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Assessing the Feasibility of Multi-modal Biosensing for Monitoring Mobility and Cognition in Older Adults

Assessing the Feasibility of Multi-modal Biosensing for Monitoring Mobility and Cognition in Older Adults

Status
Not yet recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07217951
Enrollment
20
Registered
2025-10-17
Start date
2025-12-31
Completion date
2028-09-30
Last updated
2025-10-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Chronic Stress, Mobility and Independence, Systemic Inflammation

Keywords

wearable sensors, older adults, aging, mobility, inflammation

Brief summary

Current health devices often overlook older users, who may face both health challenges and technology barriers. We are investigating the feasibility of wearable sensors to track posture, heart rate, and breathing, alongside a microneedle patch that collect body fluids to measure stress and inflammation markers. By combining this data, we aim to create an easy to use system that supports personalized, at home health monitoring for older adults.

Detailed description

Devices capable of measuring, interpreting, and providing guidance based on a range of biosignals have significantly broadened the access to health information. Nevertheless, the majority of these platforms are designed for a demographic already proficient in technology, thereby excluding older adults, a group facing unique health challenges and often experiencing technological apprehension. To address this disparity, this study aims to demonstrate the feasibility of employing a wearable eutectogel-sensor patch system to monitor and analyze posture, movement, heart rate, and respiration of older adults within their natural living environments. Furthermore, investigators seek to develop algorithms that correlate these real-time data streams with physical and cognitive performance outcomes. Concurrently, they will investigate the feasibility of employing hydrogel microneedle patches for the passive collection of interstitial fluid (ISF) to measure key biomarkers associated with stress and inflammation, specifically cortisol, dopamine, and C-reactive protein (CRP). These biomarkers serve as critical indicators of physiological stress, mood regulation, and systemic inflammation, which are closely associated with cognitive decline, frailty, and diminished quality of life in the aging population. Eutectogel sensor patches are flexible, breathable, inconspicuous, and designed for long-term wear. They can be positioned strategically to provide direct, continuous, real-time monitoring of posture, motion, heart rate, and respiration. We now propose to determine the feasibility of using a eutectogel sensor patch system to continuously collect posture, motion, heart rate, and respiration data in free-living older adults. Investigators will secondly assess the feasibility of measuring dopamine, cortisol, and CRP from ISF collected with a microneedle patch and correlate them with the same analytes measured in saliva. These analytes were selected due to their relevance in detecting chronic stress and systemic inflammation-conditions that are highly prevalent and often underdiagnosed in the aging population. The research objectives are significant because they aim to bridge the technological and demographic gap through a combined sensing system that enables seamless and meaningful health monitoring in real-world settings.

Interventions

DEVICEEutectogel Sensors and Microneedle Patches

Eutectogel Sensors: While the exact eutectogel formulation in this study has not been previously tested in humans, similar eutectogel-based materials have been employed in wearable biosensing devices with human participants. These systems are typically composed of biocompatible matrices such as gelatin or polyacrylamide combined with deep eutectic solvents (DES) to improve ionic conductivity and flexibility. Microneedle Patches: The microneedle patch in this study is composed of poly(acrylic acid)/gelatin methacryloyl (PAA/GelMA), gelatin/oxidized pectin (Gelatin/OxP), and methacrylated hyaluronic acid/maleic anhydride (MeHA/MA). Although the integrated use of these specific compositions has not yet been evaluated in humans, the materials themselves, as well as similar microneedle architectures, have been thoroughly investigated in vivo and even in clinical trials.

Sponsors

National Institute on Aging (NIA)
CollaboratorNIH
Tufts University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

1. Aged 65 or older 2. Expressed willingness to participate. 3. Able to give written informed consent 4. Able to operate a smartphone and complete surveys

Exclusion criteria

1. Reliance on assistive walking devices 2. Inability to operate a smartphone 3. Unable to complete surveys 4. Unable to give written informed consent 5. Younger than 65 years old 6. Known skin allergies to kinesiology tape (KT tape)

Design outcomes

Primary

MeasureTime frameDescription
Subtle biomechanical signals and interstitial fluid with key biomarkers.From enrollment to end of study period at 8 days.We propose the use of eutectogel-based strain sensors as a soft, conformable platform for capturing subtle biomechanical signals, including posture shifts, respiratory motion, and joint movements. These gels offer unique advantages in comfort and stretchability that make them ideal for long-term use by older adults. Complementing this, we integrate hydrogel-based microneedle patches for the continuous and minimally invasive collection of interstitial fluid to measure key biomarkers: cortisol and dopamine (using the PAA/GelMA microneedle-dPAD system) and C-reactive protein (via the Gelatin/OxP microneedle µPAD platform). These biomarkers are critically relevant to monitoring stress, inflammation, and mental health-all major contributors to declining independence in aging populations. By applying machine learning methods to passively interpret this multimodal dataset, our approach addresses the unmet need for integrated, unobtrusive health monitoring systems tailored to the aging popula

Countries

United States

Contacts

Primary ContactKyla Shea, PhD
Kyla.Shea@tufts.edu16175563370
Backup ContactEmilie Buisson, MS
emilie.buisson@tufts.edu

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026