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Dyslipidemia is a major contributor to the development of atherosclerotic cardiovascular disease. Despite high level of physical activity, athletes are not immune from dyslipidemia, but longitudinal data on the variation of lipids are currently lacking. We sought to assess lipid profile changes over time in Olympic athletes practicing different sports disciplines (power, skills, endurance, and mixed). We enrolled 957 consecutive athletes evaluated from London 2012 to Beijing 2022 Olympic Games. Dyslipidemia was defined as low-density lipoprotein (LDL) ≥115 mg/dl, high-density lipoprotein (HDL) <40 mg/dl for males, or HDL <50 mg/dl for females. Hypertriglyceridemia was defined as triglycerides >150 mg/dl. At the follow-up, a variation of ±40 mg/dl for LDL, ±6 mg/dl for HDL, and ±50 mg/dl for triglycerides was considered relevant. Athletes with follow-up <10 months or taking lower lipid agents were excluded. Follow-up was completed in 717 athletes (74.9%), with a mean duration of 55.6 months. Mean age was 27.2 ± 4.8 years old, 54.6% were male (n = 392). Overall, 19.8% (n = 142) athletes were dyslipidemic at both blood tests, being older, practicing nonendurance sports, and predominantly male. In 69.3% (n = 129) of those with elevated LDL at t ₀, altered values were confirmed at follow-up, while the same occurred in 36.5% (n = 15) with hypo-HDL and 5.3% (n = 1) in those with elevated triglycerides. Weight and fat mass percentage modifications did not affect lipid profile variation. LDL hypercholesterolemia tends to persist over time especially among male, older, and nonendurance athletes. LDL hypercholesterolemia detection in athletes should prompt early preventive intervention to reduce the risk of future development of atherosclerotic disease.

Background: Active transportation (AT), described as self-powered modes of travel (eg, walking and cycling), is an important source of health-promoting physical activity. While AT behaviors have been measured on national health surveys in Canada for over 2 decades, historic prevalence has not been previously reported. We aimed to document the measures of AT on Canada’s various national health surveys, examine AT over time, and interpret them within the context of evolving methods of assessment. Methods: We compiled and summarized the questions used to measure AT among Canadians on 4 national health surveys: National Population Health Survey (1994–1998), Canadian Community Health Survey (2000–2020), Canadian Health Measures Survey (2007–2019), and the Health Behaviour in School-aged Children Study (2010–2018). Among youth and adults (12+ y), we summarized over time: (1) the prevalence of AT participation and (2) time spent in AT (in hours per week) among those who report any AT participation. Where possible, we reported separate estimates of walking and cycling and produced an aggregate estimate of total AT. We stratified results by age group and sex. Results: Changes in AT survey questions over time and between surveys limit the interpretation and comparability of temporal trends. Nevertheless, a consistently higher proportion of females report walking, while a higher proportion of males report cycling. Irrespective of mode, males report spending more total time in AT. Participation in AT tends to decrease with age, with youth reporting the highest rates of AT and young adults often spending the most time in AT. Conclusions: Monitoring trends in AT can help assess patterns of behavior and identify whether promotion strategies are needed or whether population interventions are effective. Our evaluation of AT over time is limited by questions surveyed; however, consistent differences in AT by age and sex are evident over time. Moving forward, ensuring consistency of AT measurement over time is essential to monitoring this important behavior.

Background: There is limited evidence from globally diverse samples on the prevalence and correlates of meeting the global guideline of 180 minutes per day of total physical activity (TPA) among 3- to 4-year-olds. Methods: Cross-sectional study involving 797 (49.2% girls) 3- to 4-year-olds from 17 middle- and high-income countries who participated in the pilot phases 1 and 2 of the SUNRISE International Study of Movement Behaviours in the Early Years. Daily step count was measured using thigh-worn activPAL accelerometers. Children wore the accelerometers for at least one 24-hour period. Children were categorized as meeting the TPA guideline based on achieving ≥11,500 steps per day. Descriptive analyses were conducted to describe the proportion of meeting the TPA guideline for the overall sample and each of the sociodemographic variables, and 95% CIs were calculated. Multivariable logistic regression was used to determine the sociodemographic correlates of meeting the TPA guideline. Results: Mean daily step count was 10,295 steps per day (SD = 4084). Approximately one-third of the sample (30.9%, 95% CI, 27.6–34.2) met the TPA guideline. The proportion meeting the guideline was significantly lower among girls (adjusted OR [aOR] = 0.70, 95% CI, 0.51–0.96) and 4-year-olds (aOR = 0.50, 95% CI, 0.34–0.75) and higher among rural residents (aOR = 1.78, 95% CI, 1.27–2.49) and those from lower middle-income countries (aOR = 1.35, 95% CI, 0.89–2.04). Conclusions: The findings suggest that a minority of children might meet the TPA guideline globally, and the risk of not meeting the guideline differed by sociodemographic indicators. These findings suggest the need for more surveillance of TPA in young children globally and, possibly, interventions to improve childhood health and development.

Objective: Previous studies that looked at comparability of accelerometer data focused on epoch or recording level comparability. Our study aims to provide insight into the comparability at raw data level. Methods: We performed five experiments with accelerometers attached to a mechanical shaker machine applying movement along a single axis in the horizontal plane. In each experiment, a 1-min no-movement condition was followed by nineteen 2-min shaker frequency conditions (30–250 rpm). We analyzed accelerometer data from Axivity, ActiGraph, GENEActiv, MOX, and activPAL devices. Comparability between commonly used brands and dynamic ranges was assessed in the frequency domain with power spectra and in the time domain with maximum lagged cross-correlation analyses. The influence of sampling rate on magnitude of acceleration across brands was explored visually. All data were published open access. Results: Magnitude of noise in rest was highest in MOX and lowest in ActiGraph. The signal mean power spectral density was equal between brands at low shaker frequency conditions (<3.13 Hz) and between dynamic ranges within the Axivity brand at all shaker frequency conditions. In contrast, the cross-correlation coefficients between time series across brands and dynamic ranges were higher at higher shaking frequencies. Sampling rate affected the magnitude of acceleration most in Axivity and least in GENEActiv. Conclusions: The comparability of raw acceleration signals between brands and/or sampling rates depends on the type of movement. These findings aid a more fundamental understanding and anticipation of differences in behavior estimates between different implementations of raw accelerometry.

Background: The associations between subjective (self-reported) and objective (actigraphy) sleep measurements are not well documented among survivors of cancer. The purpose of this study was to examine actigraphy measurements across days and the associations of two self-reported sleep measures with actigraphy-measured sleep measures. Methods: Sleep data were collected using self-reported sleep diary, the Pittsburgh Sleep Quality Index, and hip-worn actigraphy at baseline for a subsample participating in the Lifestyle Intervention for oVarian cancer Enhanced Survival (N = 516) randomized controlled trial. Intraclass correlation coefficients were used to evaluate consistency of actigraphy sleep measures across days of wear and associations of sleep diary with actigraphy for total sleep time (TST), time asleep, and time awake. Bland–Altman plots were used to assess the associations of sleep duration and sleep efficiency derived from Pittsburgh Sleep Quality Index and actigraphy. Results: Participants were aged 60.3 years (SD 9.3 years). For TST, the associations were strongest after 3 weekdays of consecutive actigraphy wear (ICC = .43 95% CI [.35, .51]), and actigraphy-measured daily TST was longest (617, SD 135 min) compared with self-reported measures. Sleep diary versus actigraphy associations for TST, time asleep, and time awake were weak to moderate. Pittsburgh Sleep Quality Index versus actigraphy association was weak for all sleep constructs. Conclusion: The strength of association between self-reported and actigraphy measures of sleep ranged from weak to very strong, depending on the sleep construct. Impact: Results highlight the importance of selecting an appropriate measurement tool for estimating individual sleep constructs among survivors of cancer.

The worldwide increase in childhood overweight and obesity underscores the need to study variables like fundamental movement skill (FMS) levels from early childhood. This study investigated the bidirectional longitudinal relationship between body mass index (BMI) and process-oriented FMSs, including locomotor skills and object control skills in 675 Finnish children, aged 3–8 years at baseline (50.5% female, mean age 5.5 years) over 3 years. Standardized BMI-for-age SD scores (BMI SDS z-scores) followed Finnish national standards. The FMS assessment comprised four subtests from the Test of Gross Motor Development, third edition. Age-adjusted standardized residuals of FMS or skill domains and BMI SDS z-scores were used in a two-level, cross-classified, cross-lagged regression analysis, accounting for gender, and baseline value of the dependent variables. The results showed no statistically significant longitudinal relationship between BMI and FMS or its skill domains for either gender in either direction. This suggests that BMI and process-oriented FMS, encompassing locomotor skill and object control skill, develop independently, possibly influenced by unexplored variables. These findings contradict earlier results based on product-oriented measurements, which may include a physical capacity component. The outcomes further underscore the importance of monitoring weight status from early childhood, given its significant association with later-life weight conditions.

The dynamics of mastering the degrees of freedom in motor learning are still far from being understood. The present work explored coordination dynamics in a redundant task, relating it to performance and adaptation in a serial stimulus tracking task. One hundred and sixty-three children (10–14 years of age) continuously responded to sequential stimuli (containing five stimuli) by pressing the respective sensors before the next stimulus presentation. Participants performed 120 trials with a fixed sequence (4–2–5–3–1) and a fixed interstimuli interval (800 ms) to learn the first pattern (practice phase). Then, a changed sequence (4–2–5–1–3) with a shorter interval (700 ms) was presented for 40 trials (adaptation phase). To measure coordination and its change, we calculated the correlation matrix of the stimulus–touch interval between the five sensors in blocks of 20 trials of the practice phase and classified individuals in terms of clusters. We found associations between coordination dynamics, performance curves, and adaptation in both coordination and performance. Furthermore, using network analyses, we found a tendency for all groups to increase the clustering coefficient. We discuss the possibility of this result representing a process of progressive segregation.

Background: This article evaluates the evolution of physical activity and health research in China through a bibliometric analysis focused on number of publications, study areas, and sex balance in authorship. Methods: A systematic review was conducted by the Global Observatory for Physical Activity for “physical activity and health” publications between 1950 and 2019. Here, we focus on the 610 Chinese publications identified, defined as those in which data collection took place in China. We assessed the number of publications, classified them into 5 areas (1) surveillance, (2) correlates and determinants, (3) health consequences, (4) interventions, and (5) policy, and analyzed female participation in authorship. Results: The first Chinese publication identified in the review was in 1990. Since, the average number of physical activity and health publications increased from one per year in the 1990s to 7.6 per year in the 2000s, and to 47 per year in the 2010s. Most publications focused on the correlates and determinants (38.7%) and the health consequences of physical activity (35.9%). Physical activity policy accounted for 2.3% of the publications. In the 1990s, 64% of the publications included at least one female author; this proportion increased to 90% in the 2010s. Conclusion: Despite a slow start, China’s research on physical activity and health has grown rapidly since 2000. The distribution of publications by study areas and female participation in authorship is similar to that observed globally, with fewer publications focused on interventions and policy as compared with other topics.

In 2022, it became possible to produce ActiGraph counts from raw accelerometer data without use of ActiLife software. This supports the availability and use of transparent, open-source methods for producing physical behavior outcomes from accelerometer data. However, questions remain regarding the implications of the availability of open-source ActiGraph counts. This Expert Question and Answer paper solicited and summarized feedback from several noted physical behavior measurement experts on five questions related to open-source counts. The experts agreed that open-source, transparent, and translatable methods help with harmonization of accelerometer methods. However, there were mixed views as to the importance of open-source counts and their place in the field moving forward. This Expert Question and Answer provides initial feedback, but more research both within this special issue and to be conducted moving forward will help to inform whether and how open-source counts will be accepted and adopted for use for device-based physical behavior assessments.