@article {798, title = {Assessing the "physical cliff": detailed quantification of age-related differences in daily patterns of physical activity.}, journal = {J Gerontol A Biol Sci Med Sci}, volume = {69}, year = {2014}, month = {2014 Aug}, pages = {973-9}, abstract = {

BACKGROUND: In spite of evidence that physical activity has beneficial effects on health and age-related functional decline, there is a scarcity of detailed and accurate information on objectively measured daily activity and patterns of such activity in older adults.

METHODS: Participants in the Baltimore Longitudinal Study of Aging (n = 611, 50\% male, mean age 67, range 32-93) wore the Actiheart portable activity monitor for 7 days in the free-living environment. The association between activity and age was modeled using a continuous log-linear regression of activity counts on age with sex, body mass index, employment status, functional performance, and comorbid conditions as covariates.

RESULTS: In the fully adjusted model, continuous analyses demonstrated that overall physical activity counts were 1.3\% lower for each year increase in age. Although there were no differences among morning levels of activity, there was significantly lower afternoon and evening activity in older individuals (p < .01). After adjusting for age, poor functional performance, nonworking status, and higher body mass index were independently associated with less physical activity (p < .001).

CONCLUSIONS: The use of accelerometers to characterize minute-by-minute intensity, cumulative physical activity counts, and daily activity patterns provides detailed data not gathered by traditional subjective methods, particularly at low levels of activity. The findings of a 1.3\% decrease per year in activity from mid-to-late life, and the corresponding drop in afternoon and evening activity, provide new information that may be useful when targeting future interventions. Further, this methodology addresses essential gaps in understanding activity patterns and trends in more sedentary sectors of the population.

}, keywords = {Accelerometry, Adult, Age Factors, Aged, Aged, 80 and over, Employment, Female, Health Behavior, Humans, Linear Models, Longitudinal Studies, Male, Middle Aged, Motor Activity, Physical Fitness, Sedentary Lifestyle}, issn = {1758-535X}, doi = {10.1093/gerona/glt199}, author = {Schrack, Jennifer A and Zipunnikov, Vadim and Goldsmith, Jeff and Bai, Jiawei and Simonsick, Eleanor M and Crainiceanu, Ciprian and Ferrucci, Luigi} } @article {794, title = {Estimating energy expenditure from heart rate in older adults: a case for calibration.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e93520}, abstract = {

BACKGROUND: Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear.

OBJECTIVE: To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration.

DESIGN: Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance.

RESULTS: Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min).

CONCLUSION: These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0093520}, author = {Schrack, Jennifer A and Zipunnikov, Vadim and Goldsmith, Jeff and Bandeen-Roche, Karen and Crainiceanu, Ciprian M and Ferrucci, Luigi} } @article {804, title = {Normalization and extraction of interpretable metrics from raw accelerometry data.}, journal = {Biostatistics}, volume = {15}, year = {2014}, month = {2014 Jan}, pages = {102-16}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxt029}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M} } @article {Bai2014Metrics, title = {Normalization and extraction of interpretable metrics from raw accelerometry data}, journal = {Biostatistics}, volume = {15}, number = {1}, year = {2014}, pages = {102{\textendash}116}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Activity intensity, Movelets, Movement, Signal processing, Time active, Tri-axial accelerometer}, issn = {14654644}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A. and Crainiceanu, Ciprian M.} } @article {791, title = {Predicting human movement with multiple accelerometers using movelets.}, journal = {Med Sci Sports Exerc}, volume = {46}, year = {2014}, month = {2014 Sep}, pages = {1859-66}, abstract = {

PURPOSE: The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers.

METHODS: Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg{\textperiodcentered}m (2.5 kg{\textperiodcentered}m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers.

RESULTS: With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2\% to 99.9\% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7\% to 5.8\%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers.

DISCUSSION AND CONCLUSIONS: It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.

}, issn = {1530-0315}, doi = {10.1249/MSS.0000000000000285}, author = {He, Bing and Bai, Jiawei and Zipunnikov, Vadim V and Koster, Annemarie and Caserotti, Paolo and Lange-Maia, Brittney and Glynn, Nancy W and Harris, Tamara B and Crainiceanu, Ciprian M} } @article {787, title = {Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.}, journal = {Biostatistics}, year = {2014}, month = {2014 Oct 30}, abstract = {

Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.

}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxu045}, author = {Xiao, Luo and Huang, Lei and Schrack, Jennifer A and Ferrucci, Luigi and Zipunnikov, Vadim and Crainiceanu, Ciprian M} } @article {789, title = {Structured functional principal component analysis.}, journal = {Biometrics}, year = {2014}, month = {2014 Oct 18}, abstract = {

Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

}, issn = {1541-0420}, doi = {10.1111/biom.12236}, author = {Shou, Haochang and Zipunnikov, Vadim and Crainiceanu, Ciprian M and Greven, Sonja} } @article {799, title = {Body composition and arsenic metabolism: a cross-sectional analysis in the Strong Heart Study.}, journal = {Environ Health}, volume = {12}, year = {2013}, month = {2013}, pages = {107}, abstract = {

OBJECTIVE: The objective of this study was to evaluate the association between measures of body composition and patterns of urine arsenic metabolites in the 1989-1991 baseline visit of the Strong Heart Study, a cardiovascular disease cohort of adults recruited from rural communities in Arizona, Oklahoma, North Dakota and South Dakota.

METHODS: We evaluated 3,663 Strong Heart Study participants with urine arsenic species above the limit of detection and no missing data on body mass index, \% body fat and fat free mass measured by bioelectrical impedance, waist circumference and other variables. We summarized urine arsenic species patterns as the relative contribution of inorganic (iAs), methylarsonate (MMA) and dimethylarsinate (DMA) species to their sum. We modeled the associations of \% arsenic species biomarkers with body mass index, \% body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition. We also considered adjustment for arsenic exposure and demographics.

RESULTS: Increasing body mass index was associated with higher mean \% DMA and lower mean \% MMA before and after adjustment for sociodemographic variables, arsenic exposure, and for other measures of body composition. In unadjusted linear regression models, \% DMA was 2.4 (2.1, 2.6) \% higher per increase in body mass index category (< 25, >=25 \& <30, >=30 \& <35, >=35 kg/m2), and \% MMA was 1.6 (1.4, 1.7) \% lower. Similar patterns were observed for \% body fat, fat free mass, and waist circumference measures in unadjusted models and in models adjusted for potential confounders, but the associations were largely attenuated or disappeared when adjusted for body mass index.

CONCLUSION: Measures of body size, especially body mass index, are associated with arsenic metabolism biomarkers. The association may be related to adiposity, fat free mass or body size. Future epidemiologic studies of arsenic should consider body mass index as a potential modifier for arsenic-related health effects.

}, issn = {1476-069X}, doi = {10.1186/1476-069X-12-107}, author = {Gribble, Matthew O and Crainiceanu, Ciprian M and Howard, Barbara V and Umans, Jason G and Francesconi, Kevin A and Goessler, Walter and Zhang, Ying and Silbergeld, Ellen K and Guallar, Eliseo and Navas-Acien, Ana} } @article {821, title = {Practical Marginalized Multilevel Models.}, journal = {Stat}, volume = {2}, year = {2013}, month = {2013}, abstract = {

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate the MMM and approximate MMM approaches on a cerebrovascular deficiency crossover trial using SAS and an epidemiological study on race and visual impairment using R. Datasets, SAS and R code are included as supplemental materials.

}, issn = {0038-9986}, doi = {10.1002/sta4.22}, author = {Griswold, Michael E and Swihart, Bruce J and Caffo, Brian S and Zeger, Scott L} } @article {803, title = {Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2).}, journal = {Cogn Affect Behav Neurosci}, volume = {13}, year = {2013}, month = {2013 Dec}, pages = {714-24}, abstract = {

This article proposes the image intraclass correlation (I2C2) coefficient as a global measure of reliability for imaging studies. The I2C2 generalizes the classic intraclass correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: regional analysis of volumes in normalized space imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting-state functional magnetic resonance imaging (fMRI), and fractional anisotropy in an area surrounding the corpus callosum via diffusion tensor imaging. Notably, resting-state fMRI brain activation maps are found to have low reliability, ranging from .2 to .4. Software and data are available to provide easy access to the proposed methods.

}, keywords = {Adult, Brain, brain mapping, Computer Simulation, Female, Humans, Male, Models, Biological, Neuroimaging, Reproducibility of Results, Statistics as Topic}, issn = {1531-135X}, doi = {10.3758/s13415-013-0196-0}, author = {Shou, H and Eloyan, A and Lee, S and Zipunnikov, V and Crainiceanu, A N and Nebel, N B and Caffo, B and Lindquist, M A and Crainiceanu, C M} } @article {shee, title = {Revisiting Brain Atrophy and Its Relationship to Disability in Multiple Sclerosis}, journal = {PLoS ONE}, volume = {7}, number = {5}, year = {2012}, month = {05}, pages = {e37049}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0037049}, url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0037049}, author = {Shiee, N and Bazin, P and Zackowski, K and Farrell, S and Harrison, D and Newsome, S and Ratchford, J and Caffo, B and Calabresi, P and Pham, D and Reich, D.} } @article {reiss2011extracting, title = {Extracting information from functional connectivity maps via function-on-scalar regression}, journal = {NeuroImage}, volume = {56}, number = {1}, year = {2011}, pages = {140{\textendash}148}, author = {Reiss, P.T. and Mennes, M. and Petkova, E. and Huang, L. and Hoptman, M.J. and Biswal, B.B. and Colcombe, S.J. and Zuo, X.N. and Milham, M.P.} } @article {vadimNI, title = {Functional principal components model for high-dimensional brain imaging}, journal = {Neuroimage, NIHSMID: 316403}, volume = {58}, number = {3}, year = {2011}, pages = {772 {\textendash} 784}, author = {Zipunnikov, V. and Caffo, B and Yousem, D and Davatziko, C and Schwartz, B and Crainiceanu, C} } @article {vadimHDMFPCA, title = {Multilevel functional principal component analysis for high-dimensional data}, journal = {Journal of Computational and Graphical Statistics, NIMHID: 306063}, volume = {20}, number = {4}, year = {2011}, pages = {852{\textendash}873}, author = {Zipunnikov, V. and Caffo, B and Crainiceanu, C and Yousem, D and Davatzikos, C. and Schwartz, B} } @article {doi:10.1198/jasa.2011.ap10089, title = {Population value decomposition, a framework for the analysis of image populations}, journal = {Journal of the American Statistical Association, with discussion and rejoinder}, volume = {106}, number = {495}, year = {2011}, pages = {775-790}, doi = {10.1198/jasa.2011.ap10089}, url = {http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.ap10089}, author = {Crainiceanu, C and Caffo, B and Luo, S and Zipunnikov, V. and Punjabi, N} } @article {nazarian2011AI, title = {Prospective study to assess the safety of a protocol for magnetic resonance imaging of patients with implanted cardiac devices}, journal = {Annals of Interal Medicine}, volume = {155}, number = {7}, year = {2011}, pages = {415{\textendash}424}, publisher = {American College of Physicians}, author = {Nazarian, S and Hansford, R and Roguin, A and Goldsher, D and Zviman, M and Lardo, A and Caffo, B and Frick, K and Kraut, M and Kamel, I and Ihab, R and Calkins, H and Berger, R and Bluemke, D and Halperin, H} } @article {MRM:MRM22818, title = {On the relationship between seed-based and ICA-based measures of functional connectivity}, journal = {Magnetic Resonance in Medicine}, volume = {66}, number = {3}, year = {2011}, pages = {644{\textendash}657}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, keywords = {between network connectivity, blood oxygenation level dependent fMRI, functional connectivity, ICA, seed-based connectivity, within network connectivity}, issn = {1522-2594}, doi = {10.1002/mrm.22818}, url = {http://dx.doi.org/10.1002/mrm.22818}, author = {Joel, S. and Caffo, B and van Zijl, P and Pekar, J} } @article {nazarian:qrs, title = {QRS Prolongation in Myotonic Muscular Dystrophy and Diffuse Fibrosis on Cardiac Magnetic Resonance}, journal = {Magnetic Resonance in Medicine, PMCID: PMC3034129}, volume = {64}, number = {1}, year = {2010}, pages = {107{\textendash}114}, author = {Nazarian, S and Bluemke, D and Wagner, K and Zviman, M and Turkbey, E and Caffo, B and Shehata, M and Edwards,D and Butcher, B and Calkins, H and Berger, R and Halperin, H and Tomaselli, G} } @article {zhang2008power, title = {Power Spectral Analysis of EEG Activity During Sleep in Cigarette Smokers}, journal = {Chest}, volume = {133}, number = {2}, year = {2008}, pages = {427{\textendash}432}, publisher = {Am Coll Chest Phys}, author = {Zhang, L and Samet, J and Caffo, B and Bankman, I and Punjabi, N} } @article {lindquist2008rapid, title = {Rapid three-dimensional functional magnetic resonance imaging}, year = {2008}, author = {Lindquist, Martin A and Zhang, Cun-Hui and Glover, Gary and Shepp, Lawrence} } @article {zhang2006cigarette, title = {Cigarette smoking and nocturnal sleep architecture}, journal = {American Journal of Epidemiology}, volume = {164}, number = {6}, year = {2006}, pages = {529}, publisher = {Oxford Univ Press}, author = {Zhang, L and Samet, J and Caffo, B and Punjabi, N} } @article {bassett2006familial, title = {Familial risk for Alzheimer{\textquoteright}s disease alters fMRI activation patterns}, journal = {Brain, PMCID: PMC2744898}, volume = {129}, number = {5}, year = {2006}, pages = {1229{\textendash}1239}, publisher = {Oxford Univ Press}, author = {Bassett, S and Yousem, D and Cristinzio, C and Kusevic, I and Yassa, M and Caffo, B and Zeger, S} } @article {ndovi2006quantitative, title = {Quantitative assessment of seminal vesicle and prostate drug concentrations by use of a noninvasive method}, journal = {Clinical Pharmacology \& Therapeutics}, volume = {80}, number = {2}, year = {2006}, pages = {146{\textendash}158}, publisher = {Nature Publishing Group}, author = {Ndovi, T and Choi, L and Caffo, B and Parsons, T and Baker, S and Zhao, M and Rohde, C and Hendrix, C} }