Novel machine learning and missing data methods for improving estimates of physical activity, sedentary behavior and sleep using accelerometer data

新颖的机器学习和缺失数据方法,可使用加速度计数据改进对身体活动、久坐行为和睡眠的估计

基本信息

  • 批准号:
    10400835
  • 负责人:
  • 金额:
    $ 33.15万
  • 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:
    2021-05-03 至 2025-01-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY We propose novel statistical and machine learning methods for processing and analyzing accelerometer data for studying physical activity, sedentary behavior, and sleep and their effects on outcomes such as cardiovascular health. Methods to accurately estimate and characterize physical activity, sedentary behavior and sleep are crucially needed. Accelerometers have been widely adopted as the standard objective measure of movement in free-living humans. Recent advances have spawned instruments that collect enormous amounts of data that has far outpaced the research community’s ability to meaningfully interpret them. Current studies rely on outdated methods for identifying non-wear and addressing missing data, potentially yielding biased and inefficient estimates of relationships between behavioral activity patterns and outcomes. Importantly, methods for distinguishing between non-wear periods and those that represent sedentary behavior or sleep have not been validated using a gold standard in free-living contexts. The handling of non-wear periods using a statistically valid approach that exploits the multivariate and time- series nature of the data has yet to be developed. Thus, new methods are needed to address current gaps. We propose developing and validating an ensemble classifier to distinguish non-wear time. We will adapt and validate multiple imputation methods that exploit the multivariate and time-series nature of the data to handle non-wear time in analyses that make use of entire profiles of physical activity. Specifically, we will evaluate methods for incorporating multiple imputation for handling missing data from non-wear when applying adaptive clustering algorithms to identify distinct patterns of sleep and activity in order to relate them to outcomes in a generalized linear mixed effects model framework. We will create open-source user-friendly software that can be adopted and enhanced by the research community. Our approach integrates three novel data resources to develop our methods – two with knowledge of true activity and non-wear, and a third generated from a unique four-year longitudinal time series for both accelerometry and cardiovascular risk factor measures in a real- world setting. It offers an opportunity to develop and illustrate methods using data generated from wearable devices in a natural environment that includes missing data. This is the first study to incorporate missing data methods into learning algorithms under a generalized linear mixed effects model framework for accelerometer studies. Such methods will be critical for both observational and clinical trial research in real-world settings, where wear and non-wear time are not directly observed. The resulting insights and tools will also be highly applicable to the processing and analysis of other types of intensively sampled serial data, such as those generated from mobile digital devices.
项目总结

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

MANISHA DESAI其他文献

MANISHA DESAI的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('MANISHA DESAI', 18)}}的其他基金

Data Management and Analysis Core (DMAC) for the Air pollution disrupts Inflammasome Regulation in HEart And Lung Total Health (AIRHEALTH) Study
空气污染扰乱心肺总体健康 (AIRHEALTH) 研究中炎症小体调节的数据管理和分析核心 (DMAC)
  • 批准号:
    10684163
  • 财政年份:
    2021
  • 资助金额:
    $ 33.15万
  • 项目类别:
Data Management and Analysis Core (DMAC) for the Air pollution disrupts Inflammasome Regulation in HEart And Lung Total Health (AIRHEALTH) Study
空气污染扰乱心肺总体健康 (AIRHEALTH) 研究中炎症小体调节的数据管理和分析核心 (DMAC)
  • 批准号:
    10460329
  • 财政年份:
    2021
  • 资助金额:
    $ 33.15万
  • 项目类别:
Novel machine learning and missing data methods for improving estimates of physical activity, sedentary behavior and sleep using accelerometer data
新颖的机器学习和缺失数据方法,可使用加速度计数据改进对身体活动、久坐行为和睡眠的估计
  • 批准号:
    10548871
  • 财政年份:
    2021
  • 资助金额:
    $ 33.15万
  • 项目类别:
Data Management and Analysis Core (DMAC) for the Air pollution disrupts Inflammasome Regulation in HEart And Lung Total Health (AIRHEALTH) Study
空气污染扰乱心肺总体健康 (AIRHEALTH) 研究中炎症小体调节的数据管理和分析核心 (DMAC)
  • 批准号:
    10269333
  • 财政年份:
    2021
  • 资助金额:
    $ 33.15万
  • 项目类别:
2/1 Arrest Respiratory Failure due to Pneumonia (ARREST PNEUMONIA)
2/1 因肺炎导致呼吸衰竭(ARREST PNEUMONIA)
  • 批准号:
    10701727
  • 财政年份:
    2019
  • 资助金额:
    $ 33.15万
  • 项目类别:
2/1 Arrest Respiratory Failure due to Pneumonia (ARREST PNEUMONIA)
2/1 因肺炎导致呼吸衰竭(ARREST PNEUMONIA)
  • 批准号:
    10249960
  • 财政年份:
    2019
  • 资助金额:
    $ 33.15万
  • 项目类别:
Diabetes Clinical and Translational Core
糖尿病临床和转化核心
  • 批准号:
    10407866
  • 财政年份:
    2017
  • 资助金额:
    $ 33.15万
  • 项目类别:
Diabetes Clinical and Translational Core
糖尿病临床和转化核心
  • 批准号:
    10669023
  • 财政年份:
    2017
  • 资助金额:
    $ 33.15万
  • 项目类别:
Biostatistics
生物统计学
  • 批准号:
    10411091
  • 财政年份:
    2007
  • 资助金额:
    $ 33.15万
  • 项目类别:
Biostatistics
生物统计学
  • 批准号:
    10626974
  • 财政年份:
    2007
  • 资助金额:
    $ 33.15万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    EU-Funded
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了