Validation of Smartphone-Derived Digital Phenotypes for Cognitive Assessment in Older Adults

验证智能手机衍生的数字表型用于老年人认知评估

• 批准号: 10066084
• 负责人: Katherine Hackett
• 金额: $ 3.21万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066084
• 关键词: Accelerometer; Activities of Daily Living; Address; Aging; Alzheimer' s Disease; American; Assessment tool; Behavior; Behavioral; Bluetooth; Cellular Phone; Classification; Clinic; Clinical; Clinical Research; Clinical Trials; Cognition; Cognitive; Collection; Complement; Computer Systems; Computer software; Consultations; Cost Savings; Custom; Data; Data Analyses; Data Security; Dementia; Devices; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Early Diagnosis; Elderly; Frequencies; Future; Gold; Health; Healthcare Systems; Impaired cognition; Individual; Intervention; Laboratories; Life; Measures; Mental Depression; Metadata; Methodology; Methods; Moods; Neurodegenerative Disorders; Neuropsychology; Observational Study; Older Population; Outcome; Outcome Measure; Participant; Pattern; Phenotype; Philadelphia; Plague; Population; Population Heterogeneity; Prevalence; Procedures; Prognostic Marker; Protocols documentation; Research; Risk; Sampling; Schizophrenia; Secure; Social Interaction; Surveys; Technology; Testing; Text; Time; Training; Treatment Effectiveness; Validation; Visit; base; burden of illness; clinical diagnosis; cognitive ability; cognitive performance; cognitive process; cognitive testing; cost; cost effective; data analysis pipeline; data standards; design; diagnostic accuracy; diagnostic screening; digital; eligible participant; experience; human error; human old age (65+); improved; innovation; mild cognitive impairment; normal aging; novel; pathological aging; personalized intervention; personalized medicine; phenotypic data; prospective; recruit; sensor; social; standard measure; tool; trend; young adult

PROJECT SUMMARY/ABSTRACT At present, one in 10 Americans age 65 and older has Alzheimer's dementia. Further, the global prevalence of dementia is expected to triple from 50 million to over 150 million by 2050. Given this trend, there is a need for increasingly efficient, objective, and sensitive methods to characterize cognition, assess risk, and discriminate individuals at various stages of the disease spectrum. Traditional neuropsychological assessments have been extensively validated for these purposes, yet present several methodological drawbacks such as limited ecological validity, practice effects, and burdensome testing and scoring procedures that are prone to human error. On the other hand, smartphones are ubiquitous, are owned by older adults at increasing rates, and present a unique method to capture subtle cognitive and behavioral profiles in everyday life, outside of the laboratory or clinic. In the proposed observational study, we aim to explore the validity of a digital phenotyping protocol as a novel method for characterizing cognition and function among a heterogeneous group of older adults. Specifically, we will investigate the relations between passively captured smartphone-based digital features and gold-standard neuropsychological measures. We also will explore optimal sampling rates for collection of digital data along with clinically-useful digital phenotypes to inform future studies. A total of 90 participants age 65 and older with normal cognition, mild cognitive impairment, and mild Alzheimer's dementia will be recruited from the Philadelphia region and from a pool of eligible participants who have completed recent aging studies. Participants will use their own personal smartphones naturally during a four-week study period while a secure software application unobtrusively and continuously obtains de-identified raw sensor- based data spanning domains including device activity and usage, spatial trajectories and mobility, and social interactions. Daily surveys delivered via the study software will be used to complement the passively collected data. Participants also will complete traditional neuropsychological measures at a baseline visit to examine construct validity. This study will explore whether digital phenotyping may provide a valid, highly efficient, and naturalistic method for tracking cognition, function and disease burden both cross-sectionally and, eventually, over time. If successful, this tool can be applied in several clinical and research contexts to yield improved assessment efficiency, enhanced diagnostic accuracy, and personalized treatment interventions, which together will generate tremendous cost savings and improved health outcomes. A training plan has been designed in consultation with experts in the fields of digital phenotyping, everyday cognition and function in aging populations, and secure computer systems to develop the applicant's expertise in designing and adapting digital phenotyping platforms for use in aging populations, longitudinal data analysis for continuous multivariate data, and security protections related to personal digital data.

项目摘要/摘要 目前，有十分之一的美国人65岁及以上的美国人患有阿尔茨海默氏症的痴呆症。此外，全球流行率 预计到2050年，痴呆症将从5,000万到1.5亿。鉴于这种趋势，需要 越来越有效，客观和敏感的方法来表征认知，评估风险和歧视 疾病谱系各个阶段的个体。传统的神经心理学评估 出于这些目的进行了广泛的验证，但呈现了一些方法论上的缺点，例如有限 生态有效性，实践效果以及容易对人类的繁重测试和评分程序 错误。另一方面，智能手机无处不在，由老年人以越来越多的速度所有，并且 提出了一种独特的方法，可以在日常生活中捕获微妙的认知和行为概况 实验室或诊所。在拟议的观察性研究中，我们旨在探索数字表型的有效性 协议是一种表征认知和功能的新方法 成年人。具体来说，我们将研究被动捕获的基于智能手机的数字之间的关系 特征和金色标准的神经心理学措施。我们还将探索最佳抽样率 收集数字数据以及临床用途的数字表型，以告知未来的研究。总共90 65岁及以上的参与者患有正常认知，轻度认知障碍和轻度阿尔茨海默氏症的痴呆症 将从费城地区和一群完成的参与者中招募 最近的衰老研究。参与者将在为期四周的研究中自然使用自己的个人智能手机 期间，安全的软件应用程序无效，连续地获得了去识别的原始传感器 - 基于数据跨越域，包括设备活动和使用，空间轨迹和移动性以及社交 互动。通过研究软件进行的每日调查将用于补充被动收集的 数据。参与者还将在基线访问中完成传统的神经心理学措施以检查 构造有效性。这项研究将探讨数字表型是否可以提供有效，高效的 自然主义方法来跟踪认知，功能和疾病负担，横截面以及最终 随着时间的推移。如果成功，则可以在几种临床和研究环境中应用此工具以得到改进 评估效率，提高诊断准确性和个性化治疗干预措施，这些干预措施 共同节省成本并改善健康成果。培训计划已经 设计与数字表型，日常认知和功能领域的专家协商 人口老龄化，并确保计算机系统开发申请人在设计和设计方面的专业知识 适应用于老龄化人群的数字表型平台，连续的纵向数据分析 多元数据和与个人数字数据有关的安全保护。