Combining information from multiple circadian activity rhythm metrics to optimally detect mild cognitive impairment using a consumer wearable
结合多个昼夜节律活动指标的信息,使用消费者可穿戴设备以最佳方式检测轻度认知障碍
基本信息
- 批准号:10300129
- 负责人:
- 金额:$ 24.51万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-05 至 2023-05-31
- 项目状态:已结题
- 来源:
- 关键词:AccelerometerAdultAffectAgeAgingAlgorithmsAlzheimer&aposs disease related dementiaAlzheimer&aposs disease riskAnimalsApple watchBehavior TherapyBehavioralBig DataBiological AssayBiological MarkersBrainCharacteristicsClassificationClinicClinicalCognitiveCohort StudiesCollaborationsCommunitiesConflict (Psychology)DataDementiaDetectionDevelopmentDiagnosisDiagnosticDiseaseEarly DiagnosisEarly InterventionElderlyExploratory/Developmental Grant for Diagnostic Cancer ImagingFractalsFutureGoalsHealthHispanic Community Health Study/Study of LatinosHourHuman ActivitiesImpaired cognitionIncidenceIndividualInterventionLettersMachine LearningMeasurementMeasuresMethodsModelingMonitorNatureNerve DegenerationNeuropsychologyObservational StudyOutcomeParticipantPathogenesisPathologyPatientsPatternPhasePopulationPreventionPrevention approachProcessRegulationResearchResearch PersonnelResourcesRestRiskSamplingScienceSeriesSignal TransductionSleepSourceStructureSystemTestingTimeTrainingTranslatingTranslational ResearchUnited States Dept. of Health and Human ServicesValidationWorkagedarchive dataarchived databasecircadiancognitive functioncohortdementia riskdiagnostic biomarkerexperienceglymphatic clearancehigh rewardhigh riskimprovedindexingmild cognitive impairmentmodifiable behaviornovelpre-clinicalprediction algorithmprimary outcomeprototyperecruitrisk predictionrisk stratificationroutine carescale upscreeningstatistical learningsystematic reviewuser-friendlywearable device
项目摘要
Abstract: Widely-scalable methods for the earlier detection of elevated Alzheimer’s Disease and Related
Dementia (ADRD) would enable earlier intervention and can help reduce/delay disease incidence. Consumer
wearable technologies that passively gather “big data” signals could be leveraged to detect the early signs of
elevated ADRD risk (see NOT-AG-20-017), in a relatively inexpensive and scalable fashion. One promising set
of signals that can be captured by consumer wearable devices, but are currently only assessed in research
settings, reflects the Circadian Activity Rhythm (CAR). Human activity follows a predictable 24-hour pattern
known as the CAR. Various CAR characteristics are disrupted in ADRDs, reflect ADRD biomarkers levels
(even in the pre-clinical stage), and predict future cognitive decline. However, observational studies have yet to
conclusively demonstrate which CAR measure(s) best signal early-stage ADRD processes, and could help
with early risk stratification. Previous studies have used subsets of the available CAR metrics to establish
associations, rather than leveraging multiple metrics to improve ADRD risk prediction. We propose that using a
comprehensive panel of CAR metrics could identify combinations of CAR metrics that are sensitive to ADRD
risk. Furthermore, we propose that the translation of research findings into clinical screening has been difficult
because CAR measurement relies on researcher-, rather than clinic-/user-, friendly systems. To fill these gaps,
we propose leveraging consumer wearables, existing data, sleep/circadian science, and machine learning. Our
overarching goal is to evaluate evidence for a path forward, from observing associations, towards clinically
useful ADRD risk detection with consumer wearables. Our team includes experts in sleep/CAR-related health
risks (Dr. Smagula, PI); neuropsychology and activity in aging (Dr. Gujral, co-I); and time series
analytics/statistical learning (Dr. Krafty, co-I). We partnered with leaders of major cohorts (see letters of
support) that provide the initial data. Aim 1 will compute a comprehensive panel of CAR measures in a sample
of 766 adults aged 50+; then use machine learning to develop algorithms leveraging CAR measures to predict
the likelihood of Mild Cognitive Impairment (MCI; a diagnostic marker of elevated ADRD risk). Aim 2 will use a
new testing sample (n=25 with and n=25 without MCI) to validate if applying this algorithm to data from a
consumer-wearable accurately detects MCI. Dr. Smagula already developed a working prototype measuring
CARs using the Apple Watch called the Circadian Activity Profiling System. This R21 can have impact on the
field of ADRD risk detection by producing: evidence regarding which CAR metrics best signal MCI; an initial
algorithm that combines information regarding CARs to passively detect the likelihood of MCI; and by refining
our system for collecting these signals on a popular consumer wearable (the Apple Watch). We will also
develop collaborations with additional cohorts so that, if we find evidence supporting potential clinical utility of
this approach, we will be prepared to develop a definitive algorithm in an R01 using data from multiple studies.
摘要:广泛可扩展的阿尔茨海默病及相关疾病早期检测方法
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Stephen F Smagula其他文献
Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study
使用 myRhythmWatch 平台检测患有和未患有轻度认知障碍的老年人中与认知相关的睡眠/觉醒节律紊乱:可行性和相关性研究
- DOI:
10.2196/67294 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:4.800
- 作者:
Caleb D Jones;Rachel Wasilko;Gehui Zhang;Katie L Stone;Swathi Gujral;Juleen Rodakowski;Stephen F Smagula - 通讯作者:
Stephen F Smagula
Stephen F Smagula的其他文献
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{{ truncateString('Stephen F Smagula', 18)}}的其他基金
Developing a widely-useable wearable Circadian Profiling System to assess 24-hour behavioral rhythm disruption in people with dementia and their family caregivers
开发可广泛使用的可穿戴昼夜节律分析系统,以评估痴呆症患者及其家庭护理人员的 24 小时行为节律紊乱
- 批准号:
10321398 - 财政年份:2021
- 资助金额:
$ 24.51万 - 项目类别:
Combining information from multiple circadian activity rhythm metrics to optimally detect mild cognitive impairment using a consumer wearable
结合多个昼夜节律活动指标的信息,使用消费者可穿戴设备以最佳方式检测轻度认知障碍
- 批准号:
10478935 - 财政年份:2021
- 资助金额:
$ 24.51万 - 项目类别:
Morning Activation Deficits and Depression Symptoms: Mechanisms and Modifiability in Dementia Caregivers
早晨激活缺陷和抑郁症状:痴呆症护理人员的机制和可修改性
- 批准号:
10636933 - 财政年份:2021
- 资助金额:
$ 24.51万 - 项目类别:
Morning Activation Deficits and Depression Symptoms: Mechanisms and Modifiability in Dementia Caregivers
早晨激活缺陷和抑郁症状:痴呆症护理人员的机制和可修改性
- 批准号:
10362081 - 财政年份:2021
- 资助金额:
$ 24.51万 - 项目类别:
Developing a widely-useable wearable Circadian Profiling System to assess 24-hour behavioral rhythm disruption in people with dementia and their family caregivers
开发可广泛使用的可穿戴昼夜节律分析系统,以评估痴呆症患者及其家庭护理人员的 24 小时行为节律紊乱
- 批准号:
10612523 - 财政年份:2021
- 资助金额:
$ 24.51万 - 项目类别:
Sleep-wake, cognitive, and affective risks for a worse course of post-discharge suicidal ideation in older adults with major depression
患有重度抑郁症的老年人出院后自杀意念恶化的睡眠-觉醒、认知和情感风险
- 批准号:
9974894 - 财政年份:2020
- 资助金额:
$ 24.51万 - 项目类别:
Depression in dementia caregivers: Linking brain structure and sleep-wake risks
痴呆症护理人员的抑郁症:将大脑结构与睡眠-觉醒风险联系起来
- 批准号:
10094254 - 财政年份:2017
- 资助金额:
$ 24.51万 - 项目类别:
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