Bygge broer Grappling, en idretts bidrag til ungdommens Physical Literacy.

Master's thesis in Sports and Physical educationBakgrunn og formål: Med kroppsøvingens fremtid i sikte er kunnskap om det å utvikle kvalitativ, variert og det essensielle ved meningsfull kroppsøving et aktuelt tema. Hensikten med dette studiet er derfor å se på alternativer i bevegelsesmiljøet, som potensielt kan bidra til dette. For dette prosjektet er det valgt å erstatte eksisterende emner innenfor kroppsøving med Grappling (submission wresting) som alternativ bevegelseskultur. I kombinasjon med et eksisterende pedagogisk rammeverk, Sport Education, har vi sett på effekten denne kombinasjonen har på ulike domenene innenfor Physical Literacy som en pragmatisk holistisk filosofi rettet mot progressiv utvikling av hele barnet. Utvalg: Prosjektet har inkludert 52 elever i alder av 14 år. Alle elevene tilhørte samme ungdomsskolen i Stavanger, og har vært strategisk fordelt i en intervensjon- og kontrollgruppe med samme klassestruktur. Intervensjonsgruppen besto av 13 gutter og 12 jenter. Kontrollgruppen besto av 15 gutter og 12 jenter. Alle elevene fra både intervensjons- og kontrollgruppen fullførte intervensjonsperioden fra start til slutt. Metode: Prosjektet er basert på en 12 ukers kvasi-eksperimentell forskningsdesign. Intervensjonsgruppen hadde i denne periode Grappling som hovedtema i kroppsøving, mens kontrollgruppen hadde ordinær timeplan med Baseball, Rugby og Touch football som tema i faget. For innhenting av både kvantitativ og kvalitativ data ble det i dette prosjektet benyttet en Mixed Method med integrert design. Kvantitativ data er innhentet for athletic skills track (AST1), lengdehopp uten tilløp (SLJ), sit and reach, og gripestyrke. Dette til vurdering av motorisk ferdighetsutvikling, eksplosiv styrke, fleksibilitet og maksimal isometrisk styrke. Til vurdering av kognitive og sosiale parametere er kvalitativt data innhentet i domenene motivasjon og selvfølelse og kunnskap og forståelse ved bruk av et spørreskjema. Kvalitativ data er innhentet gjennom en semi-strukturert intervju med faglæreren som informant. Resultat: Intervensjonsgruppe viser til signifikante endringer for både jentene og guttene på alle fysiske tester. Analyser gjennom observasjonene stemmer overens med alle kvantitative funn. Hovedfunn for motivasjon og selvfølelse indikerer positiv utvikling innen alle domenene, spesielt ved elevenes tro på egne ferdigheter, indre motivasjon og vurdering av seg selv. Hovedresultater for kunnskap og forståelse viser til positiv utvikling innenfor alle domene, spesielt forståelse og kunnskap om Grappling og relevant tema i forhold til fagets kompetansemål. Kontrollgruppen viser til forbedring, men ingen signifikante endringer sammenslått. Resultatene for lengdehopp uten tilløp viser til signifikant forbedring for gutter og reduksjon for jenter. Hovedfunn for selvfølelse og motivasjon indikerer en stabil tilstand med mindre åpenbar positiv effekt. Hovedresultater innen kunnskap og forståelse indikerer positiv utvikling innen alle domene, spesielt forståelse rundt innhold i timene og relevant tema i forhold til fagets kompetansemål. Konklusjon: For dette prosjektet viser det seg at Grappling er en bevegelseskultur med stort potensialet som primært har påvirket ungdommens motoriske og fysiske ferdigheter som en del av ungdommens fysiske kompetanse. For domenet motivasjon og selvfølelse kan vi si at Grappling har bidratt til økt indre motivasjon og positivt selvbilde. For domenene kunnskap og forståelse viser funnene at selve prosjektets holistiske tilnærming har bidratt til positiv utvikling. Dette betyr at Grappling i kombinasjon med Sport Education og Physical Literacy som filosofi ansees som en potensiell aktør ved utforming av en kvalitativ bevegelseskultur innen faget kroppsøving, der barnets helhetlige utvikling står i sentrum. Fra et holistisk perspektiv kan vi dermed konkludere at Grappling bidrar til utvikling av ungdommens Physical Literacy.submittedVersio

Sample size and robust marginal methods for cluster-randomized trials with censored event times

This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901–923. doi: 10.1002/sim.6395, which has been published in final form at http://dx.doi.org/10.1002/sim.6395. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.In cluster-randomized trials, intervention effects are often formulated by specifying marginal models, fitting them under a working independence assumption, and using robust variance estimates to address the association in the responses within clusters. We develop sample size criteria within this framework, with analyses based on semiparametric Cox regression models fitted with event times subject to right censoring. At the design stage, copula models are specified to enable derivation of the asymptotic variance of estimators from a marginal Cox regression model and to compute the number of clusters necessary to satisfy power requirements. Simulation studies demonstrate the validity of the sample size formula in finite samples for a range of cluster sizes, censoring rates and degrees of within-cluster association among event times. The power and relative efficiency implications of copula misspecification is studied, as well as the effect of within-cluster dependence in the censoring times. Sample size criteria and other design issues are also addressed for the setting where the event status is only ascertained at periodic assessments and times are interval censored.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

Enhanced implementation of low back pain guidelines in general practice: study protocol of a cluster randomised controlled trial

Evidence-based clinical practice guidelines may improve treatment quality, but the uptake of guideline recommendations is often incomplete and slow. Recently new low back pain guidelines are being launched in Denmark. The guidelines are considered to reduce personal and public costs. The aim of this study is to evaluate whether a complex, multifaceted implementation strategy of the low back pain guidelines will reduce secondary care referral and improve patient outcomes compared to the usual simple implementation strategy.Methods/design: In a two-armed cluster randomised trial, 100 general practices (clusters) and 2,700 patients aged 18 to 65 years from the North Denmark region will be included. Practices are randomly allocated 1:1 to a simple or a complex implementation strategy. Intervention practices will receive a complex implementation strategy, including guideline facilitator visits, stratification tools, and quality reports on low back pain treatment. Primary outcome is referral to secondary care. Secondary outcomes are pain, physical function, health-related quality of life, patient satisfaction with care and treatment outcome, employment status, and sick leave. Primary and secondary outcomes pertain to the patient level. Assessments of outcomes are blinded and follow the intention-to-treat principle. Additionally, a process assessment will evaluate the degree to which the intervention elements will be delivered as planned, as well as measure changes in beliefs and behaviours among general practitioners and patients

Robustness and Optimal Design Issues for Cluster Randomized Trials

Cluster randomized trials (CRT), in which whole clusters instead of individuals are assigned to conditions, are not uncommon in the social, behavioral, educational, medical and organizational sciences. Though the assignment of individuals to treatment conditions is more efficient, this may not always be feasible due to ethical, financial or organizational reasons. Using a CRT has consequences with respect to the sample size and data analysis technique. The sample size should be increased to reach the same level of efficiency as with subject-level randomization and the data should be analyzed with a technique like multilevel analysis that accounts for the dependency of subjects within the same cluster. The intracluster correlation coefficient (ICC) is a measure for this dependency, but may also be seen as the proportion cluster level variance of the total variance. Thanks to extensive literature, most researchers are aware of the forenamed consequences of application of a CRT and take them into account when designing a CRT and analyzing the resulting data. However, various issues concerning design and robustness remained unclear so far and some of these issues have been covered by this thesis. In the design phase of a CRT the researcher has to determine the optimal allocation of units at the cluster and at the subject level for detecting the treatment effect -if existing- with the highest precision. The optimal allocation depends, among other things, on the magnitude of the ICC. In advance this value is unknown and the researcher has to make an educated guess. It is shown that the design is rather robust against incorrect ‘initial’ ICC estimates. The performance of multilevel structural equation modeling applied to a 2111 mediation model is investigated. It is shown that the mediation effect itself is estimated seemingly accurately. However, the parameters that determine the mediation effect are often biased, even with large sample sizes. Three different incorrect model specifications are investigated; heteroscedasticity, ignoring partially nesting, and ignoring inequality of within-cluster and contextual effects. In a CRT the variance in the experimental condition may differ from the variance in the control condition. This phenomenon is called heteroscedasticity and may be due to the treatment. When in a CRT one of the conditions consists of clusters, while the other condition consists of non-clustered individuals, the resulting data has a partially nested structure. In case a subject level covariate is taken into account, the multilevel model gives a single estimate for its effect, since the model assumes that there is no between-cluster effect. In practice the within-cluster and contextual effect may differ and when not modeled this difference is ignored. It is shown that within the framework of a CRT it is advisable to model the design specific issues appropriately when the researcher wishes to have a complete and correct view of the various effects and relations between the variables in the model. However, it is also shown that the estimation of the treatment effect and its standard error is rather robust

The Robustness of Designs for Trials With Nested Data Against Incorrect Initial Intracluster Correlation Coefficent Estimates

In the case of trials with nested data, the optimal allocation of units depends on the budget, the costs, and the intracluster correlation coefficient. In general, the intracluster correlation coefficient is unknown in advance and an initial guess has to be made based on published values or subject matter knowledge. This initial estimate is likely to deviate from the true intracluster correlation coefficient. The current study investigates the extent to which the efficiency of a design for a trial with nested data and continuous outcome variables is influenced by an incorrect initial intracluster correlation coefficient estimate. We focus on trials with nested data in both treatment conditions as well as in one treatment condition. The investigated designs prove to be rather robust against the misspecification of the intracluster correlation coefficient. Although underestimating the intracluster correlation coefficient leads to a steeper decrease in the efficiency of a design than overestimating it, the relative efficiency of the treatment effect estimate remains above 90% as long as the population intracluster correlation coefficient is not underestimated by more than 75% or overestimated by more than 175%

The Robustness of Designs for Trials With Nested Data Against Incorrect Initial Intracluster Correlation Coefficent Estimates

In the case of trials with nested data, the optimal allocation of units depends on the budget, the costs, and the intracluster correlation coefficient. In general, the intracluster correlation coefficient is unknown in advance and an initial guess has to be made based on published values or subject matter knowledge. This initial estimate is likely to deviate from the true intracluster correlation coefficient. The current study investigates the extent to which the efficiency of a design for a trial with nested data and continuous outcome variables is influenced by an incorrect initial intracluster correlation coefficient estimate. We focus on trials with nested data in both treatment conditions as well as in one treatment condition. The investigated designs prove to be rather robust against the misspecification of the intracluster correlation coefficient. Although underestimating the intracluster correlation coefficient leads to a steeper decrease in the efficiency of a design than overestimating it, the relative efficiency of the treatment effect estimate remains above 90% as long as the population intracluster correlation coefficient is not underestimated by more than 75% or overestimated by more than 175%

The robustness of designs for trials with nested data against incorrect initial intracluster correlation coefficient estimates

In the case of trials with nested data, the optimal allocation of units depends on the budget, the costs, and the intracluster correlation coefficient. In general, the intracluster correlation coefficient is unknown in advance and an initial guess has to be made based on published values or subject matter knowledge. This initial estimate is likely to deviate from the true intracluster correlation coefficient. The current study investigates the extent to which the efficiency of a design for a trial with nested data and continuous outcome variables is influenced by an incorrect initial intracluster correlation coefficient estimate. We focus on trials with nested data in both treatment conditions as well as in one treatment condition. The investigated designs prove to be rather robust against the misspecification of the intracluster correlation coefficient. Although underestimating the intracluster correlation coefficient leads to a steeper decrease in the efficiency of a design than overestimating it, the relative efficiency of the treatment effect estimate remains above 90% as long as the population intracluster correlation coefficient is not underestimated by more than 75% or overestimated by more than 175%

Robustness of parameter and standard error estimates against ignoring a contextual effect of a subject level covariate in cluster randomized trials

In experimental research, it is not uncommon to assign clusters to conditions.When analysing the data of such cluster-randomized trials, a multilevel analysis should be applied in order to take into account the dependency of firstlevel units (i.e., subjects) within a second-level unit (i.e., a cluster). Moreover, the multilevel analysis can handle covariates on both levels. If a first-level covariate is involved, usually the within-cluster effect of this covariate will be estimated, implicitly assuming the contextual effect to be equal. However, this assumption may be violated. The focus of the present simulation study is the effects of ignoring the inequality of the within-cluster and contextual covariate effects on parameter and standard error estimates of the treatment effect, which is the parameter of main interest in experimental research. We found that ignoring the inequality of the within-cluster and contextual effects does not affect the estimation of the treatment effect or its standard errors. However, estimates of the variance components, as well as standard errors of the constant, were found to be biased