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- All Subjects: mobility
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: DerAnanian, Cheryl
Description
Background: Heart failure is the leading cause of hospitalization in older adults and has the highest 30-day readmission rate of all diagnoses. An estimated 30 to 60 percent of older adults lose some degree of physical function in the course of an acute hospital stay. Few studies have addressed the role of posture and mobility in contributing to, or improving, physical function in older hospitalized adults. No study to date that we are aware of has addressed this in the older heart failure population.
Purpose: To investigate the predictive value of mobility during a hospital stay and patterns of mobility during the month following discharge on hospital readmission and 30-day changes in functional status in older heart failure patients.
Methods: This was a prospective observational study of 21 older (ages 60+) patients admitted with a primary diagnosis of heart failure. Patients wore two inclinometric accelerometers (rib area and thigh) to record posture and an accelerometer placed at the ankle to record ambulatory activity. Patients wore all sensors continuously during hospitalization and the ankle accelerometer for 30 days after hospital discharge. Function was assessed in all patients the day after hospital discharge and again at 30 days post-discharge.
Results: Five patients (23.8%) were readmitted within the 30 day post-discharge period. None of the hospital or post-discharge mobility measures were associated with readmission after adjustment for covariates. Higher percent lying time in the hospital was associated with slower Timed Up and Go (TUG) time (b = .08, p = .01) and poorer hand grip strength (b = -13.94, p = .02) at 30 days post-discharge. Higher daily stepping activity during the 30 day post-discharge period was marginally associated with improvements in SPPB scores at 30 days (b = <.001, p = .06).
Conclusion: For older heart failure patients, increased time lying while hospitalized is associated with slower walking time and poor hand grip strength 30 days after discharge. Higher daily stepping after discharge may be associated with improvements in physical function at 30 days.
Purpose: To investigate the predictive value of mobility during a hospital stay and patterns of mobility during the month following discharge on hospital readmission and 30-day changes in functional status in older heart failure patients.
Methods: This was a prospective observational study of 21 older (ages 60+) patients admitted with a primary diagnosis of heart failure. Patients wore two inclinometric accelerometers (rib area and thigh) to record posture and an accelerometer placed at the ankle to record ambulatory activity. Patients wore all sensors continuously during hospitalization and the ankle accelerometer for 30 days after hospital discharge. Function was assessed in all patients the day after hospital discharge and again at 30 days post-discharge.
Results: Five patients (23.8%) were readmitted within the 30 day post-discharge period. None of the hospital or post-discharge mobility measures were associated with readmission after adjustment for covariates. Higher percent lying time in the hospital was associated with slower Timed Up and Go (TUG) time (b = .08, p = .01) and poorer hand grip strength (b = -13.94, p = .02) at 30 days post-discharge. Higher daily stepping activity during the 30 day post-discharge period was marginally associated with improvements in SPPB scores at 30 days (b = <.001, p = .06).
Conclusion: For older heart failure patients, increased time lying while hospitalized is associated with slower walking time and poor hand grip strength 30 days after discharge. Higher daily stepping after discharge may be associated with improvements in physical function at 30 days.
ContributorsFloegel, Theresa A (Author) / Buman, Matthew P (Thesis advisor) / Hooker, Steven (Committee member) / Dickinson, Jared (Committee member) / DerAnanian, Cheryl (Committee member) / McCarthy, Marianne (Committee member) / Arizona State University (Publisher)
Created2015
Description
As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models.
Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior.
ContributorsOshan, Taylor Matthew (Author) / Fotheringham, A. S. (Thesis advisor) / Farmer, Carson J.Q. (Committee member) / Rey, Sergio S.J. (Committee member) / Nelson, Trisalyn (Committee member) / Arizona State University (Publisher)
Created2017
Description
Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020