The world's population is aging and the increasing number of older adults with Alzheimer's disease and related dementias (ADRDs) is a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine learning and activity learning technologies to design automated health assessment and intervention strategies. The primary objective of the parent study is to design a ?clinician in the loop? smart home to empower individuals in managing their chronic health conditions by automating health monitoring, assessment, and evaluation of intervention impact. This supplement extends our design to monitor, assess, and intervene for individuals with ADRDs and their caregivers. Building on our prior work, the approach will be to generate analytics describing an individual's behavior routine using smart homes, smartwatches, and activity learning (Aim 1). Our trained clinicians will use the analytics to train algorithms for health assessment (Aim 2). In addition, we will introduce brain health interventions to extend brain health and objectively capture intervention both adherence and caregiver support (Aim 3). Clinician guidance is used to train machine learning algorithms to automatically recognize health events (Aim 4). Given the unique challenges that will arise when we include individuals with ADRDs, we also introduce novel methods to track multiple residents in smart environments (Aim 5) and compare behaviors between individuals with ADRDs and the original sample of older adults with chronic health conditions (Aim 6). These contributions are significant because they can extend the health self-management of our aging society through proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers and society. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, technologies that increase functional independence and thus support aging in place while improving quality of life for both individuals and their caregivers are of significant value to both individuals and society.

Public Health Relevance

This proposed supplement builds on a parent ?clinician-in-the-loop? smart home study that automatically identifies health events for adults with chronic conditions in their own homes. In this supplement, we will use clinician-guided smart home technology to recognize health events and the impact of a brain health intervention for adults with ADRDs. This work will demonstrate that intelligent technologies can be used in real-world settings to generate analytics which reflect behavior routines and can be used for automated health assessment. This research is relevant to public health because these technologies can extend the functional independence of our aging society through technology-assisted health self-management, reduce caregiver burden and improve quality of life.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
Project #
Application #
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Bough, Kristopher J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington State University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Fritz, Roschelle L; Dermody, Gordana (2018) A nurse-driven method for developing artificial intelligence in ""smart"" homes for aging-in-place. Nurs Outlook :
Cook, Diane J; Duncan, Glen; Sprint, Gina et al. (2018) Using Smart City Technology to Make Healthcare Smarter. Proc IEEE Inst Electr Electron Eng 106:708-722
Dermody, Gordana; Fritz, Roschelle (2018) A conceptual framework for clinicians working with artificial intelligence and health-assistive Smart Homes. Nurs Inq :e12267
Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J (2017) Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications. IEEE Trans Knowl Data Eng 29:2744-2757
Williams, Jennifer A; Cook, Diane J (2017) Forecasting behavior in smart homes based on sleep and wake patterns. Technol Health Care 25:89-110
Aminikhanghahi, Samaneh; Cook, Diane J (2017) A Survey of Methods for Time Series Change Point Detection. Knowl Inf Syst 51:339-367