Ambient Independence Measures for Guiding Care Transitions

Aging Clinical Trial

— AIMs  

Official title:

Ambient Independence Measures for Guiding Care Transitions

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02566239
• Other study ID #: OHSU9944
• Secondary ID: 5R01AG042191-03
• Status: Completed
• Phase: N/A
• Start date: March 2014
• Est. completion date: August 12, 2019

Study information

• Verified date: July 2021
• Source: Oregon Health and Science University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of this study is to learn more about how to maintain health and independence for seniors by developing tools that collect data constantly from their home. Caregivers can then use this information to make decisions about their health care, such as when an individual may not be able to live independently any longer. Specific Aims of this study are:

• Aim 1: To identify trends in our data that predict health decline. To serve this aim, we want to test a number of tools that we have developed, such as in-home sensors, to determine which ones are best at measuring health risks in seniors. After collecting information for one year, we will look at which tools could be most useful to provide feedback to seniors and their communities about the process of aging.

• Aim 2: To develop a system for analyzing the data we collect and presenting a summary of the data to care teams.

• Aim 3: To validate our data and the computer-based tool in senior community settings.

Description:

The proposed study has the potential to transform current research and clinical practice paradigms of prediction and decision making about independent living. This is accomplished by shifting from reliance on episodic, self-reported or crisis event provoked data to the use of ecologically valid multidimensional and continuous physiological, activity, and behavioral data. This approach has great potential to substantially improve care need and transition decisions. In achieving this goal several innovations beyond available systems and ongoing research are notable. First, grounded by prior studies associating static clinical measures to future placement outcomes, we now contemporaneously and continuously will acquire fundamental physiological measures (weight and walking speed), activity and behavioral measures, thereby improving our ability to proactively discriminate important health and functional change in real time. Using existing in-home activity data collected longitudinally in an aging population combined with simulated data from additional new sensed measures (phone use, medication taking, body composition) we will generate derived novel metrics - AIMs - to provide objective dynamic measures of activity and behaviors that are essential to maintaining independence. These metrics will be used to develop prediction algorithms based on documented transition outcomes from the original data set to be used by care teams (Aim 1). Working care transition professionals will be iteratively queried for the refinement of these objective measures (Aim 2). These care providers' expertise and understanding of key changes that impact independence is invaluable to identification of ambient independence measures that matter, and lead to meaningful care implementation pathways. The efficacy of the final set of measures chosen and built into a user friendly interface for the care team to use (Aim 2) will then be tested (Aim 3) by comparing independently living seniors in one of three comparison groups: 1) installed technology, from which AIMs data will be extracted and provided to the care transition team to aid in transition decisions; 2) installed technology, from which AIMs data will be extracted but will not be available to the transition team; and 3) no technology. We may have insufficient power to recognize significant change between the validation group and the control group. However, this primarily study is intended to test the feasibility of the approach, and to identify those types of AIMs data that are most useful for making transition decisions, which will be used to inform larger, more definitive studies in the future.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 96
• Est. completion date: August 12, 2019
• Est. primary completion date: August 12, 2019
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 70 Years and older

Eligibility

Inclusion Criteria:

• Live alone

• Live independently

• Computer user with internet

Exclusion Criteria:

• Dementia (CDR scale score > 0.5)

• Medical illness that would limit physical participation (e.g. wheelchair use) or likely to lead to death within three years (e.g. terminal cancer)

Related Conditions & MeSH terms

• Aging

Intervention

Other:
• Share activity data with care team
Share participant in-home activity data with retirement community care team.

Locations

Country	Name	City	State
United States	Oregon Health & Science University	Portland	Oregon

Sponsors (2)

Lead Sponsor	Collaborator
Oregon Health and Science University	National Institute on Aging (NIA)

Country where clinical trial is conducted

United States, 

References & Publications (14)

Austin J, Dodge HH, Riley T, Jacobs PG, Thielke S, Kaye J. A Smart-Home System to Unobtrusively and Continuously Assess Loneliness in Older Adults. IEEE J Transl Eng Health Med. 2016 Jun 10;4:2800311. doi: 10.1109/JTEHM.2016.2579638. eCollection 2016. — View Citation

Austin J, Hollingshead K, Kaye J. Internet Searches and Their Relationship to Cognitive Function in Older Adults: Cross-Sectional Analysis. J Med Internet Res. 2017 Sep 6;19(9):e307. doi: 10.2196/jmir.7671. — View Citation

Austin J, Klein K, Mattek N, Kaye J. Variability in medication taking is associated with cognitive performance in nondemented older adults. Alzheimers Dement (Amst). 2017 Mar 6;6:210-213. doi: 10.1016/j.dadm.2017.02.003. eCollection 2017. — View Citation

Kaye J, Reynolds C, Bowman M, Sharma N, Riley T, Golonka O, Lee J, Quinn C, Beattie Z, Austin J, Seelye A, Wild K, Mattek N. Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data. J Vis Exp. 2018 Jul 27;(137). doi: 10.3791/56942. — View Citation

Kaye J. Making Pervasive Computing Technology Pervasive for Health & Wellness in Aging. Public Policy Aging Rep. 2017;27(2):53-61. doi: 10.1093/ppar/prx005. Epub 2017 Jun 9. — View Citation

Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, Sharma N, Mattek N, Wild K, Dodge H, Kaye JA. Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy. Front Aging Neurosci. 2015 Jun 10;7:102. doi: 10.3389/fnagi.2015.00102. eCollection 2015. Erratum in: Front Aging Neurosci. 2015;7:232. — View Citation

Petersen J, Austin D, Mattek N, Kaye J. Time Out-of-Home and Cognitive, Physical, and Emotional Wellbeing of Older Adults: A Longitudinal Mixed Effects Model. PLoS One. 2015 Oct 5;10(10):e0139643. doi: 10.1371/journal.pone.0139643. eCollection 2015. — View Citation

Seelye A, Hagler S, Mattek N, Howieson DB, Wild K, Dodge HH, Kaye JA. Computer mouse movement patterns: A potential marker of mild cognitive impairment. Alzheimers Dement (Amst). 2015 Dec 1;1(4):472-480. Epub 2015 Oct 19. — View Citation

Seelye A, Mattek N, Howieson D, Riley T, Wild K, Kaye J. The impact of sleep on neuropsychological performance in cognitively intact older adults using a novel in-home sensor-based sleep assessment approach. Clin Neuropsychol. 2015;29(1):53-66. doi: 10.1080/13854046.2015.1005139. Epub 2015 Feb 2. — View Citation

Seelye A, Mattek N, Howieson DB, Austin D, Wild K, Dodge HH, Kaye JA. Embedded Online Questionnaire Measures Are Sensitive to Identifying Mild Cognitive Impairment. Alzheimer Dis Assoc Disord. 2016 Apr-Jun;30(2):152-9. doi: 10.1097/WAD.0000000000000100. — View Citation

Seelye A, Mattek N, Sharma N, Riley T, Austin J, Wild K, Dodge HH, Lore E, Kaye J. Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment. Alzheimers Dement. 2018 Feb;14(2):187-194. doi: 10.1016/j.jalz.2017.07.756. Epub 2017 Oct 26. — View Citation

Seelye A, Mattek N, Sharma N, Witter P, Brenner A, Wild K, Dodge H, Kaye J. Passive Assessment of Routine Driving with Unobtrusive Sensors: A New Approach for Identifying and Monitoring Functional Level in Normal Aging and Mild Cognitive Impairment. J Alzheimers Dis. 2017;59(4):1427-1437. doi: 10.3233/JAD-170116. — View Citation

Silbert LC, Dodge HH, Lahna D, Promjunyakul NO, Austin D, Mattek N, Erten-Lyons D, Kaye JA. Less Daily Computer Use is Related to Smaller Hippocampal Volumes in Cognitively Intact Elderly. J Alzheimers Dis. 2016;52(2):713-7. doi: 10.3233/JAD-160079. — View Citation

Wild K, Sharma N, Mattek N, Karlawish J, Riley T, Kaye J. Application of In-Home Monitoring Data to Transition Decisions in Continuing Care Retirement Communities: Usability Study. J Med Internet Res. 2021 Jan 13;23(1):e18806. doi: 10.2196/18806. — View Citation

* Note: There are 14 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Number of Participants With Increased Need for Assistance During 3-year Study Period	Self-reported endorsement to the question "In the past week, is someone newly assisting you with medication management, bathing, dressing or grooming?" OR permanent move from independent living to assisted living or to a health care center	3 years