Jiawei Bai

Assessing the “Physical Cliff”: Detailed Quantification of Age-Related Differences in Daily Patterns of Physical Activity

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.

Predicting human movement with multiple accelerometers using movelets.

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·m (2.5 kg·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.

Normalization and extraction of interpretable metrics from raw accelerometry data

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.

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.

A practical guide to big data

Big Data is increasingly prevalent in science and data analysis. We provide a short tutorial for adapting to these changes and making the necessary adjustments to the academic culture to keep Biostatistics truly impactful in scientific research.

Genome-wide prediction of DNase I hypersensitivity using gene expression

We evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome–transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.A map of the activities of all genomic regulatory elements across cell types and conditions would be a tremendous resource. The computational method introduced here predicts genome-wide accessible sites from gene expression data and allows the authors to build a database of regulatory element activities using publicly available transcriptome data.

Accelerometry data in health research: challenges and opportunities. Review and examples

Wearable accelerometers provide detailed, objective, and continu-ous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popula-rity of wearable technology in health research. An ever increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper we discuss problems related to the collection and analysis of raw acce-lerometry data and provide insights into potential solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability and the effects of sensor location on the body. We also provide a short tutorial for dealing with sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these po-ints using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

Multilevel Matrix-Variate Analysis and its Application to Accelerometry-Measured Physical Activity in Clinical Populations

ABSTRACT The number of studies where the primary measurement is a matrix is exploding. In response to this, we propose a statistical framework for modeling populations of repeatedly observed matrix-variate measurements. The 2D structure is handled via a matrix-variate distribution with decomposable row/column-specific covariance matrices and a linear mixed effect framework is used to model the multilevel design. The proposed framework flexibly expands to accommodate many common crossed and nested designs and introduces two important concepts: the between-subject distance and intraclass correlation coefficient, both defined for matrix-variate data. The computational feasibility and performance of the approach is shown in extensive simulation studies. The method is motivated by and applied to a study that monitored physical activity of individuals diagnosed with congestive heart failure (CHF) over a 4- to 9-month period. The long-term patterns of physical activity are studied and compared in two CHF subgroups: with and without adverse clinical events. Supplementary materials for this article, that include de-identified accelerometry and clinical data, are available online.

A two-stage model for wearable device data.

Recent advances of wearable computing technology have allowed continuous health monitoring in large observational studies and clinical trials. Examples of data collected by wearable devices include minute-by-minute physical activity proxies measured by accelerometers or heart rate. The analysis of data generated by wearable devices has so far been quite limited to crude summaries, for example, the mean activity count over the day. To better utilize the full data and account for the dynamics of activity level in the time domain, we introduce a two-stage regression model for the minute-by-minute physical activity proxy data. The model allows for both time-varying parameters and time-invariant parameters, which helps capture both the transition dynamics between active/inactive periods (Stage 1) and the activity intensity dynamics during active periods (Stage 2). The approach extends methods developed for zero-inflated Poisson data to account for the high-dimensionality and time-dependence of the high density data generated by wearable devices. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging.

Discussion of the paper ‘A general framework for functional regression modelling’

Abstract This discussion provides our reaction to the article by Greven and Scheipl. It contains an overview of their article and a description of the many areas of research that remain open and could benefit from further methodological and computational development.