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.

Improved nationwide survival of sarcoma patients with a network of reference centers

BACKGROUND

We investigated the impact of the implementation of a network of reference centers for sarcomas (NETSARC) on the care and survival of sarcoma patients in France since 2010.

PATIENTS AND METHODS

NETSARC (netsarc.org) is a network of 26 reference sarcoma centers with specialized multidisciplinary tumor boards (MDTBs), funded by the French National Cancer Institute (INCa) since 2010. Its aims are to improve the quality of diagnosis and care of sarcoma patients. Patients' characteristics, treatments, and outcomes are collected in a nationwide database. The objective of this analysis was to compare the survival of patients in three periods: 2010-2012 (non-exhaustive), 2013-2015, and 2016-2020.

RESULTS

A total of 43 975 patients with sarcomas, gastrointestinal stromal tumors (GISTs), or connective tissue tumors of intermediate malignancy were included in the NETSARC+ database since 2010 (n = 9266 before 2013, n = 12 274 between 2013 and 2015, n = 22 435 in 2016-2020). Median age was 56 years, 50.5% were women, and 13.2% had metastasis at diagnosis. Overall survival was significantly superior in the period 2016-2020 versus 2013-2015 versus 2010-2012 for the entire population, for patients >18 years of age, and for both metastatic and non-metastatic patients in univariate and multivariate analyses (P < 0.0001). Over the three periods, we observed a significantly improved compliance to clinical practice guidelines (CPGs) nationwide: the proportion of patients biopsied before surgery increased from 62.9% to 72.6%; the percentage of patients presented to NETSARC MDTBs before first surgery increased from 31.7% to 44.4% (P < 0.0001). The proportion of patients with R0 resection on first surgery increased (from 36.1% to 46.6%), while R2 resection rate decreased (from 10.9% to 7.9%), with a better compliance and improvement in NETSARC centers.

CONCLUSIONS

The implementation of the national reference network for sarcoma was associated with an improvement of overall survival and compliance to guidelines nationwide in sarcoma patients. Referral to expert networks for sarcoma patients should be encouraged, though a better compliance to CPGs can still be achieved.

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.

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.

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.

Quantifying the Association between Objectively Measured Physical Activity and Multiple Sclerosis in the UK Biobank

INTRODUCTION

Objectively measured physical activity (PA) data were collected in the accelerometry substudy of the UK Biobank. UK Biobank also contains information about multiple sclerosis (MS) diagnosis at the time of and after PA collection. This study aimed to 1) quantify the difference in PA between prevalent MS cases and matched healthy controls, and 2) evaluate the predictive performance of objective PA measures for incident MS cases.

METHODS

The first analysis compared eight accelerometer-derived PA summaries between MS patients ( N = 316) and matched controls (30 controls for each MS case). The second analysis focused on predicting time to MS diagnosis among participants who were not diagnosed with MS. A total of 19 predictors including eight measures of objective PA were compared using Cox proportional hazards models (number of events = 47; 585,900 person-years of follow-up).

RESULTS

In the prevalent MS study, the difference between MS cases and matched controls was statistically significant for all PA summaries ( P < 0.001). In the incident MS study, the most predictive variable of progression to MS in univariate Cox regression models was lower age ( C = 0.604), and the most predictive PA variable was lower relative amplitude (RA, C = 0.594). A two-stage forward selection using Cox regression resulted in a model with concordance C = 0.693 and four predictors: age ( P = 0.015), stroke ( P = 0.009), Townsend deprivation index ( P = 0.874), and RA ( P = 0.004). A model including age, stroke, and RA had a concordance of C = 0.691.

CONCLUSIONS

Objective PA summaries were significantly different and consistent with lower activity among study participants who had MS at the time of the accelerometry study. Among individuals who did not have MS, younger age, stroke history, and lower RA were significantly associated with a higher risk of a future MS diagnosis.

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.

Specificity of affective dynamics of bipolar and major depressive disorder

OBJECTIVE

Here, we examine whether the dynamics of the four dimensions of the circumplex model of affect assessed by ecological momentary assessment (EMA) differ among those with bipolar disorder (BD) and major depressive disorder (MDD).

METHODS

Participants aged 11-85 years (n = 362) reported momentary sad, anxious, active, and energetic dimensional states four times per day for 2 weeks. Individuals with lifetime mood disorder subtypes of bipolar-I, bipolar-II, and MDD derived from a semistructured clinical interview were compared to each other and to controls without a lifetime history of psychiatric disorders. Random effects from individual means, inertias, innovation (residual) variances, and cross-lags across the four affective dimensions simultaneously were derived from multivariate dynamic structural equation models.

RESULTS

All mood disorder subtypes were associated with higher levels of sad and anxious mood and lower energy than controls. Those with bipolar-I had lower average activation, and lower energy that was independent of activation, compared to MDD or controls. However, increases in activation were more likely to perpetuate in those with bipolar-I. Bipolar-II was characterized by higher lability of sad and anxious mood compared to bipolar-I and controls but not MDD. Compared to BD and controls, those with MDD exhibited cross-augmentation of sadness and anxiety, and sadness blunted energy.

CONCLUSION

Bipolar-I is more strongly characterized by activation and energy than sad and anxious mood. This distinction has potential implications for both specificity of intervention targets and differential pathways underlying these dynamic affective systems. Confirmation of the longer term stability and generalizability of these findings in future studies is necessary.

Objectively assessed sleep and physical activity in depression subtypes and its mediating role in their association with cardiovascular risk factors

The aims of this study were to investigate the associations of major depressive disorder (MDD) and its subtypes (atypical, melancholic, combined, unspecified) with actigraphy-derived measures of sleep, physical activity and circadian rhythms; and test the potentially mediating role of sleep, physical activity and circadian rhythms in the well-established associations of the atypical MDD subtype with Body Mass Index (BMI) and the metabolic syndrome (MeS). The sample consisted of 2317 participants recruited from an urban area, who underwent comprehensive somatic and psychiatric evaluations. MDD and its subtypes were assessed via semi-structured diagnostic interviews. Sleep, physical activity and circadian rhythms were measured using actigraphy. MDD and its subtypes were associated with several actigraphy-derived variables, including later sleep midpoint, low physical activity, low inter-daily stability and larger intra-individual variability of sleep duration and relative amplitude. Sleep midpoint and physical activity fulfilled criteria for partial mediation of the association between atypical MDD and BMI, and physical activity also for partial mediation of the association between atypical MDD and MeS. Our findings confirm associations of MDD and its atypical subtype with sleep and physical activity, which are likely to partially mediate the associations of atypical MDD with BMI and MeS, although most of these associations are not explained by sleep and activity variables. This highlights the need to consider atypical MDD, sleep and sedentary behavior as cardiovascular risk factors.

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.

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.

Unilateral or bilateral adrenalectomy in PPNAD: six cases from a single family followed up over 40 years

The most frequent endocrine Carney complex manifestation is a bilateral primary pigmented nodular adrenocortical disease and bilateral adrenalectomy (BA) is therefore its main treatment. In this study, a 40 years follow-up of six members of the same family with heterozygous PRKAR1A germline mutation, is reported over two generations. The first cases, two sisters with severe hyperandrogenism and Cushing syndrome (CS) diagnosed in 1972 at age 14 and 25, were successfully treated with unilateral adrenalectomy (UA). Their two brothers were then diagnosed, one with a CS-related severe osteoporosis treated with BA and the other with CS treated with UA. The second generation was diagnosed with CS signs at 7 and 21 years of age and were treated with BA and UA respectively. Out of the four patients treated with UA, the only event possibly related to CS was spontaneous episode of pulmonary embolism, 30 years after surgery. Hormonal evaluation revealed either eucortisolism in one patient or partial adrenal deficiency in two and mild hypercortisolism in one patient. For the two patients with BA, one of them accidentally died. The second one, surprisingly, recovered progressively normal cortisol secretion and circadian variation. Steroid substitution was stopped 6 years after her surgery and we demonstrated by iodocholesterol scintigraphy the presence of bilateral adrenal remnants. In conclusion, our results of long term evolution of PPNAD patients show that UA in this subset of patients could be considered to treat CS.

Research Note: Effect of a phlorotannin extract of the brown seaweed Ascophyllum nodosum as a potential control strategy against Campylobacter in broilers

Poultry is generally recognized as the main source of human campylobacteriosis and Campylobacter is highly prevalent at the farm level. To reduce the relative risk of human campylobacteriosis attributable to broiler meat, it is necessary to reduce Campylobacter loads in broiler ceca but to date, no effective, reliable and practical strategy is available. The marine environment is a rich source of original natural compounds exhibiting different biological activities. The objective of this study was to test a phlorotannin extract of the brown seaweed Ascophyllum nodosum as a potential control strategy against Campylobacter in broilers. Bactericidal activity has been demonstrated in vitro, on several Campylobacter spp. strains at a range of 0.06 to 0.47 mg/mL. Therefore, an in vivo trial in experimental facilities was performed to evaluate addition of 0.2% (w/w) of an A. nodosum extract to feed distributed at the end of rearing from day 31 to day 35, and to assess the effect on artificial Campylobacter jejuni colonization. No statistical differences in Campylobacter enumeration were observed between the treated and control groups. Another trial was performed in a commercial broiler flock. Feed containing the extract at 0.2% (w/w) (2 kg/t) was distributed during the last 5 days of rearing (day 33-day 38). No significant effects on Campylobacter colonization and on growth parameters were observed compared to the control group. Additional studies are needed to assess whether active polyphenols are found in the cecum.

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.

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Background

Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures.

Objective

We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data.

Methods

High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data.

Results

Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published.

Conclusions

The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

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.

Defining Incidence of Acute Compartment Syndrome in the Research Setting: A Proposed Method From the PACS Study

OBJECTIVE

To compare the retrospective decision of an expert panel who assessed likelihood of acute compartment syndrome (ACS) in a patient with a high-risk tibia fracture with decision to perform fasciotomy.

DESIGN

Prospective observational study.

SETTING

Seven Level 1 trauma centers.

PATIENTS/PARTICIPANTS

One hundred eighty-two adults with severe tibia fractures.

MAIN OUTCOME MEASUREMENTS

Diagnostic performance (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver-operator curve) of an expert panel's assessment of likelihood ACS compared with fasciotomy as the reference diagnostic standard.

SECONDARY OUTCOMES

The interrater reliability of the expert panel as measured by the Krippendorff alpha. Expert panel consensus was determined using the percent of panelists in the majority group of low (expert panel likelihood of ≤0.3), uncertain (0.3-0.7), or high (>0.7) likelihood of ACS.

RESULTS

Comparing fasciotomy (the diagnostic standard) and the expert panel's assessment as the diagnostic classification (test), the expert panel's determination of uncertain or high likelihood of ACS (threshold >0.3) had a sensitivity of 0.90 (0.70, 0.99), specificity of 0.95 (0.90, 0.98), PPV of 0.70 (0.50, 0.86), and NPV of 0.99 (0.95, 1.00). When a threshold of >0.7 was set as a positive diagnosis, the expert panel assessment had a sensitivity of 0.67 (0.43, 0.85), specificity of 0.98 (0.95, 1.00), PPV of 0.82 (0.57, 0.96), and NPV of 0.96 (0.91, 0.98).

CONCLUSION

In our study, the retrospective assessment of an expert panel of the likelihood of ACS has good specificity and excellent NPV for fasciotomy, but only low-to-moderate sensitivity and PPV. The discordance between the expert panel-assessed likelihood of ACS and the decision to perform fasciotomy suggests that concern regarding potential diagnostic bias in studies of ACS is warranted.

LEVEL OF EVIDENCE

Diagnostic Level I. See Instructions for Authors for a complete description of levels of evidence.

Fast Univariate Inference for Longitudinal Functional Models

We propose fast univariate inferential approaches for longitudinal Gaussian and non-Gaussian functional data. The approach consists of three steps: (1) fit massively univariate pointwise mixed effects models; (2) apply any smoother along the functional domain; and (3) obtain joint confidence bands using analytic approaches for Gaussian data or a bootstrap of study participants for non-Gaussian data. Methods are motivated by two applications: (1) Diffusion Tensor Imaging (DTI) measured at multiple visits along the corpus callosum of multiple sclerosis (MS) patients; and (2) physical activity data measured by body-worn accelerometers for multiple days. An extensive simulation study indicates that model fitting and inference are accurate and much faster than existing approaches. Moreover, the proposed approach was the only one that was computationally feasible for the physical activity data application. Methods are accompanied by R software, though the method is "read-and-use", as it can be implemented by any analyst who is familiar with mixed effects model software.

Occupational determinants of physical activity at work: Evidence from wearable accelerometer in 2005-2006 NHANES

Occupation determines workers' physical activity (PA) in the workplace, an important health behavior contributing to health outcomes. However, self-reported measure limits our understanding of how occupational tasks differentiate workers' PA in terms of the type, frequency, intensity, and duration. In addition, accurate estimation of occupation-based PA during workers' actual working hours requires precise work schedule information. To address these limitations, this study employs data on accelerometer-monitored PA and work schedule from the 2005-2006 National Health and Nutrition Examination Survey (NHANES). It asks two questions: How do occupations determine PA among regular daytime workers in the United States? Second, how large a share of PA difference between two occupations is attributable to differences in the implicit occupational tasks, relative to workers' demographic, health preconditions, and socioeconomic attributes? Calculating PA during the 9-to-5 period among daytime regular workers on weekdays and conducting Blinder-Oaxaca decomposition analysis, we yield insights into the occupational determinant of both PA volume (total activity counts) and fragmentation (bouts of activities). Worksite health promotion can utilize the objective occupation-PA link and design occupation-tailored interventions, which is currently underdeveloped in the United States. Moreover, our findings shed light on the physical nature of occupation, suggesting a fruitful step to reconcile the documented mixed findings on occupation-based PA and health outcomes in future studies.

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.

Additive Functional Cox Model

We propose the Additive Functional Cox Model to flexibly quantify the association between functional covariates and time to event data. The model extends the linear functional proportional hazards model by allowing the association between the functional covariate and log hazard to vary non-linearly in both the functional domain and the value of the functional covariate. Additionally, we introduce critical transformations of the functional covariate which address the weak model identifiability in areas of information sparsity and discuss their impact on interpretation and inference. We also introduce a novel estimation procedure that accounts for identifiability constraints directly during model fitting. Methods are applied to the National Health and Nutrition Examination Survey (NHANES) 2003-2006 accelerometry data and quantify new and interpretable circadian patterns of physical activity that are associated with all-cause mortality. We also introduce a simple and novel simulation framework for generating survival data with functional predictors which resemble the observed data. The accompanying inferential R software is fast, open source and publicly available. Our data application and simulations are fully reproducible through the accompanying vignette.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

INTRODUCTION

We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

METHODS

We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

RESULTS

Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

CONCLUSION

There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain 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.

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

BACKGROUND

Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank.

METHOD

A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality.

RESULTS

In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578-0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value < .001).

CONCLUSIONS

Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).

The Predictive Performance of Objective Measures of Physical Activity Derived From Accelerometry Data for 5-Year All-Cause Mortality in Older Adults: National Health and Nutritional Examination Survey 2003-2006

BACKGROUND

Declining physical activity (PA) is a hallmark of aging. Wearable technology provides reliable measures of the frequency, duration, intensity, and timing of PA. Accelerometry-derived measures of PA are compared with established predictors of 5-year all-cause mortality in older adults in terms of individual, relative, and combined predictive performance.

METHODS

Participants aged between 50 and 85 years from the 2003-2006 National Health and Nutritional Examination Survey (NHANES, n = 2,978) wore a hip-worn accelerometer in the free-living environment for up to 7 days. A total of 33 predictors of 5-year all-cause mortality (number of events = 297), including 20 measures of objective PA, were compared using univariate and multivariate logistic regression.

RESULTS

In univariate logistic regression, the total activity count was the best predictor of 5-year mortality (Area under the Curve (AUC) = 0.771) followed by age (AUC = 0.758). Overall, 9 of the top 10 predictors were objective PA measures (AUC from 0.771 to 0.692). In multivariate regression, the 10-fold cross-validated AUC was 0.798 for the model without objective PA variables (9 predictors) and 0.838 for the forward selection model with objective PA variables (13 predictors). The Net Reclassification Index was substantially improved by adding objective PA variables (p < .001).

CONCLUSIONS

Objective accelerometry-derived PA measures outperform traditional predictors of 5-year mortality, including age. This highlights the importance of wearable technology for providing reproducible, unbiased, and prognostic biomarkers of health.

1043 The Relationship Between Nighttime Eating And Body Mass Index

Introduction

Late night eating has been associated with higher odds of being overweight or obese. This study aims to evaluate the relationship between late night eating and body mass index in a nationally representative sample.

Methods

Actigraphy was used to estimate the average bedtime, waketime, duration and midpoint of sleep in the National Health and Nutrition Examination Survey 2003-04 and 2005-06 cohorts. Given the circular nature of clock time, the average was calculated to be the point that minimized the sum of squares of differences between time points. Dietary data was collected through two detailed interviews of the participants. Nighttime calories were defined as the average amount of calories consumed between the average bedtime and the average midpoint of time-in-bed, based on the data recorded during the dietary interviews.

Results

Higher average nighttime caloric consumption (in units of 100 kcal) was associated with higher BMI [B(95% CI): 0.062 (0.003, 0.121)]; this remained significant after adjustment for age, gender, and race [B(95% CI): 0.084 (0.026, 0.142)]. Higher nighttime caloric consumption (as a percentage of total average daily calories consumption) was associated with higher BMI [B(95% CI): 1.522 (0.312, 2.733)]. This remained significant after adjustment for age, gender, and race [B(95% CI): 1.718 (0.505, 2.931)].

Conclusion

Higher nighttime caloric consumption, both in average amount (in units of 100 kcal) and as a percentage of average daily calories consumption, was associated with higher BMI. Additional study is needed to further elucidate the relationship between nighttime eating habits and body mass index.

Support

NHLBI T32HL110952

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.

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

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real-life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained (JIVE), a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythniicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

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.

Using Heart Rate and Accelerometry to Define Quantity and Intensity of Physical Activity in Older Adults

Background

Physical activity (PA) prevents disease and promotes longevity; yet, few older adults meet the recommended daily guidelines. Wearable PA and heart rate monitors provide the opportunity to define age-related differences in the absolute and relative intensity of daily activities, and provide insight into the underlying factors influencing PA in older adults.

Methods

Participants in the Baltimore Longitudinal Study of Aging (n = 440, 52% male, aged 31 to 88 years) completed a clinical assessment and wore an Actiheart monitor in the free-living environment. The association between age and minutes per day in sedentary, light, moderate, and vigorous PA was assessed using relative intensity, as defined by heart rate reserve, and absolute intensity using activity count thresholds.

Results

In cross-sectional analyses, time spent in sedentary and light activities as defined by relative intensity did not differ across age (p > 0.05), whereas time spent in moderate and vigorous relative PA was higher for each 1 year increase in age (p < .01). Using absolute intensity PA thresholds, older adults registered fewer activity counts per day with more sedentary time and lesser amounts of light, moderate, and vigorous PA (p < .05). Persons with higher relative and lower absolute PA intensity had poorer functional performance and higher subclinical disease indicators.

Conclusions

These findings suggest that time spent in moderate or higher intensity activities may not be lower with age after considering changes in physiology, functional ability, and subclinical disease burden and highlight the need for more age- and ability-specific PA research to inform future interventions and public health guidelines.

Organizing and analyzing the activity data in NHANES

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.

[Giant pulmonary artery aneurysms. Case report and proposal of management algorithm]

Pulmonary artery aneurysm is a rare and multiform pathology related to multiple etiologies and therefore different pathophysiological mechanisms. Delineating homogenous sub-groups is a pre-requisite to refine medico-surgical management. The case of a giant PAA without pulmonary hypertension but associated to a dysplastic pulmonary valve is reported. This association could be in some instances the result of a congenital anomaly in the development of both the pulmonary valve and the root creating the conditions for further development of a pulmonary artery aneurysm. Whilst minor forms are usually asymptomatic, they can lead to lethal complications in huge sizes and are frequently associated via pulmonary valve insufficiency to right ventricular dysfunction. This specific association is discussed and a diagnostic algorithm for nosologic classification and management is proposed.

Comparison of Three Real-Time PCR Assays for the Detection of PIK3CA Somatic Mutations in Formalin-Fixed Paraffin Embedded Tissues of Patients with Breast Carcinomas

Breast cancer is the leading cause of cancer-related death in women worldwide. Mutations of the PIK3CA gene are found in approximately 25% of breast carcinomas and are reported as activators of the PI3K/AKT/mTOR pathway. This study aims to compare three assays for the somatic mutation detection of PIK3CA gene in FFPE tissues of patients with breast cancer. We compared Cobas® PIK3CA Mutation Test (Roche Diagnostics, Meylan, France), PCR amplification-refractory mutation system Scorpions® (ARMS) and High-Resolution Melting PCR assay (HRM) for the detection of PIK3CA mutations. Discrepant samples were assessed using Next Generation Sequencing (NGS). 46 FFPE breast carcinomas samples of patients treated for breast cancer have been assessed for PIK3CA mutations using the three PCR assays. Among the 46 samples, 17 (37.8%), 13 (28.36%) and 19 (41.3%) had a PIK3CA mutation, with Cobas®, ARMS and HRM assays respectively. Three different mutations of PIK3CA have been detected for one sample. Calculated kappa were 0.95[0.86;1] between Cobas® and HRM, 0.75[0.55;0.95] between Cobas® and ARMS and 0.72[0.51;0.92] between HRM and ARMS. Five samples were found with discrepant results. Our study shows that the Cobas® assay is suitable for PIK3CA mutation assessment in patients with breast cancer. HRM assay is also suitable for PIK3CA mutation assessment but requires a mutation characterization with a specific assay.

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.

Two-dimensional speckle tracking echocardiography in calves: feasibility and repeatability study

OBJECTIVES

To assess the feasibility and repeatability of two-dimensional speckle tracking echocardiography for the evaluation of left ventricular function in healthy calves.

ANIMALS

12 Holstein calves, 62 ± 11.6 days old; 75.25 ± 5.4 Kg.

METHODS

Observational study. Right parasternal short-axis views at papillary muscle level were recorded in standing calves and subsequently analyzed by two-dimensional speckle tracking for global and regional radial and circumferential strains and strain rates and radial displacement. Echocardiographic examinations were performed by 2 observers to evaluate intra- and interobserver repeatability and variability.

RESULTS

Two-dimensional speckle tracking was feasible in all calves. Automated tracking was better in systole than in diastole. Repeatability of the technique was good in calves. Systolic radial strain and strain rate peak values showed little variability compared with systolic circumferential strain and strain rate and to all diastolic measurements. Variability of the interobserver measurements was greater than the intraobserver measurements.

CONCLUSIONS

Two-dimensional speckle tracking is feasible in calves. As in other species, evaluation of systolic radial left ventricular function is more reliable than circumferential and diastolic left ventricular function.

Statistical methods to compare functional outcomes in randomized controlled trials with high mortality

Mortality is a common primary endpoint in randomized controlled trials of patients with a high severity of illness, such as critically ill patients. However, researchers are increasingly evaluating functional outcomes, such as quality of life. Importantly, in such trials some patients may die before the assessment of a functional outcome, resulting in the functional outcome being “truncated due to death.” As described in this paper, defining and testing treatment effects on functional outcomes in this setting requires careful consideration. Data from a completed trial of critically ill patients are used to highlight key differences among three statistical approaches used when analyzing such trials.

Epidemiology of objectively measured bedtime and chronotype in US adolescents and adults: NHANES 2003-2006

BACKGROUND

We propose a method for estimating the timing of in-bed intervals using objective data in a large representative US sample, and quantify the association between these intervals and age, sex, and day of the week.

METHODS

The study included 11,951 participants 6 years and older from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, who wore accelerometers to measure physical activity for seven consecutive days. Participants were instructed to remove the device just before the nighttime sleep period and put it back on immediately after. This nighttime period of non-wear was defined in this paper as the objective bedtime (OBT), an objectively estimated record of the in-bed interval. For each night of the week, we estimated two measures: the duration of the OBT (OBT-D) and, as a measure of the chronotype, the midpoint of the OBT (OBT-M). We estimated day-of-the-week-specific OBT-D and OBT-M using gender-specific population percentile curves. Differences in OBT-M (chronotype) and OBT-D (the amount of time spent in bed) by age and sex were estimated using regression models.

RESULTS

The estimates of OBT-M and their differences among age groups were consistent with the estimates of chronotype obtained via self-report in European populations. The average OBT-M varied significantly by age, while OBT-D was less variable with age. In the reference group (females, aged 17-22 years), the average OBT-M across 7 days was 4:19 AM (SD = 30 min) and the average OBT-D was 9 h 19 min (SD = 12 min). In the same age group the average OBT-D was 18 minutes shorter for males than for females, while the average OBT-M was not significantly different between males and females. The most pronounced differences were observed between OBT-M of weekday and weekend nights. In the reference group, compared to the average OBT-M of 3:50 am on Monday through Thursday nights, there was a 57-minute delay in OBT-M on Friday nights (entering the weekend), a 69-minute delay on Saturday nights (staying in the weekend), and a 23-minute delay on Sunday night (leaving the weekend). For both OBT-M and OBT-D, in most age groups and for most days of the week, there were no statistically significant differences between males and females, except for OBT-D on Wednesdays and Thursdays, with males having 31 (p-value < 0.05) and 45 (p-value < 0.05) minutes shorter OBT-D, respectively.

CONCLUSIONS

The proposed measures, OBT-D and OBT-M, provide useful information of time in bed and chronotype in NHANES 2003-2006. They identify within-week patterns of bedtime and can be used to study associations between the bedtime and the large number of health outcomes collected in NHANES 2003-2006.

Tumor lymphocyte immune response to preoperative radiotherapy in locally advanced rectal cancer: The LYMPHOREC study

Introduction: Some studies have suggested that baseline tumor-infiltrating-lymphocytes (TILs), such as CD8+ and FoxP3+ T-cells, may be associated with a better prognosis in colorectal cancer. We sought to investigate modulation of the immune response by preoperative radiotherapy (preopRT) and its impact on survival in locally advanced rectal cancer (LARC). Materials & Methods: We analyzed data for 237 patients with LARC who received RT. Density of TILS (CD8+ and FoxP3+) in intraepithelial (iTILs) and stromal compartments (sTILs) were evaluated from surgery pathological specimens and biopsies performed at baseline. The primary endpoint was to assess the impact of infiltration of the tumor or tumor site after preopRT on progression-free survival (PFS) and overall survival (OS). Secondary endpoints were the impact of dose fractionation scheme on TILs. Results: In univariate analysis, several factors significantly correlated (p<0.05) with PFS and/or OS (T-stage, M-stage, the delay between RT and surgery). A high level of post-treatment FoxP3+ TIL density correlated significantly with a better PFS (p = 0.007). In multivariate analysis, a decrease in the CD8+/FoxP3+ iTILs ratio after preopRT correlated with better PFS and OS (p = 0.049 and p = 0.024, respectively). More particularly, patients with a delta CD8+/FoxP3+ <-3.8 had better PFS and OS. Interestingly, the dose fractionation scheme significantly influenced the CD8⁺/FoxP3⁺ ratio after treatment (p = 0.027) with a lower ratio with hypofractionated RT (≥2 Gy). Conclusion: Patients with LARC who had a significant decrease in the CD8+/FoxP3+ ratio after preopRT were more likely to live longer. This ratio needs to be validated prospectively to guide physicians in adjuvant treatment decision-making.

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.