Reference Summaries

Indirect Calorimetry: A Practical Guide for Clinicians (Haugen, 2007)

Continued changes in patient demographics, concurrent disease states and clinical interventions that affect metabolism, all impact the accuracy and reliability of traditional predictive equations for determining energy expenditure are questionable.

Furthermore the benefits of providing optimal nutrition for recovery from illness and chronic health management have been documented especially when the detrimental effects associated with under- or overfeeding are considered.

To achieve the highest quality of patient care, we should strive for patient-specific nutrition support regimens. Indirect calorimetry remains a gold standard in measuring energy expenditure in the clinical settings and offersoffers a scientifically based approach for customizing a patient's energy needs and nutrient delivery to maximize the benefits of nutrition therapy. This approach echoes already well-established clinical practice in other areas of medicine.

Traditionally, IC has been underused, mostly due to costs, shortage of personnel, and lack of education/training. With recent advances in technology, indirect calorimeters are easier to operate, more portable, and affordable.

Resting energy expenditure, body composition, and excess weight in the obese (Foster et al. 1988)

Commonly used equations for the prediction of REE are not appropriate for moderately or severely obese patients. Caloric prescription for weight reduction must be tailored to individuals rather than recommending the same caloric intake to persons with varying metabolic rates.

"There is a need for technological advances that will make the assessment of REE accurate, portable, and inexpensive."

Body-weight regulation: causes of obesity (Martinez, 2000)

Weight gain may also depend on the distribution of dietary energy substrates, which have different impacts on metabolism and food intake, as well as on the sympathetic nervous system and, thereby, on energy balance and body weight (Prentice, 1998).

Thus, short-term feeding of two formulas of different macronutrient composition, i.e. high-carbohydrate and high-fat meals, to healthy volunteers produced higher values for glucose oxidation, thermic effect of feeding and heart rate (as an indicator of sympathetic activation) in those individuals receiving the high-carbohydrate challenge as compared with the fat-rich formula (Labayen et al. 1999).

However, when a similar dietary intervention was carried out in obese individuals the results seem to indicate that these individuals were less efficient at oxidizing fat intake, while high carbohydrate feeding was accompanied by an increase in "de novo" lipogenesis (Marques-Lopes et al. 2000).

Furthermore, a relatively high RQ may reflect reduced fat oxidation, which has been suggested as a possible predictor of weight gain (Schutz, 1995b; Weinsier et al. 1998), although other investigators (Flatt & Guptta, 1999) have reported that such metabolic efficiency plays a minor role in the development or avoidance of obesity.

Ease of weight loss influenced by individual biology (NIH, 2015)

For the first time in a lab, researchers at the National Institutes of Health found evidence supporting the commonly held belief that people with certain physiologies lose less weight than others when limiting calories.

"When people who are obese decrease the amount of food they eat, metabolic responses vary greatly, with a "thrifty" metabolism possibly contributing to less weight lost," said Susanne Votruba, Ph.D., study author and PECRB clinical investigator. "While behavioral factors such as adherence to diet affect weight loss to an extent, our study suggests we should consider a larger picture that includes individual physiology " and that weight loss is one situation where being thrifty doesn't pay."

"What we've learned from this study may one day enable a more personalized approach to help people who are obese achieve a healthy weight," said NIDDK Director Griffin P. Rodgers, M.D. "This study represents the latest advance in NIDDK's ongoing efforts to increase understanding of obesity."

How are habits formed: Modelling habit formation in the real world (Lally, 2009)

How long it takes to a new habit to form can vary widely depending on behaviour, person and circumstances.

On average it take more than 2 months before a new behavior to become automatic, it can take anywhere from 18 days to 254 to form a new habit.

Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings (Bero, 1998)

Consistently effective interventions to promote behavioural change among health professionals include education, reminders and multifaceted interventions such as audit and feedback.