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Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia

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Presentation of Hexoskin Validation for KHealth's Dementia Project
The paper is available at: http://www.knoesis.org/library/resource.php?id=2155
Citation for the paper: T. Banerjee, P. Anantharam, W. L. Romine, L. Lawhorne, A. Sheth, 'Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia' in Proc. of the Intl Conf on Health Informatics and Medical Systems (HIMS), Las Vegas, July 27-30, 2015.

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Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia

  1. 1. 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, USA 3Boonshoft School of Medicine, Wright State University, USA
  2. 2. 2 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ MIT Technology Review, 2012 The Patient of the Future
  3. 3. 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 3
  4. 4. AsthmaDementia Heart Failure Liver Cirrhosis kHealth Application Areas 4
  5. 5. 5 1Alzheimer’s Association description of Alzheimer’s statistics, Available online at: http://www.alz.org/alzheimers_disease_facts_and_figures.asp#quickFacts 2 Dementia related facts, Available online at: http://www.cdc.gov/mentalhealth/basics/mental-illness/dementia.htm 3. 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
  6. 6. 6 Public level Signals Population level Signals Monitoring and Predicting Behavior Patterns in Patients with Dementia
  7. 7. 7 Clinical Collaborators Dr. Larry Lawhorne, MD
  8. 8. Hexoskin Vest 8 ● 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
  9. 9. Sample Data from a Run Sequence Using the Hexoskin Vest 9
  10. 10. • 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 sit 10 mins walk 10 mins run 1 min sprint Experimental Design: Controlled Study
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. • 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
  15. 15. 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
  16. 16. 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
  17. 17. ● 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
  18. 18. 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
  19. 19. Acknowledgements Partial support for this research was provided by Wright State University’s VP of Research under a challenge grant.
  20. 20. 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

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