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Background Mental health problems in young people have become a global health burden. The positive effects of physical activity on mental health in adults are well known but still not clear in children. The aim of this study was to investigate to what extent physical activity in early childhood would affect mental health in middle childhood. Methods From the Dutch GECKO Drenthe birth cohort, 850 children (51.5% boys) were enrolled in this analysis. Physical activity and sedentary time were measured at age 5–6 using ActiGraph GT3X. Mental health was assessed using the Strengths and Difficulties Questionnaire (SDQ) at age 5–6 and age 10–11. Multiple linear regression models were used to estimate the associations between physical activity, sedentary time and SDQ subscales, stratified by gender, adjusting for age, BMI, maternal education level, family size, accelerometer wear time and season, and additionally adjusting for SDQ scores at age 5–6 to take tracking of mental health over time into account. Results Greater physical activity volume at age 5–6 was associated with lower peer problems scores at age 10–11 in boys and girls. An increase in MVPA was associated with lower peer problems scores in boys (b = -0.445, -0.713 to -0.176) and girls (b = -0.354, -0.601 to -0.107), however, increased sedentary time was linked to higher peer problems scores in boys (b = 1.18, 0.455 to 1.906) and girls (b = 0.870, 0.191 to 1.550). For hyperactivity, higher levels of physical activity volume and MVPA were associated with higher hyperactivity scores in boys. Increased sedentary time was related to lower hyperactivity scores in boys. Further adjustment for SDQ scores at age 5–6 attenuated associations between physical activity and hyperactivity in boys but hardly changed the relationships with peer problems. No significant associations between physical activity and other SDQ subscales or total difficulties scores were observed, neither in boys nor in girls. Conclusions Children who are more physically active at age 5–6 have fewer peer problems at age 10–11, and for boys, greater activity levels at age 5–6 could be an indicator of hyperactivity at age 10–11.

Background The overall consumption of ultra-processed food (UPF) has previously been associated with type 2 diabetes. However, due to the substantial heterogeneity of this food category, in terms of their nutritional composition and product type, it remains unclear whether previous results apply to all underlying consumption patterns of UPF. Methods Of 70,421 participants (35–70 years, 58.6% women) from the Lifelines cohort study, dietary intake was assessed with a food frequency questionnaire. UPF was identified according to the NOVA classification. Principal component analysis (PCA) was performed to derive UPF consumption patterns. The associations of UPF and adherence to UPF consumption patterns with incidence of type 2 diabetes were studied with logistic regression analyses adjusted for age, sex, diet quality, energy intake, alcohol intake, physical activity, TV watching time, smoking status, and educational level. Results During a median follow-up of 41 months, a 10% increment in UPF consumption was associated with a 25% higher risk of developing type 2 diabetes (1128 cases; OR 1.25 [95% CI 1.16, 1.34]). PCA revealed four habitual UPF consumption patterns. A pattern high in cold savory snacks (OR 1.16 [95% CI 1.09, 1.22]) and a pattern high in warm savory snacks (OR 1.15 [95% CI 1.08, 1.21]) were associated with an increased risk of incident type 2 diabetes; a pattern high in traditional Dutch cuisine was not associated with type 2 diabetes incidence (OR 1.05 [95% CI 0.97, 1.14]), while a pattern high in sweet snacks and pastries was inversely associated with type 2 diabetes incidence (OR 0.82 [95% CI 0.76, 0.89]). Conclusions The heterogeneity of UPF as a general food category is reflected by the discrepancy in associations between four distinct UPF consumption patterns and incident type 2 diabetes. For better public health prevention, research is encouraged to further clarify how different UPF consumption patterns are related to type 2 diabetes.

Multimorbidity is associated with poor quality of life, polypharmacy, health care costs and mortality, with those affected potentially benefitting from a healthy lifestyle. We assessed a comprehensive set of lifestyle factors in relation to multimorbidity with major chronic diseases. This cross-sectional study utilised baseline data for adults from the prospective Lifelines Cohort in the north of the Netherlands (N = 79,345). We defined multimorbidity as the co-existence of two or more chronic diseases (i.e. cardiovascular disease, cancer, respiratory disease, type 2 diabetes) and evaluated factors in six lifestyle domains (nutrition, physical (in)activity, substance abuse, sleep, stress, relationships) among groups by the number of chronic diseases (≥2, 1, 0). Multinomial logistic regression models were created, adjusted for appropriate confounders, and odds ratios (OR) with 95% confidence intervals (95%CI) were reported. 3,712 participants had multimorbidity (4.7%, age 53.5 ± 12.5 years), and this group tended to have less healthy lifestyles. Compared to those without chronic diseases, those with multimorbidity reported physical inactivity more often (OR, 1.15; 95%CI, 1.06-1.25; not significant for one condition), chronic stress (OR, 2.14; 95%CI, 1.92-2.38) and inadequate sleep (OR, 1.70; 95%CI, 1.41-2.06); as expected, they more often watched television (OR, 1.70; 95%CI, 1.42-2.04) and currently smoked (OR, 1.91; 95%CI, 1.73-2.11), but they also had lower alcohol intakes (OR, 0.66; 95%CI, 0.59-0.74). Chronic stress and poor sleep, in addition to physical inactivity and smoking, are lifestyle factors of great concern in patients with multimorbidity.

We examined the dose-dependent association of habitual moderate-to-vigorous physical activity (MVPA) with the biochemical markers for nonalcoholic fatty liver disease (NAFLD) and whether this association changes with age and degree of impaired glucose metabolism. We also investigated whether the associations depend on the domain of MVPA. In this study, using data from the population-based Lifelines cohort (N = 42,661), MVPA was self-reported on the short questionnaire to assess health-enhancing physical activity. NAFLD was defined as a fatty liver index value of >60, based on body mass index, waist circumference, plasma triglycerides, and gamma-glutamyltransferase. Glucose metabolism was defined as normal (NGM), impaired (IGM), and type 2 diabetes mellitus (T2DM). Exclusion criteria were previously diagnosed hepatitis or cirrhosis and excessive alcohol use. All analyses were adjusted for age, sex, and education. Higher MVPA was dose dependently associated with a lower risk of having NAFLD: compared with \"No MVPA,\" the odds ratios (ORs) (95% confidence intervals) for MVPA quintiles were 0.78 (0.71-0.86), 0.64 (0.58-0.70), 0.53 (0.48-0.59), 0.51 (0.46-0.56), and 0.45 (0.41-0.50) for the highest level of MVPA. The association between MVPA and NAFLD was stronger for more impaired glucose status (ORNGM = 0.49 (0.42-0.57), ORIGM = 0.46 (0.40-0.54), ORT2DM = 0.42 (0.27-0.66)) and for older age (OR20-40 years = 0.51 (0.42-0.62), OR60-80 years = 0.37 (0.29-0.48)) with the highest level of MVPA, relative to No MVPA. No favorable association was observed for occupational MVPA. With regard to MVPA and fibrosis, associations with fibrosis markers showed contradictory results. Higher MVPA levels are dose dependently associated with a lower NAFLD risk. This association is stronger in people with diabetes and older adults.

Background Excessive weight gain during childhood is a strong predictor for adult overweight, but it remains unknown which growth measures in infancy (0–2 years of age), besides predictors known at birth, are the strongest predictors for excessive weight gain between 2 and 5–7 years of age. Methods The Amsterdam Born Children and their Development (ABCD) study formed the derivation cohort, and the Groningen Expert Center for Kids with Obesity (GECKO) Drenthe study formed the validation cohort. Change (Δ) in body mass index (BMI) z-score between 2 and 5–7 years was the outcome of interest. The growth measures considered were weight, weight-for-length (WfL), and body mass index (BMI). Formats considered for each growth measure were values at 1, 6, 12, and 24 months, at the BMI peak, the change between aforementioned ages, and prepeak velocity. 10 model structures combining different variable formats and including predictors at birth were derived for each growth measure, resulting in 30 linear regression models. A Parsimonious Model considering all growth measures and a Birth Model considering none were also derived. Results The derivation cohort consisted of 3139 infants of which 373 (11.9%) had excessive gain in BMI z-score (> 0.67). The validation cohort contained 2201 infants of which 592 (26.9%) had excessive gain. Across the 3 growth measures, 5 model structures which included measures related to the BMI peak and prepeak velocity (derivation cohort area under the curve [AUC] range = 0.765–0.855) achieved more accurate estimates than 3 model structures which included growth measure change over time (0.706–0.795). All model structures which used BMI were superior to those using weight or WfL. The AUC across all models was on average 0.126 lower in the validation cohort. The Parsimonious Model’s AUCs in the derivation and validation cohorts were 0.856 and 0.766, respectively, compared to 0.690 and 0.491, respectively, for the Birth Model. The respective false positive rates were 28.2% and 20.1% for the Parsimonious Model and 70.0% and 74.6% for the Birth Model. Conclusion Models’ performances varied significantly across model structures and growth measures. Developing the optimal model requires extensive testing of the many possibilities.

The aim of this study was to estimate the effects of lifestyle interventions on bodyweight and other cardiometabolic risk factors in people with psychotic disorders. Additionally, the long-term effects on body weight and the effects on depressive symptoms were examined. We searched four databases for randomized controlled trials (RCTs) that compared lifestyle interventions to control conditions in patients with psychotic disorders. Lifestyle interventions were aimed at weight loss or weight gain prevention, and the study outcomes included bodyweight or metabolic parameters. The search resulted in 25 RCTs -only 4 were considered high quality- showing an overall effect of lifestyle interventions on bodyweight (effect size (ES)  =  -0.63, p<0.0001). Lifestyle interventions were effective in both weight loss (ES =  -0.52, p<0.0001) and weight-gain-prevention (ES =  -0.84, p = 0.0002). There were significant long-term effects, two to six months post-intervention, for both weight-gain-prevention interventions (ES =  -0.85, p = 0.0002) and weight loss studies (ES =  -0.46, p = 0.02). Up to ten studies reported on cardiometabolic risk factors and showed that lifestyle interventions led to significant improvements in waist circumference, triglycerides, fasting glucose and insulin. No significant effects were found for blood pressure and cholesterol levels. Four studies reported on depressive symptoms and showed a significant effect (ES =  -0.95, p = 0.05). Lifestyle interventions are effective in treating and preventing obesity, and in reducing cardiometabolic risk factors. However, the quality of the studies leaves much to be desired.

Key Points Physical inactivity is a major modifiable risk factor for poor health-related quality of life, morbidity and mortality in patients with renal disease An urgent need exists for the better assessment and management of physical inactivity in patients with renal disease The level of physical activity in patients with chronic kidney disease (CKD) commonly decreases with disease progression, and does not fully recover after transplantation Regular physical activity is beneficial across all stages of CKD, improving cardiometabolic, neuromuscular, and cognitive function, and can reduce the comorbidity burden in patients with renal disease Physical activity, together with nutrition, is now recognized as an important component in the management of patients with CKD; a behavioural approach is crucial to help patients successfully adopt and maintain improved physical activity habits Regular physical activity is associated with reduced mortality in the general population and in patients with chronic kidney disease. Here, the authors discuss the importance of physical activity for patients with renal disease and patient-reported barriers and facilitators for physical activity. Regular physical activity is associated with an increased quality of life and reduced morbidity and mortality in the general population and in patients with chronic kidney disease (CKD). Physical activity, cardiorespiratory fitness, and muscle mass decrease even in the early stages of CKD, and continue to decrease with disease progression; notably, full recovery is generally not achieved with transplantation. The combined effects of uraemia and physical inactivity drive the loss of muscle mass. Regular physical activity benefits cardiometabolic, neuromuscular and cognitive function across all stages of CKD, and therefore provides an approach to address the multimorbidity of the CKD population. Interestingly, maintenance of muscle health is associated with renoprotective effects. Despite evidence of its benefits, physical activity and exercise management are not routinely addressed in the care of these patients. Although studies defining the optimum frequency, duration and intensity of physical activity are lacking, evidence from related fields can guide practical approaches to the care of patients with renal disease. Optimization of metabolic and nutritional status alongside promotion of physical activity is recommended. Behavioural approaches are now recognized as crucial in helping patients to adopt lifestyle changes and might prove valuable in integrating physical activity into renal care.

: Lifestyle factors often interact in complex ways when influencing chronic disease risk. We aimed to examine the prospective associations between empirically derived real-life lifestyle patterns (LPs) and the incidence of major chronic diseases, and to explore the linearity of the relationships between lifestyle summation scores and disease risk. : We included adults free of cardiovascular diseases (CVDs), cancer, chronic respiratory diseases (CRDs), or type 2 diabetes (T2D) at baseline (2006-2013) from the Dutch Lifelines cohort. LPs and lifestyle summation scores were derived from baseline self-reported data on diet, physical activity, substance use, sleep, stress, and social connectedness, each categorised as healthy, moderately healthy, or unhealthy. Fine-Gray sub-hazard regression models assessed associations between LPs and disease incidence, with natural spline functions used to evaluate linearity in summation scores. : Among 114,919 T2D-free, 131,248 cancer-free, 91,777 CRD-free, and 77,645 CVD-free participants, we observed 3114 T2D, 4685 cancer, 4133 CRDs, and 2850 CVD incident cases (median follow-up time: 8 years). Compared to the \"Unhealthy\" pattern, both the \"Healthy-in-a-balanced-way\" and \"Healthy-but-physically-inactive\" patterns were broadly significantly protective. The \"Unhealthy-but-no-substance-use\" pattern was associated with increased T2D risk (Sub-Hazard Ratio (SHR) = 1.27, 95% Confidence Interval (CI): 1.11-1.47) but reduced cancer risk (SHR = 0.85, 95%CI: 0.74-0.97). The \"Unhealthy-but-light-drinking-and-never-smoked\" pattern was protective for T2D (SHR = 0.89, 95%CI: 0.79-0.99). Linear associations were observed between lifestyle summation scores and disease risk, except for \"healthy lifestyle\" scores with T2D and \"unhealthy lifestyle\" scores with CRDs (non-linear -value < 0.05). : There are potential protective effects of healthy lifestyles on T2D, cancer, CRDs, and CVDs. However, the \"Unhealthy but no substance use\" demonstrated increased risk on T2D, protective effect on cancer and no significant effect on CRDs or CVDs. The relationship between combined lifestyle factors and NCD risk is complex and partly non-linear, showing diminishing benefits beyond certain thresholds, especially T2D and CRDs.

Objectives We investigated associations between leisure-time physical activity (LTPA) at different intensities (moderate and vigorous or moderate-to-vigorous) and prospective weight gain in non-obese people. We also examined whether these associations were independent of other lifestyle factors and changes in muscle mass and whether they were age-dependent and changed over a person’s life course. Methods The data were extracted from the Lifelines cohort study ( N  = 52,498; 43.5% men) and excluded obese individuals (BMI > 30 kg/m 2 ). We used the validated SQUASH questionnaire to estimate moderate-to-vigorous (MVPA; MET≥4), moderate (MPA; MET between 4 and 6.5) and vigorous PA (VPA; MET≥6.5). Body weight was objectively measured, and changes were standardized to a 4-year period. Separate analyses, adjusted for age, educational level, diet, smoking, alcohol consumption and changes in creatinine excretion (a marker of muscle mass), were performed for men and women. Results The average weight gain was + 0.45 ± 0.03 kg in women. Relative to each reference groups (No-MVPA, No-MPA and No-VPA), MVPA (Beta (95%CI): − 0.34 kg (− 0.56;-0.13)), MPA (− 0.32 kg (− 0.54;-0.10)) and VPA (− 0.30 kg (− 0.43;-0.18)) were associated with less gain in body weight in women after adjusting for potential confounders, described above. These associations were dose-dependent when physically active individuals were divided in tertiles. Beta-coefficients (95%CI) for the lowest, middle, and highest MVPA tertiles relative to the ‘No-MVPA’ were, respectively, − 0.24 (− 0.47;-0.02), − 0.31 (− 0.53;-0.08), and − 0.38 (− 0.61;-0.16) kg. The average weight gain in men was + 0.13 ± 0.03 kg, and only VPA, not MPA was associated with less body weight gain. Beta-coefficients (95%CI) for the VPA tertiles relative to the ‘No-VPA’ group were, respectively, − 0.25 (− 0.42;-0.09), − 0.19 (− 0.38;-0.01) and − 0.20 (− 0.38;-0.02) kg. However, after adjusting for potential confounders, the association was no longer significant in men. The potential benefits of leisure-time PA were age-stratified and mainly observed in younger adults (men < 35 years) or stronger with younger age (women < 55 years). Conclusion Higher leisure-time MVPA, MPA, and VPA were associated with less weight gain in women < 55 years. In younger men (< 35 years), only VPA was associated with less weight gain.

Background Given the widespread problem of physical inactivity, and the continued growth in prevalence of childhood and adolescent obesity, promotion of regular physical activity (PA) among young people has become a public priority. A greater understanding of children’s PA patterns throughout the day is needed to effectively encourage children to be more physically active. Hence this study looking at the distribution of PA in young children throughout the day and its relevance to overweight. Methods Accelerometers (ActiGraph GT3X, weartime > 600 min/day, ≥3 days) were used to measure the PA of 958 children (aged 5.7 ± 0.8 years, 52% boys) enrolled in the GECKO Drenthe cohort. Levels of sedentary time (ST), light PA (LPA) and moderate-to-vigorous PA (MVPA) were recorded throughout the day and analysed in segments (07:00–09:00, 09:00–12:00, 12:00–15:00, 15:00–18:00, 18:00–21:00). Body mass index was measured by Preventive Child Healthcare nurses and Cole’s (2012) definition of overweight was used. General linear mixed models, adjusted for age, sex and season, were used to analyse patterns of PA and ST throughout the day. Results Children were most sedentary in the early morning (07:00–09:00) and evening (18:00–21:00), and exhibited the most time spent engaged in LPA and MVPA in the afternoon (12:00–15:00) and late afternoon (15:00–18:00). The greatest inter-individual variation in ST, LPA and MVPA among the children occurred in the late afternoon and evening (approximately 40, 30 and 15 min difference per time segment between 25th and 75th percentile, respectively). The most active children (highest quartile of MVPA) were found to be more active and less sedentary throughout the entire day than the least active children (lowest quartile of MVPA). Furthermore, children with overweight were no less active than children without overweight. Conclusions At this young age, the relevance of different PA patterns to childhood overweight was minimal. Children were most active in the afternoon and late afternoon. To encourage PA in general, ST can be reduced and PA increased in the early morning and evening. Targeted PA interventions to specifically stimulate the least active children could take place in the late afternoon or evening.