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

A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews.

Systematic Reviews (SRs) are foundational in healthcare for synthesising evidence to inform clinical practices. Traditionally skewed towards English-language databases, SRs often exclude significant research in other languages, leading to potential biases. This study addresses this gap by focusing on Spanish, a language notably underrepresented in SRs. We present a foundational zero-shot dual information retrieval (IR) baseline system, integrating traditional retrieval methods with pre-trained language models and cross-attention re-rankers for enhanced accuracy in Spanish biomedical literature retrieval. Utilising the LILACS database, known for its comprehensive coverage of Latin American and Caribbean biomedical literature, we evaluate the approach with three real-life case studies in Spanish SRs. The findings demonstrate the system's efficacy and underscore the importance of query formulation. This study contributes to the field of IR by promoting language inclusivity and supports the development of more comprehensive and globally representative healthcare guidelines

Towards automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review.

Systematic reviews (SRs) constitute a critical foundation for evidence-based decision-making and policy formulation across various disciplines, particularly in healthcare and beyond. However, the inherently rigorous and structured nature of the SR process renders it laborious for human reviewers. Moreover, the exponential growth in daily published literature exacerbates the challenge, as SRs risk missing out on incorporating recent studies that could potentially influence research outcomes. This pressing need to streamline and enhance the efficiency of SRs has prompted significant interest in leveraging Artificial Intelligence (AI) techniques to automate various stages of the SR process. This review paper provides a comprehensive overview of the current AI methods employed for SR automation, a subject area that has not been exhaustively covered in previous literature. Through an extensive analysis of 52 related works and an original online survey, the primary AI techniques and their applications in automating key SR stages, such as search, screening, data extraction, and risk of bias assessment, are identified. The survey results offer practical insights into the current practices, experiences, opinions, and expectations of SR practitioners and researchers regarding future SR automation. Synthesis of the literature review and survey findings highlights gaps and challenges in the current landscape of SR automation using AI techniques. Based on these insights, potential future directions are discussed. This review aims to equip researchers and practitioners with a foundational understanding of the basic concepts, primary methodologies and recent advancements in AI-driven SR automation, while guiding computer scientists in exploring novel techniques to further invigorate and advance this field