Ford Colby T, Galler Jake A, He Yingnan, Young Cathrine, Simpson Beata Gabriela K, Wu Chao-Yi, Pfaffenroth Jake, Wah Eh So, Arnold Steven E, Dodge Hiroko H, Corkey Jon A, Das Sudeshna
Amissa Health, Charlotte, North Carolina, USA.
Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
J Gerontol A Biol Sci Med Sci. 2025 Mar 7;80(4). doi: 10.1093/gerona/glae250.
This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment.
Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of (i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and (ii) AmissaOrbis, a centralized cloud portal for the collected data. Each participant was provided an Apple Watch configured to collect steps, calories burned, accelerometer and gyroscope readings, heart rate, and sleep information.
Seven participants, with cognitive impairment diagnosed by a neurologist, were enrolled in the study from December 2023 through June 2024. The watches successfully collected more than 700 000 observations during the study. Each observation contains data recorded from over a dozen sensors (eg, heart rate, pedometer, gyroscope, and accelerometer). The participants wore Apple Watches for an average of 11.48 hours/day for 84.91% of days during a 6-month period without a decrease in usage over time. Overall, the technology yielded high wear adherence and participation within this pilot.
This study demonstrates the feasibility of using widely available Apple Watches for continuous monitoring of individuals with cognitive impairment and provides insights into their daily health and activity patterns, which could aid in future development of digital biomarkers.
本研究探讨了利用可穿戴设备开发数字生物标志物以监测阿尔茨海默病及相关痴呆症患者的潜力,重点关注使用苹果手表追踪认知障碍老年人的健康和行为的可行性。
数据收集使用了Amissa Health技术堆栈,该技术堆栈被动地从智能手表收集时间序列数据,并提供一个高频云数据库,用于临床医生和研究人员进行安全的数据存储、查询和可视化。该平台由(i)AmissaWear组成,这是一款运行在智能手表上的软件应用程序,通过安全的应用程序编程接口将信息发送到云数据库;以及(ii)AmissaOrbis,一个用于收集数据的集中式云门户。为每位参与者提供了一块配置为收集步数、燃烧的卡路里、加速度计和陀螺仪读数、心率和睡眠信息的苹果手表。
2023年12月至2024年6月,七名经神经科医生诊断为认知障碍的参与者被纳入该研究。在研究期间,手表成功收集了超过700000条观测数据。每次观测包含从十几个传感器(如心率、计步器、陀螺仪和加速度计)记录的数据。在6个月的时间里,参与者平均每天佩戴苹果手表11.48小时,84.91%的日子都在佩戴,且随着时间推移使用情况没有下降。总体而言,该技术在本次试点中产生了较高的佩戴依从性和参与度。
本研究证明了使用广泛可用的苹果手表持续监测认知障碍患者的可行性,并提供了对他们日常健康和活动模式的见解,这可能有助于数字生物标志物的未来发展。