IC-BASAROTs: New Practice Method for More Accurate Bed-side Assessment of Individual Energy Expenditure
NCT ID: NCT02682537
Last Updated: 2018-04-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
1400 participants
OBSERVATIONAL
2015-03-31
2020-12-31
Brief Summary
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Detailed Description
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The reference method for energy expenditure measurement is indirect calorimetry (IC). High costs, time requirements and the need for trained personal are main reasons for its limited use in clinical practice. Also arithmetical calculations, such as the Harris-Benedict equation, are not widely accepted.
In general energy expenditure is often estimated by so called rules of thumbs, a method requiring only one multiplication with body weight (for example: 25 kcal/kg body weight). Sex, age and BMI are usually not considered, although they are independent predictors of energy expenditure. Thus, energy expenditure estimations are often inaccurate, especially in older and overweight/obese persons.
Therefore, it is important to develop a bedside tool that is more accurate but simple enough to be accepted by practitioners. In 2004, the Austrian Society of Clinical Nutrition published the first BMI, aged and sex adapted rule of thumbs, called BASAROTs (BMI Age Sex Adjusted Rule Of Thumbs). Those were, however, based on results of the Harris Benedict equation.
The main objective of the present study is, therefore, to replace the existing BASAROTs by BASAROTs based on actual measurements of resting energy expenditure by indirect calorimetry (IC-BASAROTs).
Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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Female, BMI: 14,00-16,49 kg/m²
Age: 18 - 100 Years
No interventions assigned to this group
Female, BMI: 16,50-18,49 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 18,50-19,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 20,00-24,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 25,00-29,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 30,00-34,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 35,00-39,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 40,00-44,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Female, BMI: 45,00-49,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 14,00-16,49 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 16,50-18,49 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 18,50-19,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 20,00-24,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 25,00-29,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 30,00-34,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 35,00-39,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 40,00-44,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Male, BMI: 45,00-49,99 kg/m²
Age: 18 - 100 years
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* 18 years to 85 years
* Body Mass Index: 14,0 - 49,9 kg/m²
* Eastern Cooperative Oncology Group (ECOG) Performance Status (Grade 0 or 1)
* normal thyroid function
* subjective health in dependence of BMI (underweight, normal weight, obesity, morbid obesity)
Exclusion Criteria
* amputations
* paresis (mono- and diparesis)
* Asian or African descent
* above-average physical activity (competitive sport)
* present or suspicion of malignant neoplasms (tumors, metastases, hemato-oncological diseases)
* severe diseases (organ diseases, neurological diseases)
* severe dementia (MMSE \< 20 points)
* pregnancy
* participation in other trials
* subjects with expect non-compliance to protocol guidelines
* intake of:
* lithium compound
* neuroleptics: Olanzapine (Zyprexa ®), Clozapine, Sertindole, Ziprasidone, Haloperidol, Thioridazine
* anticonvulsant (Carbamazepin, Valproic Acid, Topiramate)
* noradrenalin-reuptake-inhibitor (NARI): Reboxetine , Atomoxetine
* tricyclic antidepressants (Amitryptiline, Clomipramine, Doxepin, Imipramine, Trimipramin)
* Lorcaserin
* interferon-alfa, interferon-beta
* Baclofen
* Orciprenaline
* Amiodarona
* Insulin
* corticoid therapy (oral)
18 Years
100 Years
ALL
Yes
Sponsors
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University of Applied Sciences Neubrandenburg
OTHER
Responsible Party
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Luzia Valentini
Prof. Dr.
Principal Investigators
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Luzia Valentini, Prof. Dr.
Role: PRINCIPAL_INVESTIGATOR
Neubrandenburg University of Applied Sciences
Locations
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Technical University of Munich
Munich, Bavaria, Germany
Fulda University of Applied Sciences
Fulda, Hesse, Germany
Dietrich Bonheoffer Hospital of Neubrandenburg
Neubrandenburg, Mecklenburg-Vorpommern, Germany
University of Applied Sciences Neubrandenburg
Neubrandenburg, Mecklenburg-Vorpommern, Germany
Profil Institut for Metabolic Research
Mainz, Rhineland-Palatinate, Germany
Leipzig University - Medical Center IFB AdiposityDiseases
Leipzig, Saxony, Germany
Countries
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Central Contacts
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Facility Contacts
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References
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Elizabeth Weekes C. Controversies in the determination of energy requirements. Proc Nutr Soc. 2007 Aug;66(3):367-77. doi: 10.1017/S0029665107005630.
Schoeller DA. Making indirect calorimetry a gold standard for predicting energy requirements for institutionalized patients. J Am Diet Assoc. 2007 Mar;107(3):390-2. doi: 10.1016/j.jada.2007.01.030. No abstract available.
Valentini L, Roth E, Jadrna K, Postrach E, Schulzke JD. The BASA-ROT table: an arithmetic-hypothetical concept for easy BMI-, age-, and sex-adjusted bedside estimation of energy expenditure. Nutrition. 2012 Jul;28(7-8):773-8. doi: 10.1016/j.nut.2011.11.020.
Lawrence M. Predicting energy requirements: is energy expenditure proportional to the BMR or to body weight? An analysis of data collected in rural Gambian women. Eur J Clin Nutr. 1988 Nov;42(11):919-27.
Piers LS, Soares MJ, McCormack LM, O'Dea K. Is there evidence for an age-related reduction in metabolic rate? J Appl Physiol (1985). 1998 Dec;85(6):2196-204. doi: 10.1152/jappl.1998.85.6.2196.
Muller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: recent lessons from imaging technologies. Obes Rev. 2002 May;3(2):113-22. doi: 10.1046/j.1467-789x.2002.00057.x.
Dickerson RN. Optimal caloric intake for critically ill patients: first, do no harm. Nutr Clin Pract. 2011 Feb;26(1):48-54. doi: 10.1177/0884533610393254.
Berger MM, Pichard C. Development and current use of parenteral nutrition in critical care - an opinion paper. Crit Care. 2014 Aug 8;18(4):478. doi: 10.1186/s13054-014-0478-0.
Heidegger CP, Berger MM, Graf S, Zingg W, Darmon P, Costanza MC, Thibault R, Pichard C. Optimisation of energy provision with supplemental parenteral nutrition in critically ill patients: a randomised controlled clinical trial. Lancet. 2013 Feb 2;381(9864):385-93. doi: 10.1016/S0140-6736(12)61351-8. Epub 2012 Dec 3.
Bader N, Bosy-Westphal A, Dilba B, Muller MJ. Intra- and interindividual variability of resting energy expenditure in healthy male subjects -- biological and methodological variability of resting energy expenditure. Br J Nutr. 2005 Nov;94(5):843-9. doi: 10.1079/bjn20051551.
Bosy-Westphal A, Eichhorn C, Kutzner D, Illner K, Heller M, Muller MJ. The age-related decline in resting energy expenditure in humans is due to the loss of fat-free mass and to alterations in its metabolically active components. J Nutr. 2003 Jul;133(7):2356-62. doi: 10.1093/jn/133.7.2356.
Gonnissen HK, Adam TC, Hursel R, Rutters F, Verhoef SP, Westerterp-Plantenga MS. Sleep duration, sleep quality and body weight: parallel developments. Physiol Behav. 2013 Sep 10;121:112-6. doi: 10.1016/j.physbeh.2013.04.007. Epub 2013 May 3.
Chaput JP, St-Onge MP. Increased food intake by insufficient sleep in humans: are we jumping the gun on the hormonal explanation? Front Endocrinol (Lausanne). 2014 Jul 15;5:116. doi: 10.3389/fendo.2014.00116. eCollection 2014. No abstract available.
Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003 Aug;35(8):1381-95. doi: 10.1249/01.MSS.0000078924.61453.FB.
Rutten A, Abu-Omar K. Prevalence of physical activity in the European Union. Soz Praventivmed. 2004;49(4):281-9. doi: 10.1007/s00038-004-3100-4.
Bosy-Westphal A, Schautz B, Later W, Kehayias JJ, Gallagher D, Muller MJ. What makes a BIA equation unique? Validity of eight-electrode multifrequency BIA to estimate body composition in a healthy adult population. Eur J Clin Nutr. 2013 Jan;67 Suppl 1:S14-21. doi: 10.1038/ejcn.2012.160.
Other Identifiers
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BB023/15
Identifier Type: -
Identifier Source: org_study_id
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