Genome-wide association studies of 27 accelerometry-derived physical activity measurements identified novel loci and genetic mechanisms.

Physical inactivity (PA) is an important risk factor for a wide range of diseases. Previous genome-wide association studies (GWAS), based on self-reported data or a small number of phenotypes derived from accelerometry, have identified a limited number of genetic loci associated with habitual PA and provided evidence for involvement of central nervous system in mediating genetic effects. In this study, we derived 27 PA phenotypes from wrist accelerometry data obtained from 88,411 UK Biobank study participants. Single-variant association analysis based on mixed-effects models and transcriptome-wide association studies (TWAS) together identified 5 novel loci that were not detected by previous studies of PA, sleep duration and self-reported chronotype. For both novel and previously known loci, we discovered associations with novel phenotypes including active-to-sedentary transition probability, light-intensity PA, activity during different times of the day and proxy phenotypes to sleep and circadian patterns. Follow-up studies including TWAS, colocalization, tissue-specific heritability enrichment, gene-set enrichment and genetic correlation analyses indicated the role of the blood and immune system in modulating the genetic effects and a secondary role of the digestive and endocrine systems. Our findings provided important insights into the genetic architecture of PA and its underlying mechanisms.

A case study of glucose levels during sleep using multilevel fast function on scalar regression inference.

Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating study contains CGM data during sleep for 174 study participants with type II diabetes mellitus measured at a 5-min frequency for an average of 10 nights. We aim to quantify the effects of diabetes medications and sleep apnea severity on glucose levels. Statistically, this is an inference question about the association between scalar covariates and functional responses observed at multiple visits (sleep periods). However, many characteristics of the data make analyses difficult, including (1) nonstationary within-period patterns; (2) substantial between-period heterogeneity, non-Gaussianity, and outliers; and (3) large dimensionality due to the number of study participants, sleep periods, and time points. For our analyses, we evaluate and compare two methods: fast univariate inference (FUI) and functional additive mixed models (FAMMs). We extend FUI and introduce a new approach for testing the hypotheses of no effect and time invariance of the covariates. We also highlight areas for further methodological development for FAMM. Our study reveals that (1) biguanide medication and sleep apnea severity significantly affect glucose trajectories during sleep and (2) the estimated effects are time invariant.

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

We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal sampling procedure. The framework provides a natural extension of standard longitudinal correlation models for scalar observations to functional observations. Using simulation studies, we compare fixed effects estimation under correctly and incorrectly specified correlation structures and also test the longitudinal correlation structure. Finally, we apply the proposed methods to a longitudinal functional dataset on physical activity. The computer code for the proposed method is available at https://github.com/rli20ST758/FILF.

Cardiovascular mortality risk prediction using objectively measured physical activity phenotypes in NHANES 2003-2006.

Increased physical activity (PA) has been associated with a decreased risk of cardiovascular disease (CVD) and mortality. However, most previous studies use self-reported PA instead of objectively measured PA assessed by wearable accelerometers. To the best of our knowledge, there have not been studies that quantified the univariate and multivariate ability of objectively measured PA summaries to predict the risk of CVD mortality. We investigate the ability of objectively measured PA summary variables to predict CVD mortality: as individual predictors, as part of the best multivariate model incorporating traditional predictors, and as additions to the best multivariate model using only traditional CVD predictors. Data were collected in the National Health and Nutrition Examination Survey 2003-2006 waves for US participants aged 50-85. The predictive ability was measured using Concordance, sometimes referred to as the C-statistic. Specifically, we calculated 10-fold cross-validated concordance (CVC) in survey-weighted Cox proportional hazard models. The best univariate predictor of CVD mortality was total activity count (outperformed age). In multivariate models, two of the eight predictors identified using the improvement in CVC threshold of 0.001 were PA measures (CVC = 0.844). The best model without physical activity (7 predictors) had CVC of 0.830. The addition of PA measures to the best traditional model was significantly better at predicting CVD mortality (P < 0.001). Accelerometer-derived PA measures have excellent cardiovascular mortality prediction performance. Wearable accelerometers have a potential for assessment of individuals' CVD mortality risks.

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study.

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals' levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42-78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.

idem: An R Package for Inferences in Clinical Trials with Death and Missingness.

In randomized controlled trials of seriously ill patients, death is common and often defined as the primary endpoint. Increasingly, non-mortality outcomes such as functional outcomes are co-primary or secondary endpoints. Functional outcomes are not defined for patients who die, referred to as "truncation due to death", and among survivors, functional outcomes are often unobserved due to missed clinic visits or loss to follow-up. It is well known that if the functional outcomes "truncated due to death" or missing are handled inappropriately, treatment effect estimation can be biased. In this paper, we describe the package idem that implements a procedure for comparing treatments that is based on a composite endpoint of mortality and the functional outcome among survivors. Among survivors, the procedure incorporates a missing data imputation procedure with a sensitivity analysis strategy. A web-based graphical user interface is provided in the idem package to facilitate users conducting the proposed analysis in an interactive and user-friendly manner. We demonstrate idem using data from a recent trial of sedation interruption among mechanically ventilated patients.

Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003-2006.

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003-2004 and 2005-2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

Day-Night Activity in Hospitalized Children after Major Surgery: An Analysis of 2271 Hospital Days.

OBJECTIVES: To characterize the day-night activity patterns of children after major surgery and describe differences in children's activity patterns between the pediatric intensive care unit (PICU) and inpatient floor setting. STUDY DESIGN: In this prospective observational study, we characterized the daytime activity ratio estimate (DARE; ratio between mean daytime activity [08:00-20:00] and mean 24-hour activity [00:00-24:00]) for children admitted to the hospital after major surgery. The study sample included 221 infants and children ages 1 day to 17 years admitted to the PICU at a tertiary, academic children's hospital. Subjects were monitored with continuous accelerometry from postoperative day 1 until hospital discharge. The National Health and Nutrition Examination Survey accelerometry data were utilized for normative data to compare DARE in a community sample of US children to hospitalized children. RESULTS: The mean DARE over 2271 hospital days was 57.8%, with a significant difference between the average DARE during PICU days and inpatient floor days (56% vs 61%, P < .0001). The average subject DARE ranged from 43% to 73%. In a covariate-adjusted mixed effects model, PICU location, lower age, orthopedic or urologic surgery, and intubation time were associated with decreased DARE. Hospitalized children had significantly lower DARE than the National Health and Nutrition Examination Survey subjects in all age groups studied, with the largest difference in the youngest PICU group analyzed (6-9 years; 59% vs 75%, P < .0001). A subset analysis of children older than 2 years (n = 144) showed that DARE was <50% on 15% of hospital days. CONCLUSIONS: Children hospitalized after major surgery experience disruptions in day-night activity patterns during their hospital stay that may reflect disturbances in circadian rhythm.

Dynamic prediction in functional concurrent regression with an application to child growth.

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques.

Novel metrics for growth model selection.

BACKGROUND: Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. METHODS: Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age. RESULTS: Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios. CONCLUSION: With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.

Re-evaluating the effect of age on physical activity over the lifespan.

Advancements in accelerometer analytic and visualization techniques allow researchers to more precisely identify and compare critical periods of physical activity (PA) decline by age across the lifespan, and describe how daily PA patterns may vary across age groups. We used accelerometer data from the 2003-2006 cohorts of the National Health and Nutrition Examination Survey (NHANES) (n=12,529) to quantify total PA as well as PA by intensity across the lifespan using sex-stratified, age specific percentile curves constructed using generalized additive models. We additionally estimated minute-to-minute diurnal PA using smoothed bivariate surfaces. We found that from childhood to adolescence (ages 6-19) across sex, PA is sharply lower by age partially due to a later initiation of morning PA. Total PA levels, at age 19 are comparable to levels at age 60. Contrary to prior evidence, during young adulthood (ages 20-30) total and light intensity PA increases by age and then stabilizes during midlife (ages 31-59) partially due to an earlier initiation of morning PA. We additionally found that males compared to females have an earlier lowering in PA by age at midlife and lower total PA, higher sedentary behavior, and lower light intensity PA in older adulthood; these trends seem to be driven by lower PA in the afternoon compared to females. Our results suggest a re-evaluation of how emerging adulthood may affect PA levels and the importance of considering time of day and sex differences when developing PA interventions.

Quantifying the time-varying association between objectively measured physical activity and mortality in US older adults over a 12-year follow-up period: the NHANES 2003-2006 study.

Objectively measuring physical activity (PA) has consistently shown an association with reduced all-cause mortality risk in cross-sectional studies. However, the strength of this association may change over time. We quantify the time-varying, covariate-adjusted association between the total volume of PA and all-cause mortality over a 12-year follow-up period using Cox regression with a time varying effect of population-referenced quantile total activity count adjusted for traditional risk factors. Analyses focus on participants 50-84 years old with adequate accelerometer wear time and without missing covariates. The findings suggest that (1) the use of baseline PA in Cox models with long follow-up periods may be inappropriate without time-varying effects and (2) the use of accelerometry derived volume of PA in risk score calculations may be most appropriate for short-term to medium-term risk scores.

Association Between Electronic Diary-Rated Sleep, Mood, Energy, and Stress With Incident Headache in a Community-Based Sample.

BACKGROUND AND OBJECTIVES: The aim of this study was to examine the diurnal links between average and changes in average levels of prospectively rated mood, sleep, energy, and stress as predictors of incident headache in a community-based sample. METHODS: This observational study included structured clinical diagnostic assessment of both headache syndromes and mental disorders and electronic diaries that were administered 4 times per day for 2 weeks yielding a total of 4,974 assessments. The chief outcomes were incident morning (am) and later-day (pm) headaches. Generalized linear mixed-effects models were used to evaluate the average and lagged values of predictors including subjectively rated mood, anxiety, energy, stress, and sleep quality and objectively measured sleep duration and efficiency on incident am and pm headaches. RESULTS: The sample included 477 participants (61% female), aged 7 through 84 years. After adjusting for demographic and clinical covariates and emotional states, incident am headache was associated with lower average (ß = -0.206*; confidence intervals: -0.397 to -0.017) and a decrease in average sleep quality on the prior day (ß = -0.172*; confidence interval: -0.305, -0.039). Average stress and changes in subjective energy levels on the prior day were associated with incident headaches but with different valence for am (decrease) (ß = -0.145* confidence interval: -0.286, -0.005) and pm (increase) (ß = 0.157*; confidence interval: 0.032, 0.281) headache. Mood and anxiety disorders were not significantly associated with incident headache after controlling for history of a diagnosis of migraine. DISCUSSION: Both persistent and acute changes in arousal states manifest by subjective sleep quality and energy are salient precursors of incident headaches. Whereas poorer sleep quality and decreased energy on the prior day were associated with incident morning headache, an increase in energy and greater average stress were associated with headache onsets later in the day. Different patterns of predictors of morning and later-day incident headache highlight the role of circadian rhythms in the manifestations of headache. These findings may provide insight into the pathophysiologic processes underlying migraine and inform clinical intervention and prevention. Tracking these systems in real time with mobile technology provides a valuable ancillary tool to traditional clinical assessments.

A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use.

Introduction: Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection. Method: Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated. Results: Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking. Conclusion: These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.

Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression.

Importance: Accelerometry has been increasingly used as an objective index of sleep, physical activity, and circadian rhythms in people with mood disorders. However, most prior research has focused on sleep or physical activity alone without consideration of the strong within- and cross-domain intercorrelations; and few studies have distinguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD). Objectives: To identify joint and individual components of the domains derived from accelerometry, including sleep, physical activity, and circadian rhythmicity using the Joint and Individual Variation Explained method (JIVE), a novel multimodal integrative dimension-reduction technique; and to examine associations between joint and individual components with current and remitted MDD. Design, Setting, and Participants: This cross-sectional study examined data from the second wave of a population cohort study from Lausanne, Switzerland. Participants included 2317 adults (1164 without MDD, 185 with current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days. Statistical analysis was conducted from January 2021 to June 2023. Main Outcomes and Measures: Features derived from accelerometry for 14 days; current and remitted MDD. Logistic regression adjusted for age, sex, body mass index, and anxiety and substance use disorders. Results: Among 2317 adults included in the study, 1261 (54.42%) were female, and mean (SD) age was 61.79 (9.97) years. JIVE reduced 28 accelerometry features to 3 joint and 6 individual components (1 sleep, 2 physical activity, 3 circadian rhythms). Joint components explained 58.5%, 79.5%, 54.5% of the total variation in sleep, physical activity, and circadian rhythm domains, respectively. Both current and remitted depression were associated with the first 2 joint components that were distinguished by the salience of high-intensity physical activity and amplitude of circadian rhythm and timing of both sleep and physical activity, respectively. MDD had significantly weaker circadian rhythmicity. Conclusions and Relevance: Application of a novel multimodal dimension-reduction technique demonstrates the importance of joint influences of physical activity, circadian rhythms, and timing of both sleep and physical activity with MDD; dampened circadian rhythmicity may constitute a trait marker for MDD. This work illustrates the value of accelerometry as a potential biomarker for subtypes of depression and highlights the importance of consideration of the full 24-hour sleep-wake cycle in future studies.

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment.

ABSTRACT: Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

Evaluating the prediction performance of objective physical activity measures for incident Parkinson's disease in the UK Biobank.

BACKGROUND: Parkinson's disease (PD) is the fastest-growing neurological condition with over 10 million cases worldwide. While age and sex are known predictors of incident PD, there is a need to identify other predictors. This study compares the prediction performance of accelerometry-derived physical activity (PA) measures and traditional risk factors for incident PD in the UK Biobank. METHODS: The study population consisted of 92,352 UK Biobank participants without PD at baseline (43.8% male, median age 63 years with interquartile range 43-69). 245 participants were diagnosed with PD by April 1, 2021 (586,604 person-years of follow-up). The incident PD prediction performances of 10 traditional predictors and 8 objective PA measures were compared using single- and multi-variable Cox models. Prediction performance was assessed using a novel, stable statistic: the repeated cross-validated concordance (rcvC). Sensitivity analyses were conducted where PD cases diagnosed within the first six months, one year, and two years were deleted. RESULTS: Single-predictor Cox regression models indicated that all PA measures were statistically significant (p-values < 0.0001). The highest-performing individual predictors were total acceleration (TA) (rcvC = 0.813) among PA measures, and age (rcvC = 0.757) among traditional predictors. The two-step forward-selection process produced a model containing age, sex, and TA (rcvC = 0.851). Adding TA to the model increased the rcvC by 9.8% (p-value < 0.0001). Results were largely unchanged in sensitivity analyses. CONCLUSIONS: Objective PA summaries have better single-predictor model performance than known risk factors and increase the prediction performance substantially when added to models with age and sex.

Fingerprinting walking using wrist-worn accelerometers.

BACKGROUND: Identifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy. RESEARCH QUESTION: We propose an open-source reproducible method to: (1) create a unique, person-specific "walking fingerprint" from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint. METHODS: Accelerometry data were collected during walking from 32 individuals (23-52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability. RESULTS: The method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals. SIGNIFICANCE: Predicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications.

Gender difference in "second-shift" physical activity: New insights from analyzing accelerometry data in a nationally representative sample.

The gendered organization of daily activities results in differential contexts of physical activity (PA) for the working population, especially during the "second shift" - a time window dominated by household-based activities. Existing research predominantly relies on self-reported leisure-time activities, yielding a partial understanding of gender difference in the source, timing, and accumulation pattern of PA. To address these limitations, this study draws on the interplay between work and family to understand how they shape gender difference in household-based PA across occupational groups. It combines work schedule and accelerometry PA data from the 2005-2006 National Health and Nutrition Examination Survey (NHANES), which permits our study of second-shift PA on workdays among full-time workers, aged 20 to 49, with a regular daytime schedule. To capture different aspects of second-shift PA, the PA outcomes are measured as both volume and accumulation patterns during time windows following (i.e., 6pm-9pm) and prior to typical working hours (7:30am-8:30am). Using generalized estimating equations, we estimate gender differences in the volume and fragmentation of second-shift PA. Overall, women with a full-time job exhibit both higher volume and higher fragmentation of second-shift PA than their male counterparts. The occupational group moderates such gender difference in PA. The gender gaps in PA volume and fragmentation are only evident for professional workers, whereas the second shift represents a gender-neutral context for PA accumulation for non-professional groups. These findings are supported by a secondary analysis when analyzing the whole-day PA data using functional data analysis. Such social patterning of second-shift PA calls for further research on gendered PA under the interplay of work and family beyond the usual focus on leisure activities.