Sleep Medicine

Exercise improves sleep quality most in poor sleepers, with benefits peaking near 25 weeks

This Bayesian meta-analysis pooled 200 adult RCTs (n=23,523) comparing exercise with non-exercise controls for subjective sleep quality. Exercise modestly improved subjective sleep quality and produced small but significant gains in objective sleep efficiency in 20 trials. Qigong, walking, and high-intensity interval training showed the largest benefits versus active controls, though overall certainty was very low. Baseline PSQI and population type strongly moderated response, with much greater clinically important improvement in those starting with poorer sleep. 1

Chronic insomnia shows reduced gBOLD–CSF coupling; low-frequency rTMS improves sleep and coupling

Middle-aged and older adults with chronic insomnia had significantly reduced global gBOLD–CSF coupling versus matched controls, especially in frontoparietal and default mode networks. Lower global and frontoparietal coupling correlated with worse sleep quality, implicating altered CSF dynamics and glymphatic-relevant processes. In the randomized, double-blind, sham-controlled trial, 26 patients received four weeks of active or sham low-frequency rTMS during standardized hypnotic tapering. Active rTMS produced greater sleep improvements at four weeks and fewer patients resumed hypnotics at 12 months than sham. 2

Diagnosed OSA and narcolepsy in England remain well below expected symptomatic prevalence

Using CPRD primary care data linked to HES, this study described diagnosed OSA and narcolepsy incidence and prevalence in England from 2000–2019. In 2019, age- and sex-standardized adult OSA prevalence was 1.40%, representing about 622,528 people, far below estimated symptomatic prevalence. Standardized narcolepsy prevalence was 0.020%, about 11,307 people, again well under expected population estimates. Rates for both disorders varied by age, sex, ethnicity, and UK nation; OSA also varied by urban–rural status, deprivation, and practice size. 3

Lower-back wearable plus machine learning enables sensitive home screening for isolated RBD

Seventy-three participants (15 iRBD, 58 controls) underwent lab vPSG, then six nights of lumbar inertial sensor monitoring at home. iRBD patients showed distinct nocturnal mobility patterns compared with controls during home recordings. Machine-learning models using mobility features classified iRBD with high sensitivity and moderate specificity, with performance improving up to about five nights. Principal component analysis demonstrated substantial differences between lab and home data, supporting the value of ecologically valid, multi-night assessment. 4