@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 {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} }