Summary: Sleep Cycle has partnered with researchers from the University of Cambridge and University College London on a study aimed at exploring potential links between sleep disruptions and early Alzheimer’s detection. The research will combine Sleep Cycle’s sleep data with navigational data collected from a separate app developed by the research team. By analyzing these data streams, the study seeks to develop new methods for early diagnosis of Alzheimer’s disease, focusing on the early stages of cognitive decline before dementia symptoms appear. This could lead to more timely interventions and improved patient outcomes.
Key Takeaways:
- Research Partnership: Sleep Cycle collaborates with Cambridge and UCL researchers to combine sleep and navigation data for Alzheimer’s early detection.
- Focus on Early Detection: The study aims to identify early Alzheimer’s signs through changes in sleep and navigational ability before dementia symptoms emerge.
- Mobile-Based Technology: The research leverages mobile apps and machine learning to analyze data collected from participants’ homes, making it scalable and less burdensome.
Sleep technology company Sleep Cycle is partnering with researchers from the University of Cambridge and University College London on an Alzheimer’s study that will combine Sleep Cycle’s sleep data with the team’s previous findings to explore potential links between navigation (sense of direction), sleep disruptions, and the early detection of Alzheimer’s disease.
The study will explore the potential of using mobile-based sensing data to assist in the early diagnosis of Alzheimer’s disease through analysis of navigation and sleep patterns. This study is expected to provide key insights into how changes in sleep, captured using an app designed for use in people’s own homes, may help identify people with early Alzheimer’s disease prior to the onset of dementia.
This research is especially important given that nearly 1 in 9 Americans aged 65 and above has Alzheimer’s disease, and early diagnosis is crucial for early treatments aimed at delaying symptom progression and improving quality of life.
“It is well known that navigational ability and sleep are affected from the earliest stages of Alzheimer’s disease, but neither of these behaviors can be measured using the traditional pen and paper cognitive tests used in memory clinics,” says Abhirup Ghosh, PhD, a researcher on the team from the University of Cambridge, in a release. “Phone apps offer a new approach that allows changes in these behaviors to be captured in and around people’s own homes, which is less burdensome for people, potentially usable at scale, and also relevant to real-life activities. As such, this research could greatly aid efforts to detect Alzheimer’s disease from its earliest stages.”
Combining Sleep and Navigational Data
As part of the collaboration, Sleep Cycle provides premium access to its app with a dedicated backend solution that guarantees privacy for all trial participants. They will combine sleep data with navigational data collected from a separate app developed by the research team. Together, these data streams will be used to create machine-learning models to aid identification of early Alzheimer’s disease.
“At Sleep Cycle, our mission is to improve health worldwide through sleep, and this partnership is an incredible opportunity to make a real difference. By leveraging our proprietary technology and vast sleep data collected from users’ homes, we hope to support this important research and deepen the understanding of Alzheimer’s,” says Mikael Kågebäck, PhD, chief technology officer at Sleep Cycle.
The study commenced in August 2024 with the initial cohort of 50-60 participants, all identified as having mild cognitive impairment. Data collected includes key metrics such as time asleep, sleep efficiency, and snoring, which will be analyzed alongside navigational data alongside a battery of other cognitive tests and amyloid/tau molecular biological markers of Alzheimer’s disease.
The first phase of data collection is now underway, with researchers prepared to analyze its potential role in identifying cognitive decline.
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