Ready for Primetime? Gregory P. Strauss, Ph.D. Assistant Professor - - PowerPoint PPT Presentation

Dig igital Phenotyping for Psychiatric Dis isorders: Ready for Primetime?

Gregory P. Strauss, Ph.D. Assistant Professor Department of Psychology University of Georgia Email: gstrauss@uga.edu

ACKNOWLEDGMENTS & DISCLOSURES

Disclosures

▪ 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

LIM LIMITATIONS OF F CLIN LINICAL 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 F DIG IGITAL PHENOTYPING

DIG IGIT ITAL PHENOTYPING METHODS

Patient Interface Active Digital Phenotyping: Subjective Passive Digital Phenotyping: Objective

DATA COLLECTION DATA TYPES ANALYSIS/FEATURE EXTRACTION SPECIAL DATA PROCESSING NEEDS

Self-Report Data (automated & “offline”) Geolocation (automated & “offline”) Accelerometry (automated & “offline”) Ambient Acoustics (automated & “offline”) None

DATA DISSEMINATION

None User Interface 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

ACT CTIVE DIG IGITAL 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

Sum 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

ACT CTIV IVE DIG IGIT ITAL PHENOTYPING: : SURVEY VALID IDIT ITY

Moran, Culbreth, Barch, 2017 Associated with clinical interview based measures of the same construct and reward processing mechanisms underlying the symptoms

ACT CTIV IVE DIG IGIT ITAL PHENOTYPING: : DATA FROM VID IDEO

• 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 Affect Alogia Emotional Experience Motivated Behavior Mood Symptoms (Mania, Depression) Disorganization Cognition

Facial Expression, Gesture, Eye movement Analysis

ACT CTIV IVE DIG IGIT ITAL PHENOTYPING: : DATA FROM VID IDEO

Signal is highly variable over time: But shows consistency for context/time: Patients Versus Controls:

Mod

• del

elli ling Group Dif Differences es: 10-folds Training Validation: Predicting Group status 339 control cases 271 patient cases Average Accuracy = 74%

Cohen et al., under review)

Highly correlated with Disorganization Self-Harm

ACT CTIV IVE DIG IGIT ITAL PHENOTYPING: : DATA FROM VID IDEO

BN BNSS: Blu Blunted ed Affect 222 control cases 49 patient cases R2 = .33 Average Accuracy = 82% BN BNSS: Alo logia ia 243 control cases 28 patient cases R2 = .36 Average Accuracy = 91% EMA Paranoid De Delu lusions 207 control cases 15 patient cases R2 = .18 Average Accuracy = 92% EMA Anxie xiety 180 control cases 42 patient cases R2 = .12 Average 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

ACT CTIV IVE DIG IGIT ITAL PHENOTYPING: : COGNITION

Holmlund et al., 2019

PASSIVE DIG IGITAL 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 =

𝑦2+𝑧2+𝑨2 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 Cohen's d Effect Size ACL Variable 9.9 10.0 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Resting Not Resting

Phone Mean 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 prep Control: n = 54; Schizophrenia: n = 54

PASSIVE DIG IGITAL PHENOTYPING: GEOLOCATION

Variable 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 2019 Strauss et al under review Control: n = 54; Schizophrenia: n = 54 Control (n = 56) Schizophre nia (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 Trials

Moore et al., 2016: Anxiety & Depression

Sensitivity to Change in Clinical Trials

Munsch 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

LIM IMITATIONS AND IS 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!