Iris Pigeot

Leibniz-Institute of Prevention Research and Epidemiology, Bremen, Bremen, Germany

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Publications (213)291.99 Total impact

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    ABSTRACT: Older adults are the most sedentary segment of society and high sedentary time is associated with poor health and wellbeing outcomes in this population. Identifying determinants of sedentary behaviour is a necessary step to develop interventions to reduce sedentary time. A systematic literature review was conducted to identify factors associated with sedentary behaviour in older adults. Pubmed, Embase, CINAHL, PsycINFO and Web of Science were searched for articles published between 2000 and May 2014. The search strategy was based on four key elements: (a) sedentary behaviour and its synonyms; (b) determinants and its synonyms (e.g. correlates, factors); (c) types of sedentary behaviour (e.g. TV viewing, sitting, gaming) and (d) types of determinants (e.g. environmental, behavioural). Articles were included in the review if specific information about sedentary behaviour in older adults was reported. Studies on samples identified by disease were excluded. Study quality was rated by means of QUALSYST. The full review protocol is available from PROSPERO (PROSPERO 2014: CRD42014009823). The analysis was guided by the socio-ecological model framework. Twenty-two original studies were identified out of 4472 returned by the systematic search. These included 19 cross-sectional, 2 longitudinal and 1 qualitative studies, all published after 2011. Half of the studies were European. The study quality was generally high with a median of 82 % (IQR 69–96 %) using Qualsyst tool. Personal factors were the most frequently investigated with consistent positive association for age, negative for retirement, obesity and health status. Only four studies considered environmental determinants suggesting possible association with mode of transport, type of housing, cultural opportunities and neighbourhood safety and availability of places to rest. Only two studies investigated mediating factors. Very limited information was available on contexts and sub-domains of sedentary behaviours. Few studies have investigated determinants of sedentary behaviour in older adults and these have to date mostly focussed on personal factors, and qualitative studies were mostly lacking. More longitudinal studies are needed as well as inclusion of a broader range of personal and contextual potential determinants towards a systems-based approach, and future studies should be more informed by qualitative work.
    International Journal of Behavioral Nutrition and Physical Activity 10/2015; 12(1). DOI:10.1186/s12966-015-0292-3 · 4.11 Impact Factor
  • Bianca Kollhorst · Michal Abrahamowicz · Iris Pigeot ·
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    ABSTRACT: To investigate whether physician's prescribing preference is a valid instrumental variable (IV) for patients' actual prescription of selective COX-2 inhibitors in the German Pharmacoepidemiological Research Database (GePaRD). We compared the effect of COX-2 inhibitors vs. traditional NSAIDs (tNSAIDs) on the risk of gastrointestinal complications using physician's preference as IV. We used different definitions of physician's preference for COX-2 inhibitors. A retrospective cohort of new users was built which was further restricted to subcohorts. We compared IV-based risk difference estimates, using a two-stage approach, to estimates from conventional multivariable models. We observed only a small proportion of COX-inhibitor users (3.2%) in our study. All instruments, in the full cohort and in the subcohorts, reduced the imbalance in most of the covariates. However, the IV treatment effect estimates had a highly inflated variance. Compared to the most recent prescription, the proportion of previous patients was a stronger instrument and reduced the variance of the estimates. The proportion of all previous patients is a potential IV for comparing COX-2 inhibitors vs. tNSAIDs in GePaRD. Our study demonstrates that valid instruments in one health care system may not be directly applicable to others. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of clinical epidemiology 09/2015; DOI:10.1016/j.jclinepi.2015.08.008 · 3.42 Impact Factor
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    ABSTRACT: Faster growth seems to be a common factor in several hypotheses relating early life exposures to subsequent health. This study aims to investigate the association between body mass index (BMI) trajectories during infancy/childhood and later metabolic risk in order to identify sensitive periods of growth affecting health. In a first step, BMI trajectories of 3301 European children that participated in the multi-centre Identification and Prevention of Dietary and Lifestyle-induced Health Effects in Children and Infants (IDEFICS) study were modelled using linear-spline mixed-effects models. The estimated random coefficients indicating initial subject-specific BMI and rates of change in BMI over time were used as exposure variables in a second step and related to a metabolic syndrome (MetS) score and its single components based on conditional regression models (mean age at outcome assessment: 8.5 years). All exposures under investigation, i.e. BMI at birth, rates of BMI change during infancy (0 to <9 months), early childhood (9 months to <6 years) and later childhood (≥6 years) as well as current BMI z-score were significantly associated with the later MetS score. Associations were strongest for the rate of BMI change in early childhood (1.78 [1.66; 1.90]; β estimate and 99 % confidence interval) and current BMI z-score (1.16 [0.96; 1.38]) and less pronounced for BMI at birth (0.62 [0.47; 0.78]). Results slightly differed with regard to the single metabolic factors. Starting from birth rapid BMI growth, especially in the time window of 9 months to <6 years, is significantly related to later metabolic risk in children. Much of the associations of early BMI growth may further be mediated through the effects on subsequent BMI growth.
    European Journal of Epidemiology 08/2015; DOI:10.1007/s10654-015-0080-z · 5.34 Impact Factor
  • Iris Pigeot · Svenja Jacobs · Uwe Koch-Gromus ·

    Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 07/2015; 58(8):785-7. DOI:10.1007/s00103-015-2194-6 · 1.42 Impact Factor
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    ABSTRACT: Overweight/obesity is an important public health burden worldwide, increasing the risk for the development of cardiovascular diseases or the metabolic syndrome. This risk may be reduced by a good aerobic fitness (AF) that can be improved by physical activity but is also influenced by genetic factors. The aim of this study was to test for familial aggregation of AF measured by maximal oxygen uptake (VO2max) and to estimate its heritability. Furthermore, an exploratory analysis of the association between overweight/obesity and AF was performed. In contrast to previous studies, all analyses were adjusted for additional environmental and behavioral factors, in particular for objectively measured physical activity (PA) in addition to body mass index (BMI). 79 families (157 parents, 132 children) performed a maximum exercise test (spiroergometry) to assess maximum oxygen uptake. PA was measured by accelerometry. Familial aggregation of AF was determined using a two-step design: AF was adjusted for age, sex and age*sex using linear regression. Afterwards, the residuals were used to determine the intraclass correlation coefficient (ICC) by ANOVA. Heritability and associations were estimated by generalized linear mixed models. Familial aggregation of AF (ICC = 0.22, p < 0.001) was significant but decreased when adjusted for PA or BMI. Its heritability was estimated as 40 % (adjusted for PA) using the mid-parent-offspring design. Relative to the middle quintile of AF residuals, the odds of being overweight/obese were three- to tenfold reduced in the upper quintile (adjusted for age, sex, age*sex, PA). AF clustered in families after controlling for PA, BMI and parental smoking. Heritability was stronger for mother-child pairs as compared to father-child pairs after controlling for PA and BMI. Above average AF was negatively associated with overweight/obesity.
    BMC Public Health 07/2015; 15(1):638. DOI:10.1186/s12889-015-2013-x · 2.26 Impact Factor
  • Edeltraut Garbe · Iris Pigeot ·
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    ABSTRACT: Large electronic healthcare databases have become an important worldwide data resource for drug safety research after approval. Signal generation methods and drug safety studies based on these data facilitate the prospective monitoring of drug safety after approval, as has been recently required by EU law and the German Medicines Act. Despite its large size, a single healthcare database may include insufficient patients for the study of a very small number of drug-exposed patients or the investigation of very rare drug risks. For that reason, in the United States, efforts have been made to work on models that provide the linkage of data from different electronic healthcare databases for monitoring the safety of medicines after authorization in (i) the Sentinel Initiative and (ii) the Observational Medical Outcomes Partnership (OMOP). In July 2014, the pilot project Mini-Sentinel included a total of 178 million people from 18 different US databases. The merging of the data is based on a distributed data network with a common data model. In the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) there has been no comparable merging of data from different databases; however, first experiences have been gained in various EU drug safety projects. In Germany, the data of the statutory health insurance providers constitute the most important resource for establishing a large healthcare database. Their use for this purpose has so far been severely restricted by the Code of Social Law (Section 75, Book 10). Therefore, a reform of this section is absolutely necessary.
    Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 06/2015; DOI:10.1007/s00103-015-2185-7 · 1.42 Impact Factor
  • Svenja Jacobs · Christoph Stallmann · Iris Pigeot ·
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    ABSTRACT: Kohortenstudien liefern unter allen Typen epidemiologischer Beobachtungsstudien die beste Evidenz für das Erkennen von kausalen Zusammenhängen zwischen Risikofaktoren und Krankheiten. Ihr Design kann jedoch auch zu Nachteilen führen, die die Validität und Aussagekraft der Ergebnisse beeinflussen können. Dazu zählen insbesondere systematische Fehler wie Selektionseffekte oder Verzerrungen aufgrund lücken- oder fehlerhafter Erinnerungen. Um diese teilweise auszugleichen, ist es möglich, die Primärdaten aus der Kohortenstudie auf Individualebene mit Sekundär- und Registerdaten zu verlinken. Diese Verknüpfung kann auch zur Validierung der verwendeten Datenquellen genutzt werden. Zu den Sekundär- und Registerdaten, die bisher in Deutschland im Rahmen von Kohortenstudien mit Primärdaten verknüpft wurden, gehören Kranken- und Rentenversicherungsdaten, Angaben der Bundesagentur für Arbeit sowie Krebsregisterdaten. Bei ihnen lassen sich zwei Gemeinsamkeiten erkennen. Zum einen verfügen alle über einen großen Umfang an Detailinformationen, die sich in der Regel auf lange Zeiträume und große Populationen beziehen. Zum anderen sind sie in der Lage, Daten auf Individualebene zur Verfügung zu stellen, sodass prinzipiell eine Verlinkung z. B. mit Primärdaten möglich ist. Jede dieser Datenquellen ist aber auch mit Einschränkungen behaftet, die zu berücksichtigen sind. Gleichzeitig muss in Deutschland eine Reihe rechtlicher Restriktionen beachtet werden, deren Ziel es ist, den Missbrauch der Daten zu vermeiden.
    Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 06/2015; 58(8):822-828. DOI:10.1007/s00103-015-2184-8 · 1.42 Impact Factor
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    ABSTRACT: Dietary patterns, physical activity (PA) and sedentary behaviours are some of the main behavioural determinants of obesity; their combined influence in children has been addressed in a limited number of studies. Children (16 228) aged 2-9 years old from eight European countries participated in the baseline survey of the IDEFICS study. A subsample of 11 674 children (50.8% males) were included in the present study. Children's food and beverage consumption (fruit and vegetables (F&V) and sugar-sweetened beverages (SSBs)), PA and sedentary behaviours were assessed via parental questionnaires. Sex-specific cluster analysis was applied to identify behavioural clusters. Analysis of covariance and logistic regression were applied to examine the association between behavioural clusters and body composition indicators (BCIs). Six behavioural clusters were identified (C1-C6) both in boys and girls. In both sexes, clusters characterised by high level of PA (C1 and C3) included a large proportion of older children, whereas clusters characterised by low SSB consumption (C5 and C6) included a large proportion of younger children. Significant associations between derived clusters and BCI were observed only in boys; those boys in the cluster with the highest time spent in sedentary activities and low PA had increased odds of having a body mass index z-score (odds ratio (OR)=1.33; 95% confidence interval (CI)=(1.01, 1.74)) and a waist circumference z-score (OR=1.41; 95%CI=(1.06, 1.86)) greater than one. Clusters characterised by high sedentary behaviour, low F&V and SSB consumption and low PA turned out to be the most obesogenic factors in this sample of European children.European Journal of Clinical Nutrition advance online publication, 3 June 2015; doi:10.1038/ejcn.2015.76.
    European journal of clinical nutrition 06/2015; 69(7). DOI:10.1038/ejcn.2015.76 · 2.71 Impact Factor
  • I. Pigeot ·

    Appetite 06/2015; 89:324. DOI:10.1016/j.appet.2014.12.077 · 2.69 Impact Factor
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    ABSTRACT: This study analyzes peer effects on childhood obesity using data from the first two waves of the IDEFICS study, which applies several anthropometric and other measures of fatness to approximately 14,000 children aged two to nine participating in both waves in 16 regions of eight European countries. Peers are defined as same-sex children in the same school and age group. The results show that peer effects do exist in this European sample but that they differ among both regions and different fatness measures. Peer effects are larger in Spain, Italy, and Cyprus - the more collectivist regions in our sample - while waist circumference generally gives rise to larger peer effects than BMI. We also provide evidence that parental misperceptions of their own children's weight goes hand in hand with fatter peer groups, supporting the notion that in making such assessments, parents compare their children's weight with that of friends and schoolmates. Copyright © 2015 Elsevier B.V. All rights reserved.
    Economics & Human Biology 05/2015; DOI:10.1016/j.ehb.2015.05.002 · 1.90 Impact Factor
  • Iris Pigeot · Fabian Sobotka · Svend Kreiner · Ronja Foraita ·
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    ABSTRACT: Graphical models are useful to detect multivariate association structures in terms of conditional independencies and to represent these structures in a graph. When fitting graphical models to multivariate data, the uncertainty of a selected graphical model cannot be directly assessed. In this paper, we therefore propose various descriptive measures to assess the uncertainty of a graphical model based on the nonparametric bootstrap. We also introduce a so-called mean graphical model. Simulations and one real data example illustrate the application and interpretation of the newly proposed measures and demonstrate that the mean graphical model performs better than a single selected graphical model.
    Journal of Applied Statistics 04/2015; 42(11):1-18. DOI:10.1080/02664763.2015.1030368 · 0.42 Impact Factor
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    Christoph Buck · Steffen Dreger · Iris Pigeot ·
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    ABSTRACT: Data privacy is a major concern in spatial epidemiology because exact residential locations or parts of participants' addresses such as street or zip codes are used to perform geospatial analyses. To overcome this concern, different levels of aggregation such as census districts or zip code areas are mainly used, though any spatial aggregation leads to a loss of spatial variability. For the assessment of urban opportunities for physical activity that was conducted in the IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) study, macrolevel analyses were performed, but the use of exact residential addresses for micro-level analyses was not permitted by the responsible office for data protection. We therefore implemented a spatial blurring to anonymise address coordinates depending on the underlying population density. We added a standard Gaussian distributed error to individual address coordinates with the variance [Formula: see text] depending on the population density and on the chosen k-anonymity. 1000 random point locations were generated and repeatedly blurred 100 times to obtain anonymised locations. For each location 1 km network-dependent neighbourhoods were used to calculate walkability indices. Indices of blurred locations were compared to indices based on their sampling origins to determine the effect of spatial blurring on the assessment of the built environment. Spatial blurring decreased with increasing population density. Similarly, mean differences in walkability indices also decreased with increasing population density. In particular for densely-populated areas with at least 1500 residents per km², differences between blurred locations and their sampling origins were small and did not affect the assessment of the built environment after spatial blurring. This approach allowed the investigation of the built environment at a microlevel using individual network-dependent neighbourhoods, while ensuring data protection requirements. Minor influence of spatial blurring on the assessment of walkability was found that slightly affected the assessment of the built environment in sparsely-populated areas. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to
    BMJ Open 03/2015; 5(3):e006481. DOI:10.1136/bmjopen-2014-006481 · 2.27 Impact Factor

  • Revista Andaluza de Medicina del Deporte 03/2015; 8(1):43. DOI:10.1016/j.ramd.2014.10.061
  • C Stallmann · W Ahrens · R Kaaks · I Pigeot · E Swart · S Jacobs ·
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    ABSTRACT: Some German cohort studies have already linked secondary and registry data with primary data from interviews and medical examinations. This offers the opportunity to obtain more valid information by taking advantage of the strengths of these data synergistically and overcome their individual weaknesses at the same time. The potential and the requirements for linking secondary and registry data with primary data from cohort studies is described generally and illustrated by the example of the "German National Cohort" (GNC). The transfer and usage of secondary and registry data require that administrative and logistic efforts be made over the whole study period. In addition, rigid data protection regulations for using social data have to be observed. The particular strengths of secondary and registry data, namely their objectivity and independence from recall bias, add to the strengths of newly collected primary data and improve the assessment of morbidity endpoints, exposure history and need of patient care. Moreover, new insights on quality and on the added value of linking different data sources may be obtained. © Georg Thieme Verlag KG Stuttgart · New York.
    Das Gesundheitswesen 02/2015; 77(2):118 - 119. DOI:10.1055/s-0034-1396805 · 0.62 Impact Factor
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    ABSTRACT: Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.
    Public Health Nutrition 01/2015; DOI:10.1017/S1368980014003243 · 2.68 Impact Factor
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    ABSTRACT: Exploring changes in children's diet over time and the relationship between these changes and socio-economic status (SES) may help to understand the impact of social inequalities on dietary patterns. The aim of the present study was to describe dietary patterns by applying a cluster analysis to 9301 children participating in the baseline (2 –9 years old) and follow-up (4– 11 years old) surveys of the Identification and Prevention of Dietary-and Lifestyle-induced Health Effects in Children and Infants Study, and to describe the cluster memberships of these children over time and their association with SES. We applied the K-means clustering algorithm based on the similarities between the relative frequencies of consumption of forty-two food items. The following three consistent clusters were obtained at baseline and follow-up: processed (higher frequency of consumption of snacks and fast food); sweet (higher frequency of consumption of sweet foods and sweetened drinks); healthy (higher frequency of consumption of fruits, vegetables and wholemeal products). Children with higher-educated mothers and fathers and the highest household income were more likely to be allocated to the healthy cluster at baseline and follow-up and less likely to be allocated to the sweet cluster. Migrants were more likely to be allocated to the processed cluster at baseline and follow-up. Applying the cluster analysis to derive dietary patterns at the two time points allowed us to identify groups of children from a lower socio-economic background presenting persistently unhealthier dietary profiles. This finding reflects the need for healthy eating interventions specifically targeting children from lower socio-economic backgrounds.
    The British journal of nutrition 01/2015; 113(03). DOI:10.1017/S0007114514003663 · 3.45 Impact Factor
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    ABSTRACT: The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
    PLoS ONE 12/2014; 9(12):e114089. DOI:10.1371/journal.pone.0114089 · 3.23 Impact Factor
  • Iris Pigeot · Benedikt Buchner ·
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    ABSTRACT: Für die Erforschung von Volkskrankheiten spielen epidemiologische Studien mit einem Life-Course Ansatz eine entscheidende Rolle. Dank langfristiger Beobachtungen auf Individualebene kann das longitudinale Risikoprofil der gesundheitlichen, wirtschaftlichen und sozialen Situation einer Population beurteilt werden. Allerdings setzt dies eine Verknüpfung von verschiedenen Datenqualitäten voraus, die erhebliche Herausforderungen nicht nur auf der Ebene der Informatik und der statistischen Modellbildung, sondern vor allem auch mit Blick auf die Einhaltung datenschutzrechtlicher Grundprinzipien birgt.
    Datenschutz und Datensicherheit - DuD 12/2014; 38(12). DOI:10.1007/s11623-014-0325-0
  • Ronja Foraita · M Jäger · I Pigeot ·
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    ABSTRACT: The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.
    Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 11/2014; 58(2). DOI:10.1007/s00103-014-2091-4 · 1.42 Impact Factor
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    ABSTRACT: Features of the built environment that may influence physical activity (PA) levels are commonly captured using a so-called walkability index. Since such indices typically describe opportunities for walking in everyday life of adults, they might not be applicable to assess urban opportunities for PA in children. Particularly, the spatial availability of recreational facilities may have an impact on PA in children and should be additionally considered. We linked individual data of 400 2- to 9-year-old children recruited in the European IDEFICS study to geographic data of one German study region, based on individual network-dependent neighborhoods. Environmental features of the walkability concept and the availability of recreational facilities, i.e. playgrounds, green spaces, and parks, were measured. Relevant features were combined to a moveability index that should capture urban opportunities for PA in children. A gamma log-regression model was used to model linear and non-linear effects of individual variables on accelerometer-based moderate-to-vigorous physical activity (MVPA) stratified by pre-school children (
    Journal of Urban Health 11/2014; 92(1). DOI:10.1007/s11524-014-9915-2 · 1.90 Impact Factor

Publication Stats

1k Citations
291.99 Total Impact Points


  • 2012-2015
    • Leibniz-Institute of Prevention Research and Epidemiology
      Bremen, Bremen, Germany
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 2002-2015
    • Universität Bremen
      • • Faculty 03: Mathematics/Computer Science
      • • Bremen Institute for Prevention Research and Social Medicine (BIPS)
      Bremen, Bremen, Germany
  • 2014
    • University of Zaragoza
      • Department of Pediatrics, Radiology and Physical Medicine
      Caesaraugusta, Aragon, Spain
  • 1999-2002
    • Technische Universität München
      München, Bavaria, Germany
    • Ludwig-Maximilian-University of Munich
      • Institut für Statistik
      München, Bavaria, Germany
  • 1991-1996
    • Technische Universität Dortmund
      • Chair of Computer Science XII
      Dortmund, North Rhine-Westphalia, Germany