Introduction: Low levels of health-enhancing physical activity require novel approaches that have the potential to reach broad populations. Web-based interventions are a popular approach for behaviour change given their wide reach and accessibility. However, challenges with participant engagement and retention reduce the long-term maintenance of behaviour change. Web 2.0 features present a new and innovative online environment supporting greater interactivity, with the potential to increase engagement and retention. In order to understand the applicability of these innovative interventions for the broader population, 'real-world' interventions implemented under 'everyday conditions' are required. The aim of this study is to investigate the difference in physical activity behaviour between individuals using a traditional Web 1.0 website with those using a novel Web 2.0 website.
Methods And Analysis: In this study we will aim to recruit 2894 participants. Participants will be recruited from individuals who register with a pre-existing health promotion website that currently provides Web 1.0 features (http://www.10000steps.org.au). Eligible participants who provide informed consent will be randomly assigned to one of the two trial conditions: the pre-existing 10 000 Steps website (with Web 1.0 features) or the newly developed WALK 2.0 website (with Web 2.0 features). Primary and secondary outcome measures will be assessed by self-report at baseline, 3 months and 12 months, and include: physical activity behaviour, height and weight, Internet self-efficacy, website usability, website usage and quality of life.
Ethics And Dissemination: This study has received ethics approval from the University of Western Sydney Human Research Ethics Committee (Reference Number H8767) and has been funded by the National Health and Medical Research Council (Reference Number 589903). Study findings will be disseminated widely through peer-reviewed publications, academic conferences and local community-based presentations.
Trial Registration Number: Australian New Zealand Clinical Trials Registry Number: ACTRN12611000253909, WHO Universal Trial Number: U111-1119-1755.
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http://dx.doi.org/10.1136/bmjopen-2014-006374 | DOI Listing |
BMC Med Res Methodol
January 2025
Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
View Article and Find Full Text PDFPlast Reconstr Surg
February 2025
From the Department of Plastic Surgery, Shanghai East Hospital, Tongji University School of Medicine.
Background: Cell-assisted lipotransfer (CAL) and platelet-rich plasma (PRP)-assisted lipotransfer have been used to overcome the low survival rate of conventional lipotransfer. However, there is still insufficient evidence to determine which technique is the best strategy for autologous fat grafting in breast cosmetic and reconstructive surgery. The present study aimed to compare the efficacy of traditional fat transplantation, CAL, and PRP-assisted lipotransfer.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.
Background: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFAnn Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
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