Dementia

Motivation and Background

Alzheimer’s disease affects more than 5 million people claiming over 500,000 Americans annually [1]. As the sixth leading cause of death in Americans [1], its management is challenging. Current reactive healthcare costs more than 17% of GDP in the US [3, 4]. Alzheimer’s related healthcare costs alone are around $150 billion a year to Medicare and Medicaid [1]. To add to the challenge, dementia is an umbrella term that encompasses various forms of the disease such as Alzheimer’s disease, vascular dementia, and Huntington’s disease, to name a few [2]. Not only are the healthcare costs associated with dementia staggering, but the impact on the caregivers is also a critical challenge; in 2013, 15.5 million family and friends provided 17.7 billion hours of unpaid care to those with Alzheimer's and other forms of dementia – care valued at $220.2 billion [1]. With the exponential rise of the older population due to the baby boomers, the number of people with Alzheimer’s disease (the most prevalent form of dementia) is estimated to reach around 13.8 million [1,6]. This creates the strong need for unobtrusive sensing modalities that can help monitor people with dementia and support caregivers.

Dementia: Challenges and Opportunities

With increasing adoption of mobile devices and low-cost sensors, an unprecedented amount of data is being collected [5]. However, in the context of dementia, it is challenging to convert this huge amount of data into actionable information that can: a) help detect behavioral changes in an individual with dementia and b) provide relevant information to the clinician supporting them in treating chronic illness. In our previous work, we derived actionable information from physical and physiological data collected from children diagnosed with asthma. We have developed kHealth kit [9, 28] a semantics-enabled smart mobile application with sensors, to capture observations from machine sensors (quantitative) and people (qualitative) in the domain of asthma [30]. We also have active clinical collaborations to investigate and evaluate the use of kHealth technology for reducing readmission of GI (gastrointestinal) and ADHF (acute decompensated heart failure) patients after their discharge from the hospital.

Preliminary Data Analysis

Figure 3. Box plots for Cadence (C) for the four participants in the controlled setting for the different activity states.

References

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