HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.

Objective: We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer's Disease Neuroimaging Initiative dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multitask baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline. Results: Out of 634 MCI patients (mean [IQR] age: 72.8 [67-78], 60% male), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all state-of-the-art longitudinal single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC = 0.623 [0.605-0.644]; all P < .05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression. Discussion: Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected electronic health records (EHR) variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.

Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US.

This cohort study uses data from electronic health records to assess variability in a sepsis prediction model across 9 hospitals.

OpenSep: a generalizable open source pipeline for SOFA score calculation and Sepsis-3 classification.

EHR-based sepsis research often uses heterogeneous definitions of sepsis leading to poor generalizability and difficulty in comparing studies to each other. We have developed OpenSep, an open-source pipeline for sepsis phenotyping according to the Sepsis-3 definition, as well as determination of time of sepsis onset and SOFA scores. The Minimal Sepsis Data Model was developed alongside the pipeline to enable the execution of the pipeline to diverse sources of electronic health record data. The pipeline's accuracy was validated by applying it to the MIMIC-IV version 1.0 data and comparing sepsis onset and SOFA scores to those produced by the pipeline developed by the curators of MIMIC. We demonstrated high reliability between both the sepsis onsets and SOFA scores, however the use of the Minimal Sepsis Data model developed for this work allows our pipeline to be applied to more broadly to data sources beyond MIMIC.

Sepsis Prediction for the General Ward Setting.

Objective: To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design. Design: Retrospective analysis of data extracted from electronic health records (EHR). Setting: Single, tertiary-care academic medical center in St. Louis, MO, USA. Patients: Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019. Interventions: None. Measurements and Main Results: Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (AUPRC) of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 h of crossing the alert threshold, 20.9% had a sepsis-related event occur. Conclusions: A machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h.

Respiratory support status from EHR data for adult population: classification, heuristics, and usage in predictive modeling.

OBJECTIVE: Respiratory support status is critical in understanding patient status, but electronic health record data are often scattered, incomplete, and contradictory. Further, there has been limited work on standardizing representations for respiratory support. The objective of this work was to (1) propose a practical terminology system for respiratory support methods; (2) develop (meta-)heuristics for constructing respiratory support episodes; and (3) evaluate the utility of respiratory support information for mortality prediction. MATERIALS AND METHODS: All analyses were performed using electronic health record data of COVID-19-tested, emergency department-admit, adult patients at a large, Midwestern healthcare system between March 1, 2020 and April 1, 2021. Logistic regression and XGBoost models were trained with and without respiratory support information, and performance metrics were compared. Importance of respiratory-support-based features was explored using absolute coefficient values for logistic regression and SHapley Additive exPlanations values for the XGBoost model. RESULTS: The proposed terminology system for respiratory support methods is as follows: Low-Flow Oxygen Therapy (LFOT), High-Flow Oxygen Therapy (HFOT), Non-Invasive Mechanical Ventilation (NIMV), Invasive Mechanical Ventilation (IMV), and ExtraCorporeal Membrane Oxygenation (ECMO). The addition of respiratory support information significantly improved mortality prediction (logistic regression area under receiver operating characteristic curve, median [IQR] from 0.855 [0.852-0.855] to 0.881 [0.876-0.884]; area under precision recall curve from 0.262 [0.245-0.268] to 0.319 [0.313-0.325], both P < 0.01). The proposed generalizable, interpretable, and episodic representation had commensurate performance compared to alternate representations despite loss of granularity. Respiratory support features were among the most important in both models. CONCLUSION: Respiratory support information is critical in understanding patient status and can facilitate downstream analyses.

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting.

The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0.803 [95% confidence interval [CI]: 0.795-0.811], area under the precision recall curves: 0.130 [95% CI: 0.121-0.140]) followed NEWS, Modified Early Warning Score, and quick Sequential Organ Failure Assessment (qSOFA). Using validated thresholds, NEWS 2 also had the highest recall (0.758 [95% CI: 0.736-0.778]) but qSOFA had the highest specificity (0.950 [95% CI: 0.948-0.952]), positive predictive value (0.184 [95% CI: 0.169-0.198]), and F1 score (0.236 [95% CI: 0.220-0.253]). While NEWS 2 outperformed all other compared EWS and patient acuity scores, due to the low prevalence of sepsis, all scoring systems were prone to false positives (low positive predictive value without drastic sacrifices in sensitivity), thus leaving room for more computationally advanced approaches.

A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infections. Using machine learning (ML), we sought to develop a prediction model for carbapenem resistance. All patients >18 years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with ≥1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. Sixty-seven features were used for predictive modeling. The most important features were number of prior antibiotic days, recent central venous catheter placement, and inpatient surgery. After model training, the area under the receiver operating characteristic curve was 0.846. The sensitivity of the model was 30%, with a positive predictive value (PPV) of 30% and a negative predictive value of 99%. Using readily available clinical data, we were able to create a ML model capable of predicting CR infections at the time of culture collection with a high PPV.

Comparison of Sepsis Definitions as Automated Criteria.

OBJECTIVES: Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. DESIGN: Retrospective analysis of data extracted from electronic health record. SETTING: Single, tertiary-care center in St. Louis, MO. PATIENTS: Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%). CONCLUSIONS: The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.

The Clinical Course of Coronavirus Disease 2019 in a US Hospital System: A Multistate Analysis.

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50-75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3-5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

Acid Resistance of Glass Ionomer Cement Restorative Materials.

In view of the need for aesthetics, restorations of teeth will typically be completed using tooth colored restorative materials. With the advent of biomimetic restorative materials, such as glass ionomer cements (GIC), much greater emphasis is now being placed on how well such materials can resist the challenge of acids that are present in foods and drinks, or gastric contents that are regurgitated. This laboratory study compared the dissolution and behavior of five GIC materials (GC Fuji® VII, GC Fuji® Bulk, GC Fuji® IX Fast, Fuji® IX Extra and GC Equia® Forte Fil) when exposed to three acids (citric acid, phosphoric acid and lactic acid), versus ultrapure deionized water, which was used as a control. Discs of each material GIC were submerged in solutions and percentage weight changes over time determined. Subsequently, the GIC materials were also placed as a part of standardized Class II sandwich restorations in bovine teeth (n = 20), and submerged in the solutions, and the extent of GIC dissolution and protection of the adjacent tooth was scored. Weight loss increased with time and with acid concentration. Overall, the most soluble material was GC Fuji® IX Extra, while GC Fuji® IX Fast and GC Fuji® Bulk were less soluble, and the least soluble material was GC Equia® Forte Fil. The most destructive solution for both the discs and for GIC restorations in teeth was 10% citric acid, while the least destructive acid was 0.1% lactic acid. The more recent GIC materials GC Fuji® Bulk and GC Equia® Forte Fil showed increased acid resistance over the older GIC materials, and this further justifies their use in open sandwich Class II restorations in more hostile environments.

Small Molecule Inhibition of CPS1 Activity through an Allosteric Pocket.

Carbamoyl phosphate synthetase 1 (CPS1) catalyzes the first step in the ammonia-detoxifying urea cycle, converting ammonia to carbamoyl phosphate under physiologic conditions. In cancer, CPS1 overexpression supports pyrimidine synthesis to promote tumor growth in some cancer types, while in others CPS1 activity prevents the buildup of toxic levels of intratumoral ammonia to allow for sustained tumor growth. Targeted CPS1 inhibitors may, therefore, provide a therapeutic benefit for cancer patients with tumors overexpressing CPS1. Herein, we describe the discovery of small-molecule CPS1 inhibitors that bind to a previously unknown allosteric pocket to block ATP hydrolysis in the first step of carbamoyl phosphate synthesis. CPS1 inhibitors are active in cellular assays, blocking both urea synthesis and CPS1 support of the pyrimidine biosynthetic pathway, while having no activity against CPS2. These newly discovered CPS1 inhibitors are a first step toward providing researchers with valuable tools for probing CPS1 cancer biology.

Spot the difference: comparing results of analyses from real patient data and synthetic derivatives.

BACKGROUND: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. OBJECTIVES: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. METHODS: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). RESULTS: For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. DISCUSSION AND CONCLUSION: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.

Novel Visualization of Clostridium difficile Infections in Intensive Care Units.

BACKGROUND: Accurate and timely surveillance and diagnosis of healthcare-facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations. OBJECTIVES: To integrate spatiotemporal factors with HO-CDI cases and develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI. METHODS: Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over two years were extracted from the Information Warehouse of a large academic medical center and processed according to Center for Disease Control (CDC) National Healthcare Safety Network (NHSN) definitions to classify Clostridium difficile infection (CDI) cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this Academic Medical Center (AMC). Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey. RESULTS: The simple classification algorithm identified all 265 HO-CDI cases from 1/1/15-11/30/15 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6% All users "strongly agreed" that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present Hospital Acquired Infection (HAI) information to others more efficiently. CONCLUSIONS: The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.

Deconvolution of images from 3D printed cells in layers on a chip.

Layer-by-layer cell printing is useful in mimicking layered tissue structures inside the human body and has great potential for being a promising tool in the field of tissue engineering, regenerative medicine, and drug discovery. However, imaging human cells cultured in multiple hydrogel layers in 3D-printed tissue constructs is challenging as the cells are not in a single focal plane. Although confocal microscopy could be a potential solution for this issue, it compromises the throughput which is a key factor in rapidly screening drug efficacy and toxicity in pharmaceutical industries. With epifluorescence microscopy, the throughput can be maintained at a cost of blurred cell images from printed tissue constructs. To rapidly acquire in-focus cell images from bioprinted tissues using an epifluorescence microscope, we created two layers of Hep3B human hepatoma cells by printing green and red fluorescently labeled Hep3B cells encapsulated in two alginate layers in a microwell chip. In-focus fluorescent cell images were obtained in high throughput using an automated epifluorescence microscopy coupled with image analysis algorithms, including three deconvolution methods in combination with three kernel estimation methods, generating a total of nine deconvolution paths. As a result, a combination of Inter-Level Intra-Level Deconvolution (ILILD) algorithm and Richardson-Lucy (RL) kernel estimation proved to be highly useful in bringing out-of-focus cell images into focus, thus rapidly yielding more sensitive and accurate fluorescence reading from the cells in different layers. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:445-454, 2018.

Physical-Performance Outcomes and Biomechanical Correlates from the 32-Week Yoga Empowers Seniors Study.

Background. Yoga Empowers Seniors Study (YESS) quantified physical demands associated with yoga performance using biomechanical methods. This study evaluated the efficacy of the program on physical function outcomes. Methods. Twenty community-dwelling older adults aged 70.7 ± 3.8 years attended biweekly 60-minute Hatha yoga classes for 32 weeks. Four domains of the physical measurements including (1) functional performance, (2) flexibility, (3) muscle strength, and (4) balance were taken at the baseline, 16-week and 32-week time points. Repeated-measures ANOVA omnibus tests and Tukey's post hoc tests were employed to examine the differences in each outcome variable across the 3 time points. Results. Improved timed chair stands (p < 0.01), 8-foot up and go (p < 0.05), 2-min step test (p < 0.05), and vertical reach (p = 0.05) performance were evident. Isometric knee flexor strength (p < 0.05) and repetitions of the heel rise test (p < 0.001) also increased following the 32-week intervention. Both flexibility and balance performance remained unchanged. Conclusions. Significant improvements in physical function and muscle-specific lower-extremity strength occur with the regular practice of a modified Hatha yoga program designed for seniors. These adaptations corresponded with the previously reported biomechanical demands of the poses.

The association between unilateral heel-rise performance with static and dynamic balance in community dwelling older adults.

As a measure of both strength and muscle endurance of the plantar flexors, the unilateral heel rise (UHR) test has been suggested as a method to evaluate balance capabilities in older adults. Thus, the purpose of this study was to examine the association between UHR performance with biomechanical measures of balance in seniors. Twenty-two older adults completed two testing sessions. The first visit included UHR performance; the second visit included dynamic and static motion analysis. UHR performance was significantly associated with dynamic balance capability as measured by medial-lateral inclination angle during gait. As indicated by an analysis of center of pressure, there were significant associations between UHR performance and measures of static balance. Balance is influenced by plantar flexor performance as measured by the UHR test. We therefore suggest incorporating the UHR test in analyses of balance in seniors.

Physical demand profiles of hatha yoga postures performed by older adults.

Understanding the physical demands placed upon the musculoskeletal system by individual postures may allow experienced instructors and therapists to develop safe and effective yoga programs which reduce undesirable side effects. Thus, we used biomechanical methods to quantify the lower extremity joint angles, joint moments of force, and muscle activities of 21 Hatha yoga postures, commonly used in senior yoga programs. Twenty older adults, 70.7 years ± 3.8 years, participated in a 32-wk yoga class (2 d/wk) where they learned introductory and intermediate postures (asanas). They then performed the asanas in a motion analysis laboratory. Kinematic, kinetic, and electromyographic data was collected over three seconds while the participants held the poses statically. Profiles illustrating the postures and including the biomechanical data were then generated for each asana. Our findings demonstrated that Hatha yoga postures engendered a range of appreciable joint angles, JMOFs, and muscle activities about the ankle, knee, and hip, and that demands associated with some postures and posture modifications were not always intuitive. They also demonstrated that all of the postures elicited appreciable rectus abdominis activity, which was up to 70% of that induced during walking.

The biomechanical demands of standing yoga poses in seniors: The Yoga empowers seniors study (YESS).

BACKGROUND: The number of older adults participating in yoga has increased dramatically in recent years; yet, the physical demands associated with yoga performance have not been reported. The primary aim of the Yoga Empowers Seniors Study (YESS) was to use biomechanical methods to quantify the physical demands associated with the performance of 7 commonly-practiced standing yoga poses in older adults. METHODS: 20 ambulatory older adults (70.7+-3.8 yrs) attended 2 weekly 60-minute Hatha yoga classes for 32 weeks. The lower-extremity net joint moments of force (JMOFs), were obtained during the performance of the following poses: Chair, Wall Plank, Tree, Warrior II, Side Stretch, Crescent, and One-Legged Balance. Repeated-measure ANOVA and Tukey's post-hoc tests were used to identify differences in JMOFs among the poses. Electromyographic analysis was used to support the JMOF findings. RESULTS: There was a significant main effect for pose, at the ankle, knee and hip, in the frontal and sagittal planes (p=0.00-0.03). The Crescent, Chair, Warrior II, and One-legged Balance poses generated the greatest average support moments. Side Stretch generated the greatest average hip extensor and knee flexor JMOFs. Crescent placed the highest demands on the hip flexors and knee extensors. All of the poses produced ankle plantar-flexor JMOFs. In the frontal plane, the Tree generated the greatest average hip and knee abductor JMOFs; whereas Warrior II generated the greatest average hip and knee adductor JMOFs. Warrior II and One-legged Balance induced the largest average ankle evertor and invertor JMOFs, respectively. The electromyographic findings were consistent with the JMOF results. CONCLUSIONS: Musculoskeletal demand varied significantly across the different poses. These findings may be used to guide the design of evidence-based yoga interventions that address individual-specific training and rehabilitation goals in seniors. CLINICAL TRIAL REGISTRATION: This study is registered with NIH Clinicaltrials.gov #NCT 01411059.

The physical demands of the tree (vriksasana) and one-leg balance (utthita hasta padangusthasana) poses performed by seniors: a biomechanical examination.

Yoga is considered especially suitable for seniors because poses can be modified to accommodate practitioners' capabilities and limitations. In this study, biomechanical assessments on healthy seniors (n = 20; 70.1 ± 3.8 yr) were used to quantify the physical demands, (net joint moments of force [JMOFs] and muscular activation in the lower extremities) associated with the performance of 3 variations (introductory, intermediate, advanced) of 2 classical Hatha yoga poses - Tree and One-Leg Balance (OLB). ANOVA and Cohen's-d were used to contrast the postural variations statistically. The advanced (single-limb, without additional support) versions were hypothesized to generate the greatest demands, followed by the intermediate (single-limb [Tree] and bilateral-limb [OLB] with support) and introductory (bilateral-limb) versions. Our findings, however, suggest that common, long-held conceptions about pose modifications can be counter-intuitive. There was no difference between the intermediate and advanced Tree variations regarding hip and knee JMOFs in both the sagittal and frontal planes (P = 0.13-0.98). Similarly, OLB introductory and intermediate variations induced sagittal JMOFs that were in the opposite direction of the classic advanced pose version at the hip and knee (P < .001; d = 0.98-2.36). These biomechanical insights provide evidence that may be used by instructors, clinicians and therapists when selecting pose modifications for their yoga participants.

Yoga Empowers Seniors Study (YESS): Design and Asana Series.

The practice of yoga asanas (postures) may be an optimal method of preserving or enhancing physical function in older men and women. However, the physical demands, efficacy and safety of an asana practice for seniors have not been well studied. The Yoga Empowers Seniors Study (YESS) is an intervention development study that created two senior-adapted series of asanas targeted for an ambulatory older population. YESS is using biomechanics and physical performance tests to acquire information about the physical demands placed on the muscles and joints by the asanas and the functional performance adaptations resulting from the yoga practice. This manuscript details the standardized, senior-adapted, YESS asana series and the additional asana modifications provided when participants had physical limitations. This presentation will enable the yoga research and teaching communities to interpret the biomechanics, physical performance and side effects outcomes of YESS.