1. Jeffrey Kaye, MD Layton Professor of Neurology & Biomedical Engineering Oregon Center for Aging & Technology NIA - Layton Aging & Alzheimer's Disease Center kaye@ohsu.edu A Vision for the Future of Aging & Technology New Yorker, Oct. 1, 2007

2. Technology in Health Research and Practice Spectacular progress has been made in applying technology to the basic biology of disease creating an embarrassment of riches of potential treatments. In contrast, clinical assessment ‘technology’ has not appreciably changed since 1747. “The Message” - Pervasive computing technologies can radically change the way we conduct clinical research and provide care. This will lead to major advances in detecting prodromal change, managing manifest disease and in transforming the effectiveness of clinical research.

3. A fundamental limitation of current assessment... detecting meaningful change Cardinal features of change - slow decline punctuated with acute, unpredictable events - are challenging to assess with current tools and methods.

4. Self Report Inaccuracy Are you sure?: Lapses in Self-Reported Activities Among Healthy Older Adults Reporting Online. Wild et al. Journal of Applied Gerontology (2015) 26% No Match Between Sensors & Report 49% Partial Agreement 25% Full Match Area Firings Time “What were you doing during the past 2 hours?” n=95; Mean age 84 yrs

5. Early detection Why detecting meaningful change is hard... And how to improve detection of change Baseline 3 years 6 years Measure Symptoms Reported Functional range Change Is this the disease onset?

6. Changing the Clinical Paradigm New Observations & Discovery Maximally Effective Clinical Research Better Outcomes for Patients & Families New Observations & Discovery Maximally Effective Clinical Research Better Outcomes for Patients & Families  Pervasive Computing  Wireless Technologies  “Big Data Analytics”  Pervasive Computing  Wireless Technologies  “Big Data Analytics”

7. Secure Internet Secure Internet Computer Activity Phone Activity MedTracker Pervasive Computing Platforms for Assessment: Community-wide ‘Life Lab’ Activity, Sleep, Mobility Time & Location Doors Opening/Closing Balance, Body Composition Heart Rate, Temperature, C0 2 Kaye et al. Journals of Gerontology: Psychological Sciences, 2011 Device/Sensor “X”

8. Motion Detectors Contact/Door Switches Phone Sensors Load Cells / Bed Sensors Location Tracking Medication Tracker Computer Raw Sensor Data Sleep Phone Use Weight Medication Events Departures Arrivals Gait Velocity Computer Interactions Weight Scale Mobility Location Estimation Sleep Hygiene Socialization Medication Adherence Depression Physical Impairments Direct AssessmentInference Memory Attention Information Level Fusion Sensor Level Fusion Functional Status Change Detection Data flow for inference engine assessing function

9. Kitchen Bath Bedroom Living room Computer Session What can you see?Total Activity: Life Space & Event Analysis Spiral plot: The plot is a 24 hour clock representing here 8 weeks of continuous data. At the top of the clock is midnight; at the bottom is noon. Each concentric blue circle outward represents 2 weeks of time. The colors of the dots represent firings of sensors by location

10. Norovirus Epidemic: All ill patients identified by decreased room transition events without self report -34% -24% -40% Campbell, 2011

11. What can you see? HealthyDiagnosed with Parkinson’s Disease Treatment with Sinemet SEPT-OCT 2012 SEPT-OCT 2013 FEB-MAR 2011

12. What can you see? June 2011 June 2012 June 2010 CDR = 0; MMSE = 28 CDR = 0.5; MMSE = 27 CDR = 0.5; MMSE = 28 xx

13. Walks: From 2 to 7000 per year Photo: NYT, 2009 Gait Mat vs Sensor Line r =.99 Hayes, 2009; Hagler, 2010; Kaye, 2012

14. Differentiation of early MCI: Total Activity & Walking Hayes et al. Alzheimer's & Dementia, 2008; 4(6): 395-405. MCI NL Hayes et al. Alzheimers Dement, 2008 MCI cases 9X more likely in Slow Group MCI cases 9X more likely in Slow Group Trajectories of walking speed over time Dodge, et al. Neurology, 2012 Activity patterns associated with mild cognitive impairment

15. Differentiation of early MCI: Night-time Behavior & Sleep Hayes, et al. Alzheimer Dis Assoc Disord. 2014 Normal NA-MCI A-MCI

16. Medication Adherence over Time EFFECT OF HOLIDAYS Weekly adherence to twice-daily dosing using the MedTracker for 1285 days (3.5 years). Blue line (top): total adherence to 2 pills per day; red line (bottom): adherence to the required time regimen of one pill at 7am and one pill at 7pm. ORCATECH MedTracker Hayes et al., Proceedings : Engineering in Medicine and Biology Soc, 2006; Leen, et al., Technology and Aging, 2007 ; Hayes et al..Journal of Aging Health, 2009; Hayes et al. Telemedicine Jounal and E-Health, 2009

17. Every Day Cognition: Medication adherence as a measure of cognitive function Adherence assessed continuously x 5 wks with MedTracker taking a Mean Age - 83 yrs Based on ADAScog: Lower Cognition Group vs Higher Cognition Group Hayes et al., Proceedings : Engineering in Medicine and Biology Soc, 2006; Leen, et al., Technology and Aging, 2007 ; Hayes et al..Journal of Aging Health, 2009; Hayes et al. Telemedicine Jounal and E-Health, 2009 0 10 20 30 40 50 60 70 80 90 100 Lower Cognition Higher Cognition % Adherent Median time within 12.0 mins of goal Median time within 53.4 mins of goal Significantly Worse Adherence in Lower Cognition Group

18. Every Day Cognition: Computer use changes over time in MCI (without formal cognitive tests) At Baseline: Mean 1.5 hours on computer/per day Over time: – Less use days per month – Less use time when in session – More variable in use pattern over time IntactMCI Kaye, et al. Alzheimers Dement. 2014; Silbert et al. submitted, 2015

19. Putting it all together: High Dimensional Data Fusion Model of Clinical Assessment Context: Weather, CCI, living in a retirement community, etc. Behavioral - Activity Data: Computer use, time out of home, etc. Weekly Self- Report: Mood, Falls, ER visits, Visitors, etc… Weekly Self- Report: Mood, Falls, ER visits, Visitors, etc… Annual Clinical Assessment: Cognition, physical function, biomarkers, etc. Annual Clinical Assessment: Cognition, physical function, biomarkers, etc. Demographics: Age, education, socioeconomic status, etc. Demographics: Age, education, socioeconomic status, etc. Controls: Number of rooms in home, etc. Controls: Number of rooms in home, etc. Model Care Transition Outcome 63,745,978 observations... Austin et al. 2014 GSA

20. Predicting Care Transitions: Sensitivity Analysis Likelihood of a person transitioning within next six months – ROC AUC under curve= 0.974 21 Austin et al. 2014 GSA

21. Home Assessment Technologies are also Treatment Technologies: RCT to Increase Social Interaction in MCI Using Home-based Technologies MCI or Normal randomized to video chat or control group 6 week of daily 30 min video chats 89% of all possible sessions completed; Exceptional adherence – no drop-out Dodge et al. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2015 Intervention group improved on executive/fluency measure. MCI participants spoke 2985 words on average while intact spoke 2423 words during sessions. Discriminated MCI from intact subjects better than the traditional cognitive tests (Animal Fluency and Delayed List Recall).

22. Clinical Practice Implications: ‘Teledementia’- Direct to home visits and assessments... http://psych.nyu.edu/freemanlab/research.htm Alzheimer’s Disease Cooperative Study Home Based Assessment Study (ORCATECH Kiosk System used in HBA Study)

23. Harnessing the power of pervasive computing systems: transform the conduct of clinical trials The Bounty of Biotechnology The Opportunity of Pervasive Computing http://www.phrma.org/sites/default/files/Alzheimer%27s%202013.pdf

24. Transforming Clinical Trials with High Frequency, Objective, Continuous Data: “Big Data” for Each Subject Dodge, 2014, AAIC 500 measured walking speeds per month...My slow walking speed ≠ Your slow walking speed Conventional Method Continuous Measures LM Delayed Recall* Computer Use** Walking Speed** SAMPLE SIZE TO SHOW 50% EFFECT 6881094 SAMPLE SIZE TO SHOW 40% EFFECT 107616148 SAMPLE SIZE TO SHOW 30% EFFECT 191226262 SAMPLE SIZE TO SHOW 20% EFFECT 430058588 MCI Prevention Trial – Sample Size Estimates More precise estimates of the trajectory of change; allows for intra-individual predictions. Reduces required sample size and/or time to identify meaningful change. Reduces exposure to harm (fewer needed/ fewer exposed) Provides the opportunity to substantially improve efficiency and inform go/no-go decisions of trials.

25. Acknowledgements "The smallest act of kindness is worth more than the grandest intention." - Oscar Wilde 26 Profound Thanks to My Amazing Colleagues and the Research Volunteers

26. Many Academic Centers - Collaborators 27

27. Funders 28

28. Companies 29

29. Thank You! kaye@ohsu.edu www.orcatech.org 20061956