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Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009–2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause. We used these patterns to characterize hospital healthcare flows in a visualization tool. We clustered these trajectories with kmlShape to identify time gap and tariff profiles. ACS hospital healthcare flows have three key categories: Angina pectoris, Myocardial Infarction or Ischemia. Elderly flows were more complex. Time gap profiles showed that readmissions were closer together as time goes by. Tariff profiles were different according to age and initial event. Our approach might be applied to monitoring other chronic diseases. Further work is needed to integrate these results into a medical decision-making tool.

One of the main problems encountered when predicting student success, as a tool to aid students, is the lack of data used to model each student. This lack of data is due in part to the small number of students in each university course and also, the limited number of features that describe the educational background for each student. In this article, we introduce new features by augmenting the student feature space to obtain an improved model. These features are divided into several groups, namely, external added data, metric and counter data, and evolutive data. We will then assess the quality of the augmented data to classify at-risk students in their first year of university. For this article, the classifiers are built using Random Forests. As this learning method measures variable importance, we can enquire on the relevance of the augmented data, as well as the data groups that allow a more significant collection of features.

Summary Background: Even in normally cycling women, hormone level shapes may widely vary between cycles and between women. Over decades, finding ways to characterize and compare cycle hormone waves was difficult and most solutions, in particular polynomials or splines, do not correspond to physiologically meaningful parameters. Objective: We present an original concept to characterize most hormone waves with only two parameters. Methods: The modelling attempt considered pregnanediol-3-alpha-glucuronide (PDG) and luteinising hormone (LH) levels in 266 cycles (with ultrasound-identified ovulation day) in 99 normally fertile women aged 18 to 45. The study searched for a convenient wave description process and carried out an extended search for the best fitting density distribution. Results: The highly flexible beta-binomial distribution offered the best fit of most hormone waves and required only two readily available and understandable wave parameters: location and scale. In bell-shaped waves (e.g., PDG curves), early peaks may be fitted with a low location parameter and a low scale parameter; plateau shapes are obtained with higher scale parameters. I-shaped, J-shaped, and U-shaped waves (sometimes the shapes of LH curves) may be fitted with high scale parameter and, respectively, low, high, and medium location parameter. These location and scale parameters will be later correlated with feminine physiological events. Conclusion: Our results demonstrate that, with unimodal waves, complex methods (e.g., functional mixed effects models using smoothing splines, second-order growth mixture models, or functional principal-component- based methods) may be avoided. The use, application, and, especially, result interpretation of four-parameter analyses might be advantageous within the context of feminine physiological events.

Part 9: Industry Case Studies; International audience; Statistical analysis is gradually entering all areas of society, be in academia or in the private sector. Statistical software is used by statisticians but also by non-experts (medical doctors, psychologists...). Unfortunately, this kind of software is integrated into obsolete interfaces that completely ignore the principles of HCI and are poorly adapted to non-expert users.R++ project aims to develop a modern statistical analysis software program integrated into a user-friendly interface. In this paper, we present the methodology that led us to the design of R++. We also give two examples that this methodology allowed us to achieve.

Background Longitudinal studies are those in which the same variable is repeatedly measured at different times. More likely than others, these studies suffer from missing values. Because the missing values may impact the statistical analyses, it is important that they be dealt with properly. Methods In this paper, we present “CopyMean”, a new method to impute (predict) monotone missing values. We compared its efficiency to sixteen imputation methods dedicated to the treatment of missing values in longitudinal data. All these methods were tested on four datasets, real or artificial, presenting markedly different caracteristics. Results The analysis showed that CopyMean was more efficient in almost all situations.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.

Background Low socio-economic context increases the risk of preterm delivery and may affect short-term outcomes in children born preterm. We described the social context of preterm delivery in France in 2011 and compared it with the general population of deliveries over the same period. We also studied how social context influenced pregnancy and delivery characteristics in the preterm population, and how it affected mortality and short-term morbidity in liveborn preterm children (

National audience; A better knowledge of patient flows can be decisive for health planning. In this article, we propose a method to characterise patients flow but also to highlight profiles of care pathways times and costs. From the medical-administrative data we extracted spatio-temporal patterns and integrated them into a visualization tool. Then, we clustered time between hospitalisation and cost trajectories in order to identify profiles profiles of change over time. This approach may support renewed management strategies.; Connaître les flux de patients peut être décisif pour la planification sa-nitaire. Dans cet article, nous proposons une méthode pour caractériser les flux de patients mais aussi pour mettre en évidence des profils de délais et de tarifs des parcours de soins. À partir des données du PMSI (Programme Médicalisé des Systèmes d'Information), nous avons extrait des motifs spatio-temporels puis les avons intégrés dans un outil de visualisation. Ensuite, nous avons classé les tra-jectoires de délais et de tarifs afin d'identifier des profils en termes d'occurrence et d'évolution de tarifs des parcours de soins. Cette approche pourrait permettre d'aider à l'élaboration de stratégies de prises en charge des patients.

International audience; The present research investigated the influence of coaches' exclamations on referees' decision-making in judo. Under time pressure, 65 judo referees judged identical throw situations played on video. The coaches' exclamations during throws were audible in the experimental condition, whereas no sound was present in the control condition. The throw situations varied in ambiguity (low vs. high) and strength (minor sanctions vs. hard sanctions), while coaches' exclamations interpreted the throws in a manner that was congenial for their own competitor. Results indicated that referees may use an audience response heuristic or a consensus heuristic to help them make decisions.

Background Longitudinal data are data in which each variable is measured repeatedly over time. One possibility for the analysis of such data is to cluster them. The majority of clustering methods group together individual that have close trajectories at given time points. These methods group trajectories that are locally close but not necessarily those that have similar shapes. However, in several circumstances, the progress of a phenomenon may be more important than the moment at which it occurs. One would thus like to achieve a partitioning where each group gathers individuals whose trajectories have similar shapes whatever the time lag between them. Method In this article, we present a longitudinal data partitioning algorithm based on the shapes of the trajectories rather than on classical distances. Because this algorithm is time consuming, we propose as well two data simplification procedures that make it applicable to high dimensional datasets. Results In an application to Alzheimer disease, this algorithm revealed a “rapid decline” patient group that was not found by the classical methods. In another application to the feminine menstrual cycle, the algorithm showed, contrarily to the current literature, that the luteinizing hormone presents two peaks in an important proportion of women (22%).

Introduction Even in normally cycling women, the shapes of hormone levels may widely vary between cycles and between women. Over the last decades, finding ways to characterize and compare the cycle hormone waves has shown to be a very difficult task and most answers, in particular polynomials or splines, do not refer to physiologically meaningful parameters. Methods We present here an original concept able to characterize most hormone waves with only two parameters. The modeling attempt considered pregnanediol-3-alpha-glucuronide (PDG) and luteinising hormone (LH) levels in 326 cycles (with ultrasound-identified ovulation day) in 107 normally fertile women aged 18 to 45. The study searched for a convenient wave description process and carried out an extended search for the best fitting density distribution. The highly flexible beta-binomial distribution was shown to offer the best fit of most hormone waves and require only two readily available and understandable wave parameters: location and scale. Results In bell-shaped waves, the case of PDG curves, early peaks (respectively late peaks) may be fitted with a small (respectively great) location parameter and a small scale parameter; plateau shapes are obtained with higher scale parameters. I-shaped, J-shaped, and U-shaped waves (which are sometimes the shapes of LH curves) may be fitted with high scale parameter and, respectively, small, high, and medium location parameter. These location and scale parameters will be later correlated with feminine physiological events. Conclusion Our results demonstrate that complex methods may be avoided and that pauci-parameter analyses might be more advantageous regarding the use, application, and, especially, result interpretation within the context of feminine physiological events. The concept and method may be used in other physiological contexts whenever biomarker waves have to be correlated with specific subject characteristics.

Cohort studies are becoming essential tools in epidemiological research. In these studies, measurements are not restricted to single variables but can be seen as trajectories. Statistical methods used to determine homogeneous patient trajectories can be separated into two families: model-based methods (like Proc Traj) and partitional clustering (non-parametric algorithms like k-means). KmL is a new implementation of k-means designed to work specifically on longitudinal data. It provides scope for dealing with missing values and runs the algorithm several times, varying the starting conditions and/or the number of clusters sought; its graphical interface helps the user to choose the appropriate number of clusters when the classic criterion is not efficient. To check KmL efficiency, we compare its performances to Proc Traj both on artificial and real data. The two techniques give very close clustering when trajectories follow polynomial curves. KmL gives much better results on non-polynomial trajectories.

Cette etude a pour objectif de montrer que les sequences de jeu en deviation (quand la balle n’est pas controlee) sont une forme particuliere de circulation du ballon. Elles s’inscrivent dans l’organisation generale de l’attaque comme des sequences ou le jeu s’accelere, provoquant le plus souvent un desequilibre momentane vis-a-vis de la defense. Nous avons choisi comme support le Championnat d’Europe des Nations de 2004. Apres avoir enregistre la totalite des rencontres, nous avons retenu 60 sequences qui comportaient trois deviations successives. Envisagee comme un systeme complexe, produisant trois types d’information, chaque combinaison a ete analysee en fonction de sa position sur le terrain. Nous avons pu ainsi discriminer 3 categories differentes de jeu en deviation qui ont comme trait commun d’etre declenchees quand les defenses sont denses et empechent l’acces direct au but. Nous avons par ailleurs utilise les proprietes liees a l’auto-organisation d’un systeme pour mettre en evidence les mecanismes du fonctionnement du jeu en deviation sous forte pression temporelle. Comme pour le jeu reduit, le jeu en deviation en situation reelle repose sur la production d’une information univoque et claire qui permet aux joueurs d’anticiper et de jouer au futur par rapport a la defense.

Longitudinal studies are essential tools in medical research. In these studies, variables are not restricted to single measurements but can be seen as variable-trajectories, either single or joint. Thus, an important question concerns the identification of homogeneous patient trajectories.kml and kml3d are R packages providing an implementation of k-means designed to work specifically on trajectories (kml) or on joint trajectories (kml3d). They provide various tools to work on longitudinal data: imputation methods for trajectories (nine classic and one original), methods to define starting conditions in k-means (four classic and three original) and quality criteria to choose the best number of clusters (four classic and one original). In addition, they offer graphic facilities to “visualize” the trajectories, either in 2D (single trajectory) or 3D (joint-trajectories). The 3D graph representing the mean joint-trajectories of each cluster can be exported through LATEX in a 3D dynamic rotating PDF graph (Figures 1 and 9).

Classifying patients according to longitudinal measures, or trajectory classification, has become frequent in clinical research. The k-means algorithm is increasingly used for this task in case of continuous variables with standard deviations that do not depend on the mean. One feature of count and binary data modeled by Poisson or logistic regression is that the variance depends on the mean; hence, the within-group variability changes from one group to another depending on the mean trajectory level. Mixture modeling could be used here for classification though its main purpose is to model the data. The results obtained may change according to the main objective. This article presents an extension of the k-means algorithm that takes into account the features of count and binary data by using the deviance as distance metric. This approach is justified by its analogy with the classification likelihood. Two applications are presented with binary and count data to show the differences between the classifications obtained with the usual Euclidean distance versus the deviance distance.

Background Several previous studies have shown relationships between adherence to HIV antiretroviral therapy (ART) and the viral load, the CD4 cell count, or mortality. However, the impact of variability in adherence to ART on the immunovirological response does not seem to have been investigated yet. Methods Monthly adherence data (November 1999 to April 2009) from 317 HIV-1 infected patients enrolled in the Senegalese ART initiative were analyzed. Latent-class trajectory models were used to build typical trajectories for the average adherence and the standardized variance of adherence. The relationship between the standardized variance of adherence and each of the change in CD4 cell count, the change in viral load, and mortality were investigated using, respectively, a mixed linear regression, a mixed logistic regression, and a Cox model with time-dependent covariates. All the models were adjusted on the average adherence. Results Three latent trajectories for the average adherence and three for the standardized variance of adherence were identified. The increase in CD4 cell count and the increase in the percentage of undetectable viral loads were negatively associated with the standardized variance of adherence but positively associated with the average adherence. The risk of death decreased significantly with the increase in the average adherence but increased significantly with the increase of the standardized variance of adherence. Conclusions The impacts of the level and the variability of adherence on the immunovirological response and survival justify the inclusion of these aspects into the process of patient education: adherence should be both high and constant. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-15-10) contains supplementary material, which is available to authorized users.

France presents one of the highest prevalence of teenagers aged 15-year-olds who report they already have experienced cannabis in Europe. Data from the French 2010 Health Behavior in School-aged Children (HSBC) survey and environmental parameters typifying schools' neighborhoods were used to study cannabis experimentation. We conducted a two-level logistic regression (clusters being schools) on 4,175 French 8th-10th graders from 156 schools. Several individual parameters were linked to cannabis experimentation. Living in a non-intact family, feeling insufficiently monitored, having poor communication with mother and being from a family with a high socio-economic status (SES) were all associated with increased risk of cannabis experimentation. At environmental level, only being in a priority education area was linked to this behavior, without explaining differences among schools.

In this 16-year longitudinal study, a new trajectory estimation approach was used to verify whether the developmental course of childhood inattention significantly predicted functional impairment. A rising childhood inattention trajectory significantly predicted graduation failure (OR: 1.76 [1.32–2.34]) independently of averaged inattention levels. Rising inattention is, in itself, important for prognosis.

Group-based trajectory models had a rapid development in the analysis of longitudinal data in clinical research. In these models, the assumption of homoscedasticity of the residuals is frequently made but this assumption is not always met. We developed here an easy-to-perform graphical method to assess the assumption of homoscedasticity of the residuals to apply especially in group-based trajectory models. The method is based on drawing an envelope to visualize the local dispersion of the residuals around each typical trajectory. Its efficiency is demonstrated using data on CD4 lymphocyte counts in patients with human immunodeficiency virus put on antiretroviral therapy. Four distinct distributions that take into account increasing parts of the variability of the observed data are presented. Significant differences in group structures and trajectory patterns were found according to the chosen distribution. These differences might have large impacts on the final trajectories and their characteristics; thus on potential medical decisions. With a single glance, the graphical criteria allow the choice of the distribution that best capture data variability and help dealing with a potential heteroscedasticity problem.

In cohort studies, variables are measured repeatedly and can be considered as trajectories. A classic way to work with trajectories is to cluster them in order to detect the existence of homogeneous patterns of evolution. Since cohort studies usually measure a large number of variables, it might be interesting to study the joint evolution of several variables (also called joint-variable trajectories). To date, the only way to cluster joint-trajectories is to cluster each trajectory independently, then to cross the partitions obtained. This approach is unsatisfactory because it does not take into account a possible co-evolution of variable-trajectories. KmL3D is an R package that implements a version of k-means dedicated to clustering joint-trajectories. It provides facilities for the management of missing values, offers several quality criteria and its graphic interface helps the user to select the best partition. KmL3D can work with any number of joint-variable trajectories. In the restricted case of two joint trajectories, it proposes 3D tools to visualize the partitioning and then export 3D dynamic rotating-graphs to PDF format.

The present paper aims to analyze trends over time in prevalence of cerebral palsy of post-neonatal origin, to investigate whether changes are similar according to severity and to describe the disability profile by etiology. Post-neonatal cases, birth years 1976 to 1998, were identified from the Surveillance of Cerebral Palsy in Europe collaboration (19 population-based registries). A recognized causal event occurring between 28 days and 24 months of age was considered to define the cases. Trends in prevalence were explored using graphical methods (Lowess and Cusum control chart) and modeled with negative binomial regressions. Over the study period, 404 cases were identified as post-neonatal cases (5.5% of the total). Mean prevalence rate was 1.20 per 10,000 live births (95% CI [1.08-1.31]). A significant downward trend was observed (p=0.001), with an accentuated decrease in the 1990 s. The prevalence of severe cases which account for around one third of the total also significantly decreased over time (p

Numerous prospective studies have shown that children diagnosed with attention deficit/hyperactivity disorder (ADHD) are at higher risk of long-term substance abuse/dependence. However, there are three important limits to these studies: (a) most did not differentiate the role of hyperactivity and inattention; (b) most did not control for associated behavioral problems; and (c) most did not consider females. Our aim was to clarify the unique and interactive contributions of childhood inattention and hyperactivity symptoms to early adulthood substance abuse/dependence. Behavioral problems of 1803 participants (814 males) in a population-based longitudinal study were assessed yearly between 6 and 12 years by mothers and teachers. The prevalence of substance abuse/dependence at age 21 years was 30.7% for nicotine, 13.4% for alcohol, 9.1% for cannabis and 2.0% for cocaine. The significant predictors of nicotine dependence were inattention (odds ratio (OR): 2.25; 95% confidence interval (CI): 1.63-3.11) and opposition (OR: 1.65; 95%: 1.20-2.28). Only opposition contributed to the prediction of cannabis dependence (OR: 2.33; 95% CI: 1.40-3.87) and cocaine dependence (OR: 2.97; 95% CI: 1.06-8.57). The best behavioral predictor of alcohol abuse/dependence (opposition) was only marginally significant (OR: 1.38; 95% CI: 0.98-1.95). Frequent oppositional behaviors during elementary school were clearly the most pervasive predictors of substance abuse/dependence in early adulthood. The association of childhood ADHD with substance abuse/dependence is largely attributable to its association with opposition problems during childhood. However, inattention remained an important predictor of nicotine dependence, in line with genetic and molecular commonalities between the two phenotypes suggested in the literature.

International audience

Objectives To identify groups of children with distinct developmental trajectories of body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, between the ages of 5 months and 8 years and identify early-life risk factors that distinguish children in an atypically elevated BMI trajectory group. Design Prospective cohort study. Setting Families with a child born between October 1997 and July 1998 in the province of Quebec, Canada. Participants A representative sample of children (N = 2120) selected through birth registries for the Quebec Longitudinal Study of Child Development. Children for whom BMI data were available for at least 5 time points were retained in the present study (n = 1957). Main Exposures Early-life factors putatively associated with BMI, assessed by maternal report. Outcome Measure Group-based trajectories of children's BMI, identified with a semiparametric modeling method from raw BMI values at each age. Results Three trajectories of BMI were identified: low-stable (54.5% of children), moderate (41.0%), and high-rising (4.5%). The high-rising group was characterized by an increasing average BMI, which exceeded international cutoff values for obesity by age 8 years. Two maternal risk factors were associated with the high-rising group as compared with the low-stable and moderate groups combined: maternal BMI (odds ratio, 2.38; 95% confidence interval, 1.38-4.54 for maternal overweight and 6.33; 3.82-11.85 for maternal obesity) and maternal smoking during pregnancy (2.28; 1.49-4.04). Conclusions Children continuing on an elevated BMI trajectory leading to obesity in middle childhood can be distinguished from children on a normative BMI trajectory as early as age 3.5 years. Important and preventable risk factors for childhood obesity are in place before birth.

Literature clearly documents the association between mental health problems, particularly attention deficit hyperactivity disorder (ADHD), and educational attainment. However, inattention and hyperactivity are generally not considered independently from each other in prospective studies. The aim of the present study was to differentiate the unique, additive, or interactive contributions of inattention and hyperactivity symptoms to educational attainment.The authors randomly selected 2,000 participants from a representative sample of Canadian children and estimated developmental trajectories of inattention and hyperactivity between the ages of 6 and 12 years using yearly assessments. High school graduation status, at age 22-23 years, was obtained from official records.Four trajectories of inattention and four trajectories of hyperactivity were observed between the ages of 6 and 12 years. After controlling for hyperactivity and other confounding variables, a high inattention trajectory (compared with low inattention) strongly predicted not having a high school diploma at 22-23 years of age (odds ratio=7.66, 95% confidence interval [CI]=5.06-11.58). To a lesser extent, a declining or rising trajectory of inattention also made a significant contribution (odds ratios of 2.67 [95% CI=1.90-3.75] and 3.87 [95% CI=2.75-5.45], respectively). Hyperactivity was not a significant predictor once inattention was taken into account.Inattention rather than hyperactivity during elementary school significantly predicts long-term educational attainment. Children with attention problems, regardless of hyperactivity, need preventive intervention early in their development.

The present longitudinal study measured student adjustment to higher education, comparing 50 participants from alternative schools (Steiner, Montessori, New Schools) with 80 students from the traditional school system. We hypothesized that students from alternative schools adapt better, because of greater perceived social support, academic self-efficacy, and task-oriented coping styles. Measures were taken during the last school year (baseline characteristics), and at the beginning of the first and last terms of the first year in higher education. The quality of adjustment was assessed through academic results, and physical and psychological well-being. The following instruments were used: the State-Trait Anxiety Inventory by Spielberger (1983), the 13-items Depression Inventory by Beck et al. (1961), the Coping Inventory for Stressful Situations by Endler and Parker (1990), and semi-directed interviews. Results show that students from alternative schools adjust better to higher education: they report less anxiety and depression symptoms, and show greater life satisfaction and academic achievement.

Coping styles are generally considered to be environmentally driven. Up to now, research has mainly focused on family influences. However, some studies underline the effect of educational settings on the development of problem-focused coping strategies. Consistently with previous reports on the enhancement of autonomy and problem-solving in alternative schools, and the relationship established by self-determination theory between autonomy-supportive climates and positive coping, we hypothesized that alternative school students develop more problem-focused coping styles. This hypothesis was tested on 80 traditional school students and 50 alternative school students (Steiner, Montessori and New schools), during their last secondary school year, using the Coping Inventory for Stressful Situations (Endler & Parker, 1990a). We also assessed psychological factors which can influence coping styles measures (anxiety and depression), using the State-Trait Anxiety Inventory (Spielberger, 1983), and the Beck Depression Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961). The proportion of problem-focused coping strategies was compared in traditional and alternative settings, adjusting for potential confounding factors, using logistic regression. Results show that the proportion of students using predominantly problem-focused coping strategies is higher in the population of alternative schools. Directions for further research on alternative schools are highlighted concerning coping as well as autonomy-supportive class climates.

CHILDHOOD OBESITY IS A PUBLIC HEALTH PROBLEM THAT IS SWEEPING THE WORLD, ESPECIALLY INDUSTRIAL COUNTRIES.1 A RECENT STUDY SHOWED THAT 10% to 15% of 2- to 5-year-olds are overweight in the United States.2 Furthermore, approximately 60% to 85% of obese children remain obese in adulthood.3 The major risk health problems of childhood obesity are cardiovascular disease (dyslipidemia, hypertension), endocrine dysfunction (type 2 diabetes, glucose intolerance, and insulin resistance) and pulmonary complications (obstructive sleep apnea syndrome, asthma, and exercise intolerance).1 Causes of the obesity are known to be multifactorial. A pediatric twin study suggests that 80% of the variation in body fat percentage and body mass index (BMI) is attributable to genetic factors.4 However, the rapid rise in obesity prevalence among genetically stable populations suggests that environmental factors should also be taken into account. It is recognized that prenatal factors, such as maternal diabetes, smoking during pregnancy, obesity or weight gain during pregnancy, birth weight of the child (as an outcome of intrauterine environment), might affect BMI in early childhood because the behaviors affecting energy balance develop early in life.5,6 In addition, sedentary lifestyles such as excessive television viewing or lack of physical activity contribute to the development of childhood obesity.7 There is growing evidence that sleep duration could be an additional factor. A comparison of 3 birth cohorts (1974,1979, and 1986) from 1 month to 16 years of age from the same Swiss population revealed that time spent in bed decreased (∼40 min) across cohorts particularly among young children.8 Also, an important 3.3-fold increase was observed in the prevalence of childhood (6–11 y) obesity between 1971–1974 and 1999.1 Previous cross-sectional studies have reported a link between short sleep duration and obesity in school-aged children.9–12 Chaput and coworkers (2006)12 found that the percentage of overweight and obese 5- to 10-year-old children was 3.5 times higher in children who slept 8 to 10 h (maternal report) than in children who slept for a total of 12 or 13 h, after adjusting for obesity risk factors such as parental obesity. Moreover, short nighttime sleep duration (

It is desirable that the smaller the number of faults hitting a network, the faster a network protocol recovers. We study the scenario where up to k (for a given k) faults hit processors of a synchronous distributed system by corrupting their state undetectably. In this context, we show that the well known step complexity model is not appropriate to study time complexity of time-adaptive protocols (i.e. protocols that recover from memory corruption in a time that depends only on the number of faults and not on the network size). In more detail, we prove that for nontrivial dynamic problems (such as token passing), there exists a lower bound of /spl Omega/(D) (where D is the network diameter) steps on the stabilization time even when as few as 1 corruption can hit the system. This implies that there exists no time adaptive protocol for those problems in the asynchronous step model, even if we assume that the number of faults is bounded by 1 and that the scheduling of the processors is almost synchronous (between two actions of an enabled processor any other processor may execute at most one action).