Objectives: To determine the efficacy of chiropractic spinal manipulative therapy (CSMT) in the resolution of the persistent or chronic middle ear effusion that is associated with otitis media. This treatment was compared to the insertion of tympanostomy tubes with regards to parent perception and satisfaction. Methods: Thirty patients under the age of seven that were diagnosed as having persistent or chronic middle ear effusion were assigned to receive either tympanostomy tubes or chiropractic treatment according to parental choice. Results: After chiropractic treatment, an objective improvement in the middle ear status was noted by serial tympanography, demonstrating resolution of middle ear effusion. This was noted in 64.3% in the right ear, where a type B tympanogram improved to a type A or type C tympanogram, and in 58.3% in the left ear, where a type B improved to a type A or C tympanogram, or a type C improved to a type A tympanogram. After chiropractic treatment, subjective improvements were noted in the patient’s health, hearing, speech and language, behaviour and social skills, balance, gross and fine motor skills, otalgia and the amount of disturbed nights. Additional benefits from chiropractic treatment were noted in 93.3% of cases. Conclusions: The manner in which the middle ear effusion was resolved by chiropractic manipulation is by normalising the function of the eustachian tube. Eustachian tube dysfunction is a commonly reported aetiological factor in the development of otitis media with effusion, yet previously there was no treatment type to address this problem. Improvement of middle ear effusion occurred in 58.3-64.3% of cases, indicating that chiropractic spinal manipulative therapy could be a viable treatment option to correct the eustachian tube dysfunction. / Dr. Harold Humphries Dr. Chris Yelverton

M.Tech.

The purpose of this study was to analyze student therapist’s level of agreement with the American Association for Marriage and Family Therapy’s (AAMFT) formal statements concerning lesbian, gay, and bisexual (LGB) persons and families; specifically, it examined student’s level of agreement with the AAMFT’s definition of Marriage/Couple and Family Therapy (CFT), and the AAMFT’s formal statement concerning same sex couples. This was explored via the participant’s qualitative and quantitative answers. The study used an existing data set consisting of 248 participants; 62.6% were enrolled in a masters program and 36.8% were enrolled in a PhD CFT program. Participant’s quantitative responses indicated that a large majority of participants agreed with the statements, and were in support of the AAMFT inviting same sex couples to receive therapeutic services. However, the qualitative responses also indicated that some participants disagreed with the statements, and did not hold accepting beliefs towards LGB persons and families.

There has been increasing development in personalized interventions that are tailored to uniquely evolving health status of each patient over time. In this dissertation, we investigate two problems: (1) the construction of individualized mobile health (mHealth) application recommender system; and (2) the estimation of optimal dynamic treatment regimes (DTRs) from a multi-stage clinical trial study. The dissertation is organized as follows. In Chapter 1, we provide a brief background on personalized medicine and two motivating examples which illustrate the needs and benefits of individualized treatment policies. We then introduce reinforcement learning and various methods to obtain the optimal DTRs as well as Q-learning procedure which is a popular method in the DTR literature. In Chapter 2, we propose a partial regularization via orthogonality using the adaptive Lasso (PRO-aLasso) to estimate the optimal policy which maximizes the expected utility in the mHealth setting. We also derive the convergence rate of the expected outcome of the estimated policy to that of the true optimal policy. The PRO-aLasso estimators are shown to enjoy the same oracle properties as the adaptive Lasso. Simulations and real data application demonstrate that the PRO-aLasso yields simple, more stable policies with better results as compared to the adaptive Lasso and other competing methods. In Chapter 3, we propose a penalized A-learning with a Lasso-type penalty for the construction of optimal DTR and derive generalization error bounds of the estimated DTR. We first examine the relationship between value and the Q-functions, and then we provide a finite sample upper bound on the difference in values between the optimal DTR and the estimated DTR. In practice, we implement a multi-stage PRO-aLasso algorithm to obtain the optimal DTR. Simulation results show advantages of the proposed methods over some existing alternatives. The proposed approach is also demonstrated with the data from a depression clinical trial study. In Chapter 4, we present future work and concluding remarks.

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