AgingAIMsNCT02566239
Ambient Independence Measures for Guiding Care Transitions
A study testing Share activity data with care team in people with Aging, run by Oregon Health and Science University at 1 site in United States.
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… Read more about this study
- Who is it for?
- Ages 70 years and olderHealthy volunteers can take part
- Conditions the study is for
- Aging
- What is being tested?
- Share activity data with care team
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 96 participants
- Where
- 1 siteUnited States
- Study dates
- Mar 2014to 12 Aug 2019
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 70 years and older
- All sexes
- Healthy volunteers accepted
- Older adult
Who may be able to join
- Live alone
- Live independently
- Computer user with internet
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
United States1 site
Oregon Health & Science University
Portland, Oregon, 97239
About this study
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.
Read the 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.
- Conditions
- Aging
- Being tested
- Share activity data with care team
- Keywords
- aging, ORCATECH, Oregon Center for Aging & Technology, Life Lab, Living Lab, in-home assessment, technology and aging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Share activity data with care teamOther
Share participant in-home activity data with retirement community care team.
| Study group | Role | What they receive |
|---|---|---|
| Shared Data | Experimental | Share activity data with care team.
Participants will have sensor technology installed in their home and caregivers will be provided with the data via our caregiver tool.
This group will be newly enrolled as part of this study and randomized to either the shared data or non-shared data groups. Randomization will be stratified by continuing care retirement community site and include statistical balancing on demographic factors. Receives: Other: Share activity data with care team |
| Non-shared Data | No intervention | Participants will have sensor technology installed in their home and caregivers will NOT have data provided via our caregiver tool. This group will be newly enrolled as part of this study and randomized to either the shared data or non-shared data groups. Randomization will be stratified by continuing care retirement community site and include statistical balancing on demographic factors. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Oct 2015 (estimated)
- Study startMar 2014
- Primary completion12 Aug 2019
- Study completion12 Aug 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for AgingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 96 participants (actual)
Sponsor and investigators
- Lead sponsor
- Oregon Health and Science University
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute on Aging (NIA)
- Responsible party
- Principal investigator, Jeffrey Kaye, Oregon Health and Science University
- Investigators
- Jeffrey Kaye, MD, Principal investigator, Oregon Health and Science University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02566239
- Sponsor's protocol ID
- OHSU9944
- Other IDs
- 5R01AG042191-03
- Acronym
- AIMs
- Status last verified
- Jul 2021
- First submitted
- 14 Aug 2015
- Last updated
- 4 Aug 2021
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 30102277Seelye 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.
PubMed 29107052Austin 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.
PubMed 28877864Seelye 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.
PubMed 28731434Austin 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.
PubMed 28349120Kaye 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.
PubMed 31148911Austin 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.
PubMed 27574577Silbert 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.
PubMed 26967228Seelye 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.
PubMed 26878035Petersen 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.
PubMed 26437228Seelye 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.
PubMed 26191967Lyons 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.
PubMed 26113819Seelye 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.
PubMed 25642948Wild 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.
PubMed 33439144
Study at a glance
From the registry record, last updated by the sponsor on 4 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ambient Independence Measures for Guiding Care Transitions
- ClinicalTrials.gov ID
- NCT02566239
- Acronym / sponsor ID
- AIMs / OHSU9944
- Conditions
- Aging
- Keywords
- aging, ORCATECH, Oregon Center for Aging & Technology, Life Lab, Living Lab, in-home assessment, technology and aging
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 96 participants (actual)
- Lead sponsor
- Oregon Health and Science UniversityUniversity, hospital or other
- Collaborators
- National Institute on Aging (NIA)
- Study start
- Mar 2014 (actual)
- Primary completion
- 12 Aug 2019 (actual)
- Study completion
- 12 Aug 2019 (actual)
- First posted
- 2 Oct 2015 (estimated)
- Results
- Posted 4 Aug 2021
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Shared Data | Experimental | Other: Share activity data with care teamShare activity data with care team. Participants will have sensor technology installed in their home and caregivers will be provided with the data via our caregiver tool. This group will be newly enrolled as part of this study and randomized to either the shared data or non-shared data groups. Randomization will be stratified by continuing care retirement community site and include statistical balancing on demographic factors. |
| Non-shared Data | No intervention | –Participants will have sensor technology installed in their home and caregivers will NOT have data provided via our caregiver tool. This group will be newly enrolled as part of this study and randomized to either the shared data or non-shared data groups. Randomization will be stratified by continuing care retirement community site and include statistical balancing on demographic factors. |
Who may qualify
- Ages
- 70 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
Key inclusion criteria (3 listed)
- Live alone
- Live independently
- Computer user with internet
Key exclusion criteria (2 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Share activity data with care teamShare participant in-home activity data with retirement community care team. | Other | Shared Data |
Primary outcome
Number of Participants With Increased Need for Assistance During 3-year Study Period
Time frame: 3 years
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
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Aug 2021.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 30102277Kaye 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.
- Background: PubMed 29107052Seelye 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.
- Background: PubMed 28877864Austin 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.
- Background: PubMed 28731434Seelye 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.
- Background: PubMed 28349120Austin 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.
- Background: PubMed 31148911Kaye 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.
- Background: PubMed 27574577Austin 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.
- Background: PubMed 26967228Silbert 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.
- Background: PubMed 26878035Seelye 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.
- Background: PubMed 26437228Petersen 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.
- Background: PubMed 26191967Seelye 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.
- Background: PubMed 26113819Lyons 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jeffrey Kaye, MD principal investigator
Oregon Health and Science University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Share activity data with care team in people with Aging, run by Oregon Health and Science University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 4 Aug 2021). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Participants With Increased Need for Assistance During 3-year Study Period (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02566239, which also lists 14 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Aug 2021.