Schritte und Schlafrhythmus: Warum Ihr Stoffwechsel beides zusammen spürt

<h4>Background</h4>Sleep and physical activity (PA) are important lifestyle-related behaviors that impact cardiometabolic health. This study investigated the joint associations of daily step count and sleep patterns (regularity and duration) with cardiometabolic biomarkers in adults.<h4>Methods</h4>We conducted a cross-sectional study using pooled data from the Prospective PA, Sitting, and Sleep Consortium, comprising 6 cohorts across Europe and Australia with thigh-worn accelerometry data collected between 2011 and 2021. The sleep regularity index, a metric that quantifies day-to-day sleep consistency, sleep duration (h/d), and steps (per day), was derived from the accelerometer data and categorized based on tertiles and sleep duration guidelines. We used multivariate generalized linear models to examine joint associations of sleep patterns and total daily step count with individual cardiometabolic biomarkers, including body mass index, waist circumference, total cholesterol, HDL (high-density lipoprotein) cholesterol, triglycerides, HbA1c (glycated hemoglobin), and a composite cardiometabolic health score (mean of the 6 standardized biomarker <i>Z</i> scores).<h4>Results</h4>The sample included 11 903 adults with a mean±SD age of 54.7±9.5 years, 54.9% female, a sleep regularity index of 78.7±10.4, and 10 206.4±3442.2 daily steps. Lower PA (<8475 steps/d) combined with either lower sleep regularity (sleep regularity index <75.9) or short sleep duration (<7 h/d) was associate

<h4>Abstract</h4>This study examines the combined effects of sleep duration and exercise habits on all-cause mortality in older Chinese adults using data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS).<h4>Methods</h4>Data were collected from 7231 residents aged 60 and above from the CLHLS. Participants were categorized based on their sleep duration (short sleep: <6 hours, normal sleep: 6-8 hours, and long sleep: >8 hours) and exercise habits (physically active, physically inactive, inactive-to-active, and active-to-inactive). The analysis was conducted over three follow-up periods (2011, 2014, and 2018). Cox proportional hazards regression models were used to assess the associations between sleep duration, exercise habits, and all-cause mortality.<h4>Results</h4>The results showed that compared to short sleepers, individuals with normal sleep (6-8 hours) had a slightly reduced risk of death, although this reduction was not statistically significant (HR = 0.97, 95% CI 0.87-1.08). In contrast, long sleepers (>8 hours) had a significantly higher risk of mortality (HR = 1.28, 95% CI 1.16-1.43). Regarding exercise habits, regular exercise or transitioning from inactivity to exercise significantly reduced mortality risk compared to those who did not exercise (HR = 0.74, 95% CI 0.66-0.83 and HR = 0.88, 95% CI 0.80-0.97, respectively). Notably, exercise habits did not significantly impact mortality among short sleepers, regardless of gender. However, both men and wome

<h4>Objectives</h4>This study aims to examine the associations of daily step count with all-cause mortality and incident cardiovascular disease (CVD) by sedentary time levels and to determine if the minimal and optimal number of daily steps is modified by high sedentary time.<h4>Methods</h4>Using data from the UK Biobank, this was a prospective dose-response analysis of total daily steps across low (<10.5 hours/day) and high (≥10.5 hours/day) sedentary time (as defined by the inflection point of the adjusted absolute risk of sedentary time with the two outcomes). Mortality and incident CVD was ascertained through 31 October 2021.<h4>Results</h4>Among 72 174 participants (age=61.1±7.8 years), 1633 deaths and 6190 CVD events occurred over 6.9 (±0.8) years of follow-up. Compared with the referent 2200 steps/day (5th percentile), the optimal dose (nadir of the curve) for all-cause mortality ranged between 9000 and 10 500 steps/day for high (HR (95% CI)=0.61 (0.51 to 0.73)) and low (0.69 (0.52 to 0.92)) sedentary time. For incident CVD, there was a subtle gradient of association by sedentary time level with the lowest risk observed at approximately 9700 steps/day for high (0.79 (0.72 to 0.86)) and low (0.71 (0.61 to 0.83)) sedentary time. The minimal dose (steps/day associated with 50% of the optimal dose) of daily steps was between 4000 and 4500 steps/day across sedentary time groups for all-cause mortality and incident CVD.<h4>Conclusions</h4>Any amount of daily steps above the

Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise dev