACL were collected over x, y, and z axes, from which a magnitude score was calculated, using:

SZ

ACL = 𝑥2+𝑦2+𝑧2

3

Representative Data Across 24Hrs Equipment

ACCELEROMETRY

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Phone Mean Phone SD Band Mean Band SD

Co

he

n's

d E

ffec

t Si

ze

ACL Variable

9.9

10.0

10.1

10.2

10.3

10.4

10.5

10.6

10.7

Resting Not Resting

Ph

on

e M

ean

Activity Type

SZ CN

Validity: At Rest vs Not Cohen’s d Effect Sizes

ACL Band Mean w/ Negative Symptoms: r = -.56, p<.001

Strauss et al in prepControl: n = 54; Schizophrenia: n = 54

PASSIVE DIGITAL PHENOTYPING: GEOLOCATIONVariable Abbreviation(s) Definition

Home time Amount of time spent at home

Distance change Δd Distance traveled in meters from previous sample

calculated by Haversine formula

Distance from

home

Δmh Meters from home for each sample calculated by

Haversine formula

Stationary location

clusters

NC Number of distinct geographical locations sampled

Location variance LV Variance within locations traveled to

Entropy ENT Amount of movement and randomness in locations

Transition time* TT Time spent moving between distinct locations

GEOLOCATION

Cohen’s d Effect Sizes

Depp et al 2019Strauss et al under reviewControl: n = 54; Schizophrenia: n = 54Control (n

= 56)Schizophrenia (n = 86)

Z, P-Value Cohen’s D

Median Distance Traveled

23.8 (17.6)

12.3(10.4)

2.5, <.001 0.80

Median Distance from home

19.8 (16.6)

8.1 (9.0)

2.3, <.001 0.88

% Samples at Home

51.1% (0.38)

74.4% (0.25)

1.9, p<.001 0.72

CAINS Negative Symptoms

Median Distance Traveled -.35***

Median Distance from home -.35***

% Samples at Home 0.29**

VALIDITY SUMMARY

• The active and passive digital phenotyping measures demonstrate validity via:• Group differences between clinical and control groups

• Correlations with symptom, functional outcome, and cognitive measures from the same constructs measured via standard clinical instruments

• Correlations between active and passive digital phenotyping measures that are temporally proximal

Sensitivity to Change in Clinical Trials

• Bell et al (2020) Blended EMA/I for Coping with Voices in Psychosis

Sensitivity to Change in Clinical Trials

• Bell et al (2020) Blended EMA/I for Coping with Voices in Psychosis

Sensitivity to Change in Clinical TrialsMoore et al., 2016: Anxiety & Depression

Sensitivity to Change in Clinical TrialsMunsch et al., 2009: Binge Eating

SUMMARY

▪ DP is a reliable and valid objective measure of psychiatric symptoms and functional outcome

▪ Objective measures overcome several limitations of traditional clinical rating scales

▪ More contextually sensitive and temporally dynamic than clinical rating scales.

▪ Combining active and passive methods may be most powerful approach

▪ Near-continuous recordings and large numbers of samples enhance probability of detecting treatment effects via enhanced power, offering promise for clinical trials.

▪ Evidence for sensitivity to change in clinical trials

LIMITATIONS AND ISSUES TO BE RESOLVED

• Do these tools measure something meaningful to those with psychiatric conditions?

• Industry-ready, compliant, validated tools

• Norms and scaling

• Automated scoring

• Data analysis

• Data management

• Compliance with FDA regulations (audit trails and security)

• Optimal use of phone vs other technologies (e.g., band)

ACKNOWLEDGMENTS & DISCLOSURES

Acknowledgments

FUNDING• NIMH

• R01-MH116039• R21-MH112925• K23-MH092530• NARSAD Young Investigator

CAN Lab Research Team

• Ian Raugh (Grad student)

• Cristina Gonzalez (lab manager)

• Sydney James (coordinator)

• Katie Visser (Grad student)

• Lisa Bartolomeo (Grad student)

Collaborators• Alex Cohen, Ph.D.• Brian Kirkpatrick, MD• Eric Granholm, PhD

Thank you!