There is something counter-intuitive about sleep and obesity. We use less energy while sleeping, yet lack of sleep may be one of the reasons why people gain weight. If so, sleep would be one of the modifiable factors for preventing overweight and obesity. We sleep less than we used to. Relatively recent changes, such as electric light, and very recent changes, such as mobile phones, have dissociated our sleeping habits from daylight which dictated the patterns for millennia. So, are we more likely to gain surplus weight if we have poor sleeping habits? Or are we looking at a reverse causality, where people with overweight or obesity are more likely to have disturbed sleep? Or are we tricked by confounders, factors common to obesity and sleep disturbances? There are plenty of candidates, such as sedentary behaviour, alcohol use, stress and psychiatric illnesses. For children, the American Association of Pediatrics has endorsed the consensus recommendation by their colleagues in the American Academy of Sleep Medicine. Children aged 3–5 years should sleep 10–13 h, including naps, children aged 6–12 years should sleep 9–12 h, and teenagers should sleep 8–10 h per 24 h.1 Data on children do not look too bad, although reports are conflicting. For girls and boys aged 6–9 years in 25 countries in the WHO European Childhood Obesity Surveillance Initiative (COSI), most children achieved 9–11 h of sleep. A fair fraction of those who did not were actually sleeping more.2 For adolescents, we know less and fear worse. Many studies have confirmed the association between lack of sleep and overweight.3 A systematic review of 42 prospective studies could demonstrate an effect size with small reductions in BMI for every additional hour of sleep.4 Some researchers have argued that the relationship is U-shaped; both children who sleep very much and those who sleep little are at increased risk of obesity.5 Others described a linear relationship between the amount of sleep and the risk of obesity.6 A lack of sleep affects metabolism and the appetite-regulating hormones, the desire to eat and food selection. Some of the landmark studies were carried out a few decades ago. In one of these studies, eleven young men were studied for 16 nights. They were allowed to sleep for 4 h per night for 6 nights and then they were able to catch up. During sleep deprivation, the insulin sensitivity decreased, and the metabolism of carbohydrates was impaired.7 In another study with a similar design, eleven adult men and women were studied for two 14 days periods and had free access to food. During the days of sleep deprivation, the participants ate more snacks.8 Researchers can keep their study participants awake and send them to bed, but they cannot make them sleep unless they use medications. The ground-breaking all-inclusive studies were probably both expensive and cumbersome. Instead, most studies rely on self-reported data on the time, duration, and quality of sleep. In the case of children, parents are often the reporters, which adds to the complexity. Important confounders in children would be neuropsychiatric illnesses and psychosocial well-being. In a Swedish study, two-thirds of adolescents seeking surgical weight loss presented with substantial mental health problems as reported by themselves or their parents.9 The everyday struggle for some of these adolescents may evolve into vicious circles of tiredness, sedentary behaviour and disturbed eating and sleeping patterns. And for the reverse, good parenting skills may affect both meal structure and sleeping habits positively. Clearly, the mechanisms, causality and confounders that may explain the association between obesity and sleep are still not fully understood. In this issue of Acta Paediatrica, Kjetså et al. make an important contribution to the field by reporting how sleeping habits at baseline affected the outcome for children who participated in obesity treatment, in addition to age and family income.10 It sounds like an obvious approach, but the effect of sleeping habits on the results of obesity treatment is much less studied than the associations between sleep and body weight for children in general. Kjetså´s study is a follow-up of 97 children aged 6–12 years with a body mass index (BMI) just under the cut-off for obesity and above, corresponding to adult BMI 27.5 kg/m2 and above. The children had been included in a 2 years randomised study, the Finnmark Activity School study in Norway, with typical lifestyle components. The authors gathered data on 62/97 (64%) at three years, one year after the end of the intervention, and regardless of randomisation.10 For a long-term evaluation of obesity treatment, 64% is a decent retention rate. At baseline, most children slept 9–11 h per night and had more than 2 h of screen time. The authors showed that the children who reported longer sleep duration at baseline had lost more weight at three years.10 This study makes good news for clinicians treating children with obesity. The healthy lifestyle adaptations that are encouraged in treatments of children with obesity should include messages on bedtime routines and adequate hours of sleep. The data from Norway support the clinical observation that sleep is beneficial to children in obesity treatment. Nothing to disclose. Annika Janson
A well-known anecdote shows a man looking for his lost keys under a lamppost at night. A passer-by stops to help and, after searching in vain, asks him ‘Are you sure this is where you lost the keys?’ The man replies ‘No. I lost them over there. But it is pitch dark there’. In research terms, only searching in the circle of light created by a streetlight would be the equivalent of selection bias and raise questions about how representative the findings would be. Instead, we love data that shed light on every corner. High coverage and large sample sizes make us trust information. We all dislike messing around in the dark. Valid data can be used to change things. In this issue of Acta Paediatrica, Miregård et al. present cross-sectional data on weight and height for 100 001 four-year-old children who were measured by Swedish Child Health Services in 18 of the 21 regions in 2020. The study covers 85% of all the children who were born in Sweden in 2016. Because 2020 was the first year of the COVID-19 pandemic, the authors chose to compare that data with 104 455 children who were 4 years of age in 2018, before the pandemic.1 At the beginning of the COVID-19 pandemic, the Swedish strategy for limiting transmission. stood out.2 Sweden chose to keep preschools and schools open and daily life was not as limited for young children as it was in many other countries. Despite this, food habits, social interactions and physical activity were still affected in Sweden. The data from Miregård et al. show a remarkable change in the prevalence of overweight and obesity among four-year-old children. The girls had a higher prevalence of overweight and obesity, which is often the case for the youngest ages. The increase from 2018 to 2020 was more notable among the boys than the girls, but significant for both sexes in 13 of the 18 regions that reported data for both years. Most importantly, the increase was more remarkable for obesity than for overweight with obesity rising by an alarming 31.8%, from 2.2% to 2.9%. It seems that COVID-19 played against a backdrop of social or genetic vulnerability, where some children were more prone to developing obesity when their support structures vanished. A similar pattern was observed in a partly longitudinal study on 25 049 children aged 3–5 years from three regions in Sweden.3 In that study, overweight and obesity increased more among children attending Child Health Services in less socially privileged areas.3 The Miregård et al. paper discusses how a regional social care need index only partly correlated with their data on overweight and obesity in four-year-old children. They state that there are likely to be many explanations. In my eyes, the central message of the Miregård et al. paper is not the likely effect of the COVID-19 pandemic on children's weight, but the uniqueness of the data that the study produced. As far as I know, these are the only comprehensive national data on four-year old children's heights and weights in the world. The maps that illustrate the differences between regions will be of particular interest. What factors make children's weight increase too fast? What successful strategies have been used? As Miregård et al. point out, parents and health professionals both prefer, and ask for, weight issues to be targeted early in a child's life.4, 5 Good data mean that the effects of interventions can be analysed. Finland is a forerunner in gathering national data on children's growth. The Finnish Institute for Health and Welfare automatically collects data from primary health care and school health services for children aged 2–16 years. The coverage is 50% for children aged 2–6 years.6 We can look on with envy at Finland's data for school-age children, whereas the Swedish data have been sparse.7 Hopefully, we may not need to depend on researchers´ stoic ambition to gather data much longer, but rather on routine data collection in an emerging patient quality register for Child Health Services, Barnhälsovårdsregistret, which is including more and more regional reports.8 At a paediatric obesity congress in 2022, Kremlin Wickramasinghe, the acting head of the World Health Organization's European Office for the Prevention and Control of Non-Communicable Disease, was particularly concerned about the lack of high-quality data on preschool children. Thanks to the hard work of the authors, Acta Paediatrica can now proudly present high-quality data on over 200 000 children from Sweden.
To assess the prevalence of neurodevelopmental problems in adolescents with severe obesity and their associations with binge eating and depression.Data were collected at inclusion in a randomised study of bariatric surgery in 48 adolescents (73% girls; mean age 15.7 ± 1.0 years; mean body mass index 42.6 ± 5.2 kg/m2 ). Parents completed questionnaires assessing their adolescents' symptoms of attention-deficit/hyperactivity disorder and autism spectrum disorder and reported earlier diagnoses. Patients answered self-report questionnaires on binge eating and depressive symptoms.The parents of 26/48 adolescents (54%) reported scores above cut-off for symptoms of the targeted disorders in their adolescents, but only 15% reported a diagnosis, 32% of adolescents reported binge eating, and 20% reported symptoms of clinical depression. No significant associations were found between neurodevelopmental problems and binge eating or depressive symptoms. Only a third of the adolescents reported no problems in either area.Two thirds of adolescents seeking surgical weight loss presented with substantial mental health problems (reported by themselves or their parents). This illustrates the importance of a multi-professional approach and the need to screen for and treat mental health disorders in adolescents with obesity.
Children's growth is mainstream paediatrics. The metaphor of the growth chart being the child's equivalent to the black box of an aeroplane is still valid. Obesity or underweight, tall or short stature and timing of puberty can point to health issues for the individual child. Data on growth for groups of children can help assess health indicators by sex, social determinants or area of residency. With good data, we can track trends over time and evaluate the effects of interventions. Sweden does not collect data on children's growth on a national level. This is likely a surprise to many as Sweden is famous for computerised medical files, long-term quality registries, and personal identification numbers. Hence, Sweden could have been expected to keep track of the extensive data collected by the school health services. That is, the data are there, but the information is not compiled. Attempts from dedicated individuals to create a national register for school health data, Elevhälsans Medicinska kvalitetsregister (EMQ), never really took off and EMQ are idle, waiting for the authorities to decide on the nature of the register and its legal aspects. Schools in Sweden are almost always paid by public financing, although independent education corporations run many schools. There are regulations as to the content of school health services. Likely, some reasons for not collecting data are purely practical and others more complex. In her autobiography, the later secretary general of WHO, Gro Harlem Brundtland, described the criticism she got of ‘sorting children' when collecting data on children's weights and heights in Oslo, Norway, in the early 1970s.1 The epidemiological study presented by Bygdell and co-workers in this issue of Acta Paediatrica provides much-needed comprehensive data on BMI in children.2 Using the established cut-offs from the International Obesity Task Force,3 the authors present recent data on obesity, overweight and underweight of almost 70,000 children age 5–18.9 years from Gothenburg, the second largest city of Sweden. The data are cross-sectional, measuring each child once in 2015–2018. The study presents data for age intervals of 2 years by sex. Only for the youngest boys, 5–6.9 years, underweight was at 11.8% more common than the combined rates of overweight and obesity. For all girls, and boys from 7 years of age, having overweight or obesity was more common than having underweight. Up to 9 years of age, girls were heavier than boys, and then, the sexes were equal in the proportion of overweight and obesity until 12.9 years. From age 13, boys had higher rates of overweight and obesity, with an alarming 6.1% of the oldest boys aged 15–18.9 years with a BMI classified as obesity. The rule of thumb that one in five children in Sweden has overweight or obesity held, with 18% of the total study population fulfilling the criteria. Height and weight in the study by Bygdell were measured by regular school health services staff. Even high-profile prevalence studies are sometimes based on questionnaires, where the risk of selection or underreporting weight is evident.4 Although Bygdell and co-workers did not present data on the size of their study sample in relation to the study population, the coverage can be estimated to be close to 100%, at least for the younger age groups. As pointed out by the authors in a useful supplement presenting other Swedish prevalence studies, the rates of overweight and obesity in this study are similar to previous studies. Another high-quality recent prevalence study from Sweden was made in Jönköping and used school-based data to cover 82%–97% of the younger children and 55%–69% of the older children during the study period 2004/5–2014/15. In that study, also published in Acta Paediatrica, Eriksson and co-workers demonstrated a slight decrease in overweight among girls and boys aged 4 years. However, there were increasing trends in overweight and obesity in both girls and boys aged 11 and 14 years of age and a sharp increase among 17-year-old boys, with 7.3% having obesity.5 The Swedish Public Health Agency performs the data collection every 3 years for the WHO Child Obesity Surveillance Initiative (COSI) and collected data 2018–19 for over 60,000 children 6–9 years old from school nurses.6 The annual number of children born in Sweden is around 120,000,7 so the sample represents an eighth of all children aged 6–9 years, and 21% had overweight or obesity. For older children, the Public Health Agency showed data based on self-reported height and weight in the school-based questionnaire Skolbarns hälsovanor that is distributed every 4 years to 11-, 13- and 14-year-old children. A meagre 4294 children participated in the last round, and the combined rate of overweight and obesity was 15%.4 In contrast, data from infants and pre-schoolers are collected and analysed in Sweden. Before starting school, almost all Swedish children attend well-child clinics, and well-designed annual reports present comprehensive data. Among health indicators, rates of caries and obesity show striking variations. The percentage of 4-year-old children having overweight or obesity in Stockholm was four times higher, 18.2%, in the most affected suburb than in the area with the lowest prevalence.8 This information is compelling for analyses of inequalities in health status and for designing means of addressing issues of concern. Today, with computers that can aid in collecting and anonymising data, Sweden should be able to follow growth also for children from age 6 years using available data from school health services. The time has come for plain annual reports of routine data from schoolchildren to replace ground-breaking research studies like the ones by Bygdell and Eriksson. With reports at hand, patterns of ill-health can be visualised. Hopefully, data will lead to action. Annika Janson
Abstract Aim Neonatal diabetes is rare, and treatment is challenging. We present aspects on treatment, genetics and incidence. Method This was a prospective cohort study including all cases in our study area in Sweden. We compared with data from the National Diabetes Registry, the Neonatal Quality Register and the National Patient Register. Results In the 19‐year study period January 1, 1998 to December 31, 2016, we treated seven infants, five of them boys. Six patients used a subcutaneous insulin pump, and the smallest patient started at a weight of 938 g. Most important was for the pump to deliver minute doses of insulin and the design of cannulas and tubing. All patients could stop insulin treatment at 17‐145 days of age. One patient relapsed at age 4.5 years. Four patients used the insulin pump after discharge. A mutation was identified in five patients, and this included all patients born after 30 weeks of gestation. The incidence of neonatal diabetes was 2/1 00 000, higher than previously estimated for Europe. Similar but lower incidences were reported in the registries. Conclusion Insulin pumps were safe in neonatal diabetes. All seven cases were transient. Neonatal diabetes was more common in our area than reported from Europe.
The lipolytic effects of growth hormone (GH) in children are not fully clarified. In this study, no lipolytic effect of GH on isolated adipocytes obtained at surgery on healthy infants aged 2-5 months and children 3-6 years was observed. Furthermore, GH did not enhance isoprenaline-induced lipolysis, as in adults. The TSH-induced lipolysis which is prominent in neonatal adipocytes was not affected by incubation of adipocytes with GH. Assuming that GH alters adipocyte metabolism primarily by increasing the sensitivity, but not the maximum response, of the beta 2-adrenergic receptor population, it follows that GH, in this sense, should be a less important co-actor in children where beta 2-adrenergic receptors are more abundant.
A routine health information system is one of the essential components of a health system. Interventions to improve routine health information system data quality and use for decision-making in low- and middle-income countries differ in design, methods, and scope. There have been limited efforts to synthesise the knowledge across the currently available intervention studies. Thus, this scoping review synthesised published results from interventions that aimed at improving data quality and use in routine health information systems in low- and middle-income countries.We included articles on intervention studies that aimed to improve data quality and use within routine health information systems in low- and middle-income countries, published in English from January 2008 to February 2020. We searched the literature in the databases Medline/PubMed, Web of Science, Embase, and Global Health. After a meticulous screening, we identified 20 articles on data quality and 16 on data use. We prepared and presented the results as a narrative.Most of the studies were from Sub-Saharan Africa and designed as case studies. Interventions enhancing the quality of data targeted health facilities and staff within districts, and district health managers for improved data use. Combinations of technology enhancement along with capacity building activities, and data quality assessment and feedback system were found useful in improving data quality. Interventions facilitating data availability combined with technology enhancement increased the use of data for planning.The studies in this scoping review showed that a combination of interventions, addressing both behavioural and technical factors, improved data quality and use. Interventions addressing organisational factors were non-existent, but these factors were reported to pose challenges to the implementation and performance of reported interventions.
Becker muscular dystrophy (BMD) is characterised by broad clinical variability. Ongoing studies exploring dystrophin restoration in Duchenne muscular dystrophy ask for better understanding of the relation between dystrophin levels and disease severity. We studied this relation in BMD patients with varying mutations, including a large subset with an exon 45–47 deletion.
Methods
Dystrophin was quantified by western blot analyses in a fresh muscle biopsy of the anterior tibial muscle. Disease severity was assessed using quantitative muscle strength measurements and functional disability scoring. MRI of the leg was performed in a subgroup to detect fatty infiltration.
Results
33 BMD patients participated. No linear relation was found between dystrophin levels (range 3%–78%) and muscle strength or age at different disease milestones, in both the whole group and the subgroup of exon 45–47 deleted patients. However, patients with less than 10% dystrophin all showed a severe disease course. No relation was found between disease severity and age when analysing the whole group. By contrast, in the exon 45–47 deleted subgroup, muscle strength and levels of fatty infiltration were significantly correlated with patients' age.
Conclusions
Our study shows that dystrophin levels appear not to be a major determinant of disease severity in BMD, as long as it is above approximately 10%. A significant relation between age and disease course was only found in the exon 45–47 deletion subgroup. This suggests that at higher dystrophin levels, the disease course depends more on the mutation site than on the amount of the dystrophin protein produced.
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