Health promotion in youth as a global public health challenge: effective strategies to encourage healthy lifestyles

La combinació de més d'un strategia metodològica (com el màrqueting social, la participació de la joventut, l'educació dirigida per iguals i l'ús dels mitjans de comunicació social) i strategias de cambio de antorn (intervenció basada en l'escola, basada en la intervenció restaurant, basat en la família de la intervenció) pot augmentar l'eficàcia de involucrar els joves en les intervencions de salut destinades a fomentar hàbits i estils de vida saludables. Aquesta tesi té com a objectiu comprendre els factors que intervenen en l'epidèmia de l'obesitat juvenil a tot el món i com influeixen en l'obesitat. En resposta a aquest desafiament global, aquest treball proporciona estratègies basades en proves científiques innovadores, eficaces i de qualitat per millorar els estils de vida saludables entre els joves. Aquestes estratègies podrien donar lloc a un enfocament d'investigació més fort que podrien beneficiar tant a la comunitat científica i el coneixement general de les parts interessades i els responsables polítics, fomentant així un enfocament multidisciplinari participatiu i inclusiu per obtenir resultats duradors i eficaçosLa combinación de más de una estrategía metodológica (como el marketing social, la participación de la juventud, la educación dirigida por pares y el uso de los medios de comunicación social) y/o de una estrategia de cambio de entorno (intervención basada en la escuela, basada en la intervención restaurante, basado en la familia de la intervención) puede aumentar la eficacia de involucrar a los jóvenes en las intervenciones de salud destinadas a fomentar hábitos y estilos de vida saludables. Esta tesis tiene como objetivo comprender los factores que intervienen en la epidemia de la obesidad juvenil en todo el mundo. En respuesta a este desafío global, este trabajo proporciona estrategias basadas en pruebas científicas innovadoras, eficaces y de calidad para mejorar los estilos de vida saludables entre los jóvenes. Estas estrategias podrían dar lugar a un enfoque de investigaciónque podrían beneficiar tanto a la comunidad científica y el conocimiento general de las partes interesadas en prevenir este problema así como a responsables políticos, fomentando así un enfoque multidisciplinario participativo e inclusivo para obtener resultados duraderos y eficaces.The combination of more than one methodological (such as social marketing, youth involvement, peer-led education and social media usage) and environmental (school-based intervention, restaurant-based intervention, family-based-intervention) strategy may increase the effectiveness of engaging young people in health interventions aimed at encouraging healthy habits and lifestyles. This thesis aims to understand the factors involved in the worldwide youth obesity epidemic and how they influence obesity. In response to this global challenge, this work provides innovative, effective and quality scientific evidence-based strategies for improving healthy lifestyles among young people. These strategies could lead to a stronger research approach that could benefit both the scientific community and the general knowledge of relevant stakeholders and policy makers, thus fostering a participatory and inclusive multidisciplinary approach for long-lasting and effective results

The effect of grape interventions on cognitive and mental performance in healthy participants and those with mild cognitive impairment : a systematic review of randomized controlled trials

Funding: We are grateful to the Scottish Government Rural and Environment Science and Analytical Services (RESAS) and the University of Aberdeen for funding.Peer reviewedPublisher PD

Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality, higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals

Open Access via the CUP Agreement Acknowledgments. Ruth L. Bates, Leone C.A. Craig, Neil Chalmers, Graham Horgan, Bram Boskamp were involved in data curation of the expanded NDNS Nutrientbank version used in this study.Peer reviewedPublisher PD

Effect of brown seaweed on plasma glucose in healthy, at-risk, and type 2 diabetic individuals : systematic review and meta-analysis

Acknowledgments Author contributions. All authors (K.V., V.R., D.C., and M.A.-M.) formulated and designed the analysis and contributed to data analysis. K.V. and M.A.-M. searched for and extracted data and evaluated the quality of the evidence. All authors contributed to and revised the submitted version of the paper. Funding. We are grateful to the Scottish Government’s Rural and Environment Science and Analytical Services (RESAS) for supporting this work and that of the University of Aberdeen.Peer reviewedPublisher PD

Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research.

When trying to synthesize information from multiple sources and perform a statistical review to compare them, particularly in the medical research field, several statistical tools are available, most common are the systematic review and the meta-analysis. These techniques allow the comparison of the effectiveness or success among a group of studies. However, a problem of these tools is that if the information to be compared is incomplete or mismatched between two or more studies, the comparison becomes an arduous task. On a parallel line, machine learning methodologies have been proven to be a reliable resource, such software is developed to classify several variables and learn from previous experiences to improve the classification. In this paper, we use unsupervised machine learning methodologies to describe a simple yet effective algorithm that, given a dataset with missing data, completes such data, which leads to a more complete systematic review and meta-analysis, capable of presenting a final effectiveness or success rating between studies. Our method is first validated in a movie ranking database scenario, and then used in a real life systematic review and meta-analysis of obesity prevention scientific papers, where 66.6% of the outcomes are missing

Child food insecurity in the UK: a rapid review

The National Institute for Health Research Public Health Research programme. The Health Services Research Unit is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates.Peer reviewedPublisher PD

Does weight management research for adults with severe obesity represent them? Analysis of systematic review data

Acknowledgments We thank the members of the REBALANCE Project and Advisory Groups for their contributions to the REBALANCE Project. We thank Shaun Treweek and Heidi Gardner, Health Services Research Unit, University of Aberdeen, for helpful discussions on trial generalisability and inclusion of underserved groups. Funding National Institute for Health Research Health Technology Assessment Programme (project number: 15/09/04).Peer reviewedPublisher PD

A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews.

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior training. This is possible by teaching the system how to codify a question and find its answer in the text. In many domains, especially health sciences, systematic reviews are evidence-based syntheses of information related to a specific topic. Producing them is demanding and time-consuming in terms of collecting, filtering, evaluating and synthesising large volumes of literature, which require significant effort performed by experts. One of its most demanding steps is abstract screening, which requires scientists to sift through various abstracts of relevant papers and include or exclude papers based on pre-established criteria. This process is time-consuming and subjective and requires a consensus between scientists, which may not always be possible. With the recent advances in machine learning and deep learning research, especially in natural language processing, it becomes possible to automate or semi-automate this task. This paper proposes a novel application of traditional machine learning and zero-shot classification methods for automated abstract screening for systematic reviews. Extensive experiments were carried out using seven public datasets. Competitive results were obtained in terms of accuracy, precision and recall across all datasets, which indicate that the burden and the human mistake in the abstract screening process might be reduced

Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions : a systematic review and meta-analysis

Peer reviewedPublisher PD

Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation.

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screening stage in the SR process to be the most burdensome for reviewers, thus automating this stage with artificial intelligence (AI). However, majority of these studies focus on using traditional machine learning classifiers for the abstract classification. Thus, there remain a gap to explore the potential of deep learning techniques for this task. This study seeks to bridge the gap by exploring how LSTM and Bi-LSTM models together with GloVe for vectorisation can accelerate this stage. As a further aim to increase precision while sustaining a recall >= 95% due to precision-recall trade-off, attention mechanics is added to these classifiers. The final experimental results obtained showed that Bi-LSTM with attention has the capacity to expedite citation screening