Simplified equation for resting energy expenditure in a population of elderly chileans compared to indirect calorimetry

Segura-Badilla, Orietta; Kammar-García, Ashuin; Vera-López, Obdulia; Aguilar-Alonso, Patrícia; Lazcano-Hernández, Martín; Avila‐Sosa, Raúl; Navarro-Cruz, Addí Rhode

doi:10.1016/j.nfs.2018.10.002

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…The studies were carried out in Europe, the USA, South America, and Asia. The participants were prevalently Caucasian [ 37 , 38 , 39 , 43 , 45 , 46 , 48 , 49 , 52 , 53 , 59 , 61 , 62 , 66 , 68 , 71 , 72 , 73 , 74 , 75 , 81 , 84 , 85 , 86 , 88 , 89 , 90 , 94 , 95 , 98 ]; other groups considered were Chinese [ 40 , 63 , 64 , 67 , 100 ], South American [ 36 , 41 , 78 , 102 ], Japanese [ 50 , 56 , 58 , 82 , 83 ], Mexican [ 65 , 76 , 93 , 97 , 102 ], African [ 21 , 54 ...…”

Section: Resultsmentioning

confidence: 99%

“…Some studies compared their results with other previously validated equations as well as with a gold standard. Twenty-six different predictive equations were used as comparisons with the new equations in the articles retrieved: the most frequently used equations were those of Harris Benedict [ 21 , 36 , 37 , 38 , 40 , 41 , 45 , 46 , 48 , 49 , 51 , 56 , 60 , 65 , 68 , 70 , 71 , 72 , 73 , 74 , 76 , 78 , 79 , 80 , 84 , 86 , 89 , 90 , 91 , 92 , 94 , 95 , 98 , 100 ], WHO/FAO/UNU, Schofield [ 36 , 37 , 38 , 57 , 60 , 65 , 74 , 80 , 84 , 89 , 98 , 100 , 102 ], Owen [ 37 , 38 ,…”

Section: Resultsmentioning

confidence: 99%

“…Of these, 13 were validated in a group of patients that did not include elderly adults, and 174 were validated in the elderly population ( Figure 1 ). The variables considered across the equations can be divided as follows ( Table S2 ): demographic characteristics (age, gender, ethnicity [ 48 , 61 , 98 ]; menopausal status [ 37 ]; smoking [ 37 ]; meal status (whether patients had eaten a meal prior to the measurement) [ 37 ]); anthropometric measurements (height, weight, BMI [ 21 , 49 , 53 , 60 , 71 , 74 , 78 , 82 , 83 , 84 , 94 , 101 ]; abdomen [ 37 ], hip [ 51 ], or wrist circumference [ 76 , 101 ]; arm span [ 37 ]; chest skinfold [ 38 ] or subscapular skinfold [ 58 ]); clinical condition (NYHA [ 93 ]; diabetes [ 55 ]); physical activity (physical activity [ 101 ], leisure time activity [ 38 ], athletics [ 73 ]); measures of fat percentage (lean body mass [ 43 ], surface area [ 39 , 81 , 100 ]); laboratory tests (glycemia [ 51 , 93 ], albumin [ 93 ], C reactive protein [ 68 ]); environmental measures (temperature [ 75 , 101 ], humidity [ …”

Section: Resultsmentioning

confidence: 99%

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Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

et al. 2021

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Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69–0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient’s overall clinical outcome.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

et al. 2021

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“…Indeed, the ability of Harris-Benedict formula to predict REE was affected by the degree of malnutrition such that the mean largest difference of predicted and measured REE was seen among patients with severe malnutrition (444 kcals, 61%). However, the literature shows that the general variation between both methods might be natural and quite acceptable (REE predicted within ±10% of measured REE) [5,10,21]. Our data demonstrated that in 74% of the patients, REE predicted exceeded the REE measured by 41% and the predicted value was within 10% of the measured value in only 13% of the subjects suggesting that malnourished patients are difficult to predict accurately.…”

Section: Discussionmentioning

confidence: 55%

Measured and Predicted Resting Energy Expenditure in Malnourished Older Hospitalized Patients: A Cross-Sectional and Longitudinal Comparison

2020

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A number of equations have been proposed to predict resting energy expenditure (REE). The role of nutritional status in the accuracy and validity of the REE predicted in older patients has been paid less attention. We aimed to compare REE measured by indirect calorimetry (IC) and REE predicted by the Harris–Benedict formula in malnourished older hospitalized patients. Twenty-three malnourished older patients (age range 67–93 years, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Long Form (MNA-SF) score of less than 17. REE was measured (REEmeasured) and predicted (REEpredicted) on admission and at discharge. REEpredicted within ±10% of the REEmeasured was considered as accuracy. Nutritional support was provided to all malnourished patients during hospitalization. All patients were malnourished with a median MNA-LF score of 14. REEmeasured and REEpredicted increased significantly during 2-week nutritional therapy (+212.6 kcal and +19.5 kcal, respectively). Mean REEpredicted (1190.4 kcal) was significantly higher than REEmeasured (967.5 kcal) on admission (p < 0.001). This difference disappeared at discharge (p = 0.713). The average REEpredicted exceeded the REEmeasured on admission and at discharge by 29% and 11%, respectively. The magnitude of difference between REEmeasured and REEpredicted increased along with the degree of malnutrition (r = 0.42, p = 0.042) as deviations ranged from −582 to +310 kcal/day in severe to mildly malnourished patients, respectively. REEpredicted by the Harris–Benedict formula is not accurate in malnourished older hospitalized patients. REE measured by IC is considered precise, but it may not represent the true energy requirements to recover from malnutrition. Therefore, the effect of malnutrition on measured REE must be taken into account when estimating energy needs in these patients.

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“…Furthermore, the combined effects of enzyme hydrolysis action and the reduced heat treatment needed for softening allowed us to obtain a TM pumpkin with greater antioxidant activity and a lower sugar content than traditionally cooked pumpkins (S15). As discussed above, increased antioxidant activity can provide benefits by counteracting oxidative stress (Achir et al, 2016;Lee et al, 2020;Wichansawakun et al, 2022), whereas lower sugar content can be an advantage for the elderly because of low total energy use (Segura-Badilla et al, 2018).…”

Section: Sugar Determinationmentioning

confidence: 99%

Physicochemical Characterization of Texture-Modified Pumpkin by Vacuum Enzyme Impregnation: Textural, Chemical, and Image Analysis

Hernández

Gallego

Verdú

et al. 2022

Food Bioprocess Technol

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Texture-modified pumpkin was developed by using vacuum enzyme impregnation to soften texture to tolerable limits for the elderly population with swallowing and chewing difficulties. The impregnation process and macrostructural and microstructural enzyme action were explored by the laser light backscattering imaging technique and a microscopic study by digital image analysis. Texture was analyzed by a compression assay. The effect of enzyme treatment on antioxidant capacity and sugar content was evaluated and compared to the traditional cooking effect. Image analysis data demonstrated the effectiveness of the impregnation process and enzyme action on plant cell walls. Enzyme-treated samples at the end of the process had lower stiffness values with no fracture point, significantly greater antioxidant capacity and significantly lower total and reducing sugars contents than traditionally cooked pumpkins. The results herein obtained demonstrate the capability of using vacuum impregnation treatment with enzymes to soften pumpkins and their positive effects on antioxidant capacity and sugar content to develop safe and sensory-accepted texture-modified products for specific elderly populations.

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Simplified equation for resting energy expenditure in a population of elderly chileans compared to indirect calorimetry

Cited by 4 publications

References 27 publications

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Measured and Predicted Resting Energy Expenditure in Malnourished Older Hospitalized Patients: A Cross-Sectional and Longitudinal Comparison

Physicochemical Characterization of Texture-Modified Pumpkin by Vacuum Enzyme Impregnation: Textural, Chemical, and Image Analysis

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