Deep Dive: Bewegungsintensität, Sitzzeit und Kadenz

<h4>Importance</h4>Recent evidence syntheses have supported the protective role of daily steps in decreasing the risk of cardiovascular disease and all-cause mortality. However, step count-based recommendations should cover additional health outcomes.<h4>Objective</h4>To synthesize the associations between objectively measured daily step counts and depression in the general adult population.<h4>Data sources</h4>In this systematic review and meta-analysis, a systematic search of the PubMed, PsycINFO, Scopus, SPORTDiscus, and Web of Science databases was conducted from inception until May 18, 2024, to identify observational studies using search terms related to physical activity, measures of daily steps, and depression, among others. Supplementary search methods were also applied.<h4>Study selection</h4>All identified studies were uploaded to an online review system and were considered without restrictions on publication date or language. Included studies had objectively measured daily step counts and depression data.<h4>Data extraction and synthesis</h4>This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines. Two independent reviewers extracted the published data.<h4>Main outcomes and measures</h4>Pooled effect sizes (correlation coefficient, standardized mean difference [SMD], and risk ratio [RR]) with 95% CIs were estimated using th

<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

<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>Background</h4>Recent studies demonstrated the validity, reliability, and accuracy of consumer smartwatches for measuring daily steps in people with Parkinson disease (PD). However, no study to date has estimated the minimal clinically important difference (MCID) for average daily steps (avDS), measured through a consumer smartwatch in people with PD.<h4>Objective</h4>This study aimed to calculate the MCID of avDS, measured through a commercial smartwatch (Garmin Vivosmart 4) in people with PD.<h4>Methods</h4>People with PD with a disease stage <4, without cognitive impairment, and who were able to walk unaided, wore a Garmin Vivosmart 4 smartwatch for 5 consecutive days on the wrist least affected by the disease, allowing the computation of avDS. To define the 3 levels of MCID for avDS, we used an anchor-based method linked to: (1) scales capturing subtle changes in global mobility and motor functions, (2) clinical and health-related measures, and (3) disease-related patient-reported outcomes. Linear regressions, Student t test, and ANOVA were used to estimate the minimal change in avDS based on anchors relevant change. For each level, the overall MCID was calculated as the average of the variables included, and the range was reported.<h4>Results</h4>A total of 100 people with PD were enrolled. Participants took on average 5949 (SD 3034) daily steps, ranging from 357 to 12,620. The MCID of avDS anchored to standardized measures of motor symptoms and mobility was 581 step