Reducing COVID-19 Related Disability in Rural Community-Dwelling Older Adults Using Smart Technology
Study Details
Study Description
Brief Summary
The social distancing requirements for COVID-19 coupled with the adverse health impacts of social isolation and decreased access to healthcare in rural areas places older adults with disabilities in a dire situation. The smart sensor system to be deployed and studied in this project aims to reduce disability for rural community-dwelling older adults and improve health-related quality of life, including depression and anxiety. An implementation guide will be developed to increase success of future scale-up evaluations.
Condition or Disease | Intervention/Treatment | Phase |
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N/A |
Detailed Description
Over 85% of Missouri is rural and individuals in these rural areas are older and have reduced access to regular healthcare as compared to individuals living in urban areas of Missouri. Those with disabilities, particularly older adults, are at higher risk for contracting COVID-19. There is a critical need to reduce disability and improve quality of life for community-dwelling older adults with disabilities for successful aging-in-place during the COVID-19 pandemic. We have developed, with our partner company Foresite Healthcare, a proven sensor-based technology solution for monitoring health-related behaviors in the home. In a multi-site randomized controlled trial, we demonstrated that the sensor system with nursing care coordination prevents declines in function for older adults living in assisted living facilities. The long-term goal of this research is to support independent living for older adults with disabilities for as long as possible. The purpose of this project is to deploy the sensor system in the homes of rural community-dwelling older adults with disabilities and evaluate the effect of the sensor system on reducing disability and improving health-related quality of life. Using a two-arm randomized controlled trial, the sensor system will be installed in the homes of 64 older adults. Participants randomized to Study Arm 1 will receive a multidisciplinary (nursing, occupational therapy, and social work) self-management intervention paired with the sensor system. This intervention is based on the 5As self-management approach and is a direct translation of the nursing care coordination in our prior research. Participants randomized to Study Arm 2 will have standard health education paired with the sensor system. An implementation guide for future use with different partner agencies will be developed using individual and setting level data collected from Aims 1, 2 and 3 using the RE-AIM framework. The project will be accomplished in three aims. In Aim 1, we evaluate the effect of a sensor system paired with a multidisciplinary self-management intervention as compared to the sensor system paired with standard health education care on disability and health-related quality of life after 1 year. In Aim 2, we will evaluate the effect of the sensor system on secondary health outcomes (depression, anxiety, occupational performance, and caregiver burden), rates of falls, and healthcare usage. In Aim 3, we will collect individual participant data for satisfaction and adoption and stakeholder data about organizational setting. Data from Aims 1, 2 and 3 will be analyzed using RE-AIM to produce implementation guidance contextualized by organizational setting. For older adults with disabilities living in rural areas, the sensor system has the potential to change the approach to healthcare and disability management.
Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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Experimental: Self Management The 5A's Behavior Change Mode [39] is the framework for the self-management intervention. The five "A"s will be addressed through the integration of the self-management intervention and the sensor system. There will be a minimum of four intervention sessions with each healthcare profession (OT, RN, and SW) for 12 visits per participant. |
Behavioral: Self Management
The self-management intervention will be delivered over the course of a year. There will be a minimum of four intervention sessions with each healthcare profession (OT, RN and SW) for 12 visits per participant. The team (OT, RN and SW) will meet twice during the first 2 months to determine a lead interventionist based on the participant's SMART goals and areas of concern. The lead interventionist will have three additional sessions with the participant and will be the point-person for sensor system alerts and messages. Goal Attainment Scaling [83] will be administered during the quarterly interview to assess participant progress on SMART goals. This measure is administered collectively with the participant, provides further accountability, offers opportunities to the participant for reflection on progress, and is a concrete measure of "success" of the self-management intervention.
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Active Comparator: Health Education Participant's randomized to the standard health education arm will receive the intervention at Month 1 and then months 3, 6, 9 and 12. |
Behavioral: Standard Health Education
Participants randomized to the standard health education arm will receive the intervention at month 1 and then months 3, 6, 9, and 12 (coinciding with the quarterly interviews). The participant will use the tablet and telehealth platform to complete the interview and education session with research staff. The content of these sessions will be focused on helping the participant (and family member/caregiver as appropriate) understand their health data, assisting them with any technology issues and providing the participant with education on their condition(s) and any requested resources. Research staff will will also provide any additional health education if there are changes to conditions or new diagnoses after an outside provider visit.
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Outcome Measures
Primary Outcome Measures
- Change in Katz ADL Index [1 year]
Disability
- Change in PROMIS-29 [1 year]
Health-related quality of life
Secondary Outcome Measures
- Change in Hospital Anxiety and Depression Scale [1 year]
Depression and anxiety
- Change in Canadian Occupational Performance Measure [1 year]
Occupational performance
- Change in Patient Activation Measure [1 year]
Patient activation/self-efficacy
- Technology Experience Profile [Baseline]
Experience with technology
Eligibility Criteria
Criteria
Inclusion Criteria:
- Over the age of 65, Live in a rural defined county, Have difficulty with at least 1 self-care task or 2 daily living tasks, Have internet access, Able to stand with or without assistance
Exclusion Criteria:
- Life expectancy less than one year, Severe cognitive impairment (mini mental state exam score <17), Life in a facility that provides care services, Katz ADL Score of 6, Receiving in-home physical therapy, occupational therapy or nursing, Have been hospitalized more than three times in teh previous 12 months, Plan to change residences within the next year
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | University of Missouri | Columbia | Missouri | United States | 65211 |
Sponsors and Collaborators
- University of Missouri-Columbia
- National Institute on Aging (NIA)
Investigators
- Principal Investigator: Rachel M Proffitt, OTD, University of Missouri-Columbia
Study Documents (Full-Text)
None provided.More Information
Publications
- Armitage R, Nellums LB. COVID-19 and the consequences of isolating the elderly. Lancet Public Health. 2020 May;5(5):e256. doi: 10.1016/S2468-2667(20)30061-X. Epub 2020 Mar 20.
- Banerjee D. The impact of Covid-19 pandemic on elderly mental health. Int J Geriatr Psychiatry. 2020 Dec;35(12):1466-1467. doi: 10.1002/gps.5320. Epub 2020 Jun 27.
- Banerjee T, Keller JM, Skubic M. Resident identification using kinect depth image data and fuzzy clustering techniques. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5102-5. doi: 10.1109/EMBC.2012.6347141.
- Barg-Walkow, L.H., Mitzner, T.L., & ROgers, W.A. (2014). Technology Experience Profile (TEP): Assessment and Scoring Guide. HFA-TR-1402). Atlanta, GA: Georgia Institute of Technology, School of Psychology, Human Factors and Aging Laboratory.
- Beutel ME, Klein EM, Brähler E, Reiner I, Jünger C, Michal M, Wiltink J, Wild PS, Münzel T, Lackner KJ, Tibubos AN. Loneliness in the general population: prevalence, determinants and relations to mental health. BMC Psychiatry. 2017 Mar 20;17(1):97. doi: 10.1186/s12888-017-1262-x.
- Boockvar K, Brodie HD, Lachs M. Nursing assistants detect behavior changes in nursing home residents that precede acute illness: development and validation of an illness warning instrument. J Am Geriatr Soc. 2000 Sep;48(9):1086-91.
- Boockvar KS, Lachs MS. Predictive value of nonspecific symptoms for acute illness in nursing home residents. J Am Geriatr Soc. 2003 Aug;51(8):1111-5.
- Boyatzis RE. Thematic analysis and code development: Transforming qualitative information. London and New Delhi: Sage Publications. 1998
- Boyle CA, Fox MH, Havercamp SM, Zubler J. The public health response to the COVID-19 pandemic for people with disabilities. Disabil Health J. 2020 Jul;13(3):100943. doi: 10.1016/j.dhjo.2020.100943. Epub 2020 May 24.
- Center for Disease Control and Prevention. (2012). Preventing Chronic disease. Multiple Chronic Conditions Among US Adults; A2012 Update. Retrieved from http://www.cdc.gov/pcd/issues/2014/13_0389.htm Accessed May 15, 2020.
- Connelly K, Molchan H, Bidanta R, Siddh S, Lowens B, Caine K, Demiris G, Siek K, Reeder B. Evaluation framework for selecting wearable activity monitors for research. Mhealth. 2021 Jan 20;7:6. doi: 10.21037/mhealth-19-253. eCollection 2021.
- Cup EH, Scholte op Reimer WJ, Thijssen MC, van Kuyk-Minis MA. Reliability and validity of the Canadian Occupational Performance Measure in stroke patients. Clin Rehabil. 2003 Jul;17(4):402-9.
- Deyo RA, Katrina Ramsey, Buckley DI, Michaels L, Kobus A, Eckstrom E, Forro V, Morris C. Performance of a Patient Reported Outcomes Measurement Information System (PROMIS) Short Form in Older Adults with Chronic Musculoskeletal Pain. Pain Med. 2016 Feb;17(2):314-24.
- Federal Interagency Forum on Aging Related Statistics. (2016). Older Americans 2016: Key Indicators of Wellbeing. Washington DC: US Government Printing Office.
- Fisk AD, Rogers WA, Charness N, Czaja SJ, & Sharit J. (2009). Designing for older adults: Principles and creative human factors approaches. Boca Raton, FL: CRC press
- Fong JH. Disability incidence and functional decline among older adults with major chronic diseases. BMC Geriatr. 2019 Nov 21;19(1):323. doi: 10.1186/s12877-019-1348-z.
- Galambos C, Rantz M, Back J, Jun JS, Skubic M, Miller SJ. Older Adults' Perceptions of and Preferences for a Fall Risk Assessment System: Exploring Stages of Acceptance Model. Comput Inform Nurs. 2017 Jul;35(7):331-337. doi: 10.1097/CIN.0000000000000330.
- Gearing RE, El-Bassel N, Ghesquiere A, Baldwin S, Gillies J, Ngeow E. Major ingredients of fidelity: a review and scientific guide to improving quality of intervention research implementation. Clin Psychol Rev. 2011 Feb;31(1):79-88. doi: 10.1016/j.cpr.2010.09.007. Epub 2010 Oct 7. Review.
- Glasgow RE, Funnell MM, Bonomi AE, Davis C, Beckham V, Wagner EH. Self-management aspects of the improving chronic illness care breakthrough series: implementation with diabetes and heart failure teams. Ann Behav Med. 2002 Spring;24(2):80-7.
- Glasgow RE, Klesges LM, Dzewaltowski DA, Bull SS, Estabrooks P. The future of health behavior change research: what is needed to improve translation of research into health promotion practice? Ann Behav Med. 2004 Feb;27(1):3-12. Review.
- Glasgow RE, Orleans CT, Wagner EH. Does the chronic care model serve also as a template for improving prevention? Milbank Q. 2001;79(4):579-612, iv-v. Review.
- Green LW, Glasgow RE, Atkins D, Stange K. Making evidence from research more relevant, useful, and actionable in policy, program planning, and practice slips "twixt cup and lip". Am J Prev Med. 2009 Dec;37(6 Suppl 1):S187-91. doi: 10.1016/j.amepre.2009.08.017.
- Griffith L, Raina P, Wu H, Zhu B, Stathokostas L. Population attributable risk for functional disability associated with chronic conditions in Canadian older adults. Age Ageing. 2010 Nov;39(6):738-45. doi: 10.1093/ageing/afq105. Epub 2010 Sep 1.
- Hays RD, Spritzer KL, Schalet BD, Cella D. PROMIS(®)-29 v2.0 profile physical and mental health summary scores. Qual Life Res. 2018 Jul;27(7):1885-1891. doi: 10.1007/s11136-018-1842-3. Epub 2018 Mar 22.
- He, W., Larsen, L. J., and U.S. Census Bureau. (2014, December). Older Americans with a disability. Washington, DC, U.S. Government Printing Office.
- Heise D, Rosales L, Skubic M, Devaney MJ. Refinement and evaluation of a hydraulic bed sensor. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4356-60. doi: 10.1109/IEMBS.2011.6091081.
- Heise D, Skubic M. Monitoring pulse and respiration with a non-invasive hydraulic bed sensor. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2119-23. doi: 10.1109/IEMBS.2010.5627219.
- Herbert C, Molinsky JH. What Can Be Done To Better Support Older Adults To Age Successfully In Their Homes And Communities? Health Aff (Millwood). 2019 May;38(5):860-864. doi: 10.1377/hlthaff.2019.00203. Epub 2019 Apr 24.
- Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004 Aug;39(4 Pt 1):1005-26.
- Hogan J. Why don't nurses monitor the respiratory rates of patients? Br J Nurs. 2006 May 11-24;15(9):489-92.
- Ibrahim, O. A., Keller, J., & Popescu, M. (2019) An unsupervised framework for detecting early signs of illness in eldercare. [Paper presentation}. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA
- Ibrahim, O.A., Keller, J.M., & Popescu, M. (2017). Context preserving representation of daily activities in elder care. {Paper presentation}. IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Ibrahim, O.A., Popescu, M., & Keller, J.M. (2017). Unsupervised Analysis of Activity Patterns in Eldercare Monitoring. {Paper presentation}. American Medical Informatics Association (AMIA) Annual Symposium.
- Jain, A., Keller, J., & Popescu, M. (2019, June 23-26). Explainable Al for dataset comparison, {Paper presentation}. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
- Kane, R, L, (1999). A new model of chronic care. Generations-Journal of the American Society on Aging, 23(2), 35-37
- Laver KE, Schoene D, Crotty M, George S, Lannin NA, Sherrington C. Telerehabilitation services for stroke. Cochrane Database Syst Rev. 2013 Dec 16;(12):CD010255. doi: 10.1002/14651858.CD010255.pub2. Review. Update in: Cochrane Database Syst Rev. 2020 Jan 31;1:CD010255.
- Lewis, T.F., Larson, M.F., & Korcuska, J.S. (2017). Strengthening the planning process of motivational interviewing using goal attainment scaling. Journal of Mental Health Counseling, 39(3), 195-210
- Little, L., Wallisch, A., Pope, E., & Dunn, W. (2018). Acceptability and cost comparison of telehealth intervention for families of children with autism.
- Little, L., Wallisch, A., Pope, El, & Dunn, W. (2018). Acceptability and cost comparison of a telehealth intervention for families of children with autism. Infants and Young Children. 31(4), 275-286
- Lydon K, Su BY, Rosales L, Enayati M, Ho KC, Rantz M, Skubic M. Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7175-9. doi: 10.1109/EMBC.2015.7320047.
- Mann DM, Chen J, Chunara R, Testa PA, Nov O. COVID-19 transforms health care through telemedicine: Evidence from the field. J Am Med Inform Assoc. 2020 Jul 1;27(7):1132-1135. doi: 10.1093/jamia/ocaa072.
- Mishra, A.K., Skubic, M., Despins, L.A., Popescu, M., Rantz, M., Keller, J., & Lane, K. (2019). Development of a functional health index for older adults using the electronic health record. {Paper presentation}. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, United States.
- Missouri Department of Health and Senior Services 2019. Health in rural Missouri: Biennial report 2018-2019. http://health.mo.gov/living/families/ruralhealth/pdf/biennial2019.pdf
- Pak, R, & McLaughlin, A. (2010). Designing displays for older adults: CRC Press
- Phillips LJ, DeRoche CB, Rantz M, Alexander GL, Skubic M, Despins L, Abbott C, Harris BH, Galambos C, Koopman RJ. Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls. West J Nurs Res. 2017 Jan;39(1):78-94. doi: 10.1177/0193945916662027. Epub 2016 Jul 28.
- Piotrowicz E, Baranowski R, Bilinska M, Stepnowska M, Piotrowska M, Wójcik A, Korewicki J, Chojnowska L, Malek LA, Klopotowski M, Piotrowski W, Piotrowicz R. A new model of home-based telemonitored cardiac rehabilitation in patients with heart failure: effectiveness, quality of life, and adherence. Eur J Heart Fail. 2010 Feb;12(2):164-71. doi: 10.1093/eurjhf/hfp181. Epub 2009 Dec 30.
- Proffitt R, Glegg S, Levac D, Lange B. End-user involvement in rehabilitation virtual reality implementation research. J Enabling Technol. 2019;13(2):92-100. doi: 10.1108/JET-10-2018-0050. Epub 2019 Jun 17.
- Proffitt R, Lange B, Chen C, Winstein C. A comparison of older adults' subjective experiences with virtual and real environments during dynamic balance activities. J Aging Phys Act. 2015 Jan;23(1):24-33. doi: 10.1123/japa.2013-0126. Epub 2013 Dec 11.
- Proffitt R, Lange B. Feasibility of a Customized, In-Home, Game-Based Stroke Exercise Program Using the Microsoft Kinect® Sensor. Int J Telerehabil. 2015 Nov 20;7(2):23-34. doi: 10.5195/ijt.2015.6177. eCollection 2015 Fall.
- Rantz M, Lane K, Phillips LJ, Despins LA, Galambos C, Alexander GL, Koopman RJ, Hicks L, Skubic M, Miller SJ. Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing. Nurs Outlook. 2015 Nov-Dec;63(6):650-5. doi: 10.1016/j.outlook.2015.08.004. Epub 2015 Sep 8.
- Rantz M, Phillips LJ, Galambos C, Lane K, Alexander GL, Despins L, Koopman RJ, Skubic M, Hicks L, Miller S, Craver A, Harris BH, Deroche CB. Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing. J Am Med Dir Assoc. 2017 Oct 1;18(10):860-870. doi: 10.1016/j.jamda.2017.05.012. Epub 2017 Jul 12.
- Rantz M, Popejoy LL, Galambos C, Phillips LJ, Lane KR, Marek KD, Hicks L, Musterman K, Back J, Miller SJ, Ge B. The continued success of registered nurse care coordination in a state evaluation of aging in place in senior housing. Nurs Outlook. 2014 Jul-Aug;62(4):237-46. doi: 10.1016/j.outlook.2014.02.005. Epub 2014 Feb 22.
- Rantz M, Skubic M, Abbott C, Galambos C, Popescu M, Keller J, Stone E, Back J, Miller SJ, Petroski GF. Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders. Gerontologist. 2015 Jun;55 Suppl 1:S78-87. doi: 10.1093/geront/gnv044.
- Rantz MJ, Scott SD, Miller SJ, Skubic M, Phillips L, Alexander G, Koopman RJ, Musterman K, Back J. Evaluation of health alerts from an early illness warning system in independent living. Comput Inform Nurs. 2013 Jun;31(6):274-80. doi: 10.1097/NXN.0b013e318296298f.
- Rantz MJ, Skubic M, Miller SJ, Galambos C, Alexander G, Keller J, Popescu M. Sensor technology to support Aging in Place. J Am Med Dir Assoc. 2013 Jun;14(6):386-91. doi: 10.1016/j.jamda.2013.02.018. Epub 2013 Apr 3.
- Reeder B, Chung J, Lazar A, Joe J, Demiris G, Thompson HJ. Testing a theory-based mobility monitoring protocol using in-home sensors: a feasibility study. Res Gerontol Nurs. 2013 Oct;6(4):253-63. doi: 10.3928/19404921-20130729-02. Epub 2013 Aug 5.
- Reeder B, Chung J, Le T, Thompson H, Demiris G. Assessing older adults' perceptions of sensor data and designing visual displays for ambient environments. An exploratory study. Methods Inf Med. 2014;53(3):152-9. doi: 10.3414/ME13-02-0009. Epub 2014 Apr 14.
- Reeder B, Chung J. Joe J, Lazar A, Thompson HJ, Demiris G. Understanding Older Adults' Perceptions of IN-Home Sensors Using an Obtrusiveness Framework. HCI International 2016; July 17-22, 2016; Toronto CA: Springer; 2016
- Reeder B, Demiris G. Building the PHARAOH framework using scenario-based design: a set of pandemic decision-making scenarios for continuity of operations in a large municipal public health agency. J Med Syst. 2010 Aug;34(4):735-9. doi: 10.1007/s10916-009-9288-3. Epub 2009 Apr 23.
- Reeder B, Hills RA, Turner AM, Demiris G. Participatory design of an integrated information system design to support public health nurses and nurse managers. Public Health Nurs. 2014 Mar-Apr;31(2):183-92. doi: 10.1111/phn.12081. Epub 2013 Sep 30.
- Reeder B, Turner AM. Scenario-based design: a method for connecting information system design with public health operations and emergency management. J Biomed Inform. 2011 Dec;44(6):978-88. doi: 10.1016/j.jbi.2011.07.004. Epub 2011 Jul 23.
- Reeder B, Zaslavksy O, Wilamowska KM, Demiris G, Thompson HJ. Modeling the oldest old: personas to design technology-based solutions for older adults. AMIA Annu Symp Proc. 2011;2011:1166-75. Epub 2011 Oct 22.
- Research and Training Center on Disability in Rural Communities. 2020. Missouri State Profile. http://rtc.ruralinstitute.umt.edu/state-profile-map-series/missouri-state-profile/
- Ridley S. The recognition and early management of critical illness. Ann R Coll Surg Engl. 2005 Sep;87(5):315-22. Review.
- Robinson EL, Park G, Lane K, Skubic M, Rantz M. Technology for Healthy Independent Living: Creating a Tailored In-Home Sensor System for Older Adults and Family Caregivers. J Gerontol Nurs. 2020 Jul 1;46(7):35-40. doi: 10.3928/00989134-20200605-06.
- Rosales, L., Bo-Yu, S., Skkubic, M. & Ho, K.C. (2017). Heart rate estimation from hydraulic bed sensor ballistocardiogram. Journal of Ambient Intelligence and Smart Environments, 9(2), 193-207
- Santos-Eggimann B, Meylan L. Older Citizens' Opinions on Long-Term Care Options: A Vignette Survey. J Am Med Dir Assoc. 2017 Apr 1;18(4):326-334. doi: 10.1016/j.jamda.2016.10.010. Epub 2016 Dec 9.
- Schoen C, Davis K, Willink A. Medicare Beneficiaries' High Out-of-Pocket Costs: Cost Burdens by Income and Health Status. Issue Brief (Commonw Fund). 2017 May;11:1-14.
- Shelani, S., Levins, T., Robinson, E.L., Lane, K., Park, G., & Skubic, M. (2019). Development and comparison of customized voice-assistant systems for independent living older adults {Paper presentation}. HCII conference, Orlando, FL, United States
- Shelkey M, Wallace M. Katz Index of Independence in Activities of Daily Living (ADL). Director. 2000 Spring;8(2):72-3.
- Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther. 2000 Sep;80(9):896-903.
- Skinner HG, Coffey R, Jones J, Heslin KC, Moy E. The effects of multiple chronic conditions on hospitalization costs and utilization for ambulatory care sensitive conditions in the United States: a nationally representative cross-sectional study. BMC Health Serv Res. 2016 Mar 1;16:77. doi: 10.1186/s12913-016-1304-y.
- Skubic M, Guevara RD, Rantz M. Automated Health Alerts Using In-Home Sensor Data for Embedded Health Assessment. IEEE J Transl Eng Health Med. 2015 Apr 10;3:2700111. doi: 10.1109/JTEHM.2015.2421499. eCollection 2015.
- Skubic, M., Guevara, R. D., & Rantz, M. (2012). Testing classifiers for embedded health assessment. Proc., International Conference on Smart Homes and Health Telematics, Artimino, Italy, pp. 198-205
- Snaith RP. The Hospital Anxiety And Depression Scale. Health Qual Life Outcomes. 2003 Aug 1;1:29.
- Starr, I., Rawson, A., Schroeder, H., & Joseph, N. (1939). Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function , from the heart's recoil and the blood's impacts; the ballistocardiogram, American Journal of Physiology--Legacy Content, 127 (1), 1-28
- Steinman MA, Perry L, Perissinotto CM. Meeting the Care Needs of Older Adults Isolated at Home During the COVID-19 Pandemic. JAMA Intern Med. 2020 Jun 1;180(6):819-820. doi: 10.1001/jamainternmed.2020.1661.
- Stone E, Skubic M, Rantz M, Abbott C, Miller S. Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders. Gait Posture. 2015 Jan;41(1):57-62. doi: 10.1016/j.gaitpost.2014.08.019. Epub 2014 Sep 6.
- Stone EE, Skubic M, Back J. Automated health alerts from Kinect-based in-home gait measurements. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2961-4. doi: 10.1109/EMBC.2014.6944244.
- Stone EE, Skubic M. Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inform. 2015 Jan;19(1):290-301. doi: 10.1109/JBHI.2014.2312180. Epub 2014 Mar 17.
- Stone EE, Skubic M. Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. IEEE Trans Biomed Eng. 2013 Oct;60(10):2925-32. doi: 10.1109/TBME.2013.2266341. Epub 2013 Jun 5.
- Stone, E., & Skubic, M. (2011). Evaluation of an inexpensive depth camera for in-home gait assessment. Journal of Ambient Intelligence and Smart Environments, 3(4), 349-361.
- Suijker JJ, Buurman BM, ter Riet G, van Rijn M, de Haan RJ, de Rooij SE, Moll van Charante EP. Comprehensive geriatric assessment, multifactorial interventions and nurse-led care coordination to prevent functional decline in community-dwelling older persons: protocol of a cluster randomized trial. BMC Health Serv Res. 2012 Apr 1;12:85. doi: 10.1186/1472-6963-12-85.
- Szanton, S.L., & Gitlin, L.N. (2016). Meeting the health care financing imperative through focusing on function. The CAPABLE studies. Public Policy & Aging Report. 26(3), 106-110
- Turk MA, McDermott S. The COVID-19 pandemic and people with disability. Disabil Health J. 2020 Jul;13(3):100944. doi: 10.1016/j.dhjo.2020.100944. Epub 2020 May 28.
- Turner AM, Reeder B, Ramey J. Scenarios, personas and user stories: user-centered evidence-based design representations of communicable disease investigations. J Biomed Inform. 2013 Aug;46(4):575-84. doi: 10.1016/j.jbi.2013.04.006. Epub 2013 Apr 22.
- U.S. Administration on Aging. (2014). A Profile of Older Americans; 2014. Department of Health and Human Services Washington, DC, Retrieved from http://www.aoa.acl.gov/Aging_Statistics/Profile/2014/docs/2014-Profile.pdf. Accessed May 15, 2020.
- Varnfield M, Karunanithi M, Lee CK, Honeyman E, Arnold D, Ding H, Smith C, Walters DL. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial. Heart. 2014 Nov;100(22):1770-9. doi: 10.1136/heartjnl-2014-305783. Epub 2014 Jun 27.
- Wagner EH, Davis C, Schaefer J, Von Korff M, Austin B. A survey of leading chronic disease management programs: are they consistent with the literature? Manag Care Q. 1999 Summer;7(3):56-66.
- Wainer, A.L., Dvortcsak, A., & Ingersoll, B. (2018). Designing for Dissemination: The Utility of the Deployment. Handbook of Parent-implemented interventions for a Very Young Children with Autism, 425
- Wang S, Skubic M, Zhu Y, Galambos C. Using Passive Sensing to Estimate Relative Energy Expenditure for Eldercare Monitoring. Proc IEEE Int Conf Pervasive Comput Commun. 2011 Mar 21:642-648.
- Wang S, Skubic M, Zhu Y. Activity density map visualization and dissimilarity comparison for eldercare monitoring. IEEE Trans Inf Technol Biomed. 2012 Jul;16(4):607-14. doi: 10.1109/TITB.2012.2196439. Epub 2012 Apr 25.
- Wang, S. (2011). Change Detection for Eldercare Using Passive Sensing. PhD. Thesis, Electrical and Computer Engineering Dept., University of Missouri, Columbia, MO
- Weisz, J.R. (2015). Bridging the research-practice divide in youth psychotherapy. The deployment-focused model and transdiagnostic treatment. Verhaltenstherapie, 25(2), 129-132
- Wu, W., Keller, J.M., Skubic, M., Popescu, M., & Lane, K.R. (in review). Early detection of health changes in the elderly using in-home multi-sensors data streams.
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- 1R01AG072935-01A1