evaluating a potential commercial tool for healthcare application for people with dementia
TRANSCRIPT
Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia
Tanvi Banerjee1, Pramod Anantharam1 , William Romine2, Larry Lawhorne3,
Amit Sheth1
1Ohio Center of Excellence in Knowledge-enabled Computing(Kno.e.sis),Wright State University, USA
2Department of Biological Sciences, Wright State University, USA3Boonshoft School of Medicine, Wright State University, USA
2
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
MIT Technology Review, 2012
The Patient of the Future
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Through analysis of physical, physiological, and environmental observations, our cellphones could act as an early warning system to detect serious health conditions, and provide actionable information
canary in a coal mine
Empowering Individuals (who are not Larry Smarr!) for their own health
kHealth: knowledge-enabled healthcare
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1Alzheimer’s Association description of Alzheimer’s statistics, Available online at: http://www.alz.org/alzheimers_disease_facts_and_figures.asp#quickFacts2 Dementia related facts, Available online at: http://www.cdc.gov/mentalhealth/basics/mental-illness/dementia.htm3. K. Vincent, V. A. Velkof, “The next four decades: The older population in the United States: 2010 to 2050.” Washington, D.C.: U.S. Census Bureau; 2010.
5 million
$150 billion
500,000
17.7 billion
People in the U.S. are diagnosed with Alzheimer’s disease1.
Spent on Alzheimer’s alone in a year2
Cause of death in Americans annually
In 2013, hours of unpaid care provided by friends and caregivers3
Dementia: Severity of the problem
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Public level Signals
Population level Signals
Monitoring and Predicting Behavior Patterns in Patients with Dementia
Hexoskin Vest
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● Heart Rate (HR)● Breathing Rate (BR)● Minute Ventilation (MV)● Cadence● Activity
http://www.hexoskin.com/blogs/news/13591246-hexoskin-wins-most-innovative-consumer-health-product-award-at-interface-future-of-health
• Test for activity states that can use some known information– Cadence
• Four healthy young subjects completed four activity states (rest, walk, run, and sprint) 10 mins sit10 mins walk10 mins run1 min sprint
Experimental Design: Controlled Study
Activity State Mean Std. Dev
Rest 0.00 0.00
Walk 103.05 25.03
Run 171.95 10.25
Sprint 185.93 22.00
Cadence Validation Across Subjects and Activity States
Key Question: ● What is the consistency of cadence measures across subjects and activity
levels?
Key Assumption:We treat subject and activity state as random effects → attempt
to generalize across all possible subjects and activity states.
Error Analysis: Variance Components Modeling
Effect Estimate % Variance
Subject 133.89 1.78
Activity 7199.19 95.51
Subject-by-Activity 153.91 2.04
Error 50.67 0.67
Results from the Generalizability Study
• Six subjects (increased age range 27 to 68 to include more older adults)
• Longer study: wore the vest for a minimum of two hours• Condition: At least one gait related activity (for cadence)
Experimental Design: Semi-controlled Study
MANOVA Lambda F* R Sq.
Subject 1 0.128 28922.56 0.871
Subject 2 0.160 26888.12 0.839
Subject 3 0.181 32369.65 0.818
Subject 4 0.255 3275.61 0.744
Subject 5 0.375 8020.30 0.624
Subject 6 0.242 6354.81 0.757
MANOVA: Trying to Run multiple regressions on HR, BR, A, MV as DV and C as IV
F critical is 5.1337 at α=.0001
Mean Std. Dev SE Tdf=5 P-value
C-BR 0.54 0.20 0.08 6.53 0.001*
C-HR 0.16 0.28 0.12 1.38 0.226
C-MV 0.66 0.15 0.06 10.9 0.000*
C-A 0.85 0.07 0.03 28.9 0.000*
BR-HR 0.18 0.28 0.11 1.56 0.180
BR-MV 0.18 0.21 0.09 2.04 0.097
BR-A 0.52 0.18 0.07 7.06 0.001*
MV-HR 0.31 0.28 0.11 2.75 0.040*
MV-A 0.64 0.18 0.07 8.93 0.000*
HR-A 0.19 0.28 0.11 1.69 0.152
*Significant at alpha = 0.05
● Cadence is a highly precise indicator of activity states for our cohort ○ Can therefore be used to detect changes in activity patterns across any
individual● Very little individual-level variation in cadence
○ While expected individual effects exist, they are not likely to confound detection of activity changes
● HR was the least correlated with the other variables
Conclusions
Future Work
Carry out a Large Scale Pilot & Clinical Trial• kHealth kit is prepared to be deployed with over 20 or more
dementia patients
Formulate Prediction of Patient’s dementia symptoms using physiological markers from the vest• Personalization is crucial in such a multispectral condition
Add New Sensors for Monitoring sleep and caregiver stress• We need these sensors for caregiver stress with dementia
episodes in patients
Acknowledgements
Partial support for this research was provided by Wright State University’s VP of Research under a challenge grant.
Thank you
Thank you, and please visit us at http://knoesis.org
For more information on kHealth, please visit us at http://knoesis.org/projects/khealth
Link to the paper: http://www.knoesis.org/library/resource.php?id=2155