The Alzheimer's Association® estimates that 6.7 million Americans have Alzheimer's disease or a related dementia. By 2050, this number is expected to double. However, exciting research is underway to monitor the health and behaviors of people with dementia, and others with a familial risk of dementia, using smartwatches including the hashtag#applewatch to help prevent the onset of this horrible and expensive disease.
Data aggregated from Apple Watches, while not directly capable of detecting dementia on their own, will contribute valuable insights when processed and analyzed using advanced techniques. The high-frequency patient-generated health and behavior data collected by the Amissa Health application on Apple Watches, such as heart rate variability, activity levels, sleep patterns, and other physiological parameters, can contain subtle patterns that could be indicative of cognitive decline associated with dementia. However, these patterns are often not easily discernible through simple observation and will require sophisticated analysis methods to identify. Amissa Health, a Charlotte, NC based digital health startup, has received funding from the National Institutes of Health and National Science Foundation to develop their research app for the Apple Watch and the team has begun pilot tests with a leading research hospital to gather real-world data from dementia patients. In the coming years, the team at Amissa Health will launch dozens of longitudinal research studies across the U.S. while centralizing data collected from millions of individuals that will help discover digital biomarkers that enable early disease detection.
The detection of dementia involves recognizing changes in cognitive, behavioral, and physiological patterns that are associated with the disease. These changes might manifest as alterations in heart rate variability, sleep disturbances, changes in daily routines, or variations in movement patterns. While raw data from Apple Watches provide a rich source of information, the detection of dementia usually requires more than just the raw data itself. Amissa's platform will integrate data from electronic health records (EHRs) to predict dementia through a comprehensive and longitudinal view of a patient's health history, medical conditions, and treatments. EHRs contain a wealth of structured and unstructured data, including medical diagnoses, laboratory results, medication records, imaging reports, and clinical notes. Through advanced data analysis techniques, including machine learning algorithms, this data can be leveraged to identify patterns and risk factors associated with dementia development. This information can assist clinicians in early identification, intervention, and tailored care plans for patients at risk of developing dementia. As a result, hashtag#datascience will help reduce the number of people at risk of dementia and improve the quality of life for generations to come.
