Matching Items (3)

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Physical activity trajectories among newly-diagnosed obstructive sleep apnea patients

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The purpose of this thesis project was to examine the trajectories of physical activity among newly-diagnosed Obstructive Sleep Apnea (OSA) patients within the UC+WS group of the SleepWell24 study across

The purpose of this thesis project was to examine the trajectories of physical activity among newly-diagnosed Obstructive Sleep Apnea (OSA) patients within the UC+WS group of the SleepWell24 study across the first 60 days of CPAP use, alone and based on Apnea-Hypopnea Index (AHI), Body Mass Index (BMI), sex, and age. The study utilizes objective data from the SleepWell24 randomized controlled trial conducted by a collaborative research team at Arizona State University and Mayo Clinic Arizona and Rochester. Participants use wearable sensors to track activity behaviors, such as sleep, sedentary behavior, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA). The primary aim of the study was to examine the physical activity trajectories among newly-diagnosed OSA patients over the first 8 weeks of CPAP use, utilizing the physical activity data from wearable sensors. The secondary aim was to assess the trajectories of physical activity between categories of AHI, BMI, sex, and age. Multilevel modeling was used to account for clustering within participants considering between and within subject variations, and week was used as a level 1 predictor in the model for LPA, and MVPA, and total activity (sum of LPA and MVPA), while between subject factors of BMI, sex, age, and AHI were also included in the model. It was found that there were no statistically significant trajectories of LPA, MVPA or total activity over the first 8 weeks of CPAP use within the sample of 30 participants. However, a few notable differences in physical activity were seen between categories of age, sex, and BMI. Also, there was a significant interaction found between BMI and each week that influenced the trajectory of physical activity within obese patients, as compared to participants considered overweight or with a lower BMI. Ultimately, this study provides insight into patterns of physical activity seen in a clinical population of OSA patients over the initial period of CPAP use.

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Created

Date Created
  • 2019-05

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Spatio-temporal data mining to detect changes and clusters in trajectories

Description

With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis

With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.

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Agent

Created

Date Created
  • 2012

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Child-level predictors of boys' and girls' trajectories of physical, verbal, and relational victimization

Description

For some children, peer victimization stops rather quickly, whereas for others it marks the beginning of a long trajectory of peer abuse (Kochenderfer-Ladd & Wardrop, 2001). Unfortunately, we know little

For some children, peer victimization stops rather quickly, whereas for others it marks the beginning of a long trajectory of peer abuse (Kochenderfer-Ladd & Wardrop, 2001). Unfortunately, we know little about these trajectories and what factors may influence membership in increasing or decreasing victimization over time. To address this question, I identified children's developmental patterns of victimization in early elementary school and examined which child-level factors influenced children's membership in victimization trajectories using latent growth mixture modeling. Results showed that boys and girls demonstrated differential victimization patterns over time that also varied by victimization type. For example, boys experienced more physical victimization than girls and increased victimization over time was predicted by boys who display high levels of negative emotion (e.g., anger) towards peers and low levels of effortful control (e.g., gets frustrated easily). Conversely, girls exhibited multiple trajectories of increasing relational victimization (i.e., talking about others behind their back) over time, whereas most boys experienced low levels or only slightly increasing relational victimization over time. For girls, withdrawn behavior lack of positive emotion, and displaying of negative emotions was predictive of experiencing high levels of victimization over time.

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Agent

Created

Date Created
  • 2015