Regularized Regression Research Articles

A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies

BackgroundAssociated with high-dimensional omics data there are often “meta-features” such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.MethodsA hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.ResultsIn a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.ConclusionsThe proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

The Effect of Regularized Regression and Tree-Based Missing Data Imputation Methods on Classification Performance in High Dimensional Data

Missing data is an important problem in the analysis and classification of high dimensional data. The aim of this study is to compare the effects of four different missing data imputation methods on classification performance in high dimensional data. In this study, missing data imputation methods were evaluated using data sets, whose independent variables between mixed correlated with each other, for binary dependent variable, p=500 independent variables, n=150 units and 1000 times running simulation. Missing data structures were created according to different missing rates. Different datasets were obtained by imputing the missing values using different methods. Regularized regression methods such as lasso and elastic net regression were used for imputation, as well as tree-based methods such as support vector machine and classification and regression trees. At the end of simulation, the classification scores of the methods were obtained by gradient boosting machines and the missing data prediction performances were evaluated according to the distance of these scores from the reference. Our simulation demonstrates that regularized regression methods outperform tree-based methods in classifying high dimensional datasets. Additionally, it was found that the increase in the amount of missing values reduced the classification performance of the methods in high dimensional data.

Model Building for Prediction of Valuation of Growth Stocks

It has been a great challenge for investors to evaluate growth stocks because these types of stocks are especially hard to forecast because of their inconsistencies and uncertainty. This paper establishes not only regression models, which include a regular Multivariable Linear Regression (MVLR) and a Fixed Effects (FE) Regression Model but also the application of a Panel Vector Autoregression (PVAR) Model on a dataset aiming to predict the Price-to-Sales Ratio (P/S) with several variables. Excel, Python, and Rstudio environments are used in the process of coding and model building. More specifically, Excel is used to conduct the MVLR model, Python is used for the FE model, and Rstudio is used for the PVAR model. By comparing different models of each type, the most optimized model was determined using a training set. After applying predictions with all types of models using another testing set, the best model is identified to be the best approach to the problem. Ultimately, all three models are compared. PVAR model and FE model are proved to be the more reliable ones out of the three models, because the R-squared for MVLR is 0.1859, lower than that of the FE model, 0.524, and the Mean Absolute Error (MAE) of for PVAR Model and FE model are respectfully 53.2139 and 55.1529. This project serves as a good starting point for future analysis and prediction of financial metrics like P/S ratio and is able to have great significance as it discovers some key points for the prediction of P/S value like its connection with time and entity despite the fact that the models themselves aren’t the best fit mainly caused by the limitations of data and variable access.

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data

BackgroundNewborns are shaped by prenatal maternal experiences. These include a pregnant person’s physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models to predict markers of fetal growth and development—specifically, newborn birthweight and head circumference (HC).MethodsWe used a pre-registered archival data analytic approach. These data consisted of maternal and newborn characteristics of 594 maternal-infant dyads in the western U.S. Participants also completed a measure of emotion dysregulation. In total, there were 22 predictors of newborn HC and birthweight. We used regularized regression for predictor selection and linear prediction, followed by nonlinear models if linear models were overfit.ResultsHC was predicted best with a linear model (ridge regression). Newborn sex (male), number of living children, and maternal BMI predicted a larger HC, whereas maternal preeclampsia, number of prior preterm births, and race/ethnicity (Latina) predicted a smaller HC. Birthweight was predicted best with a nonlinear model (support vector machine). Occupational prestige (a marker similar to socioeconomic status) predicted higher birthweight, maternal race/ethnicity (non-White and non-Latina) predicted lower birthweight, and the number of living children, prior preterm births, and difficulty with emotional clarity had nonlinear effects.ConclusionsHC and birthweight were predicted by a variety of variables associated with prenatal stressful experiences, spanning medical, psychological, and social markers of health and stress. These findings may highlight the importance of viewing prenatal maternal health across multiple dimensions. Findings also suggest that assessing difficulties with emotional clarity during standard obstetric care (in the U.S.) may help identify risk for adverse newborn outcomes.

Multigrid Preconditioning for Regularized Least-Squares Problems

Multigrid Preconditioning for Regularized Least-Squares Problems

VICTOR: Validation and inspection of cell type annotation through optimal regression

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Functional Time Series Models for K-Step Prediction of Sugar Production: A Case Study of Eswatini, Kenya, and South Africa

Sugar, produced from cane or beet, is a vital energy source and a globally traded commodity. Most business happens in a futures exchange market where speculators, hedgers, and institutional consumers and producers, make decisions based on their understanding of the future supply and demand situation. Sugar is sometimes bought and sold before the cane or beet is planted. Hence, improving sugar production forecast accuracy is vital to maximize gains and enable effective planning. The study considered the annual sugar production total as a function of the monthly output rather than a scalar quantity and analyzed historical data using three functional time series techniques. In particular, the methods used for k-steps and dynamic updating prediction of sugar production include Local Polynomial Regression (LPR), Ridge Regression (RR), and Penalized Least Squares (PLS). The performance of the three models was compared to that of the automatic ARIMA method using the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) measures to establish the suitability of each technique in sugar production forecasting. The LPR, RR, and PLS outperformed the ARIMA method in all three cases. However, the prediction accuracy was lower for highly volatile datasets from countries that overly depend on rainfall for cane development.

Circulating cell-free and extracellular vesicles-derived microRNA as prognostic biomarkers in patients with early-stage NSCLC: results from RESTING study

BackgroundFactors to accurately stratify patients with early-stage non-small cell lung cancer (NSCLC) in different prognostic groups are still needed. This study aims to investigate 1) the prognostic potential of circulating cell-free (CF) and extracellular vesicles (EVs)-derived microRNA (miRNAs), and 2) their added value with respect to known prognostic factors (PFs).MethodsThe RESTING study is a multicentre prospective observational cohort study on resected stage IA-IIIA patients with NSCLC. The primary end-point was disease-free survival (DFS), and the main analyses were carried out separately for CF- and EV-miRNAs. CF- and EV-miRNAs were isolated from plasma, and miRNA-specific libraries were prepared and sequenced. To reach the study aims, three statistical models were specified: one using the miRNA data only (Model 1); one using both miRNAs and known PFs (age, gender, and pathological stage) (Model 2), and one using the PFs alone (Model 3). Five-fold cross-validation (CV) was used to assess the predictive performance of each. Standard Cox regression and elastic net regularized Cox regression were used.ResultsA total of 222 patients were enrolled. The median follow-up time was 26.3 (95% CI 25.4–27.6) months. From Model 1, three CF-miRNAs and 21 EV-miRNAs were associated with DFS. In Model 2, two CF-miRNAs (miR-29c-3p and miR-877-3p) and five EV-miRNAs (miR-181a-2-3p, miR-182-5p, miR-192-5p, miR-532-3p and miR-589-5p) remained associated with DFS. From pathway enrichment analysis, TGF-beta and NOTCH were the most involved pathways.ConclusionThis study identified promising prognostic CF- and EV-miRNAs that could be used as a non-invasive, cost-effective tool to aid clinical decision-making. However, further evaluation of the obtained miRNAs in an external cohort of patients is warranted.

Predicting the trajectory of non-suicidal self-injury among adolescents.

Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of post-discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment. The study included adolescents (N = 612; females n = 435; 71.1%) aged 13-19-years-old (M = 15.6, SD = 1.4) undergoing inpatient treatment. Youth were administered clinical interviews and symptom questionnaires at intake (baseline) and before termination. NSSI frequency was assessed at 1-, 3-, and 6-month follow-ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI across follow-ups. Three classes were identified: Low Stable (n = 83), Moderate Fluctuating (n = 260), and High Persistent (n = 269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past-week suicidal ideation (SI) severity, lifetime average and worst-point SI intensity, and NSSI in the past 30 days (bs = 0.75-2.33). Only worst-point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class (b = -8.82); no predictors of the Moderate Fluctuating class emerged. This study found a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post-discharge. Accordingly, this may help identify youth that may benefit from additional monitoring and support post-hospitalization.

Epigenetic reprogramming driving successful and failed repair in acute kidney injury.

Acute kidney injury (AKI) causes epithelial damage followed by subsequent repair. While successful repair restores kidney function, this process is often incomplete and can lead to chronic kidney disease (CKD) in a process called failed repair. To better understand the epigenetic reprogramming driving this AKI-to-CKD transition, we generated a single-nucleus multiomic atlas for the full mouse AKI time course, consisting of ~280,000 single-nucleus transcriptomes and epigenomes. We reveal cell-specific dynamic alterations in gene regulatory landscapes reflecting, especially, activation of proinflammatory pathways. We further generated single-nucleus multiomic data from four human AKI samples including validation by genome-wide identification of nuclear factor κB binding sites. A regularized regression analysis identifies key regulators involved in both successful and failed repair cell fate, identifying the transcription factor CREB5 as a regulator of both successful and failed tubular repair that also drives proximal tubular cell proliferation after injury. Our interspecies multiomic approach provides a foundation to comprehensively understand cell states in AKI.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Lifestyle and psychosocial associations with cognition at the cusp of midlife using twins and siblings.

This study investigates the relationship between cognitive functioning and 59 modifiable and intrinsic factors at the cusp of midlife. We analyzed data from 1221 participants in the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; Mage=33.20, %Female=52.74). We assessed the impact of 59 factors on cognitive functioning using regularized regression and co-twin control models, controlling for earlier-life cognitive functioning and gray matter volume. Eight robust factors were identified, including education attainment, cognitive complexity, purpose-in-life, and smoking status. Twins reporting higher levels of cognitive complexity and purpose-in-life showed better cognitive performance than their cotwin, while smoking was negatively associated. Using meta-analytically derived effect size threshold, we additionally identified that twins experiencing more financial difficulty tend to perform less well compared with their cotwin. The findings highlight the early midlife link between cognitive functioning and lifestyle/psychological factors, beyond prior cognitive performance, brain status, genetic and familial confounders. Our results further highlight the potential of established adulthood as a crucial window for dementia prevention interventions targeting lifestyle and psychosocial factors. Cog complexity(+), purpose-in-life(+) were associated with cognition in early midlife.Smoking(-) was also associated with cognition in early midlife.Results were consistent controlling for genetic and environmental confounds.Association between EA and cognition might be mostly genetic and familial confounded.

Enhancing silicon wafer ablation using passive Q-switch [formula omitted] lasers with third-order nonlinear effects

We show that a passive Q-switch nanosecond Tm:Yap laser operating at 1.940μm is a promising candidate for conducting ablation process at the surface of silicon wafers. By modulating the fluence intensity of the lasing source, we can engrave the surface with a short series of pulses and obtain sharp and regular craters with a high level of repeatability. Our results show that we can get highly repeatable craters of diameters ranging from D=10.0μm up to D=40.0μm with a laser operating We easily manage the hole depth by choosing the correct fluence repetition rate between pulses, reducing the waste deposition on the borders of the holes. For fluences greater than 3.8J/cm2, third-order nonlinear effects, such as the lens converging Kerr n2 and the two-photon absorption βTPA effects, turn the silicon into an absorbing medium, improving the ablation process. At a room temperature of 300°K, n2=12.0×10−5±2.0×10−5cm2/GW and βTPA=0.56±0.02cm/GW, the resulting nonlinear factor of merit NFOM=2.1±2.0 is very large; conferring a self-focusing lensing action on the silicon. We also observe that these latter nonlinear effects modify the regular linear regression relating the ablated surface to the logarithm of the fluence, enhancing the efficiency of the ablation process at higher fluences due to these effects.

Functional phenotypes in schizophrenia spectrum disorders: defining the constructs and identifying biopsychosocial correlates using data-driven methods

Functional impairments contribute to poor quality of life in schizophrenia spectrum disorders (SSD). We sought to (Objective I) define the main functional phenotypes in SSD, then (Objective II) identify key biopsychosocial correlates, emphasizing interpretable data-driven methods. Objective I was tested on independent samples: Dataset I (N = 282) and Dataset II (N = 317), with SSD participants who underwent assessment of multiple functioning areas. Participants were clustered based on functioning. Objective II was evaluated in Dataset I by identifying key features for classifying functional phenotype clusters from among 65 sociodemographic, psychological, clinical, cognitive, and brain volume measures. Findings were replicated across latent discriminant analyses (LDA) and one-vs.-rest binomial regularized regressions to identify key predictors. We identified three clusters of participants in each dataset, demonstrating replicable functional phenotypes: Cluster 1—poor functioning across domains; Cluster 2—impaired Role Functioning, but partially preserved Independent and Social Functioning; Cluster 3—good functioning across domains. Key correlates were Avolition, anhedonia, left hippocampal volume, and measures of emotional intelligence and subjective social experience. Avolition appeared more closely tied to role functioning, and anhedonia to independent and social functioning. Thus, we found three replicable functional phenotypes with evidence that recovery may not be uniform across domains. Avolition and anhedonia were both critical but played different roles for different functional domains. It may be important to identify critical functional areas for individual patients and target interventions accordingly.

Traditional preference mapping and computational machine learning techniques: A comparative study of approaches to guide product development

Preference mapping, a well-known set of multivariate statistical techniques, has become widely adopted due to its demonstrated effectiveness as a powerful tool in guiding the development of new products and enhancing existing ones. Recent advancements in open-source software and computational capabilities have introduced a new set of accessible tools with the potential to address limitations associated with traditional methods. This study introduces an alternative algorithm for building predictive models, employing regularized regression in combination with Multivariate Adaptive Regression Spline (MARS). These methods make fewer assumptions about the relationship between predictors and the target and can easily capture complex and non-linear relationships. Additionally, the study presents a robust and systematic alternative approach for calculating optimum profiles and performing simulations. The paper aims to compare this new set of tools, referred to as computational machine learning techniques, with a well-established and widely recognized method − PrefMap based on Partial Least Squares Regression. The primary intention of the comparison between computational machine learning and one example of a traditional approach is not to determine a winning methodology, but rather to enhance awareness and deepen the understanding of this emerging family of models and techniques now available to sensory and consumer scientists. Results are assessed side by side to reveal their similarities and differences in terms of predictive power, drivers of liking, and the optimal profile aspects, and a list of practical considerations is provided at the end, enabling a better understanding of the trade-offs between the two approaches presented here.

Beyond Ridge Regression: Enhancing Distribution of Relaxation Times Deconvolution

The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression, which usually leverages the Euclidean norm. In this work, we show for the first time that the 1-norm is often more accurate than ridge regression and the infinity-norm. We also demonstrate that the 1-norm is more robust against discontinuities in the DRT and outliers in the impedance data. Overall, this work is expected to enhance regularized regression of non-parametric methods when analyzing EIS spectra.

Analysis of the Effective Tax Rate during the Coronavirus Pandemic on an Emerging Capital Market – Romania

Abstract In the context of the permanent global evolution and the flexible economic framework that investors encounter in the decision-making process, this paper aims to analyze the evolution of the effective tax rate in the context of the Coronavirus pandemic in Romania, as well as the factors of influence associated with it, results in the mentioned period. In order to answer these objectives, a quantitative research was carried out for the 85 companies listed on the regulated trading market on the website of the Bucharest Stock Exchange, where a retrospective methodology was applied based on the financial statements published by them for the years 2020 and 2021. Regarding the determination of the influencing factors associated with the evolution of the effective tax rate, it was performed a regular regression for both mentioned years, noticed that in 2020 the size of the companies (SIZE), their solvency (SOLV), as well as the return on assets (ROA), influenced the variation of the effective tax rate, aspect that could not the noticed in 2021. Regarding the evolution of the effective tax rate, it was found that the companies listed on the Bucharest Stock Exchange were undertaxed in the period 2020-2021.

Classification of HER2 status across multiple cancers using epigenomic profiles from a novel liquid biopsy assay.

3047 Background: The efficacy of trastuzumab deruxtecan in multiple cancers led to a recent FDA granting of priority review for the treatment of adults with HER2-positive solid tumors. This underscores the critical need for reliable assessment of HER2 expression across cancers. The current standard using IHC/ISH for HER2 scoring is fraught with challenges including scoring discordance and tumor heterogeneity. Here we describe the ability to classify HER2 status from 1 ml of plasma, using epigenomic signatures from a novel multi-analyte liquid biopsy (LBx) platform, offering a minimally invasive approach for patient (pt) selection across cancers. Methods: We selected 179 samples from 172 pts with advanced breast (BC), gastro-esophageal (GEA) and ovarian (OV) cancers who had associated HER2 status scored from tissue-based IHC/ISH according to ASCO/CAP guidelines (table). Samples were taken at baseline or at progression and those with detectable cell free DNA, as assessed by iChorCNA, were profiled for genome-wide epigenomic signals across histone modifications associated with active enhancers, promoters and DNA methylation. A regularized regression model developed to classify HER2 status in BC cell lines and was refined and validated for HER2 status prediction for each pt cohort (HER2+ = 3+, 2+/ISH+; HER2- = 2+/ISH-, 1+, 0). The BC cell-line derived HER2 classifier was refined to incorporate GEA and OV cancer specific features before being applied to those samples. Performance was assessed via AUC in a leave-one-out cross-validation schema. We also evaluated HER2 status at progression in a subset of pts with benchmarked HER2 IHC to assess dynamic changes in receptor status by LBx. Results: The epigenomic HER2 classifier was applied to all samples with ctDNA detectable by ichorCNA (90 of 179; 50%). HER2 classification of BC pts by epigenomic liquid biopsy was concordant with standard tissue-based IHC for 64/72 (89%) BC samples (AUC 0.9, table). HER2 classifier predictions for all longitudinally collected samples were concordant with IHC-based HER2 status, including the one patient whose status switched from HER2+ to HER2- at progression. Accurate classification of 11/14 (79%, GEA) and 4/4 (100%, OV) pts was achieved using the indication-refined HER2 classifier. Conclusions: We demonstrate proof of concept for a HER2 classification approach using comprehensive epigenomic signals from 1 ml of plasma that could be applied across multiple cancers. With further development, our genome-wide profiling approach could alleviate clinical constraints associated with multiple tissue-based HER2 scoring assays (IHC/ISH) and enable longitudinal monitoring of HER2 status on therapy. [Table: see text]

The oncologic burden of residual disease in incidental gallbladder cancer: An elastic net regression model to profile high-risk features

IntroductionIncidental Gallbladder Cancer (IGBC) following cholecystectomy constitutes a significant portion of gallbladder cancer diagnoses. Re-exploration is advocated to optimize disease clearance and enhance survival rates. The consistent association of residual disease (RD) with inferior oncologic outcomes prompts a critical examination of re-resection's role as a modifying factor in the natural history of IGBC. MethodsAll patients diagnosed with gallbladder cancer between 2012 and 2022 were included. An elastic net regularized regression model was employed to profile high-risk predictors of RD within the IGBC group. Survival outcomes were assessed based on resection margins and RD. ResultsAmong the 181 patients undergoing re-exploration for IGBC, 133 (73.5 %) harbored RD, while 48 (26.5 %) showed no evidence. The elastic net model, utilizing a selected λ = 0.029, identified six coefficients associated with the risk of RD: aspiration from cholecystectomy (0.141), hepatic tumor origin (1.852), time to re-exploration >8 weeks (1.879), positive margin status (2.575), higher T stage (1.473), and poorly differentiated tumors (2.241). Furthermore, the study revealed a median overall survival of 44 months (CI 38–60) for IGBC patients with no evidence of RD, compared to 31 months (23–42) for those with RD (p < 0.001). ConclusionRe-resection revealed a high incidence of RD (73.5 %), significantly correlating with poorer survival outcomes. The preoperative identification of high-risk features provides a reliable biological disease profile. This aids in strategic preselection of patients who may benefit from re-resection, underscoring the need to consolidate outcomes with tailored chemotherapy for those with unfavorable characteristics.

Analysis of multi-level barriers to physical activity among nursing students using regularized regression.

Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.