Sedentary behavior among Spanish children and adolescents: findings from the ANIBES study

Background: An increase of sedentary behaviors far from the Mediterranean lifestyle is happening in spite of the impact on health. The aims of this study were to describe sedentary behaviors in children and adolescents. Methods: A representative sample of 424 Spanish children and adolescents (38% females) involved in the ANIBES study was analyzed regarding their sedentary behaviors, together with the availability of televisions, computers, and consoles by means of the HELENA sedentary behavior questionnaire. Results: For the total sample of children, 49.3% during weekdays and 84% during weekends did not meet the recommendation of less than 2 hours of screen viewing per day. The use of TV was higher during weekdays (p < 0.05) and there were significant differences between adolescents and children (16.9 vs. 25.1%, p < 0.05). The use of computer, console games and of internet for non-study reasons was higher during weekends (p < 0.001). Adolescents played more computer games and used more internet for non-study reasons than children during both weekdays and weekends (p < 0.05 and p < 0.001, respectively). The use of internet for academic reasons was lower in children (p < 0.001) than adolescents during weekends; however, no significant differences were found between sexes. In addition, more than 30% of the children and adolescents had at least one electronic device in their bedrooms. Conclusions: Spanish children and adolescents are not meeting the recommendations regarding the maximum of screen viewing (<2 h/day), especially during the weekend, for all of sedentary behaviors. Urgent strategies and intervention studies are needed to reduce sedentary behavior in young people.The ANIBES study was financially supported by a grant from Coca-Cola Iberia through an agreement with the Spanish Nutrition Foundation (FEN). The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results

Tracking of total sedentary time and sedentary patterns in youth: a pooled analysis using the International Children’s Accelerometry Database (ICAD)

Abstract: Background: To gain more understanding of the potential health effects of sedentary time, knowledge is required about the accumulation and longitudinal development of young people’s sedentary time. This study examined tracking of young peoples’ total and prolonged sedentary time as well as their day-to-day variation using the International Children’s Accelerometry Database. Methods: Longitudinal accelerometer data of 5991 children (aged 4-17y) was used from eight studies in five countries. Children were included if they provided valid (≥8 h/day) accelerometer data on ≥4 days, including ≥1 weekend day, at both baseline and follow-up (average follow-up: 2.7y; range 0.7–8.2). Tracking of total and prolonged (i.e. ≥10-min bouts) sedentary time was examined using multilevel modelling to adjust for clustering of observations, with baseline levels of sedentary time as predictor and follow-up levels as outcome. Standardized regression coefficients were interpreted as tracking coefficients (low: 0.6). Results: Average total sedentary time at study level ranged from 246 to 387 min/day at baseline and increased annually by 21.4 min/day (95% confidence interval [19.6–23.0]) on average. This increase consisted almost entirely of prolonged sedentary time (20.9 min/day [19.2–22.7]). Total (standardized regression coefficient (B) = 0.48 [0.45–0.50]) and prolonged sedentary time (B = 0.43 [0.41–0.45]) tracked moderately. Tracking of day-to-day variation in total (B = 0.04 [0.02–0.07]) and prolonged (B = 0.07 [0.04–0.09]) sedentary time was low. Conclusion: Young people with high levels of sedentary time are likely to remain among the people with highest sedentary time as they grow older. Day-to-day variation in total and prolonged sedentary time, however, was rather variable over time

How sickening is sitting?: Sedentary behaviour among young people

Chin A Paw, J.M.M. [Promotor]Altenburg, T.M. [Copromotor]Rotteveel, J. [Copromotor

Gezondheidseffecten van veel zitten tijdens de jeugd*: Hoe sterk is het bewijs uit longitudinale studies voor negatieve effecten?

An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysis This 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

An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysis

The authors regret that in the above article a misprint appears in table two presenting the evidence synthesis stratified by main type of sedentary behaviour and overall sedentary time: Not all high quality studies were printed in bold letter type. The correct Table is shown on page 2. 2 Evidence synthesis stratified by main type of sedentary behaviour and overall sedentary time (Table presented.) Bold indicates a high-quality study. *Note that the amount of studies under the stratified evidence synthesis do not count up in het combined evidence synthesis, due to two reasons. First, some studies examined types of sedentary behaviour that could not be classified in one of the four main types (e.g. subjective sitting time). As these additional types were only examined in its relationship with one health indicator, they were not considered as an additional main type of sedentary behaviour. Second, studies reporting relationships of more than one measurement type were counted once in the combined evidence synthesis, and were considered to add evidence when consistent findings were reported (i.e. ≥75% of the relationships showing results in the same direction). +, study adding evidence for a positive relationship; − study adding evidence for an inverse relationship; 0 study indicating no evidence for a relationship; BMI, body mass index; CRF, cardiorespiratory fitness; DBP, diastolic blood pressure; FMI, fat mass index; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; SBP, systolic blood pressure; SSF, sum of skinfolds; SLJ, standing long jump; TC/HDL-c, ratio of total cholesterol to high-density lipoprotein cholesterol; TG, triglycerides; TV, television; WC, waist circumference. Reference 1. van Ekris E, Altenburg TM, Singh AS, Proper KI, Heymans MW, Chinapaw MJM. An evidence-update on the prospective relationship between childhood sedentary behaviour and biomedical health indicators: a systematic review and meta-analysis. Obes Rev 2016; 17: 833–849. https://doi.org/10.1111/obr.12426

TOPAAS : een structurele aanpak voor faalkansanalyse van software intensieve systemen

Rijkswaterstaat is bezig om op alle primaire waterkeringen en andere kunstwerken probabilistisch beheer te introduceren. Centraal in de aanpak van probabilistisch beheer is de risicoanalyse, die sturend is in de testintervallen, gegarandeerde reparatietijd en modificaties. Ook het falen van de gebruikte software is gemodelleerd. Voor de initiële inschatting van de faalkans van de software is de TDT-methode ontwikkeld. In praktijk blijkt deze onbetrouwbare resultaten te leveren. In opdracht van Rijkswaterstaat heeft een consortium van Det Norske Veritas, Movares, Technische Universiteit Eindhoven, Logica, Refis en Intermedion een verbeterde methode ontwikkeld die zowel richtlijnen geeft voor het modelleren van softwarefalen in foutenbomen als het schatten van de faalkans van een taakuitvoering door een softwaremodule. Deze methode is gerapporteerd in [8] en TOPAAS genoemd. Aan de hand hiervan zijn een aantal experimenten (pilots) uitgevoerd. De resultaten van die pilots zijn beschreven in een evaluatie [16] en deze evaluatie doet een aantal aanbevelingen voor verbetering. In deze tweede versie van [8] zijn de aanbevelingen verwerkt. Ook is de tekst hier en daar redactioneel aangepast, met name ter verduidelijking voor de niet-ICT’er. Tevens wordt aanbevolen een korte handleiding voor het toepassen van TOPAAS te maken. Kern van TOPAAS is dat software in modulen kan worden opgedeeld en dat het (mogelijk) falen van deze modulen in een foutenboom als basisgebeurtenissen kunnen worden opgenomen. Falen van een softwaremodule kan vervolgens opgedeeld worden in falen ten gevolge van onverwachtheid van input en het falen van de beslislogica van de softwaremodule zelf. Schatten van de faalkans van een softwarecomponent (module) is moeilijk: er zijn wel methoden, maar die vereisen zonder uitzondering input die vaak niet (voldoende) voorhanden is. Om toch te komen tot een faalkansschatting van een softwaremodule wordt op basis van expert opinion een schatting gemaakt, waarbij het Bayesiaanse gedachtegoed wordt gevolgd. Deze schatting is vervolgens vervat in een parametermodel, waarbij de factoren die in ogenschouw worden genomen voortkomen uit de expertgroep en internationaal onderzoek. De invloed van de factoren is ingeschat door experts en vervolgens gekalibreerd met een twintigtal referentieprojecten. Conclusie is dat de uitkomsten van het parametermodel een zeer sterke correlatie vertoont met de inschatting van de experts. Concluderend mag men stellen dat deze methode, bij afwezigheid van betere wijzen van schatting, een redelijk betrouwbare Bayesiaanse schatting van de faalkans levert

Recent Archeologisch onderzoek in West-Vlaanderen

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