Matching Items (54)
ContributorsHuberty, Jennifer (Author) / Leiferman, Jenn (Author) / Gold, Katherine (Author) / Cacciatore, Joanne (Author) / Rowedder, Lacey (Author) / Bonds, Darya Denise (Author) / Arizona State University. School of Social Work (Contributor)
Created2014
Description
The purpose of this study was to determine the feasibility of a mindfulness-based intervention among pregnant women (12-20 weeks’ gestation) using a mobile meditation app, Calm. This study involved 100 participants who were recruited nationally due to the COVID-19 pandemic. This study was reviewed and approved by the Institutional Review Board of Arizona State University (STUDY STUDY00010467). All participants were provided an informed consent document and provided electronic consent prior to enrollment and participation in this study. This study was a randomized, controlled trial (trial registration: ClinicalTrials.gov NCT04264910). Participants randomized to the intervention group were asked to participate in a minimum of 10 minutes of daily meditation using a mindfulness meditation mobile app (i.e., Calm) for the duration of their pregnancy. Participants randomized to the standard of care control group were given access to the app after they gave birth. Both the intervention and control groups were administered surveys that measured feasibility outcomes, perceived stress, mindfulness, self-compassion, impact from COVID-19, pregnancy-related anxiety, depression, emotional regulation, sleep, and childbirth experience at four time points: baseline (12-20 weeks gestation), midline (24 weeks gestation), postintervention (36 weeks gestation), and follow-up survey (3-5 weeks postpartum). Data is currently being analyzed for publication.
ContributorsLister, Haily (Author) / Huberty, Jennifer (Thesis director) / Larkey, Linda (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Earth-system models describe the interacting components of the climate system and
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.
ContributorsDurazo, Juan, Ph.D (Author) / Kostelich, Eric J. (Thesis advisor) / Mahalov, Alex (Thesis advisor) / Tang, Wenbo (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
College students experience a considerable amount of stress. Unmanaged stress is associated with poor academic performance, health risk behaviors (i.e., inadequate sleep and physical activity, alcohol consumption, poor dietary behaviors), and poor mental health. Coping with stress has become a priority among universities. The most tested stress-related programs to date have been mindfulness-based and face-to-face. These programs demonstrated significant improvements in stress, mindfulness, and self-compassion among college students. However, they may be burdensome to students as studies report low attendance and low compliance due to class conflicts or not enough time. Few interventions have used more advanced technologies (i.e., mobile apps) as a mode of delivery. The purpose of this study is to report adherence to a consumer-based mindfulness meditation mobile application (i.e., Calm) and test its effects on stress, mindfulness, and self-compassion in college students. We will also explore what the relationship is between mindfulness and health behaviors.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
ContributorsGlissmann, Christine (Author) / Huberty, Jennifer (Thesis advisor) / Sebren, Ann (Committee member) / Larkey, Linda (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Two urban flows are analyzed, one concerned with pollutant transport in a Phoenix, Arizona neighborhood and the other with windshear detection at the Hong Kong International Airport (HKIA).
Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion.
A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data.
Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.
Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion.
A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data.
Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.
ContributorsKnutson, Brent (Author) / Tang, Wenbo (Thesis advisor) / Calhoun, Ronald (Committee member) / Huang, Huei-Ping (Committee member) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2018
Description
Background: Although childhood engagement in physical activity has received growing attention, most children still do not meet the recommended daily 60 minutes of moderate to vigorous physical activity [MVPA]. Children of ethnic minorities are less likely to meet the guidelines. Interventions have been implemented in various settings to increase child physical activity levels, yet these efforts have not yielded consistent results. The purpose of this study was to assess the preliminary effects of a community-based intervention on light physical activity and MVPA among 6-11 year old children. Methods: The present study was part of a larger study called Athletes for Life [AFL], a family-based, nutrition-education and physical activity intervention. The present study focused on physical activity data from the first completed cohort of participants (n=29). This study was a randomized control trial in which participating children were randomized into a control (n=14) or intervention (n=15) group. Participants wore accelerometers at two time points. Intervention strategies were incorporated to increase child habitual physical activity. Analyses of covariance were performed to test for post 12-week differences between both groups on the average minutes of light physical activity and MVPA minutes per day.
Results: The accelerometer data demonstrated no significant difference in light physical activity or MVPA mean minutes per day between the groups. Few children reported engaging in activities sufficient for meeting the physical activity guidelines outside the AFL program. Of the 119 total distributed child physical activity tracker sheets (7 per family), 55 were returned. Of the 55 returned physical activity tracker sheets, parents reported engaging in physical activity with their children only 7 times outside of the program over seven weeks.
Conclusion: The combined intervention strategies implemented throughout the 12-week study did not appear to be effective at increasing habitual mean minutes per day spent engaging in light and MVPA among children beyond the directed program. Methodological limitations and low adherence to intervention strategies may partially explain these findings. Further research is needed to test successful strategies within community programs to increase habitual light physical activity and MVPA among 6-11 year old children.
Results: The accelerometer data demonstrated no significant difference in light physical activity or MVPA mean minutes per day between the groups. Few children reported engaging in activities sufficient for meeting the physical activity guidelines outside the AFL program. Of the 119 total distributed child physical activity tracker sheets (7 per family), 55 were returned. Of the 55 returned physical activity tracker sheets, parents reported engaging in physical activity with their children only 7 times outside of the program over seven weeks.
Conclusion: The combined intervention strategies implemented throughout the 12-week study did not appear to be effective at increasing habitual mean minutes per day spent engaging in light and MVPA among children beyond the directed program. Methodological limitations and low adherence to intervention strategies may partially explain these findings. Further research is needed to test successful strategies within community programs to increase habitual light physical activity and MVPA among 6-11 year old children.
ContributorsQuezada, Blanca (Author) / Crespo, Noe (Thesis advisor) / Huberty, Jennifer (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
The role of environmental factors that influence atmospheric propagation of sound originating from freeway noise sources is studied with a combination of field experiments and numerical simulations. Acoustic propagation models are developed and adapted for refractive index depending upon meteorological conditions. A high-resolution multi-nested environmental forecasting model forced by coarse global analysis is applied to predict real meteorological profiles at fine scales. These profiles are then used as input for the acoustic models. Numerical methods for producing higher resolution acoustic refractive index fields are proposed. These include spatial and temporal nested meteorological simulations with vertical grid refinement. It is shown that vertical nesting can improve the prediction of finer structures in near-ground temperature and velocity profiles, such as morning temperature inversions and low level jet-like features. Accurate representation of these features is shown to be important for modeling sound refraction phenomena and for enabling accurate noise assessment. Comparisons are made using the acoustic model for predictions with profiles derived from meteorological simulations and from field experiment observations in Phoenix, Arizona. The challenges faced in simulating accurate meteorological profiles at high resolution for sound propagation applications are highlighted and areas for possible improvement are discussed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
A detailed evaluation of the environmental forecast is conducted by investigating the Surface Energy Balance (SEB) obtained from observations made with an eddy-covariance flux tower compared with SEB from simulations using several physical parameterizations of urban effects and planetary boundary layer schemes. Diurnal variation in SEB constituent fluxes are examined in relation to surface layer stability and modeled diagnostic variables. Improvement is found when adapting parameterizations for Phoenix with reduced errors in the SEB components. Finer model resolution (to 333 m) is seen to have insignificant ($<1\sigma$) influence on mean absolute percent difference of 30-minute diurnal mean SEB terms. A new method of representing inhomogeneous urban development density derived from observations of impervious surfaces with sub-grid scale resolution is then proposed for mesoscale applications. This method was implemented and evaluated within the environmental modeling framework. Finally, a new semi-implicit scheme based on Leapfrog and a fourth-order implicit time-filter is developed.
ContributorsShaffer, Stephen R. (Author) / Moustaoui, Mohamed (Thesis advisor) / Mahalov, Alex (Committee member) / Fernando, Harindra J.S. (Committee member) / Ovenden, Nicholas C. (Committee member) / Huang, Huei-Ping (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2014
Description
Pitchers are a vital part of the game of baseball and may account for up to two-thirds of the variance in win percentage. As they rise through the ranks of competition, physical skill set becomes less of a factor when compared to mentality. Pitchers are the “first line of defense” for keeping opponents from having an opportunity to score, as well as for holding onto their own team’s lead. Baseball pitchers not only face pressure to perform, but also experience stress from factors such as low pay, adjusting to higher levels of competition, and internal team competition for a limited number of spots. Athletes are often resistant to seeking aid from sport psychologists and often turn to unfavorable means to cope (i.e. drugs/alcohol, excessive exercise) with stress instead. Meditation has been shown to have beneficial effects on psychological factors associated with performance including emotional regulation, anxiety, confidence, focus, and mindfulness. Mobile applications have become a popular means of delivering mindfulness. The purpose of this study was to determine the feasibility and preliminary effectiveness of delivering a mindful meditation intervention using a mobile meditation application to improve psychological factors associated with performance (i.e. emotional regulation, anxiety (somatic and cognitive), confidence, focus, mindfulness) to minor league baseball pitchers. Pitchers in instructional league (Phase one) and off season (Phase two) were asked to meditate daily for 10-minutes each day for three weeks (Phase one) and eight weeks (Phase two). Pitchers were asked to complete self-report questionnaires and satisfaction surveys at pre- and post-intervention. Pitchers in phase one reported enjoying meditation, had improvements in self-confidence and sport confidence, and reported moderate decreases in cognitive anxiety and concentration disruption. Pitchers in phase two also enjoyed meditating (94.7%) and had improvements in self-confidence and moderate decreases in somatic anxiety. Low adherence due to timing (off-season) of intervention may have been a contributing factor to fewer outcomes. Future research should explore the feasibility and effectiveness of implementing meditation during the baseball season.
ContributorsDowling, Tiffany (Author) / Huberty, Jennifer (Thesis advisor) / Ransdell, Lynda (Committee member) / Buman, Matthew (Committee member) / Michel, Jesse (Committee member) / Arizona State University (Publisher)
Created2018
Description
The tools developed for the use of investigating dynamical systems have provided critical understanding to a wide range of physical phenomena. Here these tools are used to gain further insight into scalar transport, and how it is affected by mixing. The aim of this research is to investigate the efficiency of several different partitioning methods which demarcate flow fields into dynamically distinct regions, and the correlation of finite-time statistics from the advection-diffusion equation to these regions.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
For autonomous systems, invariant manifold theory can be used to separate the system into dynamically distinct regions. Despite there being no equivalent method for nonautonomous systems, a similar analysis can be done. Systems with general time dependencies must resort to using finite-time transport barriers for partitioning; these barriers are the edges of Lagrangian coherent structures (LCS), the analog to the stable and unstable manifolds of invariant manifold theory. Using the coherent structures of a flow to analyze the statistics of trapping, flight, and residence times, the signature of anomalous diffusion are obtained.
This research also investigates the use of linear models for approximating the elements of the covariance matrix of nonlinear flows, and then applying the covariance matrix approximation over coherent regions. The first and second-order moments can be used to fully describe an ensemble evolution in linear systems, however there is no direct method for nonlinear systems. The problem is only compounded by the fact that the moments for nonlinear flows typically don't have analytic representations, therefore direct numerical simulations would be needed to obtain the moments throughout the domain. To circumvent these many computations, the nonlinear system is approximated as many linear systems for which analytic expressions for the moments exist. The parameters introduced in the linear models are obtained locally from the nonlinear deformation tensor.
ContributorsWalker, Phillip (Author) / Tang, Wenbo (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Moustaoui, Mohamed (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2018
Description
Myeloproliferative neoplasm (MPN) patients suffer from fatigue and a reduced overall quality of life, both of which are not resolved with current pharmacologic therapy. The purpose of this study was to examine the effects of a 12-week online-streamed yoga intervention on fatigue and QoL in MPN patients as compared to a wait-list control group as well as to determine the feasibility of remotely collecting blood and saliva samples in a national sample. MPN patients were asked to complete 60 min/week of online yoga for 12 weeks. MPN fatigue and QoL were assessed online with single-item questions taken from the MPN SAF (fatigue and QoL) and NIH PROMIS (QoL) at baseline, week 7, and week 12. The practicality of the blood and saliva measures were defined as >70% completion rate at both baseline and week 12. Fidelity of the intervention (i.e., weekly yoga participation) was assessed via both self-report (i.e., daily log) and objective measurement (i.e., Clicky). Of the 62 MPN patients that enrolled in the study, 48 completed the intervention with 27 participating in the yoga group and 21 participating in the wait-list control group. Weekly yoga participation averaged ~41 min/week as measured objectively, whereas self-report yoga participation averaged ~56 min/week. The blood draw was determined to be practical with a 92.6% completion rate at baseline and a 70.4% completion rate at week 12. There were no significant differences from baseline to week 12 in MPN SAF fatigue (ES=0.18; p=0.724) or MPN SAF QoL (ES=-0.53; p=0.19), however, NIH PROMIS QoL was significantly improved from baseline to week 12 (ES=0.7; p=0.031) when compared to the control group. This study builds upon the findings from a prior feasibility study in demonstrating the feasibility of online yoga as well as its preliminary effects of improving total symptom burden, fatigue, pain, depression, anxiety, and sleep disturbance in MPN patients. Given the effects of yoga demonstrated both in the feasibility study and the current pilot study, a future randomized controlled trial with a larger sample size is warranted in order to further investigate the effectiveness of online yoga for MPN patient symptom burden and QoL.
ContributorsEckert, Ryan (Author) / Huberty, Jennifer (Thesis advisor) / Mesa, Ruben (Committee member) / Gowin, Krisstina (Committee member) / Dueck, Amylou (Committee member) / Kosiorek, Heidi (Committee member) / Larkey, Linda (Committee member) / Arizona State University (Publisher)
Created2017