Clinical trial NCT02566239: Ambient Independence Measures for Guiding Care Transitions
Official title: Ambient Independence Measures for Guiding Care Transitions
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Aging
Keywords: aging
ORCATECH
Oregon Center for Aging & Technology
Life Lab
Living Lab
in-home assessment
technology and aging
Lead sponsor: Oregon Health and Science University
Sponsor class: OTHER
Collaborators: name: National Institute on Aging (NIA)
First posted: 2015-10-02
Enrollment: 96
Sex: ALL
Minimum age (years): 70
Standard ages: OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief 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.

Detailed 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.

Eligibility criteria: 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)

Primary outcomes:
measure: Number of Participants With Increased Need for Assistance During 3-year Study Period
timeFrame: 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Portland
country: United States
facility: Oregon Health & Science University
state: Oregon
Study officials:
affiliation: Oregon Health and Science University
name: Jeffrey Kaye, MD
role: PRINCIPAL_INVESTIGATOR
References:
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):56942. doi: 10.3791/56942.
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.
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.
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.
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.
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. No abstract available.
citation: 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.
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.
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. doi: 10.1016/j.dadm.2015.09.006. Epub 2015 Oct 19.
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.
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.
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.
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.
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.