A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach.
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http://dx.doi.org/10.1089/tmj.2012.0129 | DOI Listing |
Transl Behav Med
January 2025
University of Pittsburgh School of Public Health, Department of Epidemiology, Pittsburgh, PA, USA.
Background: In previous efforts, health-related quality of life (HRQoL) improved for individuals at high risk of type 2 diabetes and cardiovascular disease after participation in community-based lifestyle interventions (LI) with a moderate-to-vigorous physical activity (MVPA) movement goal.
Purpose: It is unknown whether HRQoL improves with LI when the primary movement goal is to reduce sedentary behavior. HRQoL changes were examined among adults with overweight and prediabetes and/or metabolic syndrome randomized to a 12-month Diabetes Prevention Program-based Group Lifestyle Balance (DPP-GLB) community LI work with goals of weight-loss and either increasing MVPA (DPP-GLB) or reducing sedentary time (GLB-SED).
Front Public Health
January 2025
Faculty of Nursing, Department of Medical Nursing, Aydin Adnan Menderes University, Aydin, Türkiye.
Background: Non-communicable diseases (NCDs) are a major global concern. This study aimed to examine the prevalence and co-occurrence of lifestyle risk factors among university students.
Methods: This analytical, cross-sectional study was conducted between January and April 2022.
Diabetes Metab Syndr Obes
January 2025
School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People's Republic of China.
Purpose: Readmission within a period time of discharge is common and costly. Diabetic patients are at risk of readmission because of comorbidities and complications. It is crucial to monitor patients with diabetes with risk factors for readmission and provide them with target suggestions.
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
Background: Few studies have explored the association between DNA methylation and physical activity. The aim of this study was to evaluate the association of objectively measured hours of sedentary behavior (SB) and moderate physical activity (MPA) with DNA methylation. We further aimed to explore the association between SB or MPA related CpG sites and cardiometabolic traits, gene expression, and genetic variation.
View Article and Find Full Text PDFFront Sports Act Living
January 2025
Department of Sport and Health, School of Health and Human Development, University of Evora, Évora, Portugal.
Background: This study aims to investigate the effects of a multimodal program using augmented reality on the functional fitness and physical activity of older adults living in the community.
Method: Seventy-eight older adults living in the community participated in this study. Participants were divided into three groups: a control group that maintained their usual activities, and two experimental groups, one with multimodal training (EG1) and the other with multimodal training combined with augmented reality (EG2).
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