See how research teams can start using wearables to identify novel digital biomarkers to efficiently generate real-world patient evidence in clinical trials.

Many clinical researchers are exploring novel digital endpoints derived from wearable and connected digital health technologies (DHTs) to generate real-world patient evidence in clinical trials.

DHTs can be deployed remotely to collect data continuously for extended periods of time. High-frequency data streams can be used to derive novel digital endpoints that capture treatment-induced changes more rapidly and with higher levels of sensitivity than established episodic endpoints. Ultimately, novel digital endpoints can improve the efficiency and efficacy of the drug development process by increasing the speed of clinical trials, reducing costs, and enabling faster decision-making.

The FDA’s Patient-Focused Drug Development initiative encourages sponsors to systematically incorporate the patient’s voice throughout the entire drug development process and capture endpoints that represent meaningful aspects of the patient experience. Aspects of health like physical function, mobility, and sleep are relevant and meaningful to patients across multiple therapeutic areas and can be assessed using wearable sensors in home environments to provide truly patient-centric and meaningful endpoints in clinical trials.

In the following presentation, Dr. Jen Blankenship shared details about one of VivoSense’s ongoing pilot studies, which aims to develop and validate novel methods to capture aspects of real-world walking behavior with actigraphy sensors in older adults with and without mild Alzheimer’s disease.

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On-Demand Webinar Presentation

Developing Digital Measures of Function Using Wearable Sensors for Alzheimer’s Disease

Formatted Transcript

Functional Independence Matters to Patients with Alzheimer’s Disease

Functional independence is an aspect of health that matters enormously to patients with Alzheimer’s disease. When you ask patients, their caregivers, and healthcare professionals, as seen in this Venn diagram (Fig. 1), maintaining independence, autonomy, and functioning is among the most important aspects of life for this patient population.

Established Approach to Measuring Function in Alzheimer’s

The aspects of physical function important to key stakeholders are also crucial in evaluating drugs for Alzheimer’s disease. Recently, the FDA granted traditional approval for Lecanemab, a drug for early Alzheimer’s disease.

Figure 2 shows data from Lecanemab’s clinical trial. The primary endpoint was the clinical dementia rating sum of boxes, a composite assessment of cognitive and functional domains. This endpoint asks people close to Alzheimer’s patients about their functional and memory abilities.

It captures eight different domains and serves as a primary endpoint in many Alzheimer’s clinical trials. It indicated that Lecanemab delayed Alzheimer’s progression.

Another commonly used endpoint measures daily living activities, including instrumental and basic activities of daily living (Fig. 3). These endpoints are assessed intermittently, typically every three months. There’s significant variability in patients’ conditions between these assessments.

Limitations of Existing Methods of Function

Additionally, these endpoints are subject to rater and recall bias, relying on informants recalling long periods. For instance, ADL questionnaires often ask, “Over the last four weeks, has Bob been able to dress independently?”

This approach assesses high-level function but captures only a limited window and doesn’t holistically reflect lived experiences. Clinic-based assessments miss the everyday functional challenges older adults face in real-world settings.

The Problem

There is an unmet need right now to develop new methods to capture functioning more holistically and other aspects related to function that matter to patients with Alzheimer’s disease.

Opportunities for Wearable Actigraphy Sensors in Alzheimer’s

We see many opportunities with wearable actigraphy sensors to fill existing gaps. These low-burden sensors capture various aspects of physical behavior in daily life, such as walking, activities, sedentary behaviors, and sleep, which are hard to measure through self-reporting. Patients can wear these devices continuously and passively to monitor movement and function both at home and outside.

While wearable sensors don’t provide a complete picture, they offer a complementary data stream to subjective measures of function. A significant advantage in drug development is their potential to detect treatment effects more efficiently than traditional endpoints.

Endpoints in Alzheimer’s and other clinical trials are often assessed sporadically over long periods, leading to lengthy, costly trials. By using wearable sensors and digital health technologies, we can identify more sensitive endpoints, speeding up the detection of treatment effects or lack thereof. This accelerates the drug development cycle and reduces costs.

Assessing Walking Behaviors in AD with Actigraphy Sensors

Wearable actigraphy sensors offer significant opportunities to fill gaps in clinical research. These low-burden devices capture daily physical behaviors like walking, activities, sedentary behaviors, and sleep. Unlike self-reported measures, which rely on memory, wearable sensors provide continuous, passive monitoring, offering a more accurate view of physical function at home and outside.

While wearable sensors don’t provide a complete picture, they complement existing subjective measures with objective data. Their major advantage in drug development is detecting treatment effects more efficiently than traditional endpoints.

In Alzheimer’s and other clinical trials, endpoints are often assessed sporadically over long periods, resulting in lengthy, costly trials. Integrating wearable sensors and digital health technologies helps identify more sensitive endpoints, speeding up the detection of treatment effects or lack thereof. This accelerates the drug development cycle and reduces costs.

Obtaining Regulatory Qualification

Our long-term research goal is to obtain regulatory qualification for a real-world digital measure of function for patients with Alzheimer’s disease. A qualification is a regulatory opinion that the physical function you are trying to measure is well-defined and a reliable assessment of that function. It allows pharmaceutical companies to use that measure in their clinical trials, and the FDA will accept that this is good enough for regulatory decision-making.

The evidence needed to get that regulatory qualification opinion is that the measure you capture matters to patients: you can reliably quantify it with a given piece of technology or approach, and there’s clinical significance that it matters to the disease itself.

MassAITC Pilot Study Specific Aims

This approach to digital biomarker discovery feeds into our MassAITC pilot study, which has three aims.

1.  Address the measurement problem by developing and validating novel machine-learning methods to detect walking behaviors from inertial sensors.
2. Validate the clinical meaningfulness of digital biomarkers of real-world walking behavior in real-world environments.
3. Evaluate whether these technologies are feasible and acceptable for use in real-world environments.

These aims align with the evidence needed for regulatory qualification. We are at the beginning of this work, but here’s a quick snapshot of where we’re going in the long term.

Muli-disciplinary Academic-Industry Partnership

Our team is a multidisciplinary academic-industry collaboration, bringing together experts in digital health, aging signal processing, and motion capture systems to execute this project.

Participants

In our study, we’ll recruit 20 older adults, both with and without a self-reported diagnosis of mild Alzheimer’s disease. All participants will be at least 65 years or older and free from other types of dementia, traumatic brain injury or cerebrovascular stroke. All participants will have a self-reported diagnosis.

Study Design: Aim 1 -In-Clinic Measures

Our study has two main components. The first is an in-lab component where participants will visit the motion capture laboratory at UMass Amherst. They will perform multiple walking tasks while wearing several wearable sensors at various locations. A motion capture system will assess them, providing our truth data. This data will help develop machine learning algorithms to measure real-world walking behavior.

Study Design: Aims 2 and 3 – Real-World Measures

Those algorithms will be applied to another data stream we’re collecting: two weeks of real-world monitoring. Participants will wear monitors on their wrists and thighs to assess their functioning and walking behaviors at home.

We’ll evaluate group differences in these walking behavior measures to see if some outcomes can detect differences in older adults with and without Alzheimer’s disease.

After completing the wearable sensor period, participants will fill out a questionnaire about their experience with the technology. This is relatively novel, as there’s limited data on how Alzheimer’s patients accept this type of technology.

We’ll generate a summary report of the patient’s walking behavior and provide it to them after the two-week period. They’ll complete another questionnaire to evaluate how meaningful that data is to them.

Delivering wearable sensor data back to participants in a meaningful way is crucial in the pharma world, and this data will help generate that evidence.

Improved Alzheimer’s Care and Treatment

We see the potential for digital measures and endpoints in drug development to improve care for Alzheimer’s patients and older adults. This monitoring can facilitate conversations, identify significant changes, and guide treatment decisions. This technology will enable a more connected care model in the future.