Background: The growing improvement in urbanisation, modes of transportation and the expansion of sedentary behaviour, both at work and home, have resulted in declining rates of physical activity (PA) worldwide. Nearly one-third of the global population aged 15 and over are insufficiently active. The negative effect of physical inactivity has been evidenced and ranked fourth as the lethal cause of death globally. Therefore, the aim of this research was to explore the factors influencing PA participation among youths from different geographical locations in the Kingdom of Saudi Arabia.
Methods: Sixteen focus groups (males = 8 and females = 8) were conducted with a total of 120 secondary school students (male = 63 and female = 57) aged between 15 and 19 years. The focus groups were analysed to identify key themes through the process of thematic analysis.
Results: Results from the focus groups indicated that a lack of time, safety, parental support, policies, access to sport and PA facilities, and transportation, as well as climate were reported as barriers to PA participation.
Discussion And Conclusion: The current research contributes to the scarce literature focused on the multidimensional effects on Saudi youth PA behaviour from different geographical locations. This qualitative approach has provided the participants a voice, and the overall study offers valuable evidence as well as invaluable information to policymakers, public health departments, and local authorities for PA intervention based on the environment and the community.
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http://dx.doi.org/10.3390/ijerph20105785 | DOI Listing |
Gerontologist
January 2025
Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
Background And Objectives: The Housing and Urban Development-Veterans Affairs Supported Housing (HUD-VASH) program provides rental subsidies, case management, and supportive services to Veterans who are currently or formerly homeless, 77% of whom are ages ≥50. Few interventions have been developed to address the needs of older Veterans in HUD-VASH.
Research Design And Methods: We conducted a 2-stage study to inform the development of an intervention to promote aging in place in HUD-VASH.
Int J Audiol
January 2025
National Centre for Audiology, Western University, London, Ontario, Canada.
Objective: The purpose of the study was to qualitatively describe the experiences of hearing aid and physical fit accessories use during physical activity and exercise participation in a sample of older adults with hearing loss.
Design: A prospective qualitative research design was employed with the use of focus groups with older adult participants who were fitted with hearing aids and physical fit accessories.
Study Sample: Twelve older adults with hearing loss (six experienced and six new hearing aid users, age range 64 - 88 years) were recruited in this study.
Gerontologist
January 2025
Population Health Initiative, University of Washington, Seattle, WA, USA.
Background And Objectives: The study aimed to identify key drivers of vaccine hesitancy among healthcare workers (HCWs) employed at Long-term care facilities (LTCF) within selected states. It also sought to determine which interventions, policies, and programs effectively reduced HCW vaccine hesitancy for COVID-19 and influenza.
Research Design And Methods: The study employed a mixed methods approach, combining secondary analysis of the Behavioral Risk Factor Surveillance System (BRFSS) data, survey research, and focus groups.
Community Dent Oral Epidemiol
January 2025
School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
Objectives: Supervised toothbrushing programmes (STPs), whereby children brush their teeth at nursery or school with a fluoride toothpaste under staff supervision, are a clinically and cost-effective intervention to reduce dental caries. However, uptake is varied, and the reasons unknown. The aim was to use an implementation science approach to explore the perspectives of key stakeholders on the barriers and facilitators at each level of implementation of STPs.
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December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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