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BackgroundGiven the evolution of processing and analyzing accelerometry data over the past decade, it is of utmost importance that we as a field understand how newer (e.g., MIMS) summary measures compare to long-established ones (e.g., ActiGraph activity counts).ObjectiveOur study aims to compare and harmonize accelerometry-based measures of physical activity (PA) to increase the comparability, generalizability, and translation of findings across studies using objective measures of PA.MethodsHigh resolution accelerometry data were collected from 655 participants in the Baltimore Longitudinal Study on Aging who wore an ActiGraph GT9X device at wrist continuously for a week. Data were summarized at the minute-level as activity counts (AC; measure obtained from ActiGraph’s ActiLife software) and MIMS, ENMO, MAD, and AI (open-source measures implemented in R). The correlation between AC and other measures was quantified both marginally and conditionally on age, sex and BMI. Next, each pair of measures were harmonized using nonparametric regression of minute-level measurements.ResultsThe study sample had the following characteristics: mean (sd) age of 69.8 (14.2), BMI of 27.3 (5.0) kg/m2, 54.5% females, and 67.9% white. The marginal participant-specific correlation between AC and MIMS, ENMO, MAD, and AI were 0.988, 0.867, 0.913 and 0.970, respectively. After harmonization, the mean absolute percentage error for predicting total AC from MIMS, ENMO, MAD, and AI was 2.5, 14.3, 11.3 and 6.3, respectively. The accuracy for predicting sedentary minutes based on AC (AC > 1853) using MIMS, ENMO, MAD and AI was 0.981, 0.928, 0.904, and 0.960, respectively. An R software with a unified interface for computation of the open-source measures from raw accelerometry data was developed and published as SummarizedActigraphy R package.ConclusionsOur comparison of accelerometry-based measures of PA enables researchers to extend the knowledge from the thousands of manuscripts that have been published using ActiGraph AC to MIMS and other measures by demonstrating their high correlation and providing a harmonization mapping.

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. CLINICALTRIAL

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.

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.

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&ndash, 2004 and 2005&ndash, 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.

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.Prospective observational study.Seven Level 1 trauma centers.One hundred eighty-two adults with severe tibia fractures.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.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.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 (threshold0.3) 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 of0.7 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).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.Diagnostic Level I. See Instructions for Authors for a complete description of levels of evidence.

Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003–2004 and 2005–2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups.

Background:People living with human immunodeficiency virus (HIV+) have greater risk for sudden arrhythmic death than HIV-uninfected (HIV–) individuals. HIV-associated abnormal cardiac repolarization may contribute to this risk. We investigated whether HIV serostatus is associated with ventricular repolarization lability by using the QT variability index (QTVI), defined as a log measure of QT-interval variance indexed to heart rate variance.Methods:We studied 1123 men (589 HIV+ and 534 HIV–) from MACS (Multicenter AIDS Cohort Study), using the ZioXT ambulatory electrocardiography patch. Beat-to-beat analysis of up to 4 full days of electrocardiographic data per participant was performed using an automated algorithm (median analyzed duration [quartile 1–quartile 3]: 78.3 [66.3–83.0] hours/person). QTVI was modeled using linear mixed-effects models adjusted for demographics, cardiac risk factors, and HIV-related and inflammatory biomarkers.Results:Mean (SD) age was 60.1 (11.9) years among HIV– and 54.2 (11.2) years among HIV+ participants (PConclusions:HIV+ men have greater beat-to-beat variability in QT interval (QTVI) than HIV– men, especially in the setting of HIV viremia and heightened inflammation. Among HIV+ men, higher QTVI suggests ventricular repolarization lability, which can increase susceptibility to arrhythmias, whereas lower heart rate variability signals a component of autonomic dysfunction.

Objective Early life physical activity may help prevent obesity, but objective quantification in infants is challenging. Methods A total of 506 infants were examined from 2013 to 2016. Infants wore accelerometers for 4 days at ages 3, 6, 9, and 12 months. Daily log‐transformed physical activity counts were computed, averaged, and standardized across assessments. A linear mixed model was used to examine trends in standardized physical activity counts as well as associations between physical activity and BMI z score, sum of subscapular and triceps skinfold thickness for overall adiposity (SS+TR), and their ratio for central adiposity (SS:TR). Results Among infants, 66% were black and 50% were female. For each additional visit, standardized physical activity counts increased by 0.23 (CI: 0.18 to 0.27; P

We introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation. We refer to this technique as variable-domain functional principal ...

Power and sample size calculation are major components of statistical analyses. The upstrap resampling method introduced by Crainiceanu and Crainiceanu (2018) was proposed as a general solution to this problem but has not been assessed in numerical experiments. We evaluate the power estimation properties of the upstrap for target data sets that are larger or smaller than the original observed data set. We also expand the scope of upstrap and propose a solution to estimate the power to detect: (1) an effect size observed in the original data; and (2) an effect size chosen by a researcher. Simulations include the following scenarios: one- and two-sample t-tests; linear regression with both Gaussian and binary outcomes; multilevel mixed effects models with both Gaussian and binary outcomes. In addition, our simulations consider cases where the distribution of a covariate in the target setting is preserved and when it is purposefully changed compared to the original data set. We illustrate the approach using a reanalysis of a cluster-randomized controlled trial of malaria transmission. The GitHub repository with R code used in manuscript analyses is available at https://git.io/J0TH1. The accompanying data are publicly available.

SummaryAs health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We provide the first published analysis of tri-axial accelerometry data from Zio XT patch and introduce an extension of posture classification algorithms for use with ECG patches worn in the free-living environment. Our novel extensions to posture classification include (1) estimation of an upright posture for each individual without the reference measurements used by existing posture classification algorithms; (2) correction for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. Methods were built using data from 14 participants from the Multicenter AIDS Cohort Study (MACS), and applied to 1, 250 MACS participants. As no posture labels exist in the free-living environment, we evaluate the algorithm against labelled data from the Towson Accelerometer Study and against data labelled by hand from the MACS study.

Difficulty recognizing affective prosody (receptive aprosodia) can occur following right hemisphere damage (RHD). Not all individuals spontaneously recover their ability to recognize affective prosody, warranting behavioral intervention. However, there is a dearth of evidence-based receptive aprosodia treatment research in this clinical population. The purpose of the current study was to investigate an explicit training protocol targeting affective prosody recognition in adults with RHD and receptive aprosodia. Eighteen adults with receptive aprosodia due to acute RHD completed affective prosody recognition before and after a short training session that targeted proposed underlying perceptual and conceptual processes. Behavioral impairment and lesion characteristics were investigated as possible influences on training effectiveness. Affective prosody recognition improved following training, and recognition accuracy was higher for pseudo- vs. real-word sentences. Perceptual deficits were associated with the most posterior infarcts, conceptual deficits were associated with frontal infarcts, and a combination of perceptual-conceptual deficits were related to temporoparietal and subcortical infarcts. Several right hemisphere ventral stream regions and pathways along with frontal and parietal hypoperfusion predicted training effectiveness. Explicit acoustic-prosodic-emotion training improves affective prosody recognition, but it may not be appropriate for everyone. Factors such as linguistic context and lesion location should be considered when planning prosody training.

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.

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.

The extensible neuroimaging archive toolkit (XNAT) is a common platform for storing and distributing neuroimaging data and is used by many key repositories of public neuroimaging data. Some examples include the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://nitrc.org/), the ConnectomeDB for the Human Connectome Project (https://db.humanconnectome.org/), and XNAT Central (https://central.xnat.org/). We introduce Rxnat (https://github.com/adigherman/Rxnat), an open-source R package designed to interact with any XNAT-based repository. The program has similar capabilities with PyXNAT and XNATpy, which were developed for Python users. Rxnat was developed to address the increased popularity of R among neuroimaging researchers. The Rxnat package can query multiple XNAT repositories and download all or a specific subset of images for further processing. This provides a lingua franca for the large community of R analysts to interface with multiple XNAT-based publicly available neuroimaging repositories. The potential of Rxnat is illustrated using an example of neuroimaging data normalization from two neuroimaging repositories, NITRC and HCP.

Background Portable monitoring is a convenient means for diagnosing sleep apnea. However, data on whether one night of monitoring is sufficiently precise for the diagnosis of sleep apnea are limited. Research Question The current study sought to determine the variability and misclassification in disease severity over three consecutive nights in a large sample of patients referred for sleep apnea. Methods A sample of 10,340 adults referred for sleep apnea testing was assessed. A self-applied type III monitor was used for three consecutive nights. The apnea-hypopnea index (AHI) was determined for each night, and a reference AHI was computed by using data from all 3 nights. Pairwise correlations and the proportion misclassified regarding disease severity were computed for each of the three AHI values against the reference AHI. Results Strong correlations were observed between the AHI from each of the 3 nights (r = 0.87-0.89). However, substantial within-patient variability in the AHI and significant misclassification in sleep apnea severity were observed based on any 1 night of monitoring. Approximately 93% of the patients with a normal study on the first night and 87% of those with severe sleep apnea on the first night were correctly classified compared with the reference derived from all three nights. However, approximately 20% of the patients with mild and moderate sleep apnea on the first night were misdiagnosed either as not having sleep apnea or as having mild disease, respectively. Conclusions In patients with mild to moderate sleep apnea, one night of portable testing can lead to misclassification of disease severity given the substantial night-to-night variability in the AHI.

International audience; IMPORTANCE Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems.OBJECTIVES To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.DESIGN, SETTING, AND PARTICIPANTS This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018.MAIN OUTCOMES AND MEASURES Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks.RESULTS Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (beta = -0.018, P = .04). Bidirectional associations were observed between motor activity (beta = 0.176; P = .03) and subjective energy level (beta = 0.027; P = .03) as well as between motor activity (beta = -0.027; P = .04) and sleep duration (beta = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy.CONCLUSIONS AND RELEVANCE These findings suggest that interventions focused on motor activity and energymay have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

Objective Optical coherence tomography (OCT)‐derived measures of the retina correlate with disability and cortical gray matter atrophy in multiple sclerosis (MS); however, whether such measures predict long‐term disability is unknown. We evaluated whether a single OCT and visual function assessment predict the disability status 10 years later. Methods Between 2006 and 2008, 172 people with MS underwent Stratus time domain‐OCT imaging [160 with measurement of total macular volume (TMV)] and high and low‐contrast letter acuity (LCLA) testing (n = 150; 87%). All participants had Expanded Disability Status Scale (EDSS) assessments at baseline and at 10‐year follow‐up. We applied generalized linear regression models to assess associations between baseline TMV, peripapillary retinal nerve fiber layer (pRNFL) thickness, and LCLA with 10‐year EDSS scores (linear) and with clinically significant EDSS worsening (binary), adjusting for age, sex, optic neuritis history, and baseline disability status. Results In multivariable models, lower baseline TMV was associated with higher 10‐year EDSS scores (mean increase in EDSS of 0.75 per 1 mm3 loss in TMV (mean difference = 0.75; 95% CI: 0.11–1.39; P = 0.02). In analyses using tertiles, individuals in the lowest tertile of baseline TMV had an average 0.86 higher EDSS scores at 10 years (mean difference = 0.86; 95% CI: 0.23–1.48) and had over 3.5‐fold increased odds of clinically significant EDSS worsening relative to those in the highest tertile of baseline TMV (OR: 3.58; 95% CI: 1.30–9.82; P trend = 0.008). pRNFL and LCLA predicted the 10‐year EDSS scores only in univariate models. Interpretation Lower baseline TMV measured by OCT significantly predicts higher disability at 10 years, even after accounting for baseline disability status.

OBJECTIVE Therapeutic development in progressive multiple sclerosis (PMS) has been hampered by a lack of reliable biomarkers to monitor neurodegeneration. Optical coherence tomography (OCT)-derived retinal measures have been proposed as promising biomarkers to fulfill this role. However, it is unclear whether retinal atrophy persists in PMS, exceeds normal aging, or can be distinguished from relapsing-remitting multiple sclerosis (RRMS). METHODS 178 RRMS, 186 PMS, and 66 control participants were followed with serial OCT for a median follow-up of 3.7 years. RESULTS The estimated proportion of peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell + inner plexiform layer (GCIPL) thinning in multiple sclerosis (MS) attributable to normal aging increased from 42.7% and 16.7% respectively at age 25 years, to 83.7% and 81.1% at age 65 years. However, independent of age, PMS was associated with faster pRNFL (-0.34 ± 0.09%/yr, p

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).