Environmental influences on food choice, physical activity and energy balance

Extensive human and animal model data show that environmental influences during critical periods of prenatal and early postnatal development can cause persistent alterations in energy balance regulation. Although a potentially important factor in the worldwide obesity epidemic, the fundamental mechanisms underlying such developmental programming of energy balance are poorly understood, limiting our ability to intervene. Most studies of developmental programming of energy balance have focused on persistent alterations in the regulation of energy intake; energy expenditure has been relatively underemphasised. In particular, very few studies have evaluated developmental programming of physical activity. The aim of this review is to summarise recent evidence that early environment may have a profound impact on establishment of individual propensity for physical activity. Recently, we characterised two different mouse models of developmental programming of obesity; one models fetal growth restriction followed by catch-up growth, and the other models early postnatal overnutrition. In both studies, we observed alterations in body-weight regulation that persisted to adulthood, but no group differences in food intake. Rather, in both cases, programming of energy balance appeared to be due to persistent alterations in energy expenditure and spontaneous physical activity (SPA). These effects were stronger in female offspring. We are currently exploring the hypothesis that developmental programming of SPA occurs via induced sex-specific alterations in epigenetic regulation in the hypothalamus and other regions of the central nervous system. We will summarise the current progress towards testing this hypothesis. Early environmental influences on establishment of physical activity are likely an important factor in developmental programming of energy balance. Understanding the fundamental underlying mechanisms in appropriate animal models will help determine whether early life interventions may be a practical approach to promote physical activity in man.

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Integrating cognitive and environmental influences on energy-balance related behaviour

PsycEXTRA Dataset ◽

10.1037/e524352011-101 ◽

2006 ◽

Cited By ~ 1

Author(s):

G. J. De Bruijn ◽

S. Kremers ◽

Mechelen W. Van ◽

J. Brug

Keyword(s):

Energy Balance ◽

Environmental Influences

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Individual, social environmental, and physical environmental influences on physical activity among black and white Adults: A Structural Equation Analysis

Annals of Behavioral Medicine ◽

10.1207/s15324796abm3101_7 ◽

2006 ◽

Vol 31 (1) ◽

pp. 36-44 ◽

Cited By ~ 128

Author(s):

Lorna Haughton McNeill ◽

Kathleen W. Wyrwich ◽

Ross C. Brownson ◽

Eddie M. Clark ◽

Matthew W. Kreuter

Keyword(s):

Physical Activity ◽

Structural Equation ◽

Environmental Influences ◽

Black And White ◽

Structural Equation Analysis ◽

Equation Analysis ◽

Social Environmental ◽

White Adults

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The EnergyKids Pilot Study: Comparing Energy Balance of Primary School Children during School and Summer Camp

Nutrients ◽

10.3390/nu13010092 ◽

2020 ◽

Vol 13 (1) ◽

pp. 92

Author(s):

Cinzia Franchini ◽

Alice Rosi ◽

Cristian Ricci ◽

Francesca Scazzina

Keyword(s):

Physical Activity ◽

Energy Balance ◽

Weight Status ◽

Summer Camp ◽

Negative Energy ◽

Primary School Children ◽

Food Diary ◽

Activity Tracker ◽

School Time ◽

Study Participants

Children’s energy requirements may vary during school and summer camp days. To evaluate energy balance during these two periods, seventy-eight children (45% females, 8–10 years) living in Parma, Italy, were enrolled in this observational study. Participants completed a 3-day food diary and wore an activity tracker for three consecutive days during a school- and a summer camp-week to estimate energy intake (EI) and energy expenditure (TEE). Height and body weight were measured at the beginning of each period to define children’s weight status. BMI and EI (school: 1692 ± 265 kcal/day; summer camp: 1738 ± 262 kcal/day) were similar during both periods. Both physical activity and TEE (summer camp: 1948 ± 312; school: 1704 ± 263 kcal/day) were higher during summer camp compared to school time. Therefore, energy balance was more negative during summer camp (−209 ± 366 kcal/day) compared to school time (−12 ± 331 kcal/day). Similar results were observed when males and females were analyzed separately but, comparing the sexes, males had a higher TEE and a more negative energy balance than females, during both periods. The results strongly suggest that an accurate evaluation of children’s energy balance, that considers both diet and physical activity, is needed when planning adequate diets for different situations.

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Systematic review of interventions in the childcare setting with direct parental involvement: effectiveness on child weight status and energy balance-related behaviours

International Journal of Behavioral Nutrition and Physical Activity ◽

10.1186/s12966-019-0874-6 ◽

2019 ◽

Vol 16 (1) ◽

Cited By ~ 5

Author(s):

I. van de Kolk ◽

S. R. B. Verjans-Janssen ◽

J. S. Gubbels ◽

S. P. J. Kremers ◽

S. M. P. L. Gerards

Keyword(s):

Physical Activity ◽

Systematic Review ◽

Energy Balance ◽

Parental Involvement ◽

Sedentary Behaviour ◽

Weight Status ◽

Overweight And Obesity ◽

Early Years ◽

Effect Sizes ◽

Intervention Effectiveness

Abstract Background The early years are a crucial period to promote healthy energy balance-related behaviours in children and prevent overweight and obesity. The childcare setting is important for health-promoting interventions. Increasingly, attention has been paid to parental involvement in childcare-based interventions. The aim of this systematic review is to evaluate the effectiveness of these interventions with direct parental involvement on the children’s weight status and behavioural outcomes. Methods A systematic search was conducted in four electronic databases to include studies up until January 2019. Studies written in English, describing results on relevant outcomes (weight status, physical activity, sedentary behaviour and/or nutrition-related behaviour) of childcare-based interventions with direct parental involvement were included. Studies not adopting a pre-post-test design or reporting on pilot studies were excluded. To improve comparability, effect sizes (Cohen’s d) were calculated. Information on different types of environment targeted (e.g., social, physical, political and economic) was extracted in order to narratively examine potential working principles of effective interventions. Results A total of 22 studies, describing 17 different interventions, were included. With regard to the intervention group, 61.1% found some favourable results on weight status, 73.3% on physical activity, 88.9% on sedentary behaviour, and all on nutrition-related behaviour. There were studies that also showed unfavourable results. Only a small number of studies was able to show significant differences between the intervention and control group (22.2% weight status, 60.0% physical activity, 66.6% sedentary behaviour, 76.9% nutrition behaviour). Effect sizes, if available, were predominantly small to moderate, with some exceptions with large effect sizes. The interventions predominantly targeted the socio-cultural and physical environments in both the childcare and home settings. Including changes in the political environment in the intervention and a higher level of intensity of parental involvement appeared to positively impact intervention effectiveness. Conclusion Childcare-based interventions with direct parental involvement show promising effects on the children’s energy balance-related behaviours. However, evidence on effectiveness is limited, particularly for weight-related outcomes. Better understanding of how to reach and involve parents may be essential for strengthening intervention effectiveness.

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Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools

Nutrients ◽

10.3390/nu11071681 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1681 ◽

Cited By ~ 1

Author(s):

Ramyaa Ramyaa ◽

Omid Hosseini ◽

Giri P. Krishnan ◽

Sridevi Krishnan

Keyword(s):

Physical Activity ◽

Machine Learning ◽

Cluster Analysis ◽

Body Weight ◽

Energy Balance ◽

Numerical Data ◽

Support Vector ◽

Learning Tools ◽

K Nearest Neighbor ◽

Numerical Predictions

Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters. From the Women’s Health Initiative Observational Study (WHI OS), carbohydrates, proteins, fats, fibers, sugars, and physical activity variables, namely energy expended from mild, moderate, and vigorous intensity activity, were used to predict current body weight (both as body weight in kilograms and as a body mass index (BMI) category). Several machine learning tools were used for this prediction. Finally, cluster analysis was used to identify putative phenotypes. For the numerical predictions, the support vector machine (SVM), neural network, and k-nearest neighbor (kNN) algorithms performed modestly, with mean approximate errors (MAEs) of 6.70 kg, 6.98 kg, and 6.90 kg, respectively. For categorical prediction, SVM performed the best (54.5% accuracy), followed closely by the bagged tree ensemble and kNN algorithms. K-means cluster analysis improved prediction using numerical data, identified 10 clusters suggestive of phenotypes, with a minimum MAE of ~1.1 kg. A classifier was used to phenotype subjects into the identified clusters, with MAEs <5 kg for 15% of the test set (n = ~2000). This study highlights the challenges, limitations, and successes in using machine learning tools on self-reported data to identify determinants of energy balance.

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Social and Environmental Influences on Physical Activity Levels in Latina Adolescents

Public Health Nursing ◽

10.1111/phn.12278 ◽

2016 ◽

Vol 34 (2) ◽

pp. 101-111 ◽

Cited By ~ 2

Author(s):

Deborah Benes ◽

Jacqueline Dowling ◽

Sybil Crawford ◽

Laura L. Hayman

Keyword(s):

Physical Activity ◽

Environmental Influences ◽

Activity Levels ◽

Latina Adolescents ◽

Physical Activity Levels

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Energy Balance and Energy Availability During a Selection Course for Belgian Paratroopers

Military Medicine ◽

10.1093/milmed/usab140 ◽

2021 ◽

Author(s):

Patrick Mullie ◽

Pieter Maes ◽

Laurens van Veelen ◽

Damien Van Tiggelen ◽

Peter Clarys

Keyword(s):

Physical Activity ◽

Energy Balance ◽

Energy Expenditure ◽

Metabolic Rate ◽

Energy Intake ◽

Rest Metabolic Rate ◽

Negative Energy ◽

Fat Free Mass ◽

Negative Energy Balance ◽

Energy Availability

ABSTRACT Introduction Adequate energy supply is a prerequisite for optimal performances and recovery. The aims of the present study were to estimate energy balance and energy availability during a selection course for Belgian paratroopers. Methods Energy expenditure by physical activity was measured with accelerometer (ActiGraph GT3X+, ActiGraph LLC, Pensacola, FL, USA) and rest metabolic rate in Cal.d−1 with Tinsley et al.’s equation based on fat-free mass = 25.9 × fat-free mass in kg + 284. Participants had only access to the French individual combat rations of 3,600 Cal.d−1, and body fat mass was measured with quadripolar impedance (Omron BF508, Omron, Osaka, Japan). Energy availability was calculated by the formula: ([energy intake in foods and beverages] − [energy expenditure physical activity])/kg FFM−1.d−1, with FFM = fat-free mass. Results Mean (SD) age of the 35 participants was 25.1 (4.18) years, and mean (SD) percentage fat mass was 12.0% (3.82). Mean (SD) total energy expenditure, i.e., the sum of rest metabolic rate, dietary-induced thermogenesis, and physical activity, was 5,262 Cal.d−1 (621.2), with percentile 25 at 4,791 Cal.d−1 and percentile 75 at 5,647 Cal.d−1, a difference of 856 Cal.d−1. Mean daily energy intake was 3,600 Cal.d−1, giving a negative energy balance of 1,662 (621.2) Cal.d−1. Mean energy availability was 9.3 Cal.kg FFM−1.d−1. Eleven of the 35 participants performed with a negative energy balance of 2,000 Cal.d−1, and only five participants out of 35 participants performed at a less than 1,000 Cal.d−1 negative energy balance level. Conclusions Energy intake is not optimal as indicated by the negative energy balance and the low energy availability, which means that the participants to this selection course had to perform in suboptimal conditions.

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