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We investigated the association between mobile phone use characteristics and Parkinson's disease (PD) risk and whether genetic predisposition modified these associations, utilising data from the UK Biobank. Participants using mobile phones for at least one hour weekly had a lower risk of PD compared to those using less than five minutes weekly (HR: 0.75, 95% CI: 0.65-0.87). Long-term users of mobile phones over eight years had an 18% reduced risk compared to participants with a length of mobile phone use ≤1 year (HR: 0.82, 95% CI: 0.68-1.00), and increased frequency of mobile phone use over two years was associated with a 12% lower risk than those who did not change (HR: 0.88, 95% CI: 0.79-0.98). No interaction between mobile phone use characteristics and PD-PRS was detected. Total brain volume, grey matter and white matter were positively associated with mobile phone use, while white matter hyperintensity was negatively associated.
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http://dx.doi.org/10.1093/ageing/afaf143 | DOI Listing |
JMIR Pediatr Parent
September 2025
Center for the Promotion of Interdisciplinary Research in Medicine and Life Science, Keio University School of Medicine, Mori JP Tower F7, 1-3-1, Azabudai, Minato-ku, Tokyo, 160-0041, Japan, 81 353633219.
Background: Children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) often face structural and psychological barriers in accessing medical care, including economic costs, long wait times, and stress of attending new medical environments. The COVID-19 pandemic accelerated the adoption of telehealth services to overcome these challenges. However, few studies have assessed the satisfaction levels of children and adolescents diagnosed with neurodevelopmental disorders and their caregivers when they use telepsychiatry, particularly in Japan.
View Article and Find Full Text PDFAnal Chim Acta
November 2025
Guangxi Key Laboratory of Natural Polymer Chemistry and Physics, Key Laboratory of Nanobiosensor Analysis, College of Chemistry and Materials, Nanning Normal University, Nanning, 530001, PR China. Electronic address:
Background: Hexavalent chromium ions (Cr(VI)), a notorious toxic heavy metal pollutant with proven carcinogenicity, endangers human health and the environment. Meanwhile, l-ascorbic acid (L-AA), a vital biological antioxidant, has abnormal levels closely tied to various diseases. Developing efficient synchronous detection methods for these two key analytes is of great value in clinical and environmental monitoring.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
The Hong Kong Jockey Club Centre for Suicide Research and Prevention, University of Hong Kong, 5 Sassoon Rd, Sandy Bay, Hong Kong, 999077, China (Hong Kong), 852 2831 5232.
Background: Online text-based counseling services are becoming increasingly popular. However, their text-based nature and anonymity pose challenges in tracking and understanding shifts in help-seekers' emotional experience within a session. These characteristics make it difficult for service providers to tailor interventions to individual needs, potentially diminishing service effectiveness and user satisfaction.
View Article and Find Full Text PDFJMIR Nurs
September 2025
Université de Montréal, Montreal, QC, Canada.
Background: The integration of artificial intelligence (AI) into health care is set to revolutionize the sector, offering opportunities to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. However, little is known about nurses' readiness to integrate AI into their professional practice.
Objective: This study aimed to identify the key factors influencing nurses' intention to integrate AI into their practice.
Sleep Breath
September 2025
Université Paris Cité, NeuroDiderot, Inserm U1141, Paris, F-75019, France.
Purpose: obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal, an application recording sound and movements thanks to a smartphone's microphone, accelerometer and gyroscope, to estimate patients' apnea-hypopnea index (AHI).
Methods: monocentric proof-of-concept study with a first manual scoring step, then automatic detection of respiratory events from recorded signals using a sequential deep-learning model (version 0.