Dementia Care Research and Psychosocial Factors

Area of Science:

• Gerontology and Biomedical Engineering
• Digital Health and Human-Computer Interaction
• Neurodegenerative Disease Research

Background:

• Wearable technologies offer continuous real-world data on mobility, cognition, and behaviors, presenting opportunities for aging individuals, including those with mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD).
• Translating wearable data into personalized feedback is key to promoting sustained behavior change and improving adherence in clinical trials and observational studies.

Purpose of the Study:

• To illustrate the challenges and opportunities in delivering personalized feedback from wearables for aging and dementia research.
• To focus on co-design of feedback mechanisms, integration of multimodal sensor data, and lessons learned from real-world implementations.

Main Methods:

• Co-designing feedback mechanisms with patients, care partners, and clinicians.
• Integrating multimodal sensor data into personalized interventions.
• Analyzing real-world implementation data from clinical trials and longitudinal studies.

Main Results:

• Wearable-based studies demonstrate effective co-design processes for personalized feedback in cognitive impairment.
• Tailored feedback from wearables influences health behavior changes, participant retention, and adherence in aging and ADRD research.
• Identified challenges include ensuring feedback is valuable and understandable for individuals with cognitive difficulties.

Conclusions:

• Personalized feedback from wearable sensors can enhance health behaviors, improve participant retention, and maximize the effectiveness of clinical trials and observational studies in aging and ADRD.
• A scientific and practical framework for utilizing wearable technology can foster engagement, facilitate behavior change, and improve health outcomes for older adults and those with ADRD.