Literature DB >> 32047572

Organizing and analyzing the activity data in NHANES.

Andrew Leroux1, Junrui Di1, Ekaterina Smirnova2,3, Elizabeth J Mcguffey4, Quy Cao3, Elham Bayatmokhtari3, Lucia Tabacu5, Vadim Zipunnikov1, Jacek K Urbanek6, Ciprian Crainiceanu1.   

Abstract

The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. However, using the accelerometry data has proven daunting because: 1) currently, there are no agreed upon standard protocols for data storage and analysis; 2) data exhibit heterogeneous patterns of missingness due to varying degrees of adherence to wear-time protocols; 3) sampling weights need to be carefully adjusted and accounted for in individual analyses; 4) there is a lack of reproducible software that transforms the data from its published format into analytic form; and 5) the high dimensional nature of accelerometry data complicates analyses. Here, we provide a framework for processing, storing, and analyzing the NHANES accelerometry data for the 2003-2004 and 2005-2006 surveys. We also provide an NHANES data package in R, to help disseminate high quality, processed activity data combined with mortality and demographic information. Thus, we provide the tools to transition from "available data online" to "easily accessible and usable data", which substantially reduces the large upfront costs of initiating studies of association between physical activity and human health outcomes using NHANES. We apply these tools in an analysis showing that accelerometry features have the potential to predict 5-year all cause mortality better than known risk factors such as age, cigarette smoking, and various comorbidities.

Entities:  

Keywords:  Accelerometry; NHANES; Phyiscal Activity; Prediction

Year:  2019        PMID: 32047572      PMCID: PMC7012355          DOI: 10.1007/s12561-018-09229-9

Source DB:  PubMed          Journal:  Stat Biosci        ISSN: 1867-1764


  19 in total

1.  Assessing the "physical cliff": detailed quantification of age-related differences in daily patterns of physical activity.

Authors:  Jennifer A Schrack; Vadim Zipunnikov; Jeff Goldsmith; Jiawei Bai; Eleanor M Simonsick; Ciprian Crainiceanu; Luigi Ferrucci
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2013-12-14       Impact factor: 6.053

2.  BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.

Authors:  Eunjee Lee; Hongtu Zhu; Dehan Kong; Yalin Wang; Kelly Sullivan Giovanello; Joseph G Ibrahim
Journal:  Ann Appl Stat       Date:  2015-12       Impact factor: 2.083

3.  Multilevel Functional Principal Component Analysis for High-Dimensional Data.

Authors:  Vadim Zipunnikov; Brian Caffo; David M Yousem; Christos Davatzikos; Brian S Schwartz; Ciprian Crainiceanu
Journal:  J Comput Graph Stat       Date:  2011       Impact factor: 2.302

4.  MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS.

Authors:  Chong-Zhi Di; Ciprian M Crainiceanu; Brian S Caffo; Naresh M Punjabi
Journal:  Ann Appl Stat       Date:  2009-03-01       Impact factor: 2.083

5.  Ambulatory Activity Components Deteriorate Differently across Neurodegenerative Diseases: A Cross-Sectional Sensor-Based Study.

Authors:  Jochen Klenk; Karin Srulijes; Cornelia Schatton; Lars Schwickert; Walter Maetzler; Clemens Becker; Matthis Synofzik
Journal:  Neurodegener Dis       Date:  2016-05-21       Impact factor: 2.977

6.  Ambulatory sleep-wake patterns and variability in young people with emerging mental disorders.

Authors:  Rébecca Robillard; Daniel F Hermens; Sharon L Naismith; Django White; Naomi L Rogers; Tony K C Ip; Sharon J Mullin; Gail A Alvares; Adam J Guastella; Kristie Leigh Smith; Ye Rong; Bradley Whitwell; James Southan; Nick Glozier; Elizabeth M Scott; Ian B Hickie
Journal:  J Psychiatry Neurosci       Date:  2015-01       Impact factor: 6.186

7.  Cox Regression Models with Functional Covariates for Survival Data.

Authors:  Jonathan E Gellar; Elizabeth Colantuoni; Dale M Needham; Ciprian M Crainiceanu
Journal:  Stat Modelling       Date:  2015-01-09       Impact factor: 2.039

8.  UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.

Authors:  Cathie Sudlow; John Gallacher; Naomi Allen; Valerie Beral; Paul Burton; John Danesh; Paul Downey; Paul Elliott; Jane Green; Martin Landray; Bette Liu; Paul Matthews; Giok Ong; Jill Pell; Alan Silman; Alan Young; Tim Sprosen; Tim Peakman; Rory Collins
Journal:  PLoS Med       Date:  2015-03-31       Impact factor: 11.069

9.  Daily Patterns of Physical Activity by Type 2 Diabetes Definition: Comparing Diabetes, Prediabetes, and Participants with Normal Glucose Levels in NHANES 2003-2006.

Authors:  Jeremy A Steeves; Rachel A Murphy; Ciprian M Crainiceanu; Vadim Zipunnikov; Dane R Van Domelen; Tamara B Harris
Journal:  Prev Med Rep       Date:  2015

10.  Obesity History and Daily Patterns of Physical Activity at Age 60-64 Years: Findings From the MRC National Survey of Health and Development.

Authors:  Rachel Cooper; Lei Huang; Rebecca Hardy; Adina Crainiceanu; Tamara Harris; Jennifer A Schrack; Ciprian Crainiceanu; Diana Kuh
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2017-10-01       Impact factor: 6.053

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  20 in total

1.  Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity.

Authors:  Junrui Di; Adam Spira; Jiawei Bai; Jacek Urbanek; Andrew Leroux; Mark Wu; Susan Resnick; Eleanor Simonsick; Luigi Ferrucci; Jennifer Schrack; Vadim Zipunnikov
Journal:  Stat Biosci       Date:  2019-04-15

2.  Additive Functional Cox Model.

Authors:  Erjia Cui; Ciprian M Crainiceanu; Andrew Leroux
Journal:  J Comput Graph Stat       Date:  2021-01-01       Impact factor: 2.302

3.  A semiparametric risk score for physical activity.

Authors:  Erjia Cui; E Christi Thompson; Raymond J Carroll; David Ruppert
Journal:  Stat Med       Date:  2021-11-21       Impact factor: 2.373

4.  Registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes.

Authors:  Erin I McDonnell; Vadim Zipunnikov; Jennifer A Schrack; Jeff Goldsmith; Julia Wrobel
Journal:  Biol Rhythm Res       Date:  2022-05-31       Impact factor: 1.362

5.  Fast Univariate Inference for Longitudinal Functional Models.

Authors:  Erjia Cui; Andrew Leroux; Ekaterina Smirnova; Ciprian M Crainiceanu
Journal:  J Comput Graph Stat       Date:  2021-08-04       Impact factor: 1.884

6.  Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.

Authors:  Rahul Ghosal; Vijay R Varma; Dmitri Volfson; Jacek Urbanek; Jeffrey M Hausdorff; Amber Watts; Vadim Zipunnikov
Journal:  Sci Rep       Date:  2022-07-07       Impact factor: 4.996

7.  Evaluating the timing of differences in activity related to depression symptoms across adulthood in the United States.

Authors:  Stephen F Smagula; Chandler S Capps; Robert T Krafty
Journal:  J Affect Disord       Date:  2021-02-02       Impact factor: 4.839

Review 8.  Wearable Devices: Current Status and Opportunities in Pain Assessment and Management.

Authors:  Andrew Leroux; Rachael Rzasa-Lynn; Ciprian Crainiceanu; Tushar Sharma
Journal:  Digit Biomark       Date:  2021-04-19

9.  Fixed-effects inference and tests of correlation for longitudinal functional data.

Authors:  Ruonan Li; Luo Xiao; Ekaterina Smirnova; Erjia Cui; Andrew Leroux; Ciprian M Crainiceanu
Journal:  Stat Med       Date:  2022-05-01       Impact factor: 2.497

10.  Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank.

Authors:  Andrew Leroux; Shiyao Xu; Prosenjit Kundu; John Muschelli; Ekaterina Smirnova; Nilanjan Chatterjee; Ciprian Crainiceanu
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2021-07-13       Impact factor: 6.053

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