Ankle sprains are one of the most common musculoskeletal injuries. Physiotherapy treatment and advice to rest, use ice, use compression, and elevate the ankle (RICE advice) is believed to speed up the functional recovery and enhance healing associated with acute ankle sprains. However, there is limited evidence to support the efficacy of RICE treatment. This study investigated whether physiotherapy (including RICE advice) was as effective as RICE advice alone in improving the time to recovery in a clinical situation.The evidence for RICE advice was reviewed along with the different treatment modalities currently used by physiotherapists in New Zealand for acute ankle sprain. This review highlighted the low number and poor quality of studies investigating RICE and early intervention physiotherapy management for ankle sprains. RICE principals appear to be relatively inexpensive and somewhat effective for pain relief and may reduce further tissue damage in the acute stage of Grade I and II ankle sprains. Evidence extrapolated from studies investigating the use of post surgical ice appears to support the use of ice in the acute stage of an ankle sprain to minimise bleeding and oedema. The intermittent application of ice is more effective for pain relief in the acute phase than sustained icing. Physiotherapy interventions such as TENS for pain relief and bracing for the support of Grade II - III ankle sprains have been shown to be beneficial for pain relief in the acute phase. A need for further high quality, randomised controlled trials (RCT's) was identified.Subsequently a RCT was conducted with 28 participants to investigate the difference between (a) early intervention physiotherapy management combined with RICE advice, and (b) self management RICE advice without physiotherapy.Twenty eight individuals (males n = 22, females n = 6), between the ages of 16 and 40 with acute ankle sprains, who met the inclusion criteria, were approached by physiotherapists working on this project and invited to participate. Dependant variables were pain, function, swelling, compliance and medication use up to Day 11 post injury. Swelling, pain and function were measured over three assessments on Days 1, 3 and 11, using volumetric analysis, a visual analogue scale (VAS) and a functional question derived from a validated functional questionnaire respectively. Medication use and compliance were elicited from information gathered in a participant home diary. Both groups were similar on Day 1 in respect to their initial pain, swelling, the number of participants who were referred for X-rays, and the time taken to present to the physiotherapist. However the RICE group had significantly higher function scores (p = 0.042). The RICE group also had a significantly higher use of medication on Day 1 (p = 0.035) and Day 11 (p = 0.048). For both groups there was a statistically significant decrease in swelling (p = 0.003), pain scores (p = 0.000), and an increase in function scores (p = 0.000) in relation to time over the eleven days of assessment. The physiotherapy group had significantly improved function scores (p = 0.042) from Day 1 to Day 11 compared to the RICE group. There were no significant differences between groups for swelling, pain scores, and their first day of documented non-compliance. The within day range of error in the volumetric measurements was within 189.9 ml and 1.2 ml. Three trials were conducted per person within a Day session. The first volumetric analysis was significantly less than the subsequent two measurements (p = 0.040).It was concluded that, in the early stage of an ankle injury both physiotherapy and RICE, and RICE advice alone, resulted in significant improvements in swelling, pain and function. Early intervention physiotherapy was significantly better at improving the functional ability of participants by Day 11. Early intervention physiotherapy may also identify complications associated with ankle sprains.Despite its limitations this research could potentially lead to changes in the standard treatment protocols for soft tissue ankle injuries. Implementation of self management RICE by patients in the acute stage would initially reduce the cost of physiotherapy treatments, and may lead to equal improvements in pain and swelling outcomes. However, it appears that physiotherapy may lead to better functional outcomes which would reduce the costs associated with time off work, and rehabilitation. It is important to note that these findings are based on a small sample size and on Grade I or II ankle ligament sprains, and that treatment for more severe ankle injuries may be better with physiotherapy, or surgery, rather than self management RICE by patients. Findings contribute to the growing body of 'best practice' evidence for health practitioners. Keywords: Acute soft tissue injuries, ankle, sprain, early intervention, physiotherapy, RICE, volumetric measurement.

Axial rotation is regarded as an essential movement of the trunk that allows many individuals to participate in vocations, sports and activities of daily living. Unfortunately when the destabilising nature of rotation is combined with that of spinal flexion, the risk of injuring the spine can increase significantly. Few studies have investigated the potential benefits that maximizing trunk rotation has in certain vocation and sport-related arenas and none have looked at whether adopting certain spinal postures in the sagittal plane can maximise trunk rotation more than others. The aim of the study was to determine the effects of alterations of trunk inclination, spinal posture, pelvic fixation and turning direction on the active range of motion (ROM) of trunk rotation. Twenty healthy individuals participated in the main study. Retro-reflective markers were placed on key anatomical locations and used to track the movement of the thorax and pelvis during a series of repeated maximal trunk rotations in ten different spinal positions within the sagittal plane. Trunk kinematics and kinetics were recorded simultaneously using an optoelectronic motion analysis and force platform measuring system. A repeated-measures multiple analysis of variance (MANOVA) was used to test for the main effects of trunk inclination, spinal posture, fixation of pelvis and direction of turn on maximum active ROM of trunk rotation, maximum pelvic rotation and the anterior-posterior and lateral displacement of the centre of pressure (COP). To investigate test-retest reliability, ten participants were tested on two separate days. Repeatability for each outcome measure was investigated using interclass correlation coefficients (ICC) and Bland Altman graphs. The majority of subjects showed reasonable test-retest reliability for trunk rotation measures in each of the test positions, with ICC’s ranging between 0.562 – 0.731. Overall, trunk inclination (0°, 22.5°, 45°) forward in the sagittal plane had a significant effect on trunk and pelvic rotation (p<0.001) and lateral displacement of the COP (p<0.005) during trunk rotation. As trunk inclination increased from 0° to 45° there was an average increase in trunk rotation ROM of approximately 10 % (approximately 3.4°). Furthermore, increasing trunk inclination led to an increase in lateral displacement of the COP and a decrease in pelvic rotation. Spinal posture (neutral, flexed, extended) at a forward inclination of 45° had a significant effect on trunk rotation (p<0.01) and pelvic rotation (p<0.05), with a neutral spine averaging approximately 3 % (approximately 1.1°) more trunk rotation than a flexed or extended posture. The position and posture of the spine in the sagittal plane appears to have a significant influence on ranges of trunk rotation. The study suggests that rotating the trunk when adopting a neutral spine inclined to 45° will maximise range of trunk rotation and encourage a natural stabilisation of the lower body. This posture meets the unique set of biomechanical requirements for the sport of golf and may help to reduce the risk of injury in manual material handling tasks. Conversely, rotating the trunk whilst the thoracolumbar spine is flexed leads to a reduction in trunk rotation ROM, encourages greater pelvic and lower body rotation, reduces torque production of the trunk and may increase the risk of lower back injury. These findings have important implications in relation to the teaching of spinal position during vocations, sports and activities of daily living that seek to maximise trunk rotation.

Children experience a multitude of benefits in response to interactions with nature. Despite documented effects, children have increasingly spent less time outdoors over the last century and experienced higher rates of physical and emotional illnesses. Although child-centered play therapy (CCPT) is a culturally and developmentally responsive mental health treatment for children, researchers have limited study of environmental structure and materials employed in the therapeutic process of CCPT. In this study, I sought to further research on the integration of nature with CCPT by providing CCPT in an outdoor, contained playroom equipped with traditional CCPT toys and additional nature materials. Participants were 13 children in the southwestern U.S. with parent-reported attentional or self-regulation concerns (9 males, 6 females; ages 5-10, mean age M = 8.0). Parents reported participants' racial identities were 13% Black (n = 2), 13% Latinx (n = 2), 7% Turkish (n = 1), and 67% White (n = 10). Participants received 8 weeks of twice-weekly CCPT in an outdoor playroom. Results of two repeated measures ANOVAs revealed statistically significant improvement in attention on the Brown EF/A Scales and statistically significant improvement in social-emotional competencies on the SEARS-P. Results of this study illustrate the possible benefits of theoretically integrating CCPT and nature and the clinical impacts the novel approach could have on children's attention and social-emotional competencies. The study also provided insight into the viability of providing an outdoor CCPT intervention at a larger scale and some problems that may arise in creating and maintaining an outdoor playroom.

Em muitos trabalhos científicos, é comum encontrar os dados estruturados de forma hierarquica, ou seja, os indivíduos em estudo estão agrupados em unidades de nível mais baixo, que por sua vez pertencem a unidades de um nível mais alto e assim sucessivamente. Na análise desse tipo de dados é importante levar em conta a estrutura hierarquica uma vez que, não faze-la, pode implicar na superestimação dos coecientes do modelo em estudo. Assim, para facilitar a análise de dados seguindo uma estrutura hierarquica, foram desenvolvidos os modelos multiníveis. Tais modelos levam em conta toda a variabilidade existente para os dados num mesmo nível como nos diferentes níveis da hierarquia. No caso da análise de dados de medidas repetidas no tempo, uma estrutura hierarquica em dois níveis pode ser considerada, organizando as ocasiões de medidas, no primeiro nível, para cada indivíduo no segundo nível. Neste trabalho, é feita uma abordagem dos modelos multiníveis para vários níveis da hierarquia bem como os métodos de estimação e teste dos parâmetros envolvidos no modelo. Como aplicação, foram analisados dados provenientes do Programa de Atenção ao Idoso (PAI), desenvolvido no ambulatório municipal Dr. Plinio do Prado Coutinho em Alfenas, M.G., em que foram observadas as variáveis Indice de Massa Corporea (imc) e Pressão Arterial dos idosos durante 22 meses. Também, foram analisados dados referentes ao teor de proteína no leite de 79 vacas australianas, coletados durante 19 semanas após o parto e submetidas a três dietas (Diggle et al., 1994). Para os dados do "PAI", foi possível verificar que as diferentes medidas de pressão arterial estão relacionadas positivamente com o imcao longo do tempo, independente de sexo, idade e estado civil. Já nos dados relativos ao teor de proteína no leite, notou-se uma redução do teor de proteína no leite ao longo do tempo, independente dos tratamentos aplicados. Foram utilizados os softwares MLwiN e SAS para a realização das análises. / It is common to and data structured in a hierarchical form in several scientific works, that is, the studied subjects are nested in the lowest level unites, that belong to the highest level unites, and so on. To analyze these sort of data it is important to take in account the hierarchical structure once, if does not do it, the coeficients can be overestimated in the studied model. Then, in order to become easier the data analysis according to the hierarchical structure, multilevel models were developed. Such models take into account all the existing variability for the data at the same level as well as in diferent levels of the hierarchy. In the case of repeated measure data, a two levels hierarchical structure can be considered, organizing the occasions at the first level for each subject at the second level. In this work, na approach of the multilevel models for several levels are made as well as the estimation methods and the tests for the involved parameters in the model. As an application, data from the Elderly Care Program (ECP), developed at outpatient clinic Dr. Plinio do Prado Coutinho at Alfenas, M.G., where the Body Mass Index and the Bloody Pressure were observed from elderly people for 22 months. Also, it was analyzed the milk protein content of 79 australian cows during 19 weeks after calving and subject to three diets (Diggle et al., 1994). For the data of the ECP it was possible to observe that the bloody pressure are positively related to the occasions, independently of sex, race and marital status. For the data form the milk protein content, a reduce in the content in the occasions even after the diets are included. MLwiN and SAS softwares were used to run the analysis.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T20:19:01Z No. of bitstreams: 1 Franklin Tupinamba Paes de Andrade Viera.pdf: 337732 bytes, checksum: 0600dc25608a782e0a42c10d6cdd44f2 (MD5) / Made available in DSpace on 2016-07-05T20:19:01Z (GMT). No. of bitstreams: 1 Franklin Tupinamba Paes de Andrade Viera.pdf: 337732 bytes, checksum: 0600dc25608a782e0a42c10d6cdd44f2 (MD5) Previous issue date: 2006-02-22 / The main objective of the present work was to evaluate the use of the leucena [Leucaena leucocephala (Lam.) of Wit.] as direct pasture in the field. A multivariate silvopastoral experiment with repeated measures, carried out in the experimental station of Pernambucana Company of Agricultural Research (IPA) in Caruaru, Agreste of Pernambuco, was used for this purpose. The plant spacing of 0,5mX1,0m, 1,0mX1,0m and 1,5mX1,0m with the height of cut of 20cm, 40cm and 60cm, was tested. The measurements have been carried through at same month of the year (August) along five years. The presented result shows that the measures along the time are correlated and there were significant differences among the plant spacing during the period of the experiment. No significant differences among the heights of cuts and its interactions with the plant spacing occurred. Because of a larger number of plants per unit area, yielding greater productivity of biomass per hectare, the plant spacing of 0,5mX1,0m is recommended for silvopastoral systems in the Agreste of Pernambuco. / O principal objetivo do presente trabalho foi avaliar o uso da leucena [Leucaena leucocephala (Lam.) de Wit.] como pastagem direta no campo, utilizandose para tanto de um experimento silvipastoril multivariado, com medidas repetidas, realizado na região Agreste do Estado de Pernambuco. Estudou-se a leucena submetida a diferentes espaçamentos e alturas de corte com a finalidade de produção de biomassa. O experimento foi realizado na estação experimental da Empresa Pernambucana de Pesquisa Agropecuária (IPA), no município de Caruaru. Testaram-se os espaçamentos 0,5mX1,0m ; 1,0mX1,0m e 1,5mX1,0m e as alturas de corte 20cm, 40cm e 60cm. As medições foram realizadas numa mesma época do ano (mês de agosto) em cinco anos. Os resultados apresentados mostraram que as medidas ao longo do tempo são correlacionadas e que houve diferenças significativas entre os espaçamentos durante todo o período do experimento. Não ocorreram diferenças significativas entre as alturas de cortes e suas interações com os espaçamentos em todos os tempos. Por se usar um maior número de plantas por unidades de área e conseqüentemente, maior produtividade de biomassa por hectare, recomenda-se o uso de espaçamento 0,5mX1,0m em sistemas silvipastoris com leucena no Agreste de Pernambuco.

Os modelos de transição de Markov constituem uma ferramenta de grande importância para diversas áreas do conhecimento quando são desenvolvidos estudos com medidas repetidas. Eles caracterizam-se por modelar a variável resposta ao longo do tempo condicionada a uma ou mais respostas anteriores, conhecidas como a história do processo. Além disso, é possível a inclusão de outras covariáveis. No caso das respostas binárias, pode-se construir uma matriz com as probabilidades de transição de um estado para outro. Neste trabalho, quatro abordagens diferentes de modelos de transição foram comparadas para avaliar qual estima melhor o efeito causal de tratamentos em um estudo experimental em que a variável resposta é um vetor binário medido ao longo do tempo. Estudos de simulação foram realizados levando em consideração experimentos balanceados com três tratamentos de natureza categórica. Para avaliar as estimativas foram utilizados o erro padrão, viés e percentual de cobertura dos intervalos de confiança. Os resultados mostraram que os modelos de transição marginalizados são mais indicados na situação em que um experimento é desenvolvido com um reduzido número de medidas repetidas. Como complementação, apresenta-se uma forma alternativa de realizar comparações múltiplas, uma vez que os pressupostos como normalidade, independência e homocedasticidade são violados impossibilitando o uso dos métodos tradicionais. Um experimento com dados reais no qual se registrou a presença de fungos (considerada como sucesso) em cultivos de citros e morango foi analisado por meio do modelo de transição apropriado. Para as comparações múltiplas, intervalos de confiança simultâneos foram construídos para o preditor linear e os resultados foram estendidos para a resposta média que neste caso são as probabilidades de sucesso. / The transition Markov models are a very important tool for several areas of knowledge when studies are developed with repeated measures. They are characterized by modeling the response variable over time conditional to the previous response which is known as the history. In addtion it is possible to include other covariates. In the case of binary responses, can be constructed a matrix of transition probabilities from one state to another. In this work, four different approaches to transition models were compared in order to assess which best estimates of the causal effect of treatments in an experimental studies where the outcome is a vector of binary response measured over time. Simulation study was held taking into account a balanced experiments with three treatments of categorical nature. To assess the best estimates standard error and bias, beyond the percentage of coverage were used. The results showed that the marginalized transition models are more appropriate in situation where an experiment is developed with a reduced number of repeated measurements. As complementation is presented an alternative way to perform multiple comparisons, since the assumptions as normality, independence and homoscedasticity are violated precluding the use of traditional methods. An experiment with real data where we recorded the presence of fungi (deemed successful) in citrus and strawberry crops was analyzed through the appropriate transition model. For multiple comparisons, simultaneous confidence intervals were constructed for the linear predictor and the results have been extended to the mean response in this case are the probabilities of success.

Logic regression (LR) is a methodology to identify logic combinations of binary predictors in the form of intersections (and), unions (or) and negations (not) that are linearly associated with an outcome variable. Logic regression uses the predictors as inputs and enables us to identify important logic combinations of independent variables using a computationally efficient tree-based stochastic search algorithm, unlike the classical regression models, which only consider pre-determined conventional interactions (the “and” rules). In the thesis, we focused on LR with a binary outcome in a logistic regression framework. Simulation studies were conducted to examine the performance of LR under the assumption of independent and correlated observations, respectively, for various characteristics of the data sets and LR search parameters. We found that the proportion of times that LR selected the correct logic rule was usually low when the signal and/or prevalence of the true logic rule were relatively low. The method performed satisfactorily under easy learning conditions such as high signal, simple logic rules and/or small numbers of predictors. Given the simulation characteristics and correlation structures tested, we found some but not significant difference in performance when LR was applied to dependent observations compared to the independent case. In addition to simulation studies, an advanced application method was proposed to integrate LR and resampling methods in order to enhance LR performance. The proposed method was illustrated using two simulated data sets as well as a data set from a real-life situation. The proposed method showed some evidence of being effective in discerning the correct logic rule, even for unfavorable learning conditions.

Racial minorities, women, and people with disabilities are underrepresented in the fields of Science, Technology, Engineering, and Mathematics (STEM). Attitude towards science has been shown to be a reliable predictor of science achievement. Project-Based Learning (PBL) has been shown to improve attitude towards a topic. The sample selected consisted of 113 African American high school students (68% to 32% female to male ratio) from Alabama, Louisiana, and Mississippi. A quasi-experimental research design which consisted of pre and post intervention measures of participants’ attitudes towards science was utilized in this study. Overall, Phase 1, a week-long residential camp, saw greater increases with direct respect to time or gender due to the immersive nature of the camp, whereas Phase 2, an eight week long outreach, saw a more complex interaction of the two factors. PBL was shown to be an effective method of instruction to reach African American and women populations.

<p>Clinical trials are the gold standard for inferring the causal effects of treatments or interventions. This thesis is concerned with the development of methodologies for two problems in modern clinical trials. First is analyzing binary repeated measures in clinical trials using models that reflect the complicated autocorrelation patterns in the data, so as to obtain high power when inferring treatment effects. Second is simulating realistic outcomes and subject nonadherence mechanisms in Phase III pharmaceutical clinical trials under the Tripartite Framework.</p><p> </p><p><b>Bayesian Models for Binary Repeated Data: The Bayesian General Logistic Autoregressive Model and the Polya-Gamma Logistic Autoregressive Model</b></p><p>Autoregressive processes in generalized linear mixed effects regression models are convenient for the analysis of clinical trials that have a moderate to large number of binary repeated measurements, collected across a fixed set of structured time points, for each subject. However, much of the existing literature and methods for autoregressive processes on repeated binary measurements permit only one order and only one autoregressive process in the model. This limits the flexibility of the resulting generalized linear mixed effects regression model to fully capture the dynamics in the data, which can result in decreased power for testing treatment effects. Nested autoregressive structures enable more holistic modeling of clinical trials that can lead to increased power for testing effects.</p><p> </p><p>We introduce the Bayesian General Logistic Autoregressive Model (BGLAM) for the analysis of repeated binary measures in clinical trials. The BGLAM extends previous Bayesian models for binary repeated measures by accommodating flexible and nested autoregressive processes with non-informative priors. We describe methods for selecting the order of the autoregressive process in the BGLAM based on the Deviance Information Criterion (DIC) and marginal log-likelihood, and develop an importance sampling-weighted posterior predictive p-value to test for treatment effects in BGLAM. The frequentist properties of BGLAM compared to existing likelihood- and non-likelihood-based statistical models are evaluated by means of extensive simulation studies involving different data generation mechanisms.</p><p> </p><p>Two features of BGLAM that can limit its application in practice is the computational effort involved in executing it and the inability to integrate added heterogeneity across time in its autoregressive processes. We develop the Polya-Gamma Logistic Autoregressive Model (PGLAM) for addressing these limiting features of the BGLAM. This new model enables the integration of additional layers of variability through random effects and heterogeneity across time in nested autoregressive processes. Furthermore, PGLAM is computationally more efficient than BGLAM because it eliminates the need to use the complex types of samplers for truncated latent variables that is involved in the Markov Chain Monte Carlo algorithm for BGLAM.</p><p> </p><p><b>Data Generating Model for Phase III Clinical Trials With Intercurrent Events</b></p><p>Although clinical trials are designed with strict controls, inevitably complications will arise during the course of the trials. One significant type of complication is missing subject outcomes due to subject drop-out or nonadherence during the trial, which are referred to in general as intercurrent events. This complication can arise from, among other causes, adverse reactions, lack of efficacy of the assigned treatment, administrative reasons, and excess efficacy from the assigned treatment. Intercurrent events typically confound causal inferences on the effects of the treatments under investigation because the missingness that occurs as a result corresponds to a Missing Not at Random missing data mechanism, the pharmaceutical industry is increasingly focused on developing methods for obtaining valid causal inferences on the receipt of treatment in clinical trials with intercurrent events. However, it is extremely difficult to compare the frequentist properties and performance of these competing methods, as real-life clinical trial data cannot be easily accessed or shared, and as the different methods consider distinct assumptions for the underlying data generating mechanism in the clinical trial. We develop a novel simulation model for clinical trials with intercurrent events. Our simulator operates under the Rubin Causal Model. We implement the simulator by means of an R Shiny application. This app enables users to control patient compliance through different sources of discontinuity with varying functional trends, and understand the frequentist properties of treatment effect estimators obtained by different models for various estimands.</p>

Data from the 2001 and 2003 timber product output (TPO) studies for Georgia were explored to determine new methods for handling missing data and finding suitable sampling estimators. Mean roundwood volume receipts per mill for the year 2003 were calculated using the methods developed by Rubin (1987). Mean receipts per mill ranged from 4.4 to 14.2 million ft3. The mean value of 9.3 million ft3 did not statistically differ from the NONMISS, SINGLE1, and SINGLE2 references means (p=.68, .75, and .76 respectively). Fourteen estimators were investigated to investigate sampling approaches, with estimators being of several means types (simple random sample, ratio, stratified sample, and combined ratio) as well as employing two methods for stratification (Dalenius-Hodges (DH) square root of the Frequency method and a cluster analysis method. Relative efficiency (RE) improved when the number of groups increased and when employing a ratio estimator, particularly a combined ratio. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1, 5, 10, 15, 20, and 25 percent) were considered for deriving samples sizes for the total volume of roundwood. The minimum achievable bound size was found to be 10 percent of the total receipts volume for the DH-method using a two group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 10 percent bound on the total (6 of the 12 stratified estimators). The remaining six stratified estimators were able to achieve a 20 percent bound of the total. Finally, nonlinear repeated measures models were developed to spatially allocate mill receipts to surrounding counties in the event of obtaining only a mill's total receipt volume. A Gompertz model with a power spatial covariance was found to be the best performing when using road distances from the mills to either county center type (geographic or forest mass). These models utilized the cumulative frequency of mill receipts as the response variable, with cumulative frequencies based on distance from the mill to the county. / Ph. D.