Attained body mass index among children attending rural outdoor or urban conventional kindergartens

ObjectiveThis study aimed to examine whether children in rural outdoor kindergartens had attained a lower body mass index z-score (BMIz) and were at lower risk of overweight after school entrance compared to children in urban conventional kindergartens.MethodsThis is a longitudinal observational study of 1,544 children from outdoor kindergartens and 1,640 from conventional kindergartens. The mean age at kindergarten enrolment was 3.5 years (SD: 0.9) in the outdoor kindergartens and 3.6 years (SD: 1.0) in the conventional kindergartens. Anthropometry was measured after school entry by school health nurses when the children were 6 to 8 years old. Attained BMIz was included as the primary outcome. The risk of attaining overweight (including obesity) was included as a secondary outcome. Register-based information was available on potential confounding factors. Linear and logistic regression models were used to assess group differences in outcome measures.ResultsOur basic models, with information on outcome, kindergarten type, and birth weight showed a borderline statistically significantly lower attained BMIz (−0.07 [95% CI: −0.14, 0.00], P = 0.060) and a lower risk of overweight (adjusted risk ratio: 0.83 [95% CI: 0.72, 0.97], P = 0.016) among children attending outdoor kindergartens. However, when adjusting for sociodemographic factors and parental BMI, there was no evidence of differences in attained BMIz (P = 0.153) or overweight (P = 0.967).ConclusionWhen considering confounding factors, our findings indicate no differences in attained BMIz or risk of overweight after school entry among children attending rural outdoor kindergartens compared to those attending urban conventional kindergartens

Evidence for the protein leverage hypothesis in preschool children prone to obesity.

BACKGROUND & AIMS The protein leverage hypothesis (PLH) proposed that strict regulation of protein intake drives energy overconsumption and obesity when diets are diluted by fat and/or carbohydrates. Evidence about the PLH has been found in adults, while studies in children are limited. Thus, we aimed to test the PLH by assessing the role of dietary protein on macronutrients, energy intake, and obesity risk using data from preschool children followed for 1.3 years. METHODS 553 preschool children aged 2-6 years from the 'Healthy Start' project were included. EXPOSURES The proportion of energy intake from protein, fat, and carbohydrates collected from a 4-day dietary record. OUTCOMES Energy intake, BMI z-score, fat mass (FM) %, waist- (WHtR) and hip-height ratio (HHtR). Power function analysis was used to test the leverage of protein on energy intake. Mixture models were used to explore interactive associations of macronutrient composition on all these outcomes, with results visualized as response surfaces on the nutritional geometry. RESULTS Evidence for the PLH was confirmed in preschool children. The distribution of protein intake (% of MJ, IQR: 3.2) varied substantially less than for carbohydrate (IQR: 5.7) or fat (IQR: 6.3) intakes, suggesting protein intake is most tightly regulated. Absolute energy intake varied inversely with dietary percentage energy from protein (L = -0.14, 95% CI: -0.25, -0.04). Compared to children with high fat or carbohydrate intakes, children with high dietary protein intake (>20% of MJ) had a greater decrease in WHtR and HHtR over the 1.3-year follow-up, offering evidence for the PLH in prospective analysis. But no association was observed between macronutrient distribution and changes in BMI z-score or FM%. CONCLUSIONS In this study in preschool children, protein intake was the most tightly regulated macronutrient, and energy intake was an inverse function of dietary protein concentration, indicating the evidence for protein leverage. Increases in WHtR and HHtR were principally associated with the dietary protein dilution, supporting the PLH. These findings highlight the importance of protein in children's diets, which seems to have significant implications for childhood obesity risk and overall health

Consistent sleep onset and maintenance of body weight after weight loss:An analysis of data from the NoHoW trial

BackgroundSeveral studies have suggested that reduced sleep duration and quality are associated with an increased risk of obesity and related metabolic disorders, but the role of sleep in long-term weight loss maintenance (WLM) has not been thoroughly explored using prospective data.Methods and findingsThe present study is an ancillary study based on data collected on participants from the Navigating to a Healthy Weight (NoHoW) trial, for which the aim was to test the efficacy of an evidence-based digital toolkit, targeting self-regulation, motivation, and emotion regulation, on WLM among 1,627 British, Danish, and Portuguese adults. Before enrolment, participants had achieved a weight loss of ≥5% and had a BMI of ≥25 kg/m2 prior to losing weight. Participants were enrolled between March 2017 and March 2018 and followed during the subsequent 12-month period for change in weight (primary trial outcome), body composition, metabolic markers, diet, physical activity, sleep, and psychological mediators/moderators of WLM (secondary trial outcomes). For the present study, a total of 967 NoHoW participants were included, of which 69.6% were women, the mean age was 45.8 years (SD 11.5), the mean baseline BMI was 29.5 kg/m2 (SD 5.1), and the mean weight loss prior to baseline assessments was 11.4 kg (SD 6.4). Objectively measured sleep was collected using the Fitbit Charge 2 (FC2), from which sleep duration, sleep duration variability, sleep onset, and sleep onset variability were assessed across 14 days close to baseline examinations. The primary outcomes were 12-month changes in body weight (BW) and body fat percentage (BF%). The secondary outcomes were 12-month changes in obesity-related metabolic markers (blood pressure, low- and high-density lipoproteins [LDL and HDL], triglycerides [TGs], and glycated haemoglobin [HbA1c]). Analysis of covariance and multivariate linear regressions were conducted with sleep-related variables as explanatory and subsequent changes in BW, BF%, and metabolic markers as response variables. We found no evidence that sleep duration, sleep duration variability, or sleep onset were associated with 12-month weight regain or change in BF%. A higher between-day variability in sleep onset, assessed using the standard deviation across all nights recorded, was associated with weight regain (0.55 kg per hour [95% CI 0.10 to 0.99]; P = 0.016) and an increase in BF% (0.41% per hour [95% CI 0.04 to 0.78]; P = 0.031). Analyses of the secondary outcomes showed that a higher between-day variability in sleep duration was associated with an increase in HbA1c (0.02% per hour [95% CI 0.00 to 0.05]; P = 0.045). Participants with a sleep onset between 19:00 and 22:00 had the greatest reduction in diastolic blood pressure (DBP) (P = 0.02) but also the most pronounced increase in TGs (P = 0.03). The main limitation of this study is the observational design. Hence, the observed associations do not necessarily reflect causal effects.ConclusionOur results suggest that maintaining a consistent sleep onset is associated with improved WLM and body composition. Sleep onset and variability in sleep duration may be associated with subsequent change in different obesity-related metabolic markers, but due to multiple-testing, the secondary exploratory outcomes should be interpreted cautiously.Trial registrationThe trial was registered with the ISRCTN registry (ISRCTN88405328)

Hair cortisol concentration, weight loss maintenance and body weight variability: A prospective study based on data from the european nohow trial

Several cross-sectional studies have shown hair cortisol concentration to be associated with adiposity, but the relationship between hair cortisol concentration and longitudinal changes in measures of adiposity are largely unknown. We included 786 adults from the NoHoW trial, who had achieved a successful weight loss of ≥5% and had a body mass index of ≥25 kg/m2 prior to losing weight. Hair cortisol concentration (pg/mg hair) was measured at baseline and after 12 months. Body weight and body fat percentage were measured at baseline, 6-month, 12-month and 18-month visits. Participants weighed themselves at home ≥2 weekly using a Wi-Fi scale for the 18-month study duration, from which body weight variability was estimated using linear and non-linear approaches. Regression models were conducted to examine log hair cortisol concentration and change in log hair cortisol concentration as predictors of changes in body weight, change in body fat percentage and body weight variability. After adjustment for lifestyle and demographic factors, no associations between baseline log hair cortisol concentration and outcome measures were observed. Similar results were seen when analysing the association between 12-month concurrent development in log hair cortisol concentration and outcomes. However, an initial 12-month increase in log hair cortisol concentration was associated with a higher subsequent body weight variability between month 12 and 18, based on deviations from a nonlinear trend (β: 0.02% per unit increase in log hair cortisol concentration [95% CI: 0.00, 0.04]; P =0.016). Our data suggest that an association between hair cortisol concentration and subsequent change in body weight or body fat percentage is absent or marginal, but that an increase in hair cortisol concentration during a 12-month weight loss maintenance effort may predict a slightly higher subsequent 6-months body weight variability. Clinical Trial Registration: ISRCTN registry, identifier ISRCTN88405328. [ABSTRACT FROM AUTHOR]info:eu-repo/semantics/publishedVersio

Are motivational and self-regulation factors associated with 12 months’ weight regain prevention in the NoHoW study? An analysis of European adults

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the dataPurpose: Preventing weight regain can only be achieved by sustained changes in energy balance-related behaviors that are associated with weight, such as diet and physical activity. Changes in motivation and self-regulatory skills can support long-term behavioral changes in the context of weight loss maintenance. We propose that experiencing a supportive climate care is associated with enhanced satisfaction of basic psychological needs, intrinsic goals, and autonomous motivation. These factors are expected to be associate with the utilization of self-regulation skills, leading to more sustained behavior changes and ultimately preventing weight regain. This hypothesis was tested in this ancillary analysis of the NoHoW trial, where the study arms were pooled and followed for 12 months. Methods: The NoHoW was a three-center, large-scale weight regain prevention full factorial trial. In this longitudinal study, data were collected in adults who lost > 5% weight in the past year (N = 870, complete data only, 68.7% female, 44.10 ± 11.86 years, 84.47 ± 17.03 kg) during their participation in a 12-month digital behavior change intervention. Weight and validated measures of motivational- and self-regulatory skills-related variables were collected at baseline, six- and 12 months. Change variables were used in Mplus' path analytical models informed by NoHoW's logic model. Results: The bivariate correlations confirmed key mediators' potential effect on weight outcomes in the expected causal direction. The primary analysis showed that a quarter of the variance (r2 = 23.5%) of weight regain prevention was achieved via the mechanisms of action predicted in the logic model. Specifically, our results show that supportive climate care is associated with needs satisfaction and intrinsic goal content leading to better weight regain prevention via improvements in self-regulatory skills and exercise-controlled motivation. The secondary analysis showed that more mechanisms of action are significant in participants who regained or maintained their weight. Conclusions: These results contribute to a better understanding of the mechanisms of action leading to behavior change in weight regain prevention. The most successful participants used only a few intrinsic motivation-related mechanisms of action, suggesting that habits may have been learned. While developing a digital behavior change intervention, researchers and practitioners should consider creating supportive climate care to improve needs satisfaction and intrinsic goal contents. Trial registration: ISRCTN, ISRCTN88405328 , registered 12/22/2016.The NoHoW study has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement number 643309).info:eu-repo/semantics/publishedVersio

Long-term Randomized Controlled Trial

Funding Information: The authors thank Sarah E Scott for her valuable contributions as the trial manager and in the user experience evaluation, and Susana Cunha for her contribution in conducting and reporting the focus groups. This project has received funding from the European Union?s Horizon 2020 research and innovation program under grant agreement number 643309. The material presented and views expressed here are the responsibility of the authors only. The European Union Commission does not take responsibility for any use made of the information set out.Background: Digital behavior change interventions (DBCIs) offer a promising channel for providing health promotion services. However, user experience largely determines whether they are used, which is a precondition for effectiveness. Objective: The primary aim of this study is to evaluate user experiences with the NoHoW Toolkit (TK)—a DBCI that targets weight loss maintenance—over a 12-month period by using a mixed methods approach and to identify the main strengths and weaknesses of the TK and the external factors affecting its adoption. The secondary aim is to objectively describe the measured use of the TK and its association with user experience. Methods: An 18-month, 2×2 factorial randomized controlled trial was conducted. The trial included 3 intervention arms receiving an 18-week active intervention and a control arm. The user experience of the TK was assessed quantitatively through electronic questionnaires after 1, 3, 6, and 12 months of use. The questionnaires also included open-ended items that were thematically analyzed. Focus group interviews were conducted after 6 months of use and thematically analyzed to gain deeper insight into the user experience. Log files of the TK were used to evaluate the number of visits to the TK, the total duration of time spent in the TK, and information on intervention completion. Results: The usability level of the TK was rated as satisfactory. User acceptance was rated as modest; this declined during the trial in all the arms, as did the objectively measured use of the TK. The most appreciated features were weekly emails, graphs, goal setting, and interactive exercises. The following 4 themes were identified in the qualitative data: engagement with features, decline in use, external factors affecting user experience, and suggestions for improvements. Conclusions: The long-term user experience of the TK highlighted the need to optimize the technical functioning, appearance, and content of the DBCI before and during the trial, similar to how a commercial app would be optimized. In a trial setting, the users should be made aware of how to use the intervention and what its requirements are, especially when there is more intensive intervention content.publishersversionpublishe

Substituting sedentary time with sleep or physical activity and subsequent weight‐loss maintenance

Objective: In this study, the associations between the substitution of sedentary time with sleep or physical activity at different intensities and subsequent weight‐loss maintenance were examined. Methods: This prospective study included 1152 adults from the NoHoW trial who had achieved a successful weight loss of ≥5% during the 12 months prior to baseline and had BMI ≥25 kg/m2 before losing weight. Physical activity and sleep were objectively measured during a 14‐day period at baseline. Change in body weight was included as the primary outcome. Secondary outcomes were changes in body fat percentage and waist circumference. Cardiometabolic variables were included as exploratory outcomes. Results: Using isotemporal substitution models, no associations were found between activity substitutions and changes in body weight or waist circumference. However, the substitution of sedentary behavior with moderate‐to‐vigorous physical activity was associated with a decrease in body fat percentage during the first 6 months of the trial (−0.33% per 30 minutes higher moderate‐to‐vigorous physical activity [95% CI: −0.60% to −0.07%], p = 0.013). Conclusions: Sedentary behavior had little or no influence on subsequent weight‐loss maintenance, but during the early stages of a weight‐loss maintenance program, substituting sedentary behavior with moderate‐to‐vigorous physical activity may prevent a gain in body fat percentage

Investigating the causal effect of smoking on hay fever and asthma: a Mendelian randomization meta-analysis in the CARTA consortium

AbstractObservational studies on smoking and risk of hay fever and asthma have shown inconsistent results. However, observational studies may be biased by confounding and reverse causation. Mendelian randomization uses genetic variants as markers of exposures to examine causal effects. We examined the causal effect of smoking on hay fever and asthma by using the smoking-associated single nucleotide polymorphism (SNP) rs16969968/rs1051730. We included 231,020 participants from 22 population-based studies. Observational analyses showed that current vs never smokers had lower risk of hay fever (odds ratio (OR) = 0·68, 95% confidence interval (CI): 0·61, 0·76; P &lt; 0·001) and allergic sensitization (OR = 0·74, 95% CI: 0·64, 0·86; P &lt; 0·001), but similar asthma risk (OR = 1·00, 95% CI: 0·91, 1·09; P = 0·967). Mendelian randomization analyses in current smokers showed a slightly lower risk of hay fever (OR = 0·958, 95% CI: 0·920, 0·998; P = 0·041), a lower risk of allergic sensitization (OR = 0·92, 95% CI: 0·84, 1·02; P = 0·117), but higher risk of asthma (OR = 1·06, 95% CI: 1·01, 1·11; P = 0·020) per smoking-increasing allele. Our results suggest that smoking may be causally related to a higher risk of asthma and a slightly lower risk of hay fever. However, the adverse events associated with smoking limit its clinical significance.</jats:p