Psychological approaches to obesity interventions

Obesity is one of the most significant health concerns across the world, particularly in developed countries, and there is no evidence to suggest that obesity is becoming less prevalent. There are many physical, psychological and social consequences from being obese. Nevertheless, interventions to date have been generally unsuccessful at providing long lasting effects. Furthermore, there has been a general lack of attention given to non-weight factors relating to overall well-being. The literature review presented in chapter 1 critically evaluates the research to date exploring the efficacy of mindfulness-based interventions for overweight and obese adults. The promising initial indications that this ‘third-wave’ approach offers are discussed within the methodological limitations of current research. Clinically, the approach appears to have potential, although future research should aim to have greater consistency in defining what constitutes a mindfulness approach. The empirical paper presented in chapter two explores how the attributions made by trainee teachers about the cause of weight gain may impact on helping behaviour in a school setting. A mixed methodology questionnaire design is used. This incorporates a vignette about a significantly overweight child and a series of open ended questions to explore the factors that trainee teachers see as key considerations for making interventions for obese children more effective. The results are discussed within the context of how healthcare professionals may offer a valuable role in training and coordinating intervention efforts. The final chapter is a reflective paper summarising some personal, professional and academic experiences that occurred during my thesis. Current and alternative approaches taken towards childhood obesity are discussed and reflected upon

The influence of alcohol content variation in UK packaged beers on the uncertainty of calculations using the Widmark equation

It is common for forensic practitioners to calculate an individual's likely blood alcohol concentration following the consumption of alcoholic beverage(s) for legal purposes, such as in driving under the influence (DUI) cases. It is important in these cases to be able to give the uncertainty of measurement on any calculated result, for this reason uncertainty data for the variables used for any calculation are required. In order to determine the uncertainty associated with the alcohol concentration of beer in the UK the alcohol concentration (%v/v) of 218 packaged beers (112 with an alcohol concentration of ≤5.5%v/v and 106 with an alcohol concentration of >5.5%v/v) were tested using an industry standard near infra-red (NIR) analyser. The range of labelled beer alcohol by volume (ABV's) tested was 3.4%v/v – 14%v/v. The beers were obtained from a range of outlets throughout the UK over a period of 12 months. The root mean square error (RMSE) was found to be ±0.43%v/v (beers with declared %ABV of ≤5.5%v/v) and ±0.53%v/v (beers with declared %ABV of >5.5%v/v) the RMSE for all beers was ±0.48%v/v. The standard deviation from the declared %ABV is larger than those previously utilised for uncertainty calculations and illustrates the importance of appropriate experimental data for use in the determination of uncertainty in forensic calculations

Wisdom of crowds for robust gene network inference

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.National Institutes of Health (U.S.)National Centers for Biomedical Computing (U.S.) (Roadmap Initiative (U54CA121852))Howard Hughes Medical InstituteNational Institutes of Health (U.S.) (Director's Pioneer Award DPI OD003644)Swiss National Science Foundation (Fellowship

The Executive Revolving Door: New Dataset on the Career Moves of Former Danish Ministers and Permanent Secretaries

Concerns have been raised that transfers of bureaucrats and politicians into the private sector might create unfair advantages for their future employers and even lead to distrust in government. Not surprisingly, the study of the revolving door has therefore gained prominence in the academic literature. Importantly, however, less attention has been paid to the executive branch. We add to the study of the revolving door by presenting the first dataset on the executive revolving door in Denmark. To do so, we trace the frequency, timing and character of the career moves of Danish Ministers and Permanent Secretaries who held office from 2009 to 2019. Our data document that the Danish executive revolving door is widespread: more than a third of Danish Ministers and Permanent Secretaries end up in a private job within the same year or the year after they stop their job. If we extend the period and investigate the entire period after public service, the number is above 60 percent. Moreover, a substantial share of the jobs obtained is in companies and at a senior level. Our note concludes with reflections on how our data can be used to fill existing research gaps and should be complemented in future research.publishedVersio

Quantitative Essays on Mixed Martial Arts A Markov Chain Based Forecasting Model and Analyses of the Judges

Whilst Mixed Martial Arts (MMA) has only recently gained mainstream popularity, the rapid rise of it, particularly the Ultimate Fighting Championship (UFC–the most popular MMA promotion), has been unparalleled in sports. Academic research on MMA is still scarce, and the vast majority has focused on the sport’s health implications. This thesis comprises three articles which contribute to the knowledge on MMA, as well as the wider literature regarding sports forecasting and biases. The first article, now published in the International Journal of Forecasting (Holmes et al., 2022), introduces a Markov chain (MC) based model to predict MMA bouts. The states of the MC are associated with key techniques or positions within MMA. Various models based on the athletes’ historical in-fight statistics determine the transition probabilities between states, thus accounting for individual fighting styles. By simulating the chain many times, we obtain probabilities of fight outcomes. These predictions were comparable to the bookmakers, and generated positive returns when used for betting. Compared to other subjectively judges sports, for instance, diving, the performance data available for UFC fights provides an ideal environment to model the judges’ behaviours. Thus, the remaining two papers examined the judges in the UFC. First, we explored the potential of several biases within MMA judging. We find evidence suggesting two biases exist: the judges are influenced by a live audience, thus favouring a home athlete; and the judges favour athletes higher in the official rankings. One issue with previous work was establishing whether the significant effects were due to bias or fighter skill. Under the hypothesis that the betting market is efficient, we address this issue by including the bookmakers’ odds to account for unseen skills. Market efficiency suggests the bias variables don’t add any information on skills beyond what is contained in the odds, and thus significance is indicative of bias, not skill. We demonstrate that the market is efficient, and thus we can be more confident in our conclusions. Second, we use a Bayesian hierarchical model to show that the judges have different preferences towards each action. We identify several actions where judges have a wide range of opinions, even to the extent of actions being valued in opposite directions. Using this model, we demonstrate how the judges’ preferences may themselves determine the winner of a fight, and also develop a “fair”-scoring model that could be used by promotions or athletic commissions for a number of purposes. We apply the concept of variable significance to determine whether a judge’s verdict was mathematically controversial or within reason. Further, we estimate a similar model using scores submitted by fans. This model suggests that fans are more likely to give rarer scores, such as draws. Interestingly, it appears fans are less influenced by bias variables than the judges

Factors in Patient Responsiveness to Directional Preference-Matched Treatment of Neck Pain With or Without Upper Extremity Radiation

Purpose: Patient-related predictive factors in responsiveness to directional preference therapy for neck pain with or without upper extremity radiation (NP/R) have not been reported. A directional preference is any neck movement that, when performed repeatedly to end range, results in centralization and/or alleviation of NP/R. It was hypothesized that patient compliance with a prescribed, directional preference-matched home exercise program would improve positive responsiveness to NP/R treatment. Methods: Patient-related factors thought to affect responsiveness to care were collected retrospectively from charts and de-identified for patients with NP/R who underwent chiropractic treatment at a multispecialty spine clinic from January 2014 through June 2015. Responsiveness was measured by calculating the percentage change in Neck Bournemouth Questionnaire (NBQ) scores over treatment time. Multiple linear regression was used to identify factors associated with positive responsiveness. Results: Mean percentage change in patient NBQ score from initial intake to discharge was 50% (standard deviation: 32%). Of 104 patients meeting study inclusion criteria, 86 (83%) reported experiencing improvement after the first treatment session. Bivariate analysis of patient characteristics by compliance with directional preference-matched exercise indicated that compliant patients (n = 95, 91%) demonstrated significantly greater responsiveness to care than did noncompliant patients, at 55% versus 25% change in NBQ score, respectively (P = 0.0041). Four factors were statistically significant predictors of patient responsiveness to directional preference therapy for NP/R: patient compliance with directional preference-matched exercise (P = 0.0023), patient age (P = 0.0029), condition chronicity (P < 0.0001), and whether the patient reported improvement of symptoms following initial treatment session (P = 0.0003). Conclusions: The results of this study suggest that patient compliance with directional preference exercise is associated with patient responsiveness to conservative treatment of NP/R, as are age, chronicity and report of immediate symptom improvement

Qualitative Methods of Road Traffic Crash Research in Low- and Middle-income Countries: A Review

Road traffic crashes are rapidly becoming one of the leading causes of injury and death globally. It is predicted that by 2030 crashes will become the fourth leading cause of disability-adjusted life years (DALYs) (Mathers & Loncar, [11]) and the seventh leading global cause of death (World Health Organization [WHO], [26]). The global death toll due to crashes has already escalated by 46% over the past two decades (The World Bank, [21]). Low- and middle-income countries (LMICs) are acutely affected by this \u27hidden epidemic\u27 (Balch, [ 1]). Ninety per cent of the world\u27s crash-related deaths occur in LMICs where only 54% of its motor vehicles are registered (WHO, [25]). Furthermore, the economic toll of crashes in LMICs is concerning because nearly one half of all health care expenditures in LMICs is used to treat injuries related to motor vehicle crashes (Zakeri & Nosratnejad, [28]). This epidemic deserves urgent attention (Lin, [10]). Research on the epidemiology of crash problems in LMICs is increasing but these research efforts predominantly report statistics. There is a paucity of qualitative research that could help to explain the statistics. Qualitative exploration has the potential to enhance crash research by describing and explicating the contexts and social processes surrounding crashes, such as the antecedents, the environments in which crashes occur and injuries are produced, and the behaviours of people which make crashes more likely (Roberts, [14]; Rothe, [16]). Qualitative research methods can spark and mobilize the ideas and efforts of affected community members, thereby optimizing crash prevention interventions. Additionally, incorporating local citizens\u27 perspectives on the nature, causes and potential solutions of traffic problems in their locale increases the likelihood that proposed solutions will be effective, wanted and beneficial (Roberts, Smith, & Bryce, [15]). This article will review the literature to assess the extent to which qualitative methods have been implemented to research road traffic crashes in LMICs and to inform future methodological decision-making