Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia
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The document evaluates a commercial healthcare application aimed at monitoring individuals with dementia through the use of advanced wearable technology, specifically a vest that tracks physiological markers. It highlights the potential for early detection of health conditions and the importance of personalized care in managing dementia symptoms. The research calls for a large-scale pilot and clinical trial to validate findings and enhance caregiver support.
Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia
- 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 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ MIT Technology Review, 2012 The Patient of the Future
- 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. AsthmaDementia Heart Failure Liver Cirrhosis kHealth Application Areas 4
- 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 Public level Signals Population level Signals Monitoring and Predicting Behavior Patterns in Patients with Dementia
- 7. 7 Clinical Collaborators Dr. Larry Lawhorne, MD
- 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. Sample Data from a Run Sequence Using the Hexoskin Vest 9
- 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. 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. 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. 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. • 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. 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. 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. ● 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. 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. Acknowledgements Partial support for this research was provided by Wright State University’s VP of Research under a challenge grant.
- 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
Editor's Notes
- #2: Time series observations are readily and naturally available in domains such as finance, health care, smart cities, and system health monitoring. Increasingly, time series observations include both sensor and textual data generated in the same spatio-temporal context creating both challenges for dealing with heterogeneous data and opportunities for obtaining comprehensive situational awareness. For example, in a city, there are machine sensors and citizen sensors observing the city infrastructure (e.g., bridges, power grids) and city dynamics (e.g., traffic flow, power consumption). In this research, we investigate extraction of city events from textual observations and utilize them explain variations in the sensor observations. This will improve our understanding of city events and their manifestations due to the complementary nature of observations provided by the machine sensors and citizen sensors.
- #3: - Larry Smarr is a professor at the University of California, San Diego And he was diagnosed with Chrones Disease What’s interesting about this case is that Larry diagnosed himself He is a pioneer in the area of Quantified-Self, which uses sensors to monitor physiological symptoms Through this process he discovered inflammation, which led him to discovery of Chrones Disease This type of self-tracking is becoming more and more common sdd link to video
- #4: - With this ability, many problems could be solved - For example: we could help solve health problems (before they become serious health problems) through monitoring symptoms and real-time sense making, acting as an early warning system to detect problematic health conditions
- #9: heart rate (HR) in beats per minute (BPM), breathing rate (BR) in BPM, minute ventilation (MV) to detect the volume of gas inhaled or exhaled by the lungs in lungs per minute (LPM), cadence (C), as well as the activity level (A) on a scale of 0 to 1 using accelerometers in the X, Y, and Z directions (resolution of 0.004g)
- #10: New domain for validation of commercial tool heart rate (HR) in beats per minute (BPM), breathing rate (BR) in BPM, minute ventilation (MV) to detect the volume of gas inhaled or exhaled by the lungs in lungs per minute (LPM), cadence (C), as well as the activity level (A) on a scale of 0 to 1 using accelerometers in the X, Y, and Z directions (resolution of 0.004g)
- #13: subject 1: run/ walk For example, Subject 2 has much lower variance for the Sprint activity state whereas Subject 4 has a high variance for the same activity state
- #15: basically the variance within people and within activities: these are the generalizability 2 way random effects ANOVA => take grand mean of all the data, take mean cadence of each person across each activity
- #18: http://en.wikipedia.org/wiki/F-test#One-way_ANOVA_example http://www.utexas.edu/courses/schwab/sw388r7/Tutorials/TwoGroupHatcoDiscriminantAnalysis_doc_html/035_Overall_significance_of_the_discriminant_function_s_.html
- #19: From the r sq. we can see that cadence explained the majority of the variance in the dependent variables F critical K−1 = 4-1, N −K =order of several 1000s http://www.socr.ucla.edu/applets.dir/f_table.html so F critical is 5.1337 at alpha =.0001 H0: model is not useful C explains between 62% - 87% of the variance for all the DVs across the six participants
- #20: http://www.statisticshowto.com/p-value/ Degree of freedom = 5