Background: Childhood and adolescent obesity constitute a significant public health concern. Family health care settings with multidisciplinary teams provide an opportunity for weight loss treatment. The objective of this study was to examine the effect of intensive treatment designed to reduce weight using a parent-child lifestyle modification intervention in a family health care clinic for obese and overweight children who had failed previous treatment attempts.
Methods: This was a practice-based 6-month intervention at Maccabi Health Care Services, an Israeli health maintenance organization, consisting of parental education, individual child consultation, and physical activity classes. We included in the intervention 100 obese or overweight children aged 5 to 14 years and their parents and 943 comparison children and their parents. Changes in body mass index z-scores, adjusted for socioeconomic status, were analyzed, with a follow-up at 14 months and a delayed follow-up at an average of 46.7 months.
Results: The mean z-score after the intervention was lower in the intervention group compared to the comparison group (1.74 and 1.95, respectively; P = .019). The intervention group sustained the reduction in z-score after an average of 46.7 months (P < .001). Of the overweight or obese children, 13% became normal weight after the intervention, compared with 4% of the comparison children.
Conclusion: This multidisciplinary team treatment of children and their parents in family health care clinics positively affected measures of childhood obesity. Additional randomized trials are required to verify these findings.
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http://dx.doi.org/10.3122/jabfm.2014.03.130243 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFAnnu Rev Clin Psychol
January 2025
3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Most people with mental health needs cannot access treatment; among those who do, many access services only once. Accordingly, single-session interventions (SSIs) may help bridge the treatment gap. We conducted the first umbrella review synthesizing research on SSIs for mental health problems and service engagement in youth and adults.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
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 PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
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