Online health promotion program and individualized health coaching for veteran wellbeing

The pandemic has highlighted the need for accessible and effective health promotion as Canadians are isolated from their communities during social distancing measures. A web-based health promotion program in which participants also received individualized email-based health coaching from medical students has been available during the pandemic to empower veterans and their family members to engage in healthy lifestyle change. Health coaches’ email interactions with participants used techniques of motivational interviewing, including an empathetic style, statements of affirmation, and reflections. Open-ended questions were useful in gaining insight into the participant’s current lifestyle, including habits, challenges, and coping strategies. As services have transitioned online and individuals have become more isolated, the connection formed between online health coaches and individuals participating in the health promotion program became crucial in countering the mental and physical health repercussions of the pandemic. In a preliminary analysis, we show that web-based health promotion with health coaching, for Canadian Veterans and their families, leads to significant weight loss, increased activity and improvement in wellbeing metrics such as sleep and stress. The medical students acting as health coaches were able to gain a deeper understanding of the challenges involved in behaviour change, something that is seldom covered in detail in the medical school curricula. Medical students were also able to practice their motivational counseling skills surrounding lifestyle changes. Given the lack of available evidence for web-based health promotion that targets veterans and their families, these preliminary results appear promising, with longer-term follow-up planned for the next two years

Bayesian modeling of continuous diagnostic test data: sample size and Polya trees

Parametric models such as the bi-normal have been widely used to analyse datafrom imperfect continuous diagnostic tests. Such models rely on assumptions thatmay often be unrealistic and/or unveri_able, and in such cases nonparametric modelspresent an attractive alternative. Further, even when normality holds, researcherstend to underestimate the sample size required to accurately estimate disease preva-lence from bi-normal models when densities from diseased and non-diseased subjectsoverlap. In this thesis we investigate both of these problems. First, we study theuse of nonparametric Polya tree models to analyze continuous diagnostic test data.Since we do not assume a gold standard test is available, our model includes a latentclass component, the latent data being the unknown true disease status for each sub-ject. Second, we develop methods for the sample size determination when designingstudies with continuous diagnostic tests. Finally, we show how Bayes factors can beused to compare the _t of Polya tree models to parametric bi-normal models. Bothsimulations and a real data illustration are included.Les modèles paramétriques tel que le modèle binormal ont été largement utilisés pour analyser les données provenant de tests de diagnostic continus et non parfaits. De tels modèles reposent sur des suppositions souvent non réalistes et/ou non verifiables, et dans de tels cas les modèles nonparamétriques représentent une alternative attrayante. De plus, même quand la supposition de normalité est rencontrée les chercheurs ont tendence à sous-estimer la taille d'échantillon requise pour estimer avec exactitude la prédominance d'une maladie à partir de ces modèles bi-normaux quand les densités associées aux sujets malades se chevauchent avec celles associées aux sujets non malades. D'abord, nous étudions l'utilisation de modèles nonparametriques d'arbres de Polya pour analyser les données provenant de tests de diagnostic continus. Puisque nous ne supposons pas l'existance d'un test étalon d'or, notre modèle contient une composante de classe latente, les données latentes étant le vrai état de maladie de chaque sujet. Ensuite nous développons des méthodes pourla determination de la taille d'échantillon quand on planifie des études avec des tests de diagnostic continus. Finalement, nous montrons comment les facteurs de Bayes peuvent être utilisés pour comparer la qualité d'ajustement de modèles d'arbres de Polya à celles de modèles paramétriques binormaux. Des simulations ansi que des données réelles sont incluses

Articles Years of life lost and healthy life-years lost from diabetes and cardiovascular disease in overweight and obese people: a modelling study

Summary Background Despite the increased risk of cardiovascular disease and type 2 diabetes associated with excess bodyweight, development of a clinically meaningful metric for health professionals remains a challenge. We estimated the years of life lost and the life-years lost from diabetes and cardiovascular disease associated with excess bodyweight