This study assessed whether sleeping with versus without a mobile phone (two-week intervals) affects sleep in medical students, using smartwatch-based monitoring. It reports no statistically significant differences in sleep quality or time spent in wakefulness, REM, light, or deep sleep between conditions. The authors report a statistically significant effect on minimum and average blood oxygen saturation during sleep and call for further research on nightly RF-EMF exposure.

This retrospective observational study analyzed Murmuras app data from 1074 participants in 2022 to examine demographic differences in smartphone use and nocturnal smartphone inactivity duration (a proxy for sleep-related behavior). Nighttime smartphone use increased, especially for social media and entertainment, and usage patterns varied by gender, age, education, and employment status. Most demographic groups showed no significant correlation between usage duration and nocturnal inactivity, although some subgroups showed correlations in either direction. The authors frame excessive nighttime smartphone use as potentially adverse for sleep and link this behavioral exposure to electromagnetic fields with sleep health risks.

This randomized, double-blind, sham-controlled study tested whether CACNA1C rs7304986 genotype modifies sleep EEG responses to 5G RF-EMF exposure. The authors report a genotype-by-exposure interaction, with 3.6 GHz exposure in T/C carriers associated with a faster NREM sleep spindle center frequency versus sham. The abstract also notes longer sleep latency in T/C compared with T/T carriers, and concludes that genetically susceptible groups may show differential physiological responses to 5G RF-EMF.

This review/overview argues that ultrafine particulate matter and industrial nanoparticles can reach the brain and accumulate in sleep and arousal regulatory regions, including orexinergic neuron hubs. It reports that ferromagnetic particles in these regions show motion responsive to low-intensity electromagnetic fields (30–50 μT) and describes links to sleep disturbances and neurodegenerative disease markers in young urban residents. The authors frame combined air pollution nanoparticle exposure and low-level EMF as a significant threat and call for monitoring and protective strategies.

This analytical study evaluated machine learning models (SVM and Random Forest) to predict health symptoms in adults living near mobile phone base stations. The SVM model reportedly achieved high predictive performance for headache, sleep disturbance, dizziness, vertigo, and fatigue, and outperformed Random Forest and prior models. The abstract concludes that proximity to base stations is connected with increased prevalence of several symptoms and emphasizes distance, age, and duration of residence as key predictors.