Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis

Systemic central venous oxygen saturation is associated with clot strength during traumatic hemorrhagic shock: A preclinical observational model

<p>Abstract</p> <p>Background</p> <p>Clot strength by Thrombelastography (TEG) is associated with mortality during trauma and has been linked to severity of tissue hypoperfusion. However, the optimal method for monitoring this important relationship remains undefined. We hypothesize that oxygen transport measurements will be associated with clot strength during traumatic shock, and test this hypothesis using a swine model of controlled traumatic shock.</p> <p>Methods</p> <p>N = 33 swine were subjected to femur fracture and hemorrhagic shock by controlled arterial bleeding to a predetermined level of oxygen debt measured by continuous indirect calorimetry. Hemodynamics, oxygen consumption, systemic central venous oxygenation (ScvO₂), base excess, lactate, and clot maximal amplitude by TEG (TEG-MA) as clot strength were measured at baseline and again when oxygen debt = 80 ml/kg during shock. Oxygen transport and metabolic markers of tissue perfusion were then evaluated for significant associations with TEG-MA. Forward stepwise selection was then used to create regression models identifying the strongest associations between oxygen transport and TEG-MA independent of other known determinants of clot strength.</p> <p>Results</p> <p>Multiple markers of tissue perfusion, oxygen transport, and TEG-MA were all significantly altered during shock compared to baseline measurements (p < 0.05). However, only ScvO₂demonstrated a strong bivariate association with TEG-MA measured during shock (R = 0.7, p < 0.001). ScvO₂measured during shock was also selected by forward stepwise selection as an important covariate in linear regression models of TEG-MA after adjusting for the covariates fibrinogen, pH, platelet count, and hematocrit (Whole model R²= 0.99, p ≤ 0.032).</p> <p>Conclusions</p> <p>Among multiple measurements of oxygen transport, only ScvO₂was found to retain a significant association with TEG-MA during shock after adjusting for multiple covariates. ScvO₂should be further studied for its utility as a clinical marker of both tissue hypoxia and clot formation during traumatic shock.</p

Randomised Trial to Evaluate the Effectiveness and Impact of Offering Postvisit Decision Support and Assistance in Obtaining Physician-Recommended Colorectal Cancer Screening: The e-Assist: Colon Health Study - A Protocol Study

INTRODUCTION: How to provide practice-integrated decision support to patients remains a challenge. We are testing the effectiveness of a practice-integrated programme targeting patients with a physician recommendation for colorectal cancer (CRC) screening. METHODS AND ANALYSIS: In partnership with healthcare teams, we developed \u27e-assist: Colon Health\u27, a patient-targeted, postvisit CRC screening decision support programme. The programme is housed within an electronic health record (EHR)-embedded patient portal. It leverages a physician screening recommendation as the cue to action and uses the portal to enrol and intervene with patients. Programme content complements patient-physician discussions by encouraging screening, addressing common questions and assisting with barrier removal. For evaluation, we are using a randomised trial in which patients are randomised to receive e-assist: Colon Health or one of two controls (usual care plus or usual care). Trial participants are average-risk, aged 50-75 years, due for CRC screening and received a physician order for stool testing or colonoscopy. Effectiveness will be evaluated by comparing screening use, as documented in the EHR, between trial enrollees in the e-assist: Colon Health and usual care plus (CRC screening information receipt) groups. Secondary outcomes include patient-perceived benefits of, barriers to and support for CRC screening and patient-reported CRC screening intent. The usual care group will be used to estimate screening use without intervention and programme impact at the population level. Differences in outcomes by study arm will be estimated with hierarchical logit models where patients are nested within physicians. ETHICS AND DISSEMINATION: All trial aspects have been approved by the Institutional Review Board of the health system in which the trial is being conducted. We will disseminate findings in diverse scientific venues and will target clinical and quality improvement audiences via other venues. The intervention could serve as a model for filling the gap between physician recommendations and patient action. TRIAL REGISTRATION NUMBER: NCT02798224; Pre-results

A calibration method of USBL installation error based on attitude determination

The Ultra-short baseline (USBL) positioning system has important application in the positioning of underwater vehicles. The installation error angle of the USBL positioning system has an important influence on the positioning accuracy of USBL system. The traditional calibration methods have limited estimation accuracy for installation error angles and have high route requirements. To solve the above problems, a calibration method of installation error angle based on attitude determination is proposed in this paper. When strapdown inertial navigation system (SINS) and USBL are fixed together in the application process, the installation error angle of USBL is fixed and unchanged. Then the calibration of installation error angle can be accomplished with the idea of attitude determination. The vector observation model based on the installation error angle matrix is established first. Observation vectors are obtained by the relative position of transponders in the USBL coordinate frame. The reference vector is calculated by position of transponder, position and attitude of SINS and lever arm between SINS and USBL. By constructing the observation vectors and the reference vectors, the proposed method can calibrate the installation error angle of SINS and USBL in real time. The advantages of the proposed method are that it has no specific requirements for the calibration route and can calibrate the installation error angle in real time with high accuracy. In order to verify the performance of the proposed algorithm, simulation experiment and field experiment are carried out in this paper. The results of simulation experiment and field experiment show that the proposed method can give the estimated installation error angle of USBL in real time, and the estimated result is the best among several methods. The proposed method can not only achieve the calibration of the installation error angle in circular trajectory, but also in straight trajectory

Inference and applications in hierarchical linear models with missing data.

The development of model-based methods for missing data has been a seminal contribution to statistical inference and data analysis (Orchard and Woodbury 1972; Rubin 1976; Dempster, Laird and Rubin 1977; Rubin 1987; Schafer 1997; Little and Rubin 2002). These methods apply when observations are independently distributed. This paper extends the model-based methods to two-level data where the observations within each cluster are dependent. When such data are complete, analysis using a hierarchical linear model (also known as a multilevel linear model or a random coefficient model) proceeds using maximum likelihood (Dempster, Laird and Rubin 1977; Dempster, Rubin, and Tsutakawa 1981; Laird and Ware 1982; Longford 1993; Goldstein 1995; Schafer 1997; Pinheiro and Bates 2000; Little and Rubin 2002; Raudenbush and Bryk 2002) or Bayes methods (Lindley and Smith 1972; Carlin and Louis 1996; Gelman, Carlin, Stern and Rubin 1997; Schafer 1997; Little and Rubin 2002). The key assumptions are that the data at the within-cluster or cluster level, or both, are missing at random (MAR); that parameter spaces for the complete data model and missing data mechanism are distinct (Rubin 1976); and that the data subject to missingness are multivariate normal conditional on all observed data. We maximize the observed data likelihood via the EM algorithm (Dempster, Laird and Rubin 1977; Wu 1993) or a mixture of EM algorithm and Fisher scoring (Laird and Ware 1982; Longford 1987) to obtain the maximum likelihood (ML) estimates of the parameters of interest using all available data. We consider a general missing data pattern via an observed-value indicator matrix. Applications include regression of a subset of complete data on a disjoint subset, a random-coefficients model, multiple model-based imputation, a simultaneous-equations model, a contextual-effects model, and a level-2 response model. We illustrate these applications using national survey data on US high schools and simulated data sets.Ph.D.Pure SciencesStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/123703/2/3096200.pd

Racial disparities in cancer genetic counseling encounters: study protocol for investigating patient-genetic counselor communication in the naturalistic clinical setting using a convergent mixed methods design

Abstract Background Despite decades of effort to reduce racial cancer disparities, Black people continue to die at higher rates from cancer than any other U.S. racial group. Because prevention is a key to the cost-effective and long-term control of cancer, the potential for cancer genetic counseling to play a central role in reducing racial cancer disparities is high. However, the benefits of genetic counseling are not equitable across race. Only 2% of genetic counselors self-identify as Black/African American, so most genetic counseling encounters with Black patients are racially discordant. Patients in racially discordant medical interactions tend to have poorer quality patient-provider communication and receive suboptimal clinical recommendations. One major factor that contributes to these healthcare disparities is racial bias. Drawing on findings from prior research, we hypothesize that genetic counselor providers’ implicit racial prejudice will be associated negatively with the quality of patient-provider communication, while providers’ explicit negative racial stereotypes will be associated negatively with the comprehensiveness of clinical discussions of cancer risk and genetic testing for Black (vs. White) patients. Methods Using a convergent mixed methods research design, we will collect data from at least 15 genetic counseling providers, from two different institutions, and their 220 patients (approximately equal number of Black and White patients per provider) whose appointments are for a hereditary cancer condition. The data sources will include two provider surveys, two patient surveys, video- and/or audio-recordings of genetic counseling encounters, and medical chart reviews. The recorded cancer genetic counseling in-person and telehealth encounters will be analyzed both qualitatively and quantitatively to assess the quality of patient-provider communication and the comprehensiveness of clinical discussion. Those data will be linked to pre- and post-encounter survey data and data from medical chart reviews to test our hypotheses. Discussion Findings from this multi-site study will highlight specific aspects of cancer genetic counseling encounters (patient-provider communication and clinical recommendations) that are directly associated with patient-centered outcomes (e.g., satisfaction, trust, genetic testing completion). Patient-provider communication and clinical recommendations are modifiable factors that can be integrated into current genetic counseling training curricula and thus can have immediate impact on genetic counseling training and practice