Jeffrey Kaye, MD Layton Professor of Neurology & Biomedical Engineering Oregon Center for Aging & Technology NIA - Layton Aging & Alzheimer's Disease Center [email protected] A Vision for the Future of Aging & Technology New Yorker, Oct. 1, 2007
Jan 12, 2016
Jeffrey Kaye, MDLayton Professor of Neurology & Biomedical EngineeringOregon Center for Aging & TechnologyNIA - Layton Aging & Alzheimer's Disease [email protected]
A Vision for the Future of Aging & Technology
New Yorker, Oct. 1, 2007
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.
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.
Self Report InaccuracyAre 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
Early detection
Why detecting meaningful change is hard ... And how to improve detection of change
Baseline 3 years 6 years
Mea
sure
Symptoms Reported
Functional range
Change
Is this the disease onset?
Changing the Clinical Paradigm
• New Observations & Discovery• Maximally Effective Clinical Research• Better Outcomes for Patients & Families
• Brief• Episodic• Clinic-based• Subjective• Obtrusive• Inconvenient
Pervasive Computing Wireless Technologies “Big Data Analytics”
• Real-time• Continuous• Home-based• Objective• Unobtrusive• Ambient
SecureInternet
ComputerActivity
Phone Activity
MedTracker
Pervasive Computing Platforms for Assessment: Community-wide ‘Life Lab’
Activity, Sleep, Mobility Time & Location
Doors Opening/Closing
Balance, Body CompositionHeart Rate, Temperature, C02
Kaye et al. Journals of Gerontology: Psychological Sciences, 2011
Device/Sensor “X”
MotionDetectors
Contact/DoorSwitches
PhoneSensors
Load Cells /Bed Sensors
LocationTracking
MedicationTracker
Computer
Raw Sensor Data
Sleep
Phone Use
Weight
MedicationEvents
DeparturesArrivals
GaitVelocity
ComputerInteractions
WeightScale
Mobility
LocationEstimation
Sleep Hygiene
Socialization
MedicationAdherence
Depression
PhysicalImpairments
Direct Assessment Inference
Memory
Attention
Information Level Fusion
Sensor Level Fusion
Functional Status
Change Detection
Data flow for inference engine assessing function
Kitchen Bath Bedroom Living room Computer Session
What can you see?Total Activity: Life Space & EventAnalysisSpiral 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
Norovirus Epidemic: All ill patients identified by decreased room transition events without self report
-34%-34%
-24%-24%
-40%-40%
Campbell, 2011
What can you see?
Healthy Diagnosed with Parkinson’s Disease
Treatment with Sinemet
SEPT-OCT 2012 SEPT-OCT 2013FEB-MAR 2011
What can you see?
June 2011 June 2012June 2010
CDR = 0; MMSE = 28 CDR = 0.5; MMSE = 27 CDR = 0.5; MMSE = 28
xx
Walks: From 2 to 7000 per year
Photo: NYT, 2009
Gait Mat vs Sensor Line
r = .99
Hayes, 2009; Hagler, 2010; Kaye, 2012
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 9Xmore likely in Slow Group
Trajectories of walking speed over time
Dodge, et al. Neurology, 2012
Activity patterns associated with mild cognitive impairment
Differentiation of early MCI: Night-time Behavior & Sleep
Hayes, et al. Alzheimer Dis Assoc Disord. 2014
Normal
NA-MCI
A-MCI
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
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
% A
dher
ent
Median time within 12.0 mins of goal
Median time within 53.4 mins of goal
Significantly Worse Adherence in Lower Cognition Group
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 time0 4 8 12 16 20 24 28 32
14
16
18
20
Months of Continuous Monitoring
Me
an
Da
ys
on
Co
mp
ute
r
Intact MCI
Kaye, et al. Alzheimers Dement. 2014; Silbert et al. submitted, 2015
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…
Annual Clinical Assessment:
Cognition, physical function,
biomarkers, etc.
Demographics:Age, education, socioeconomic
status, etc.
Controls:Number of rooms in
home, etc.
Model CareTransition
Outcome
63,745,978 observations...
Austin et al. 2014 GSA
Predicting Care Transitions: Sensitivity Analysis
• Likelihood of a person transitioning within next six months – ROC AUC under curve= 0.974
20Austin et al. 2014 GSA
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).
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)
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
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 SHOW50% EFFECT
688 10 94
SAMPLE SIZE TO SHOW 40% EFFECT
1076 16 148
SAMPLE SIZE TO SHOW 30% EFFECT
1912 26 262
SAMPLE SIZE TO SHOW 20% EFFECT
4300 58 588
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.
Acknowledgements
"The smallest act of kindness is worth more than the grandest intention."
- Oscar Wilde
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Profound Thanks to My Amazing Colleagues and the Research Volunteers
Many Academic Centers - Collaborators
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Funders
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Companies
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