scholarly journals The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Geraldine Lo Siou ◽  
Alianu K. Akawung ◽  
Nathan M. Solbak ◽  
Kathryn L. McDonald ◽  
Ala Al Rajabi ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Dietary Patterns ◽  
Total Energy Expenditure ◽  
Nutritional Epidemiology ◽  
Adjusted Rand Index ◽  
Cluster Solution ◽  
The Impact

Abstract Background All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods Using self-reported food frequency and physical activity data from Alberta’s Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie’s adjusted Rand Index, Kappa and Cramer’s V statistics < 0.8). Conclusions Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.

scholarly journals The Effect of Different Methods to Identify, and Scenarios Used to Address Energy Intake Misestimation on Dietary Patterns Derived by Cluster Analysis

2020 ◽  
Author(s):  
Geraldine Lo Siou ◽  
Alianu K. Akawung ◽  
Nathan M. Solbak ◽  
Kathryn McDonald ◽  
Ala Al Rajabi ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Dietary Patterns ◽  
Nearest Neighbor ◽  
Total Energy Expenditure ◽  
Self Report ◽  
Cluster Solution ◽  
Activity Data

Abstract Background: All self-report dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods: Using self-report food frequency and physical activity data from Alberta’s Tomorrow Project participants (n=9,847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results: The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios. Conclusions: Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns.

scholarly journals Validation of energy intake by 24-hour multiple pass recall: comparison with total energy expenditure in children aged 5–7 years

2005 ◽  
Vol 93 (5) ◽  
pp. 671-676 ◽  
Author(s):  
Colette Montgomery ◽  
John J. Reilly ◽  
Diane M. Jackson ◽  
Louise A. Kelly ◽  
Christine Slater ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
School Children ◽  
Age Groups ◽  
Total Energy Expenditure ◽  
Doubly Labelled Water ◽  
Individual Level ◽  
Multiple Pass ◽  
The Individual

Accurate measurement of energy intake (EI) is essential in studies of energy balance in all age groups. Reported values for EI can be validated against total energy expenditure (TEE) measured using doubly labelled water (DLW). Our previous work has indicated that the use of the standardized 24 h multiple pass recall (24 h MPR) method produces slight overestimates of EI in pre-school children which are inaccurate at individual level but acceptable at group level. To extend this work, the current study validated EI by 24 h MPR against TEE by DLW in sixty-three (thirty-two boys) school-aged children (median age 6 years). In both boys and girls, reported EI was higher than TEE, although this difference was only significant in the girls (median difference 420 kJ/d, P=0·05). On analysis of agreement between TEE and EI, the group bias was an overestimation of EI by 250 kJ/d with wide limits of agreement (−2880, 2380 kJ/d). EI was over-reported relative to TEE by 7 % and 0·9 % in girls and boys, respectively. The bias in the current study was lower than in our previous study of pre-school children, suggesting that estimates of EI become less inaccurate as children age. However, the current study suggests that the 24 h MPR is inaccurate at the individual level.

Assessment of energy intake and total energy expenditure in a series of patients who have undergone oesophagectomy following neoadjuvant treatment

2020 ◽  
Vol 37 ◽  
pp. 121-128
Author(s):  
Jessica Ericson ◽  
Lars Lundell ◽  
Mats Lindblad ◽  
Fredrik Klevebro ◽  
Magnus Nilsson ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Neoadjuvant Treatment ◽  
Total Energy Expenditure

Measurements of total energy expenditure provide insights into the validity of dietary measurements of energy intake

1993 ◽  
Vol 93 (5) ◽  
pp. 572-579 ◽  
Author(s):  
Alison E Black ◽  
Andrew M Prentice ◽  
Gail R Goldberg ◽  
Susan A Jebb ◽  
Sheila A Bingham ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Total Energy Expenditure

scholarly journals Lower resting and total energy expenditure in postmenopausal compared with premenopausal women matched for abdominal obesity

2014 ◽  
Vol 3 ◽  
Author(s):  
Leanne Hodson ◽  
Karin Harnden ◽  
Rajarshi Banerjee ◽  
Belen Real ◽  
Kyriakoula Marinou ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Adipose Tissue ◽  
Fatty Acid ◽  
Energy Expenditure ◽  
Total Energy ◽  
Postmenopausal Women ◽  
Energy Intake ◽  
Premenopausal Women ◽  
Total Energy Expenditure ◽  
Mineral Density

AbstractThe menopause is accompanied by increased risk of obesity, altered body fat distribution and decreased skeletal muscle mass. The resulting decrease in RMR should be accompanied by a compensatory change in energy balance to avoid weight gain. We aimed to investigate habitual energy intake and expenditure in pre- and postmenopausal women matched for abdominal obesity. We recruited fifty-one healthy Caucasian women, BMI > 18·5 and <35 kg/m2, aged 35–45 years (premenopausal, n 26) and 55–65 years (postmenopausal, n 25). Energy intake was measured using 3 d diet diaries and dietary fat quality assessed using adipose tissue fatty acid biomarkers. RMR was measured using indirect calorimetry, and total energy expenditure (TEE) and activity energy expenditure using a combined accelerometer and heart rate monitor. Postmenopausal women had lower RMR and TEE and spent significantly less time undertaking moderate exercise than premenopausal women. Postmenopausal women had a tendency for a lower energy intake, and a similar macronutrient intake but a significantly lower adipose tissue n-6:n-3 ratio (24·6 (se 1·6) v. 37·7 (se 3·1); P < 0·001). The main lifestyle determinant of bone mineral density (which was significantly lower in postmenopausal women) was TEE for premenopausal women, and dietary n-6:n-3 ratio for postmenopausal women. The present results suggest that weight maintenance is achieved in the post- compared with premenopausal status through a combination of reduced energy intake and reduced TEE in a regimen that compromises micronutrient intake and has a negative impact on lean tissue mass. However, lower n-6:n-3 fatty acid intake in postmenopausal women is associated with greater bone mineral density.

scholarly journals Physical activity and energy balance

1999 ◽  
Vol 2 (3a) ◽  
pp. 335-339 ◽  
Author(s):  
Marleen A. Van Baak
Keyword(s):  
Physical Activity ◽  
Energy Balance ◽  
Energy Expenditure ◽  
Total Energy ◽  
Energy Intake ◽  
Body Mass ◽  
Resting Energy Expenditure ◽  
Total Energy Expenditure ◽  
Activity Level ◽  
Resting Energy

AbstractEnergy expenditure rises above resting energy expenditure when physical activity is performed. The activity-induced energy expenditure varies with the muscle mass involved and the intensity at which the activity is performed: it ranges between 2 and 18 METs approximately. Differences in duration, frequency and intensity of physical activities may create considerable variations in total energy expenditure. The Physical Activity Level (= total energy expenditure divided by resting energy expenditure) varies between 1.2 and 2.2–2.5 in healthy adults. Increases in activity-induced energy expenditure have been shown to result in increases in total energy expenditure, which are usually greater than the increase in activity-induced energy expenditure itself. No evidence for increased spontaneous physical activity, measured by diary, interview or accelerometer, was found. However, this does not exclude increased physical activity that can not be measured by these methods. Part of the difference may also be explained by the post-exercise elevation of metabolic rate.If changes in the level of physical activity affect energy balance, this should result in changes in body mass or body composition. Modest decreases of body mass and fat mass are found in response to increases in physical activity, induced by exercise training, which are usually smaller than predicted from the increase in energy expenditure. This indicates that the training-induced increase in total energy expenditure is at least partly compensated for by an increase in energy intake. There is some evidence that the coupling between energy expenditure and energy intake is less at low levels of physical activity. Increasing the level of physical activity for weight loss may therefore be most effective in the most sedentary individuals.

scholarly journals Dietary patterns in Irish adolescents: a comparison of cluster and principal component analyses

2011 ◽  
Vol 16 (5) ◽  
pp. 848-857 ◽  
Author(s):  
Áine P Hearty ◽  
Michael J Gibney
Keyword(s):  
Cluster Analysis ◽  
Total Energy ◽  
Dietary Patterns ◽  
Principal Component ◽  
Food Groups ◽  
Food Diary ◽  
Breakfast Cereal ◽  
Percentage Contribution ◽  
Main Meal ◽  
The Impact

AbstractObjectivePattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions.DesignAnalysis was based on the Irish National Teens Food Survey, in which food intake data were collected using a semi-quantitative 7 d food diary. Thirty-two food groups were created and were expressed as either g/d or percentage contribution to total energy. Dietary patterns were identified using cluster analysis (k-means) and PCA.SettingRepublic of Ireland, 2005–2006.SubjectsA representative sample of 441 adolescents aged 13–17 years.ResultsFive clusters based on percentage contribution to total energy were identified, ‘Healthy’, ‘Unhealthy’, ‘Rice/Pasta dishes’, ‘Sandwich’ and ‘Breakfast cereal & Main meal-type foods’. Four principal components based on g/d were identified which explained 28 % of total variance: ‘Healthy foods’, ‘Traditional foods’, ‘Sandwich foods’ and ‘Unhealthy foods’.ConclusionsA ‘Sandwich’ and an ‘Unhealthy’ pattern are the main dietary patterns in this sample. Patterns derived from either cluster analysis or PCA were comparable, although it appears that cluster analysis also identifies dietary patterns not identified through PCA, such as a ‘Breakfast cereal & Main meal-type foods’ pattern. Consideration of the format of the dietary variable is important as it can directly impact on the patterns obtained for both cluster analysis and PCA.

Sign in / Sign up

Export Citation Format

Share Document