Studiendatenbank

<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>Digital interventions integrating gamification features hold promise to promote daily steps. However, results regarding the effectiveness of this type of intervention are heterogeneous and not yet confirmed in real-life contexts.<h4>Objective</h4>This study aims to examine the effectiveness of a gamified intervention and its potential moderators in a large sample using real-world data. Specifically, we tested (1) whether a gamified intervention enhanced daily steps during the intervention and follow-up periods compared to baseline, (2) whether this enhancement was higher in participants in the intervention than in nonparticipants, and (3) what participant characteristics or intervention parameters moderated the effect of the program.<h4>Methods</h4>Data from 4819 individuals who registered for a mobile health Kiplin program between 2019 and 2022 were retrospectively analyzed. In this intervention, participants could take part in one or several games in which their daily step count was tracked, allowing individuals to play with their overall activity. Nonparticipants were people who registered for the program but did not take part in the intervention and were considered as a control group. Daily step counts were measured via accelerometers embedded in either commercial wearables or smartphones of the participants. Exposure to the intervention, the intervention content, and participants' characteristics were included in multilevel models to test the study obj

<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

<h4>Background</h4>Average daily steps (avDS) could be a valuable indicator of real-world ambulation in people with Parkinson disease (PD), and previous studies have reported the validity and reliability of this measure. Nonetheless, no study has considered disease phenotype, stage, and severity when assessing the reliability of consumer wrist-worn devices to estimate daily step count in unsupervised, free-living conditions in PD.<h4>Objective</h4>This study aims to assess and compare the reliability of a consumer wrist-worn smartwatch (Garmin Vivosmart 4) in counting avDS in people with PD in unsupervised, free-living conditions among disease phenotypes, stages, and severity groups.<h4>Methods</h4>A total of 104 people with PD were monitored through Garmin Vivosmart 4 for 5 consecutive days. Total daily steps were recorded and avDS were calculated. Participants were dichotomized into tremor dominant (TD; n=39) or postural instability and gait disorder (PIGD; n=65), presence (n=57) or absence (n=47) of tremor, and mild (n=65) or moderate (n=39) disease severity. Based on the modified Hoehn and Yahr scale (mHY), participants were further dichotomized into earlier (mHY 1-2; n=68) or intermediate (mHY 2.5-3; n=36) disease stages. Intraclass correlation coefficient (ICC; 3,k), standard error of measurement (SEM), and minimal detectable change (MDC) were used to evaluate the reliability of avDS for each subgroup. The threshold for acceptability was set at an ICC ≥0.8 with a lower

<h4>Background</h4>Mobile technology offers great potential for physical activity promotion, especially by facilitating online communication, however, the impact of group communication norms on intervention effectiveness remains unclear. This study aimed to evaluate the effect on daily steps of a team-based social norms-related intervention using a mobile application.<h4>Methods</h4>The 13-week quasi-experimental study was conducted in Shanghai, China, from September to November 2019, involving 2,985 employees from 32 worksites. For the intervention group (n = 2,049), participants set a goal of 10,000 steps per day. The teams and individual members would receive points for meeting the daily goal, contributing to team-based rankings and financial rewards for the teams and their members. In addition, the intervention teams created dedicated WeChat groups to facilitate communication, which were also used to collect group chat messages. The communication type in these groups was classified into four types: (1) nudging - encouraging team members to be more active, (2) sharing - exchanging the completion of daily step goals, (3) feedback - providing responses or suggestions to team members, and (4) other -diverse topics that could not be classified otherwise. The control group only tracked their steps online.<h4>Results</h4>The weekly average steps of the intervention group increased by 2,523 steps, while the control group increased by 470 steps. In the first 3 weeks of follow-up,

Previous studies link lower physical activity with incident Parkinson's disease (PD) but rely on self-reported data and fail to address reverse causation. This study used accelerometer-derived daily step count, an objective measure of physical activity, to examine its association with incident PD in the UK Biobank, within successive periods of follow-up. PD cases were identified through hospital inpatient and death data, and associated with machine learning-derived step counts using Cox regression models, adjusted for age, sex, demographic and lifestyle factors. For 94,696 valid participants and a median follow-up of 7.9 years, 407 incident PD cases occurred. Every 1000 steps higher were associated with 8% lower risk of PD (hazard ratio 0.92, 95% confidence interval 0.89-0.94). However, this association was no longer statistically significant when excluding follow-up periods closer to the time of accelerometer wear, suggesting that low activity may be an early marker, but not a risk factor for PD.

This study examines the stressor-response process in physical activity among 226 participants across four countries. We analyzed their step count collected via activity monitors before and after a significant stressor: the COVID-19 lockdown. Results showed that a 'local dynamic complexity' metric significantly predicts the rate of recovery to pre-COVID levels of physical activity. These findings provide new opportunities for just-in-time interventions to support physical activity recovery after disruptive stressors.

BACKGROUND: Adolescence is a critical period for developing behaviors that affect lifelong cardiovascular health. While physical activity improves cardiovascular outcomes, excessive recreational screen time is linked to negative cardiovascular indicators. This study examined associations between screen time and physical activity with cardiovascular risk factors in a large, diverse sample of early adolescents. METHODS: This study included 4,443 adolescents from the Adolescent Brain Cognitive Development (ABCD) Study collected at Year 2 (2018–2020) and Year 4 (2020–2022). Screen time was self-reported and categorized as low (0–4), medium (> 4–8), or high (> 8) hours/day. Physical activity was measured via Fitbit step count over 3 weeks and categorized as low (1,000–6,000), medium (> 6,000–12,000), and high (> 12,000) steps/day. Outcomes included blood pressure percentile and hypertensive-range blood pressure, which were available for the full sample, while total cholesterol, high-density lipoprotein (HDL) cholesterol, non-HDL cholesterol, hemoglobin A1c, and testing consistent with diabetes were available in a subset (one-third) at Year 4. Adjusted linear and Poisson regression models examined joint associations of screen time and step count with cardiovascular health outcomes. RESULTS: Adolescents averaged 6.1 (± 5.1) hours/day of screen use and 9,280 (± 3,279) steps/day. Higher screen time and lower step count showed dose-response associations with higher diastolic blood pres

<h4>Purpose</h4>Low physical activity has been shown to be associated with a higher risk of nonalcoholic fatty liver disease (NAFLD). However, the strength and shape of this association are currently uncertain due to a reliance on self-reported physical activity measures. This report aims to investigate the relationship of median daily step count with NAFLD using accelerometer-derived step count from a large prospective cohort study.<h4>Methods</h4>The wrist-worn accelerometer substudy of the UK Biobank ( N = ~100,000) was used to characterize median daily step count over a 7-d period. NAFLD cases were ascertained via record linkage with hospital inpatient data and death registers or by using a measure of liver fat from imaging. Cox proportional hazards models were employed to assess the association between step count and NAFLD, adjusting for age, sociodemographic, and lifestyle factors. Mediation analyses were conducted.<h4>Results</h4>Among 91,031 participants (709,440 person-years of follow-up), there were 762 incident NAFLD cases. Higher step count was log-linearly and inversely associated with risk of NAFLD. A 1000-step increase (representing 10 min of walking) was associated with a 12% (95% confidence interval, 10%-14%) lower hazard of NAFLD. When using imaging to identify NAFLD, a 1000-step increase was associated with a 6% (95% confidence interval, 6%-7%) lower risk. There was evidence for mediation by adiposity, accounting for 39% of the observed association.<h4>Conc

<h4>Background</h4>Physical activity has been reported to be associated with surgical outcomes, but most previous studies have focused solely on postoperative step counts. To better understand the relationship between step count at different phases and surgical outcomes, we prospectively recorded patients' step counts before and after lung surgery.<h4>Methods</h4>Step count data were collected from 244 patients who underwent minimally invasive surgery for lung cancer using Mi Band 5 to track preoperative and 3-day postoperative activity. Patients' quality of life was assessed using the 12-Item Short Form Health Survey (SF-12) preoperatively and at 1 and 3 months postoperatively. Correlation and regression analyses were conducted to evaluate the impact of perioperative step count on hospital length of stay and quality of life.<h4>Results</h4>Preoperative (<i>r</i> = -0.146, <i>p</i> = 0.023) and postoperative day 1 (<i>r</i> = -0.172, <i>p</i> = 0.018) step count were significantly correlated with the length of hospital stay. Postoperative day 1 step count was positively correlated with changes in SF-12 Physical Component Score (PCS) at 1 month (<i>r</i> = 0.186, <i>p</i> = 0.013). Pain significantly affected PCS changes at both 1 (<i>β</i> = -3.33, <i>p</i> < 0.001) and 3 months (<i>β</i> = -3.06, <i>p</i> < 0.001).<h4>Conclusion</h4>Higher preoperative step counts are associated with a shorter hospital stay, while early postoperative physical activity is linked to both reduc

<b>Background</b>: Step count has emerged as an objective indicator of physical activity, yet its association with functional recovery following hip fracture remains unclear. <b>Objective</b>: This study aimed to evaluate daily step counts after hospital discharge in older adults with hip fracture and to determine thresholds associated with functional improvement. <b>Methods</b>: A prospective, observational study was conducted in patients aged over 75 years admitted with hip fracture. Daily steps were recorded using validated activity trackers. Functional status was assessed at one, three, and six months after discharge through telephone interviews. Functional improvement was defined as an increase of at least 5 points on the Barthel Index. Step count thresholds were estimated using Liu's method based on receiver operating characteristic (ROC) analysis. <b>Results</b>: Ninety-four patients were included with a mean age of 83.2 ± 6 years, with 80.8% being female. Of the patients included in the study who recorded their daily steps after hip surgery, 59 patients (72%) improved during the first month after discharge with a median of 220 daily steps (IQR: 103.5-494.5; cut-off point at 150 steps). At the third month after hip fracture, 77 patients (86.5%) showed functional improvement with a median of 778 steps (IQR: 263-1697; cut-off point at 425 steps). At month six, 65 patients (80.2%) showed functional improvement with a median of 1757 steps (IQR: 696-3388; cut-off point at 2

The need to collect objective outcome parameters digitally is increasing in both clinical practice and research. Step count is a frequently utilized digital mobility outcome (DMO) in orthopedic traumatology; however, its usefulness to monitor the patient recovery process remains unclear. The aim of this scoping review is to investigate the application and utility of daily patient step count as a DMO in musculoskeletal injuries. PubMed and consensus.app were queried. Eligibility criteria included the following: articles published within 20 years including patients with orthopedic trauma conditions and utilizing daily step count as an outcome. The type of study, case numbers, conditions investigated, use/usefulness of step count, duration of assessment, sensor use and location, and data harvesting specifics were assessed. Totally, 40 articles were analyzed, revealing an increasing trend in annual publications. The majority of studies were observational (93%), with a mean of 103 participants (range: 9-666). Proximal femur fractures (n = 7), anterior curciate ligament (ACL) injuries (n = 6), and joint replacement (n = 5) were the most frequently investigated conditions. Overall, 30% of studies used step count to demonstrate an association with patient-reported outcome measures, while 27% employed it to identify differences between study groups. Research-grade accelerometers/inertial measurement units (73%) were the most common sensors, with continuous measurement durations from 4

<h4>Background/objectives</h4>This study explored the association between daily step count (DSC) and health outcomes in older adults in Spain. A total of 668 individuals aged 60-100 years (mean = 71.33 ± 8.11 years) participated.<h4>Methods</h4>Participants wore a Xiaomi Mi Band 4 accelerometer continuously for seven days. Physical and cognitive tests were conducted, along with questionnaires on depression, quality of life, and physical activity.<h4>Results</h4>On average, men walked 8919.08 ± 4455.65 steps/day, significantly more than women (7855.46 ± 7855.46 steps/day, <i>p</i> = 0.002). A moderate negative correlation was found between age and DSC (r = -0.460, <i>p</i> < 0.001). The coefficient of variation in DSC increased across age groups, indicating growing heterogeneity with advancing age. Individuals in the high International Physical Activity Questionnaire (IPAQ) category walked 1517 more steps/day than those in the low activity group (<i>p</i> < 0.001), confirming IPAQ level as a strong determinant of physical activity. Participation in organized physical activity was associated with an additional 909 steps/day (<i>p</i> = 0.004). Meeting age-specific step recommendations is associated with better anthropometric, psychosocial, and cardiometabolic markers, but many of these differences disappear after adjusting for age and sex.<h4>Conclusions</h4>DSC in older adults is strongly influenced by age, sex, and physical activity level. DSC may not adequately assess health