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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Contemp Clin Trials. 2024 Jan 26;138:107461. doi: 10.1016/j.cct.2024.107461

Smart Technology for Aging and Reducing Disability (STAReD): Study Protocol for a Randomized Pragmatic Clinical Trial

Rachel Proffitt 1, Erin L Robinson 2, Blaine Reeder 3, Emily Leary 4, Leah Botkin 1, Sheila Marushak 1, Lori L Popejoy 3, Marjorie Skubic 5
PMCID: PMC10922904  NIHMSID: NIHMS1965366  PMID: 38280484

Abstract

Background

There is a critical need to improve quality of life for community-dwelling older adults with disabilities. Prior research has demonstrated that a smart, in-home sensor system can facilitate aging in place for older adults living in independent living apartments with care coordination support by identifying early illness and injury detection. Self-management approaches have shown positive outcomes for many client populations. Pairing the smart, in-home sensor system with a self-management intervention for community-dwelling older adults with disabilities may lead to positive outcomes.

Methods

This study is a prospective, two-arm, randomized, pragmatic clinical trial to compare the effect of a technology-supported self-management intervention on disability and health-related quality of life to that of a health education control, for rural, community-dwelling older adults. Individuals randomized to the self-management study arm will receive a multidisciplinary (nursing, occupational therapist, and social work) self-management approach coupled with the smart-home sensor system. Individuals randomized to the health education study arm will receive standard health education coupled with the smart-home sensor system. The primary outcomes of disability and health-related quality of life will be assessed at baseline and post-intervention. Generalizable guidance to scale the technology-supported self-management intervention will be developed from qualitatively developed exemplar cases.

Conclusion

This study has the potential to impact the health and well-being of rural, community-dwelling older adults with disabilities. We have overcome barriers including recruitment in a rural population and supply chain issues for the sensor system. Our team remains on track to meet our study aims.

Keywords: Independent living, Aging, Remote sensing technology, Disabled persons, COVID-19, Rural population

Background

Over 85% of Missouri is rural and individuals in these rural areas are older and have reduced access to regular healthcare compared to individuals living in urban areas of Missouri.1 Limitations in access to healthcare disparities closely align with rates of disability, particularly in the older adult population.2 COVID-19, with the resulting stay-at-home orders and social distancing requirements, has affected those living in Missouri’s rural communities differently than those in denser urban areas.3 Those with disabilities, particularly older adults, comprise a higher risk population for contracting COVID-19 as regular visits for preventive healthcare and management of chronic conditions have been severely disrupted.4 Due to social isolation and stay-at-home orders, many informal caregivers of older adults with disabilities often learn about preventable exacerbations of chronic healthcare conditions or missed visits after-the-fact. Worsening of chronic healthcare conditions or disability due to these factors can lead to an irreversible path to costly nursing home care and increased COVID-19 risk.4 Given the impact of the COVID-19 pandemic, it has become increasingly evident that there is a crucial need to enhance the quality of life for community-dwelling older adults with disabilities and to promote successful aging-in-place.

Smart-home technologies have been used in the past few decades as a novel approach to supporting aging-in-place.5 Home-based monitoring systems6, mobile apps7, and telehealth8,9 have all been explored to support aging-in-place. However, the combination of these technologies paired with self-management approach has not yet been explored. We have developed and tested a smart, in-home sensor system (Figure 1) that monitors health-related activity in independent living apartments.10–22 The sensor system is comprised of multiple components including: (1) a depth sensor that tracks components of walking (e.g., average in-home walking speed) and detects when a fall occurs;23 (2) a bed sensor placed under the mattress that monitors heartrate (ballistocardiogram), respiration rate, and sleep restlessness;24 and (3) multiple motion sensors placed in various rooms of the living space that detect when motion has occurred in the space.25 These sensors have integrated algorithms to detect patterns in activities and routines (e.g., number of times entering/exiting the apartment, amount of time spent in bed) and provide alerts (deviations from average) and actionable data to nursing staff. In a multi-site randomized controlled trial (n = 171), we demonstrated that the system facilitates Aging in Place (care coordination) for older adults with chronic health conditions by identifying early illness and/or injury detection.11 Our work has resulted in successful translation of this technology to our industry partner (Foresite Healthcare, Inc.).

Figure 1:

Figure 1:

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(a) On the left is a depth sensor mounted in the kitchen of a study participant. On the upper right is an ABS bed sensor, optimized for single bed occupants, placed between the mattress and the bed platform. On the lower right is the hydromat bed mat sensor, placed between the mattress and the bed platform. This can be used in beds with more than one occupant. (b) A screenshot of the Clinician Dashboard. In the top graph, the motion sensor data has been processed by the arural Missouri: Biennial report 2018–2019lgorithm to display motion density in the home overall. Darker blue colors indicate more motion. Black indicates the study participant is out of the home. The middle graph indicates a visit to the bathroom by the study participant. In the lower graph, the data from the bed sensor has been processed to display overall sleep restlessness, with dark blue indicating more restless sleep. (c) A screenshot of the Participant Portal Dashboard. Processed data from the depth sensor is displayed in two graphs. The top graph indicates fall risk as calculated by the algorithms. Data points in the green are good, with yellow and red indicating trends towards greater fall risk. The lower graph indicates average walking speed. Again, data points in the green are good, with yellow and red indicating a slower walking speed from the study participant’s average.

Following the success in independent living apartments that had built-in care coordination, we deployed the sensor system in independent senior living apartments (no care coordination/clinical support) and tested participant-facing health messages.26,27 Consumers in this study were able to view the health messages generated from the smart-home sensor system and their own health data in simplified graphs. However, most consumers requested more help to better understand the health messages that were generated automatically from trends in the sensor data. Additionally, many consumers did not have the initiative or desire to regularly view their own health data. Thus, many study participants still experienced significant health events and falls.

Older adults may be able to better understand and interpret their own health data generated from the sensor system, set appropriate goals, and collaboratively develop strategies to achieve those goals when there is integrated clinical support. Self-management interventions in healthcare, broadly, empower individuals to make decisions about their health and manage new and existing conditions.28–30 The 5As Behavior Change Model is a framework for a self-management approach to healthcare. Specifically, the 5As Behavior Change Model is designed to guide interventions at the patient-provider interaction and is multidisciplinary in approach.28 We propose to pair the 5As Behavior Change Model self-management intervention with the smart-home sensor system to support rural, community-dwelling older adults living in rural Missouri.

The specific aims of the project are to: (1) Evaluate the effect of a technology-supported self-management intervention as compared to a standard health education intervention on disability and health-related quality of life after 1 year; and (2) Evaluate the effect of a technology-supported self-management intervention as compared to a standard health education intervention on secondary health outcomes (depression, anxiety, occupational performance, and caregiver burden), rates of falls, and healthcare usage; and (3) Develop implementation guidance for scaling the technology-supported self-management intervention beyond this study.

Methods

Design

This study is a prospective two-arm randomized, pragmatic clinical trial. Participants randomized to the self-management study arm (experimental) will receive a multidisciplinary (nursing, occupational therapy, and social work) self-management intervention paired with the smart-home sensor system. Participants randomized to the health education study arm (control) will receive standard health education from an occupational therapist (OT) paired with the smart-home sensor system (Figure 2). This study has been registered at clinicaltrials.gov (Identifier: NCT05379504).

Figure 2:

Figure 2:

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The flow of study participants through the study from initial screening to the final post-intervention visit. Quarterly reviews are conducted for both study arms at 3, 6, 9, and 12 months.

Sample Size Calculation

A total sample size of 64 will allow for 32 participants in each group required for this study after attrition. We estimate the sample size based on preliminary data, the sample size required for the planned statistical analyses, and the minimal clinically important difference (MCID) for the primary outcome measures. For this sample, we also assumed 80% power to detect a medium effect size with a significance level of 0.05 and a correlation of 0.5 between time points. For the PROMIS-29, the MCID ranges from 2 to 5 points depending on the specific population of interest.31 Assuming a 4-point change from baseline, a sample size of 48 will be able to detect a medium effect size (0.4) given a mean score of 50 with a standard deviation of 10.31 For the Katz ADL Index, the MCID of 1 point is based on similar studies.32 The same sample size of 48 will be able to detect a medium effect size assuming a standard deviation of 1.5. Attrition is to be expected for this study. In our prior and ongoing research, we have observed attrition rates ranging from 10–50%. The most common reasons for attrition were participant death and leaving the facility (e.g., transfer to a higher level of care). We anticipate an attrition rate in those bound (40%) for the proposed study because the intervention will occur in private community dwellings. Therefore, we will have a target sample size of 64 to account for attrition.

Recruitment and Screening

Participants will be recruited to the study through convenience sampling (local print, electronic, and radio media advertisements), targeted outreach (local Area Agencies on Aging, University of Missouri Health clinics, local stroke and Parkinson’s disease support groups, rural pharmacy networks, local home health agencies, and local health fairs), and screening electronic health records (University of Missouri Healthcare) for adults that meet the first two inclusion criteria. All potential participants will be screened against the full inclusion and exclusion criteria listed below. A blank informed consent form will be sent to eligible individuals who express interest in the study. Eligible individuals will be encouraged to review the informed consent form before the baseline visit, including reviewing it with their trusted support. The University of Missouri Institutional Review Board approved this study (IRB #2043542). Written, informed consent to participate will be obtained from all participants.

Inclusion and Exclusion Criteria

Participants will be included if they: (1) are over the age of 65; (2) reside in a rural area (defined as core census block <1000 people per square mile per 2020 US Census data); (3) have self-reported difficulty with at least 1 self-care task or 2 daily living tasks as assessed with the Katz ADL screening instrument; (4) have internet access; (5) are able to stand with or without assistance (verbal confirmation); and (6) can identify a trusted support individual (does not have to live with them) that is willing to receive fall alerts.

Participants will be excluded if they: (1) have a life expectancy of less than one year; (2) have severe cognitive impairment as assessed with the Mini-Mental State Exam instrument with a score <17); reside in a facility that provides care services; (3) have a Katz ADL score of 6; (4) are currently receiving in-home physical therapy, occupational therapy, or nursing; (5) have been hospitalized more than three times in the previous 12 months; and (6) plan to change residences in the next year.

Randomization

At the time of screening, participants will be randomly assigned to one of two study arms (experimental or control). A computer-generated list with block order (factor of 4, ABAB-style) will be used to ensure random assignment. If participants do not initially meet the inclusion criteria, withdraw prior to the baseline visit, or do not complete the study, their specific study arm assignment in the randomization list will not be re-used or saved for the next screening. To account for anticipated withdrawals, the randomization list will be 30% greater than the calculated sample size including anticipated attrition. However, if needed, the study’s statistician will generate an additional list if the first one is exhausted during the recruitment phase. The study participant will not know their group assignment until after baseline assessments are completed.

Intervention

Smart Home Sensor System

All participants will have the sensor system installed in their home at the initial baseline study visit. At the time of this writing, the total cost of a sensor system for one home is approximately $3,500 is covered in full by the funding for this study. The sensor system includes a hydraulic bed sensor, motion sensors, and two depth sensors placed in high traffic areas (Figure 1a). Raw data (e.g., motion triggers, depth frames) from the sensors will be sent to our secure servers via the study participant’s internet (see inclusion criteria #4). The raw data will be stored and processed on our secure, HIPAA compliant servers. During processing, the data will be run through the health assessment algorithms and clinically actionable data, health alerts, and health messages will be displayed via the Clinician Dashboard (Figure 1b). A very simplified version of the processed health data and consumer-facing health messages will be displayed on the Participant Portal Dashboard (Figure 1c) for the study participant and their trusted support to view as desired. All study participants will be provided with a tablet computer (Lenovo m8) to access the Participant Portal Dashboard.

Self-Management Intervention (Experimental)

Participants randomized to the Self-Management study arm will receive the technology-supported self-management intervention from a multidisciplinary team of an OT, nurse (RN), and social worker (SW). These three disciplines were chosen to deliver the intervention from our prior work in care coordination in independent living facilities11 and related research focused on reducing disability in the older adult population33. There will be a minimum of four intervention sessions with each healthcare profession (OT, RN, and SW). The multidisciplinary team will appoint one member of the team as the lead intervener for each study participant. The lead intervener will be the main point of contact for the study participant and recipient of the smart-home sensor system health alerts, health messages, and fall alerts (see “Fall detection” section below). The lead intervener will have three additional sessions with the participant (Figure 2). All intervention sessions will be conducted via telehealth (Zoom34) using the tablet computer provided to the study participant.

Upon evaluation, areas of concern will be identified, and SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) goals35 will be proposed to address these concerns. A personal action plan to be used for the duration of the study will be developed by the care team in collaboration with the participant. Quarterly interviews, occurring at months 3, 6, 9 and 12, (see “Outcomes” below) will use Goal Attainment Scaling to assess participant progress on SMART goals and adjust goals if necessary. The content of these sessions will focus on helping the participant understand their health data, assisting them with any technology issues, and providing the participant with education on their condition(s) and any requested resources.

The study interveners will guide the study participant towards their goals using problem-solving strategies, strategy training, and accountability checks. This approach is based on the 5As Behavior Change Model.28–30 The 5As (Ask, Advise, Assess, Assist, and Arrange) will be addressed through the integration of the self-management intervention and sensor system. See the Case Study in Figure 3 for an example of the integration of the self-management intervention with the smart-home sensor system. If the participant identifies a goal that might require something hands-on, the team uses a guided problem-solving approach to have the participant identify the most appropriate healthcare provider to contact. The team may also work with the participant to develop a list of questions for their healthcare provider, identify local contacts or resources, or review their current health insurance plan for coverage. The study interveners work as a true team and rely on shared expertise to work with the participants towards achieving their goals.

Figure 3:

Figure 3:

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A case study example of the 5As self-management intervention integrated with the smart-home sensor system.

Standard Health Education Intervention (Control)

Participants randomized to the Standard Health Education study arm will receive standard health education. This arm will serve as the control for the study. All intervention sessions will be delivered by an OT via telehealth (Zoom34) using the tablet computer provided to the study participant. The intervention sessions will occur in months 1, 3, 6, 9 and 12, coinciding with the quarterly interviews in months 3, 6, 9, and 12 (see “Outcomes” section below) (Figure 2). During the intervention sessions, the OT will assist the study participant with any technology issues, provide the participant with education on their condition(s), provide any requested resources. The OT will not coach the participant, use problem-solving approaches, or provide strategy training.

Smart-Home Sensor System Fall Detection

The smart-home sensor system algorithms have been trained over several years to detect falls, specifically falls that have the potential to be injurious.13,14 If the depth sensor component of the sensor system identifies a fall (high velocity with ground contact), the system will trigger text alerts to the lead intervener, the research manager, the study participant’s trusted support, and the study participant. A link to a short depth video (usually 10 seconds or less) will be sent via text message. The research team will determine if the fall alert was a true fall or if it was a false alert by viewing the short depth video. A true fall is when a participant contacts the ground at a high velocity unintentionally, whether that be a controlled or an uncontrolled descent.

When the alert is confirmed to be a true fall, a member of the research team will contact the participant and/or their trusted support. The research team member will check on the status of the participant and assist them in contacting emergency services if needed. If the team member is unable to contact the participant, the trusted support will be notified. Should the team member be unable to contact the participant and their trusted support, and the participant appears to be on the floor with a possible injury, the team member will call the participant’s local emergency services.

Participants and their trusted support are informed at the beginning of and throughout the study that this system will not be used in lieu of systems like Life-Alert®. The research team will respond to fall alerts during business hours, Monday-Friday from 7am-7pm CST excluding holidays. The trusted support will be responsible for contacting emergency services in the event of a true fall outside of business hours. If a true fall occurs outside of business hours, the research manager will follow up with the study participant and/or their trusted support upon discovery of the fall event during return to business hours.

Assessment and Outcomes

Following informed consent, study outcomes will be assessed at baseline and post-intervention (12 months). At baseline, all assessments will be completed in-person. A blinded assessor, who is an OT, will complete all assessments at the post-intervention visit. See Table 1 for details on the study outcome measures.

Table 1.

Study Outcomes and Assessment Timeline

Assessment Construct Measured Administration Timepoint
Baseline Post-Intervention
Demographics and Health History Demographic information and health history ✓
Technology Experience Profile36 Use of different technologies ✓
Patient-Reported Outcomes Measurement Information System (PROMIS)31 Self-reported physical, mental and social health ✓ ✓
Hospital Anxiety and Depression Scale (HADS)37 Anxiety and depression ✓ ✓
Patient Activation Measure38 Individual knowledge, skills and confident in managing one’s own health and healthcare ✓ ✓
Telehealth Usability Questionnaire (TUQ)39 Usability factors of telehealth systems ✓
Katz ADL*32 Functional status based on ability to perform activities of daily living independently ✓ ✓
Lawton IADL*40 Independent living skills ✓ ✓
Timed Up and Go (TUG)*41 Mobility ✓ ✓
Range of motion, manual muscle testing, upper extremity coordination* Upper extremity range of motion, strength, and coordination ✓ ✓
Environmental Checklist* Environmental issues and barriers ✓ ✓
Canadian Occupational Performance Measure (COPM)*42 Self-perception of performance and satisfaction with performance in everyday activities ✓ ✓
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Note:

*

Administered by an occupational therapist-registered.

Quarterly interviews will be conducted at months 3, 6, 9 and 12, with the participant to understand if and how they interact with their health data through the Participant Portal Dashboard. Healthcare utilization (e.g., specialist visits, emergency room visits), updates on medical conditions/diagnoses, and updates on medications taken will be recorded during the quarterly interviews.

Intervention Fidelity

All telehealth visits for both study arms will be recorded via Zoom and stored for later review to ensure fidelity of treatment delivery. Monthly, a senior investigator in each discipline (OT, RN, and SW) will review the recording for their respective discipline. A checklist will be completed for each visit to assess protocol fidelity. Deviations and concerns will trigger a meeting of the senior investigators to review performance. If necessary, a meeting with the study intervener(s) will be scheduled to update training and ensure understanding and best practice. The study intervener will then be monitored weekly until compliance reaches 80% for a period of 4 weeks.

Retention

Multiple methods of contact (e.g., phone, email, trusted support phone) will be solicited from the study participant during enrollment. Reminder phone calls will be made one day prior to each telehealth session, baseline, and post-intervention. At the beginning of the study, all participants will be provided with an information packet that will include an overview of the study and the commitments required to participate. This includes a list of all study visits with spaces to write in dates and times of their scheduled (virtual) visits. Scheduling will be based on the participant’s availability. Participants will be reminded of their study appointment times and provided with contact information in case he/she needs to cancel or reschedule. All participants will be paid $50 at baseline and at each of the four quarterly interviews.

Adverse Events and Safety

All adverse events, including serious adverse events, and unanticipated problems will be collected on the Adverse Event Form for each study participant from enrollment to intervention completion. All serious adverse events and unanticipated problems will be reported to the Safety Officer, the IRB, and the funding Program Officer. An individual who dies, moves out of their home and is unable to move the sensor system with them to their new home, or moves into a higher level of care (e.g., nursing home) will be discontinued and unenrolled from the study. The Safety Officer will be consulted for all individuals who experience significant health events or have more than one fall in a month. The Safety Officer has the final decision on removal of the participant from the study and discontinuation of the intervention.

Proposed Data Analysis Plan

Demographic characteristics will be compared at baseline between study arms to examine any differences that may have occurred during randomization, overall and accounting for sex. Descriptive statistics and confidence interval estimates will be calculated for the two time points by study arm to provide preliminary estimates of change without adjustment for covariates. Data will be examined to ensure statistical test assumptions are met prior to analysis. A fixed effects repeated measures ANOVA model with study arm, time, and arm*time interaction will be fit for each primary outcome of interest with covariates in the model, age, sex, and technology proficiency to identify intervention effects (as measured by the Technology Experience Profile31). This model will allow comparisons between study arms, within study arms, and over time.

To evaluate the effect of the sensor system on secondary health outcomes (depression, anxiety, occupational performance, and trusted support burden), rates of falls, and healthcare usage while accounting for possible influencing factors such as internet capabilities, dwelling square footage, participant routines, social support, and caregivers, generalized estimating equations model will be fit. To track the effect of the sensor system over time, time will be included as a variable in the model. This will estimate the population-averaged effect over time adjusted for the factors included in the model. Sex will be included as a covariate.

Protocol non-adherence is unlikely given the intervention, which relies upon health care professionals and care teams, however, this will be assessed in the planned sensitivity analyses to identify characteristics that may be associated. Missing data will also be assessed. If less than 10% of values for each metric are missing (at each time point, if applicable), then multiple imputation methods will be considered.

Data collected about home context from participants (quarterly interviews, home safety checklist) will be integrated with intervention team notes and observations. We will use an inductive coding approach and thematic analysis to develop exemplar cases for different types of individuals and living situations. These cases will inform future implementations efforts in the areas of technology installation, configuration, participant training, and technical support. The goal is to develop generalizable guidance to scale technology-supported self-management interventions for older adults and adapt and translate these interventions to other populations.

Discussion

At the time of this publication, two main hurdles have emerged. Gaining the trust of the communities served is important when completing research in any field and we have found that, initially, gaining the trust of the rural communities has been a challenge. Specifically, the rural, older population we are seeking to serve are especially wary of technology. Both the idea of telehealth and the idea of sensors in the home tend to make this population uncomfortable. Participants with less experience in using technology and/or telehealth have continued to need additional assistance with taking part in their telehealth visits and using the tablet computers provided. This population also has expressed concern with the depth sensor recording their movement throughout their home. We have found that this barrier can be overcome by utilizing partnerships with established community providers and current participants to spread awareness about our study via word-of-mouth. Sharing an illustration or a sample video clip of the silhouette-like image from the depth sensor showing no distinguishable features has alleviated the concerns of prospective participants and their trusted support.

The second barrier is supply-chain issues. This study requires a large amount of costly electronic equipment. We have installed a series of sensors in community homes, while also providing the installation equipment, bed sensors and a computer tablet produced by preferred, approved vendors. We have been impacted by shipping delays, order delays due to decreased inventory, and limited stock availability of certain items. This barrier has, at times, slowed our progress and pushed us to have multiple methods to obtain equipment.

Despite these challenges, we have successfully implemented this study protocol and installed sensor systems in the homes of rural, older adults in Mid-Missouri. At the time of this publication, we have 30 participants enrolled and receiving intervention. The findings from this research study will provide evidence of the effect of a self-management intervention paired with a smart home sensor system in reducing disability and improving quality of life for rural older adults.

Acknowledgements

The authors wish to thank Ava Wilson, Sarah Bowes, Alexis Nelson, Courtney Durben, Lexi Nichols, and Charles Gentile for their assistance with study recruitment.

Funding

This work is supported by the National Institute on Aging through the National Institutes of Health [grant #R01AG072935-01A1}. The design of the study and collection, analysis, and interpretation of data is solely the authors’ and does not reflect the views of the National Institutes of Health.

Abbreviations:

MCID

Minimal clinically important difference

OT

Occupational therapist

RN

Registered nurse

SMART

Specific, Measurable, Achievable, Relevant, and Time-bound

SW

Social worker

Footnotes

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Competing interests

RP, ELR, BR, LB, SM, and LLP declare that they have no competing interests. MS has equity stake in Foresite Healthcare, Inc., the manufacturer of the sensor system used in this study.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and materials

The final trial dataset will be made available through the National Archive of Computerized Data on Aging (NACDA), which is an NIH-funded repository. Data will be shared with investigators working under an institution with a Federal Wide Assurance (FWA). The repository has data access policies and procedures consistent with NIH data sharing policies.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The final trial dataset will be made available through the National Archive of Computerized Data on Aging (NACDA), which is an NIH-funded repository. Data will be shared with investigators working under an institution with a Federal Wide Assurance (FWA). The repository has data access policies and procedures consistent with NIH data sharing policies.

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