Colby T Ford, Jake A Galler, Yingnan He, Cathrine Young, Beata Gabriela K Simpson, Chao-Yi Wu, Jake Pfaffenroth, Eh So Wah, Steven E Arnold, Hiroko H Dodge, Jon A Corkey, Sudeshna Das
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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.</p><p><strong>Results: </strong>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 (e.g., heart rate, pedometer, gyroscope, 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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":94243,"journal":{"name":"The journals of gerontology. 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引用次数: 0
摘要
研究目的本研究探讨了利用可穿戴设备开发数字生物标志物监测阿尔茨海默病和相关痴呆症患者的潜力,重点研究了使用苹果手表跟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 技术栈,该技术栈从智能手表中被动收集时间序列数据,并为临床医生和研究人员提供安全的数据存储、查询和可视化的高频云数据库。该平台包括 i) AmissaWear,这是一款在智能手表上运行的软件应用程序,可通过安全的应用程序接口将信息发送到云数据库;以及 ii) AmissaOrbis,这是一个收集数据的集中式云门户。每位参与者都配备了一块 Apple Watch,用于收集步数、消耗的卡路里、加速计和陀螺仪读数、心率和睡眠信息:从 2023 年 12 月到 2024 年 6 月,七名经神经科医生诊断患有认知障碍的参与者参加了这项研究。研究期间,手表成功收集了 70 多万个观测数据。每个观察结果都包含十多个传感器(如心率、计步器、陀螺仪、加速计)记录的数据。在为期 6 个月的时间里,参与者平均每天佩戴 11.48 小时的 Apple 手表,占 84.91% 的天数,使用率没有随着时间的推移而降低。总体而言,该技术在此次试点中的佩戴依从性和参与度都很高:本研究证明了使用市面上广泛销售的苹果手表对认知障碍患者进行持续监测的可行性,并提供了对他们日常健康和活动模式的深入了解,这将有助于未来数字生物标志物的开发。
Using Apple Watches to Monitor Health and Behaviors of Individuals with Cognitive Impairment: A Case Series Study.
Objectives: 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.
Method: 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.
Results: 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 (e.g., heart rate, pedometer, gyroscope, 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.
Discussion: 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.