Sleep duration modifies effects of free ad libitum school meals on adiposity and blood pressure

Interventions to prevent obesity in children aged 5 to 11 years old

BACKGROUND

Prevention of obesity in children is an international public health priority given the prevalence of the condition (and its significant impact on health, development and well-being). Interventions that aim to prevent obesity involve behavioural change strategies that promote healthy eating or 'activity' levels (physical activity, sedentary behaviour and/or sleep) or both, and work by reducing energy intake and/or increasing energy expenditure, respectively. There is uncertainty over which approaches are more effective and numerous new studies have been published over the last five years, since the previous version of this Cochrane review.

OBJECTIVES

To assess the effects of interventions that aim to prevent obesity in children by modifying dietary intake or 'activity' levels, or a combination of both, on changes in BMI, zBMI score and serious adverse events.

SEARCH METHODS

We used standard, extensive Cochrane search methods. The latest search date was February 2023.

SELECTION CRITERIA

Randomised controlled trials in children (mean age 5 years and above but less than 12 years), comparing diet or 'activity' interventions (or both) to prevent obesity with no intervention, usual care, or with another eligible intervention, in any setting. Studies had to measure outcomes at a minimum of 12 weeks post baseline. We excluded interventions designed primarily to improve sporting performance.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our outcomes were body mass index (BMI), zBMI score and serious adverse events, assessed at short- (12 weeks to < 9 months from baseline), medium- (9 months to < 15 months) and long-term (≥ 15 months) follow-up. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

This review includes 172 studies (189,707 participants); 149 studies (160,267 participants) were included in meta-analyses. One hundred forty-six studies were based in high-income countries. The main setting for intervention delivery was schools (111 studies), followed by the community (15 studies), the home (eight studies) and a clinical setting (seven studies); one intervention was conducted by telehealth and 31 studies were conducted in more than one setting. Eighty-six interventions were implemented for less than nine months; the shortest was conducted over one visit and the longest over four years. Non-industry funding was declared by 132 studies; 24 studies were funded in part or wholly by industry. Dietary interventions versus control Dietary interventions, compared with control, may have little to no effect on BMI at short-term follow-up (mean difference (MD) 0, 95% confidence interval (CI) -0.10 to 0.10; 5 studies, 2107 participants; low-certainty evidence) and at medium-term follow-up (MD -0.01, 95% CI -0.15 to 0.12; 9 studies, 6815 participants; low-certainty evidence) or zBMI at long-term follow-up (MD -0.05, 95% CI -0.10 to 0.01; 7 studies, 5285 participants; low-certainty evidence). Dietary interventions, compared with control, probably have little to no effect on BMI at long-term follow-up (MD -0.17, 95% CI -0.48 to 0.13; 2 studies, 945 participants; moderate-certainty evidence) and zBMI at short- or medium-term follow-up (MD -0.06, 95% CI -0.13 to 0.01; 8 studies, 3695 participants; MD -0.04, 95% CI -0.10 to 0.02; 9 studies, 7048 participants; moderate-certainty evidence). Five studies (1913 participants; very low-certainty evidence) reported data on serious adverse events: one reported serious adverse events (e.g. allergy, behavioural problems and abdominal discomfort) that may have occurred as a result of the intervention; four reported no effect. Activity interventions versus control Activity interventions, compared with control, may have little to no effect on BMI and zBMI at short-term or long-term follow-up (BMI short-term: MD -0.02, 95% CI -0.17 to 0.13; 14 studies, 4069 participants; zBMI short-term: MD -0.02, 95% CI -0.07 to 0.02; 6 studies, 3580 participants; low-certainty evidence; BMI long-term: MD -0.07, 95% CI -0.24 to 0.10; 8 studies, 8302 participants; zBMI long-term: MD -0.02, 95% CI -0.09 to 0.04; 6 studies, 6940 participants; low-certainty evidence). Activity interventions likely result in a slight reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.18 to -0.05; 16 studies, 21,286 participants; zBMI: MD -0.05, 95% CI -0.09 to -0.02; 13 studies, 20,600 participants; moderate-certainty evidence). Eleven studies (21,278 participants; low-certainty evidence) reported data on serious adverse events; one study reported two minor ankle sprains and one study reported the incident rate of adverse events (e.g. musculoskeletal injuries) that may have occurred as a result of the intervention; nine studies reported no effect. Dietary and activity interventions versus control Dietary and activity interventions, compared with control, may result in a slight reduction in BMI and zBMI at short-term follow-up (BMI: MD -0.11, 95% CI -0.21 to -0.01; 27 studies, 16,066 participants; zBMI: MD -0.03, 95% CI -0.06 to 0.00; 26 studies, 12,784 participants; low-certainty evidence) and likely result in a reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.21 to 0.00; 21 studies, 17,547 participants; zBMI: MD -0.05, 95% CI -0.07 to -0.02; 24 studies, 20,998 participants; moderate-certainty evidence). Dietary and activity interventions compared with control may result in little to no difference in BMI and zBMI at long-term follow-up (BMI: MD 0.03, 95% CI -0.11 to 0.16; 16 studies, 22,098 participants; zBMI: MD -0.02, 95% CI -0.06 to 0.01; 22 studies, 23,594 participants; low-certainty evidence). Nineteen studies (27,882 participants; low-certainty evidence) reported data on serious adverse events: four studies reported occurrence of serious adverse events (e.g. injuries, low levels of extreme dieting behaviour); 15 studies reported no effect. Heterogeneity was apparent in the results for all outcomes at the three follow-up times, which could not be explained by the main setting of the interventions (school, home, school and home, other), country income status (high-income versus non-high-income), participants' socioeconomic status (low versus mixed) and duration of the intervention. Most studies excluded children with a mental or physical disability.

AUTHORS' CONCLUSIONS

The body of evidence in this review demonstrates that a range of school-based 'activity' interventions, alone or in combination with dietary interventions, may have a modest beneficial effect on obesity in childhood at short- and medium-term, but not at long-term follow-up. Dietary interventions alone may result in little to no difference. Limited evidence of low quality was identified on the effect of dietary and/or activity interventions on severe adverse events and health inequalities; exploratory analyses of these data suggest no meaningful impact. We identified a dearth of evidence for home and community-based settings (e.g. delivered through local youth groups), for children living with disabilities and indicators of health inequities.

Sleep Deprivation and Central Appetite Regulation

Research shows that reduced sleep duration is related to an increased risk of obesity. The relationship between sleep deprivation and obesity, type 2 diabetes, and other chronic diseases may be related to the imbalance of appetite regulation. To comprehensively illustrate the specific relationship between sleep deprivation and appetite regulation, this review introduces the pathophysiology of sleep deprivation, the research cutting edge of animal models, and the central regulatory mechanism of appetite under sleep deprivation. This paper summarizes the changes in appetite-related hormones orexin, ghrelin, leptin, and insulin secretion caused by long-term sleep deprivation based on the epidemiology data and animal studies that have established sleep deprivation models. Moreover, this review analyzes the potential mechanism of associations between appetite regulation and sleep deprivation, providing more clues on further studies and new strategies to access obesity and metabolic disease.

Sleep profile status based on substance use, lipids and demographic variables in Tabari cohort study

BACKGROUND

This study aims to investigate the situation of sleep profile and its related factors in the Tabari Cohort Tabari (TCS) population.

METHODS

The information of 10255 of the Tabari cohort population in the enrolment phase was used in this study. The sleep profile data was collected and recorded by trained questioners. The sleep duration in day & night, the time interval between going bed and falling asleep, continuous use of sedatives, involuntary nap, limb hypermobility during sleep and shift working were determined for each person. Data analysis was performed by independent T test and Pearson correlation coefficient.

RESULTS

Mean, standard deviation, median, minimum and maximum of sleep duration in this population were 7.6, 1.6, 7.5, 0.5 and 17 h. Frequency of sleeping less than 6 h, 6-10 h and more than 10 h were 1168(11.4%), 8463(82.5%) and 624(6.1%) respectively. Prevalence of sleeping more than 10 h among men and women were 5% and 6.8% respectively (P < 0.001). Prevalence of sedative routine use among men and women were 4.7% and 9.6% respectively (P < 0.001). There were significant relationships between sleep duration and area residence, age group (P < 0.001), education level (P < 0.001), socioeconomic level (P < 0.001), triglyceride (P = 0.002), HDL-cholesterol (P = 0.013) and Cholesterol total (P = 0.021). There was a negative correlation between age and sleep duration (r = -0.062, P < 0.001).

CONCLUSION

The results showed the association of the quality and quantity of sleep with personal, social, environmental and biological factors such as gender, age, economic status, educational status, and lipid profile. Therefore without proper intervention, the incidence of outcomes associated with these risk factors can be predicted in TCS In later years.

Effect of sleep duration on dietary intake, desire to eat, measures of food intake and metabolic hormones: A systematic review of clinical trials

BACKGROUND AND AIMS

Sleep, as well as diet and physical activity, plays a significant role in growth, maturation, health, and regulation of energy homeostasis. Recently, there is increasing evidence indicating a possible causal association between sleep duration and energy balance. We aimed to examine the relationship between sleep duration and food consumption, energy intake, anthropometric characteristics, and appetite-regulating hormones by randomized controlled trials (RCTs).

METHODS

Electronic literature searches were conducted on Medline, Web of Science, and Google Scholar until July 2020. The search was conducted with the following words: "Sleep Duration", "Circadian Rhythm", "Sleep Disorders" in combination with "Obesity", "Overweight", "Abdominal Obesity", "Physical Activity", "Energy Intake", "Body Mass Index", "Lipid Metabolism", "Caloric Restriction", Leptin, "Weight Gain", and "Appetite Regulation" using human studies.methods RESULTS: After screening 708 abstracts, 50 RCTs (7 on children or adolescents and 43 on adults) were identified and met the inclusion criteria. In general, the findings suggested that sleep restriction may leads to a significant increment in energy intake, fat intake, body weight, appetite, hunger, eating occasions, and portion size, while protein and carbohydrate consumption, total energy expenditure, and respiratory quotient remained unaffected as a result of sleep restriction. Serum leptin, ghrelin, and cortisol concentrations were not influenced by sleep duration as well.

CONCLUSION

Insufficient sleep can be considered as a contributing factor for energy imbalance, weight gain, and metabolic disorders and it is suggested that to tackle disordered eating it may be necessary to pay more attention to sleep duration.

A systematic review of the association between sleep health and stress biomarkers in children

Sleep is intimately linked with the stress response system. While the evidence for this connection has been systematically reviewed in the adult literature, to our knowledge no studies have examined this relationship in young children. Recent scientific interest in understanding the effects of adverse environments in early childhood, including an emphasis on understanding the role of sleep, highlights the importance of synthesizing the current evidence on the relationship between sleep and the stress response system in early childhood. The aim of this systematic review is to examine the relationship between sleep health and biomarkers of physiologic stress (neuroendocrine, immune, metabolic, cardiovascular) in healthy children ages 0-12 y. Following PRISMA guidelines, we identified 68 empirical articles and critically reviewed and synthesized the results across studies. The majority of studies included school-age children and reported sleep dimensions of duration or efficiency. Overall, evidence of associations between sleep health and stress biomarkers was strongest for neuroendocrine variables, and limited or inconsistent for studies of immune, cardiovascular, and metabolic outcomes. Gaps in the literature include prospective, longitudinal studies, inclusion of children under the age of 5 y, and studies using objective measures of sleep.

Bidirectional associations between sleep quality or quantity, and dietary intakes or eating behaviors in children 6-12 years old: a systematic review with evidence mapping

CONTEXT

Although dietary advice has long been a cornerstone of a healthy lifestyle, how sleep quality and quantity may interact with dietary intake or eating behaviors remains unclear.

OBJECTIVE

To consider a bidirectional relationship between sleep and diet in children aged 6-12 years via a systematic review following PRISMA guidelines.

DATA SOURCES

Relevant trials and observational studies were identified by searching the PubMed, Medline, Embase, and CENTRAL databases up to June 1, 2019, without language or date restrictions and supplemented with hand searching. Recognized procedures and reporting standards were applied.

DATA EXTRACTION

Data on participant characteristics, study parameters, diet measures, sleep measures, and findings of study quality assessment criteria were collected.

DATA ANALYSIS

Forty-five articles involving 308 332 participants on a diverse range of topics were included. Meta-analyses were planned but were impossible to perform due to high study heterogeneity. Most studies (82%) were cross-sectional, which prevented examining directionality of the observed associations. Risk of bias was assessed for trial, cohort studies, and cross-sectional studies, using the Cochrane Risk of Bias Tool or Newcastle Ottawa Scale.

RESULTS

Of 16 studies in which the effect of sleep on dietary intake was investigated, 81% (n = 13) reported a significant association. All studies (n = 8) of sugar-sweetened or caffeinated beverages reported a negative association with sleep, and in 6 of 7 studies in which eating behaviors were investigated, associations with sleep were reported. The use of objective measures of sleep and diet were scarce, with most trials and studies relying on subjective measures of sleep (68%) or diet (93%).

CONCLUSION

Because most studies investigating the relationship between sleep and diet in this age group are cross-sectional, temporality could not be determined. Additional randomized controlled trials and long-term cohort studies in middle childhood, particularly those using objective rather than questionnaire measures of sleep, are required to better understand interactions between diet and sleep.

SYSTEMATIC REVIEW REGISTRATION

Prospectively registered with PROSPERO International Prospective Register of Systematic Reviews (CRD42018091647).

Sleep, Diet, and Cardiometabolic Health Investigations: a Systematic Review of Analytic Strategies

PURPOSE OF REVIEW

Poor sleep is a risk factor for cardiometabolic morbidity. The relationship of sleep and cardiometabolic health could be confounded, mediated, or modified by diet, yet the incorporation of diet in sleep-cardiometabolic health studies is inconsistent. This rapid systematic literature review evaluates the conceptualization of diet as a confounder, mediator, or effect modifier within sleep-cardiometabolic health investigations, and the statistical approaches utilized.

RECENT FINDINGS

Of 4692 studies identified, 60 were retained (28 adult, 32 pediatric). Most studies included diet patterns, quality, or energy intake as confounders, while a few examined these dietary variables as mediators or effect modifiers. There was some evidence, mostly in pediatric studies, that inclusion of diet altered sleep-cardiometabolic health associations. Diet plays a diverse role within sleep-cardiometabolic health associations. Investigators should carefully consider the conceptualization of diet variables in these relationships and utilize contemporary statistical approaches when applicable.

Interventions for preventing obesity in children

BACKGROUND

Prevention of childhood obesity is an international public health priority given the significant impact of obesity on acute and chronic diseases, general health, development and well-being. The international evidence base for strategies to prevent obesity is very large and is accumulating rapidly. This is an update of a previous review.

OBJECTIVES

To determine the effectiveness of a range of interventions that include diet or physical activity components, or both, designed to prevent obesity in children.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, PsychINFO and CINAHL in June 2015. We re-ran the search from June 2015 to January 2018 and included a search of trial registers.

SELECTION CRITERIA

Randomised controlled trials (RCTs) of diet or physical activity interventions, or combined diet and physical activity interventions, for preventing overweight or obesity in children (0-17 years) that reported outcomes at a minimum of 12 weeks from baseline.

DATA COLLECTION AND ANALYSIS

Two authors independently extracted data, assessed risk-of-bias and evaluated overall certainty of the evidence using GRADE. We extracted data on adiposity outcomes, sociodemographic characteristics, adverse events, intervention process and costs. We meta-analysed data as guided by the Cochrane Handbook for Systematic Reviews of Interventions and presented separate meta-analyses by age group for child 0 to 5 years, 6 to 12 years, and 13 to 18 years for zBMI and BMI.

MAIN RESULTS

We included 153 RCTs, mostly from the USA or Europe. Thirteen studies were based in upper-middle-income countries (UMIC: Brazil, Ecuador, Lebanon, Mexico, Thailand, Turkey, US-Mexico border), and one was based in a lower middle-income country (LMIC: Egypt). The majority (85) targeted children aged 6 to 12 years.Children aged 0-5 years: There is moderate-certainty evidence from 16 RCTs (n = 6261) that diet combined with physical activity interventions, compared with control, reduced BMI (mean difference (MD) -0.07 kg/m2, 95% confidence interval (CI) -0.14 to -0.01), and had a similar effect (11 RCTs, n = 5536) on zBMI (MD -0.11, 95% CI -0.21 to 0.01). Neither diet (moderate-certainty evidence) nor physical activity interventions alone (high-certainty evidence) compared with control reduced BMI (physical activity alone: MD -0.22 kg/m2, 95% CI -0.44 to 0.01) or zBMI (diet alone: MD -0.14, 95% CI -0.32 to 0.04; physical activity alone: MD 0.01, 95% CI -0.10 to 0.13) in children aged 0-5 years.Children aged 6 to 12 years: There is moderate-certainty evidence from 14 RCTs (n = 16,410) that physical activity interventions, compared with control, reduced BMI (MD -0.10 kg/m2, 95% CI -0.14 to -0.05). However, there is moderate-certainty evidence that they had little or no effect on zBMI (MD -0.02, 95% CI -0.06 to 0.02). There is low-certainty evidence from 20 RCTs (n = 24,043) that diet combined with physical activity interventions, compared with control, reduced zBMI (MD -0.05 kg/m2, 95% CI -0.10 to -0.01). There is high-certainty evidence that diet interventions, compared with control, had little impact on zBMI (MD -0.03, 95% CI -0.06 to 0.01) or BMI (-0.02 kg/m2, 95% CI -0.11 to 0.06).Children aged 13 to 18 years: There is very low-certainty evidence that physical activity interventions, compared with control reduced BMI (MD -1.53 kg/m2, 95% CI -2.67 to -0.39; 4 RCTs; n = 720); and low-certainty evidence for a reduction in zBMI (MD -0.2, 95% CI -0.3 to -0.1; 1 RCT; n = 100). There is low-certainty evidence from eight RCTs (n = 16,583) that diet combined with physical activity interventions, compared with control, had no effect on BMI (MD -0.02 kg/m2, 95% CI -0.10 to 0.05); or zBMI (MD 0.01, 95% CI -0.05 to 0.07; 6 RCTs; n = 16,543). Evidence from two RCTs (low-certainty evidence; n = 294) found no effect of diet interventions on BMI.Direct comparisons of interventions: Two RCTs reported data directly comparing diet with either physical activity or diet combined with physical activity interventions for children aged 6 to 12 years and reported no differences.Heterogeneity was apparent in the results from all three age groups, which could not be entirely explained by setting or duration of the interventions. Where reported, interventions did not appear to result in adverse effects (16 RCTs) or increase health inequalities (gender: 30 RCTs; socioeconomic status: 18 RCTs), although relatively few studies examined these factors.Re-running the searches in January 2018 identified 315 records with potential relevance to this review, which will be synthesised in the next update.

AUTHORS' CONCLUSIONS

Interventions that include diet combined with physical activity interventions can reduce the risk of obesity (zBMI and BMI) in young children aged 0 to 5 years. There is weaker evidence from a single study that dietary interventions may be beneficial.However, interventions that focus only on physical activity do not appear to be effective in children of this age. In contrast, interventions that only focus on physical activity can reduce the risk of obesity (BMI) in children aged 6 to 12 years, and adolescents aged 13 to 18 years. In these age groups, there is no evidence that interventions that only focus on diet are effective, and some evidence that diet combined with physical activity interventions may be effective. Importantly, this updated review also suggests that interventions to prevent childhood obesity do not appear to result in adverse effects or health inequalities.The review will not be updated in its current form. To manage the growth in RCTs of child obesity prevention interventions, in future, this review will be split into three separate reviews based on child age.

Does stress influence sleep patterns, food intake, weight gain, abdominal obesity and weight loss interventions and vice versa?

Decades of research have reported only weak associations between the intakes of specific foods or drinks and weight gain and obesity. Randomized controlled dietary intervention trials have only shown very modest effects of changes in nutrient intake and diet composition on body weight in obese subjects. This review summarizes the scientific evidence on the role mental stress (either in or not in association with impaired sleep) may play in poor sleep, enhanced appetite, cravings and decreased motivation for physical activity. All these factors contribute to weight gain and obesity, possibly via decreasing the efficacy of weight loss interventions. We also review evidence for the role that lifestyle and stress management may play in achieving weight loss in stress-vulnerable individuals with overweight.

Relationship between sleep duration and childhood obesity: Systematic review including the potential underlying mechanisms

AIM

The prevalence of obesity is continually increasing worldwide. Determining risk factors for obesity may facilitate effective preventive programs. The present review focuses on sleep duration as a potential risk factor for childhood obesity. The aim is to summarize the evidence on the association of sleep duration and obesity and to discuss the underlying potential physiological and/or pathophysiological mechanisms.

DATA SYNTHESIS

The Ovid MEDLINE, Scopus and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched for papers using text words with appropriate truncation and relevant indexing terms. All studies objectively measuring sleep duration and investigating the association between sleep duration and obesity or factors (lifestyle and hormonal) possibly associated with obesity were included, without making restrictions based on study design or language. Data from eligible studies were extracted in tabular form and summarized narratively. After removing duplicates, 3540 articles were obtained. Finally, 33 studies (including 3 randomized controlled trials and 30 observational studies) were included in the review.

CONCLUSION

Sleep duration seems to influence weight gain in children, however, the underlying explanatory mechanisms are still uncertain. In our review only the link between short sleep duration and the development of insulin resistance, sedentarism and unhealthy dietary patterns could be verified, while the role of other mediators, such as physical activity, screen time, change in ghrelin and leptin levels, remained uncertain. There are numerous evidence gaps. To answer the remaining questions, there is a need for studies meeting high methodological standards and including a large number of children.

Sleep quality and duration are associated with performance in maximal incremental test

BACKGROUND AND OBJECTIVE

Inadequate sleep patterns may be considered a trigger to development of several metabolic diseases. Additionally, sleep deprivation and poor sleep quality can negatively impact performance in exercise training. However, the impact of sleep duration and sleep quality on performance during incremental maximal test performed by healthy men is unclear. Therefore, the purpose of the study was to analyze the association between sleep pattern (duration and quality) and performance during maximal incremental test in healthy male individuals.

METHODS

A total of 28 healthy males volunteered to take part in the study. Sleep quality, sleep duration and physical activity were subjectively assessed by questionnaires. Sleep pattern was classified by sleep duration (>7h or <7h of sleep per night) and sleep quality according to the sum of measured points and/or scores by the Pittsburgh Sleep Quality Index (PSQI). Incremental exercise test was performed at 35 watts for untrained subjects, 70 watts for physically active subjects and 105 watts for well-trained subjects.

RESULTS

HR_max was correlated with sleep quality (r=0.411, p=0.030) and sleep duration (r=-0.430, p=0.022). Participants reporting good sleep quality presented higher values of W_max, VO_2max and lower values of HR_max when compared to participants with altered sleep. Regarding sleep duration, only W_max was influenced by the amount of sleeping hours per night and this association remained significant even after adjustment by VO_2max.

CONCLUSION

Sleep duration and quality are associated, at least in part, with performance during maximal incremental test among healthy men, with losses in W_max and HR_max. In addition, our results suggest that the relationship between sleep patterns and performance, mainly in W_max, is independent of fitness condition.