Digital Measures of Physical Behavior for Alzheimer's Clinical Trials

Capturing Measures that Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development

In collaboration with researchers at the University of Massachusetts Amherst, VivoSense published a new review paper in the Journal of Alzheimer’s Disease. The review summarizes how measures of real-world physical behavior, captured with digital health technologies, can contribute to more holistic assessment of functional independence in Alzheimer’s disease clinical research.

Changes in functional independence are required for a diagnosis of Alzheimer’s disease and meaningful to patients. However, established assessments of functional independence are dependent on informants, prone to ceiling effects, and only capture a limited scope of activities. These challenges necessitate innovative approaches to assess how individuals with Alzheimer’s disease are functioning. This review summarizes how passive, continuous, and remote measurement of real-world physical behavior with digital health technologies has the potential to improve the assessment of functional independence in Alzheimer’s disease.

The article provides evidence of measurement domains that may offer value in Alzheimer’s disease clinical research, including real-world gait, physical activity and sedentary behavior, and life-space mobility. It outlines a roadmap of the research needed to develop, validate, and deploy digital measures of real-world physical behavior in Alzheimer’s disease clinical trials.

View the Paper

This work was supported by the National Institute on Aging and the Massachusetts Artificial Intelligence and Technology Center for Connected Care in Aging & Alzheimer’s Disease (grant number 5P30AG073107-02 Pilot A2 and Pilot 3), as well as the Army Research Laboratory Cooperative Agreement W911NF2120208 and the Massachusetts Life Sciences Center.

Shelby Bachman, PhD

Shelby Bachman, PhD

Shelby Bachman, PhD VivoSense Research Scientist, uses data from wearable sensors to develop real-world digital measures that can be deployed in clinical trials. Her academic background is in neuroscience and gerontology. During her PhD, she studied neural, physiological, and behavioral factors associated with successful aging. Shelby has extensive experience designing human-centered studies, collecting physiological data, and applying statistical methods to gather insights from complex datasets.

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