scholarly journals The forgotten agriculture-nutrition link: farm technologies and human energy requirements

Food Security ◽  
2021 ◽  
Author(s):  
Thomas Daum ◽  
Regina Birner
Keyword(s):  
Physical Activity ◽  
Time Use ◽  
Smallholder Farmers ◽  
High Energy ◽  
Energy Requirements ◽  
Physical Work ◽  
Limited Attention ◽  
Activity Levels ◽  
Hand Tools ◽  
Nutritional Outcomes

AbstractIn the quest to reduce global under- and malnutrition, which are particularly high among smallholder farmers, agriculture-nutrition linkages are receiving increasing attention. Researchers have analyzed the link between the quantity and diversity of food that farmers produce and nutritional outcomes but paid limited attention to a third agriculture-nutrition link: the link between how food is produced and nutritional outcomes. This neglect persists despite the majority of smallholder farmers relying on hand tools for farming, which implies heavy physical work and, thus, high energy requirements. To address this research gap, this study compares the energy requirements of farm households in rural Zambia that are characterized by three different levels of mechanization: hand tools, animal drought power, and tractors. 1638 days of detailed time-use and nutrition data were collected from 186 male and female adults and boys and girls during different seasons (land preparation, weeding, and harvesting/processing) using an innovative picture-based smartphone app called “Timetracker”. This data served to calculate different proxies for physical activity and energy requirements using “Ainsworth’s Compendium of Physical Activities”. The results suggest that detailed time-use data offers great potentials to study physical activity and energy requirements. The findings show strong linkages between farm technologies, physical activity levels, and energy requirements, suggesting that this agriculture-nutrition link deserves more scientific and political attention to reduce under- and malnutrition among smallholder farmers.

Implications of Engaging in Regular Exercise and Reducing Sedentary Behavior During a Global Pandemic: An Immuno-metabolic Perspective in Patients with Obesity and Type 2 Diabetes

2020 ◽  
Author(s):  
Jabeur Methnani ◽  
Dorra Amor ◽  
Narimen Yousfi ◽  
Ali Bouslama ◽  
Asma Omezzine ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Sedentary Behavior ◽  
Metabolic Diseases ◽  
High Energy ◽  
Activity Levels ◽  
Adaptive Mechanism ◽  
Global Pandemic ◽  
Light Activity

Many reports showed a dramatic decrease in the levels of physical activity during the current pandemic of SARS-COV-2. This has substantial immunometabolic implications, especially in those at risk or with metabolic diseases including individuals with obesity and Type 2 diabetes. Here we discuss the route from physical inactivity to immnometabolic aberrancies; focusing on how insulin resistance could represent an adaptive mechanism to the low physical activity levels and/or high energy intake and on how such an adaptive mechanism could derail to be a pathognomonic feature of metabolic diseases creating a vicious circle of immune and metabolic aberrancies. We provide a theoretical framework to the severe immunopathology of COVID-19 in patients with metabolic diseases. We finally discuss the idea of exercise as a potential adjuvant against COVID-19 and emphasize how even interrupting prolonged periods of sitting with short time breaks of very light activity could be a feasible strategy to limit the deleterious effects of sedentary behavior.

The Relationship between Peak Oxygen Uptake and Physical Activity in 6- to 8-Year-Old Children

1989 ◽  
Vol 1 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Juliane R. Fenster ◽  
Patty S. Freedson ◽  
Richard A. Washburn ◽  
R. Curtis Ellison
Keyword(s):  
Physical Activity ◽  
Aerobic Capacity ◽  
Large Scale ◽  
Work Capacity ◽  
Peak Oxygen Uptake ◽  
Physical Work ◽  
Physical Work Capacity ◽  
Activity Levels ◽  
Activity Method ◽  
The Relationship

The relationship between physical activity measured using the LSI (Large Scale Integrated Activity Monitor), and questionnaire, with physical work capacity 170 (PWC 170) and aerobic capacity (peak V̇O2) was evaluated in 6- to 8-year-old children (n = 18). The mean (± SD) peak V̇O2 was 44.1 ± 5.6 ml • kg−1 • min−1. Peak V̇O2 was not significantly different for children (n = 8) who had completed two treadmill trials (45.4 vs. 43.5 ml • kg−1 • min−1; R = 0.67, p<0.05). The log LSI expressed as counts per hour (M ± SD = 2.1 ±.22 cts/hr) was the only activity method significantly related to peak V̇O2 (r = 0.59, p<0.05). The correlation between peak V̇O2 with the questionnaire was positive but nonsignificant (r = 0.20). PWC 170 was not related to peak V̇O2 (r = 0.21) or the activity variables (r = 0.12 questionnaire; r = 0.18 log LSI). When the group was divided into high and low peak V̇O2 groups (high: M = 48.8 ml • kg−1 • min−1; low: M = 39.5 ml • kg−1 • min−1), the log LSI was able to distinguish significant differences in activity levels (high: 2.23 ±. 19 cts/hr; low: 1.99±.19 cts/hr). This study suggests that activity measured with the LSI and aerobic capacity are related in this sample of 6- to 8-year-old children.

scholarly journals Estimating energy requirements: regression based prediction equations or multiples of resting metabolic rate

2005 ◽  
Vol 8 (7a) ◽  
pp. 1184-1186 ◽  
Author(s):  
Michael I Goran
Keyword(s):  
Physical Activity ◽  
Body Weight ◽  
Energy Expenditure ◽  
Metabolic Rate ◽  
Resting Metabolic Rate ◽  
Total Energy Expenditure ◽  
Energy Requirements ◽  
Activity Levels ◽  
Doubly Labelled Water ◽  
Alternative Approaches

AbstractEnergy requirements have traditionally been determined based on multiples of resting metabolic rate (RMR), known as Physical Activity Levels (PAL). With more data from doubly labelled water studies alternative approaches for estimating energy requirements have been suggested. Statistical analysis reveals that body weight explains more of the variance in total energy expenditure (TEE) than does RMR. The explanation for this phenomenon is that body weight contributes to the variance of both RMR and the other major determinant of TEE, i.e. physical activity related energy expenditure. Thus, in effect, the regression-based approach provides a more physiological appropriate model for TEE. Its major departure from tradition, difference from current adult proposals, and time taken for acceptance are the disadvantages of the regression-based approach.

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