The objective of this work is assessing if sleep problems at ages 8–9 represent an alarm sign for presenting ADHD problems at ages 10–11 in three cohorts from INMA Study. Participants were 1244 children from Gipuzkoa, Sabadell, and Valencia cohorts. Sleep problems were assessed (ages 8–9) with the sleep items of the Child’s Behaviour Checklist (CBCL), and ADHD problems were collected through the Conner’s Parent Rating Scales-Revised: Short Form (CPRS-R:S) (age 10–11). Minimally and fully adjusted negative binomial models were fitted for each CPRS-R:S scale. Linearity of the relationship was assessed with generalized additive models . For sensitivity analyses, children with previous symptoms, those born preterm and small for gestational age, and cases with extreme values, were excluded. Sleep problems presented IRR (95% CI) of 1.14 (1.10–1.19), 1.20 (1.14–1.26), 1.18 (1.11–1.25), and 1.18 (1.13–1.23) for opposition, inattention, hyperactivity, and ADHD scales, respectively. Fully adjusted models slightly decreased the IRR, but the association remained similar and significant. Sensitivity analyses showed similar results to fully adjusted models with only hyperactivity shown a slight decrease on significance (Conclusion: Sleep problems are an alarm sign for later neurodevelopment problems such as ADHD. Healthcare systems could take advantage implementing policies to pay special attention on the sleep habits and sleep hygiene. This could contribute to add evidence to public health programmes such as the Healthy Child Programme.  The online version contains supplementary material available at 10.1007/s00431-023-05145-3. Sleep is “a normal, reversible, recurrent state of reduced responsiveness to external stimulation that is accompanied by complex and predictable changes in physiology” , necessaIn preadolescence, there are influences which may alter quantity, quality, and schedule of sleep: (A) the brain reduces synaptic density and increases white matter; (B) sleep architecture and circadian rhythms shift; and (C) poor sleep hygiene is more common  . These cAttention deficit hyperactivity disorder (ADHD) is a disorder characterized by symptoms of inattention (inability to concentrate), hyperactivity , and/or impulsivity  , which sSleep problems are even more common in children with ADHD symptoms, with their prevalence increasing to 25–73.3% of children with this disorder , 15, 16.On the one hand, sleep problems could be producing ADHD-like symptoms. Sleep problems drive to sleep deprivation, which provokes a low activation of the prefrontal cortex. This leads to oxidative stress and neuronal loss and impact on cognitive functions , 3 , Sabadell (N = 657), and Gipuzkoa (N = 638), respectively. Successive follow-up visits and sample evolution are described in Fig. The INMA Study is a Spanish population-based mother-and-child multicentre cohort set up in 2003 in 7 areas. This work uses data from INMA Valencia, Sabadell, and Gipuzkoa. Recruitment and subsequent procedures are described elsewhere . BrieflyChild Behaviour Checklist 6–18 (CBCL 6–18)  was usedConner’s Parent Rating Scales-Revised: Short Form (CPRS-R:S) was employed to measure ADHD symptoms when children were 10–11 years old. It consists in 27 items rated by mothers about her child’s behaviour in the last month. Each item was responded as 0 “Not true at all”, 1 “Just a little true”, 2 “Pretty much true”, and 3 “Very much true”. Inattention, hyperactivity, opposition, and ADHD index raw scores were employed in this work . These sFamily and parental characteristics as well as perinatal and child characteristics were collected by means of medical records and structured questionnaires at different follow-up visits .Parental age and country of birth, occupational social class , parentaPrevious symptoms of ADHD were assessed with the ADHD DSM-IV formlist  and the U and Kruskal-Wallis tests were used to assess the relation between categorical covariates and ADHD scores. To assess the bivariate relation of continuous covariates and CBCL’s sleep problems ADHD scores, Spearman correlations were employed.For descriptive analyses, frequencies and percentages were used for categorical variables, while medians and interquartile ranges were used for continuous variables. For bivariate analyses, Mann-Whitney p < 0.20 in the bivariate analyses and consecutively excluding those variables with a p value > 0.20 in the adjusted model based on the likelihood ratio test. (2) Sleep problems were included in each of the regressions of the previous step, to assess its effect on ADHD scores.We evaluated the linearity of the association between sleep problems and ADHD score, comparing linear and non-linear models  with the aid of graphical examination and the AIC test. Because non-linear models did not provide a better fit, the relationship between sleep problems and ADHD symptoms was assessed linearly by a negative binomial regression  and children with previous symptoms of ADHD. The fully adjusted models were re-ran excluding extreme values  for sleep and CPRS-R:S scales. A supplementary analysis was performed repeating the sensitivity analyses but employing the hit reaction time standard error (HRT-SE) from the attention network and flanker tasks , we performed stratified models and represented them graphically by the interaction variable. Statistical analyses were performed with IBM SPSS Statistics package version 26, R and RStudio  with the packages of MASS, haven, foreign, ggplot2, mgcv, and sjPlot. Flowcharts were designed with draw.io.Interactions were also checked in the fully adjusted models to assess the role of potential key variables , and if there was any effect modifier  (Table Child’s median and interquartile range (IQR) age was 10.9 (10.6–11.1). Parents’ ages during pregnancy were 31 (29–33) and 32 (30–36) for mothers and fathers, respectively; however, children were slightly younger, and parents were slightly older in Gipuzkoa.Sleep problems, opposition, inattention, hyperactivity, and ADHD presented a median and IQR of 1 (0–2), 3 (1–5), 2 (0–5), 1 (0–3), and 5 (2–11), respectively. Sleep problems did not differ across cohorts. Opposition was lower in Valencia, inattention was lower in Gipuzkoa, and in Sabadell, hyperactivity was more dispersed, and ADHD was higher.Minimally and fully adjusted models for ADHD problems are shown in Table p value = 0.051) when ADHD cases at ages 8-9 were excluded. Analyses excluding extreme values did not change the fully adjusted models. In general terms, sleep items describing overtiredness, sleeping less or more than other kids, and trouble sleeping were those more related to Conner’s scales  . We stratified and represented our ADHD and inattention models by paternal employment  were related to ADHD symptoms even after adjusting for other covariates . The secSocial determinants were included in our work, and we also explored if they provoked an interaction in our models. We observed that paternal employment was an effect modifier, meaning that the effect of sleep problems on ADHD was different depending on the father’s employment situation. We further explored the linear association stratifying by father’s employment situation finding that children from working fathers presented higher ADHD risk at higher sleep problems, while children from not working fathers did not present an association between sleep problems and ADHD. One possible explanation for this is that children from non-working parents could present higher ADHD scores but the same distribution for sleep problems scores for both working and not working fathers. Presenting more ADHD symptoms in more economically disadvantaged contexts has been explored previously in the literature and coincides with the current evidence . A recenThe present work has some limitations: Firstly, we could not include some relevant factors which could be determining the relation between sleep problems and ADHD, such as parenting stress . ParentiThe present work also presents several strengths: it includes sample from different areas in Spain, and this may avoid regional variability and could improve the power of the analyses. A whole range of factors of children’s environment that could be affecting ADHD symptoms was also included. This was possible due to its prospective nature, which allows the inclusion of information collected at different time point. One of the major strengths of this work is its specific design, employing the information on sleep problems collected in a previous time point to ADHD symptoms. This data collection in two steps vanishes the cross-sectional correlation, and the sensitivity analyses provide more strength to our findings. Lastly, the search of interactions and stratification by socioeconomic factors allowed us to observe specific patterns of the relationship studied.The present work contributes to the evidence that sleep problems in childhood may have an influence on ADHD symptoms even at early pre-pubertal stages. However, our evidence is not strong to establish a causal mechanism, but sleep problems could be considered an alarm sign for general health, and specifically, for ADHD symptoms. Healthcare systems could take advantage of these findings, implementing policies to pay special attention on the sleep habits and sleep hygiene. This could contribute to add evidence to public health programmes such as the Healthy Child Programme. Moreover, our work employed parent-reported questionnaires to detect both sleep and ADHD problems, and this system of detection could be easily and cost-effective translated into clinical practice. If results were confirmed in a wider sample, an assessment on sleep quality could be implemented in middle childhood. Given the high prevalence of these problems in this age period, the development and modification of sleep routines could easily ameliorate sleep problems and, also, future ADHD symptoms during pre-puberty.Supplementary file1 (DOCX 12249 KB)Below is the link to the electronic supplementary material.