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Children with a low level of neuromotor fitness are less skilled to participate in sports activities. Moreover, lower levels of neuromotor fitness are related to adiposity, lower cardiovascular health, and poor self-esteem in children. The aim of this paper was to determine neuromotor fitness in 10–12-year-old Dutch children over a 10-year period. Test scores measured in 2015/2017 (N = 533 in 2015, N = 941 in 2017) were compared with scores of same-aged children measured in 2006 (N = 1986). Neuromotor fitness was assessed using theMOPER fitness test battery, including speed and agility, strength, flexibility, and coordination and upper-limb speed. Data were analyzed using multilevellinear regression models and tobit regression analyses in case of skewed distributions with an excess of zeros. Analyses were stratified by age and gender, and adjusted for level of urbanization. Children in 2015/2017 performed significantly worse on speed and agility (b = 0.8 to 1.1 s), significantly better on coordination/upper-limb speed (b = −1.0 to −0.6 s), and–except for 12-year-old girls–significantly worse on flexibility vs. childrenin 2006 (b = −3.4 to −1.8 cm). Additionally, upper-body strength was significantly worse among 10-year olds (b = −3.2 to −2.5 s) while leg strength was significantly worse among 11-year-olds in 2015/2017 vs. 2006 (b = −1.8 to −1.7 cm). Trunk strength was worse among 11- and 12-year old boys (b = 1.1 to 1.2 s). In line with a previously observed downward trend in neuromotor fitness among children (1980–2006), we found worse scores on speed and agility, and flexibility in 2015/2017 vs. 2006, stressing the need for interventions aimed at improving neuromotor fitness in order to promote physical activity and future health.

An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysisThis systematic review and meta-analysis summarizes the evidence on the prospective relationship between childhood sedentary behaviour and biomedical health indicators, overall and stratified by type of sedentary behaviour (TV viewing, computer use/games, screen time and objective sedentary time).PubMed, EMBASE, PsycINFO and Cochrane were systematically searched till January 2015. Methodological quality of all included studies was scored and a best evidence synthesis was applied.We included 109 studies of which 19 were of high quality. We found moderate-to-strong evidence for a relationship of overall sedentary time with some anthropometrics (overweight/obesity, weight-for-height), one cardiometabolic biomarker (HDL-cholesterol) and some fitness indicators (fitness, being unfit). For other health indicators, we found no convincing evidence due to inconsistent or non-significant findings. The evidence varied by type of sedentary behaviour. The meta-analysis indicated that each additional baseline hour of TV viewing (β = 0.01; 95%-CI: -0.002-0.02) or computer use (β = 0.00; 95%-CI: -0.004-0.01) per day was not significantly related with BMI at follow-up.We conclude that the evidence for a prospective relationship between childhood sedentary behaviour and biomedical health is in general unconvincing.Conflict of interest and financial support: ICMJE forms provided by the authors are available online along with the full text of this article.

Background Physical activity has beneficial effects on the health of cancer survivors. We aimed to investigate accelerometer-assessed physical activity and sedentary time in cancer survivors, and describe activity profiles. Additionally, we identify demographic and clinical correlates of physical activity, sedentary time and activity profiles. Methods Accelerometer, questionnaire and clinical data from eight studies conducted in four countries (n = 1447) were pooled. We calculated sedentary time and time spent in physical activity at various intensities using Freedson cut-points. We used latent profile analysis to identify activity profiles, and multilevel linear regression analyses to identify demographic and clinical variables associated with accelerometer-assessed moderate to vigorous physical activity (MVPA), sedentary time, the highly active and highly sedentary profile, adjusting for confounders identified using a directed acyclic graph. Results Participants spent on average 26 min (3%) in MVPA and 568 min (66%) sedentary per day. We identified six activity profiles. Older participants, smokers and participants with obesity had significantly lower MVPA and higher sedentary time. Furthermore, men had significantly higher MVPA and sedentary time than women and participants who reported less fatigue had higher MVPA time. The highly active profile included survivors with high education level and normal body mass index. Haematological cancer survivors were less likely to have a highly active profile compared to breast cancer survivors. The highly sedentary profile included older participants, males, participants who were not married, obese, smokers, and those