Examining Plausibility of Self-Reported Energy Intake Data: Considerations for Method Selection

Abstract

Self-reported dietary intake data contain valuable information and have long been used in the development of nutrition programs and policy. Some degree of measurement error is always present in such data. Biological plausibility, assessed by determining whether self-reported energy intake (rEI) reflects physiological status and physical activity level, must be examined and accounted for before drawing conclusions about intake. Methods that may be used to account for plausibility of rEI include crude methods such as excluding participants reporting EIs at the extremes of a range of intake and individualized methods such as statistical adjustment and applying cutoffs that account for the errors associated with within-participant variation in EI and total energy expenditure (TEE). These approaches allow researchers to determine how accounting for under- and overreporting affects study results and to appropriately address misreporting in drawing conclusions with data collected and in interpreting reported research. In selecting a procedure to assess and account for plausibility of intake, there are a number of key considerations, such as resources available, the dietary-report instrument, as well as the advantages and disadvantages of each method. While additional studies are warranted to recommend one procedure as superior to another, researchers should apply one of the available methods to address the issue of implausible rEI. If no method is applied, then at minimum, mean TEE or rEI/TEE should be reported to allow readers to ascertain the degree of misreporting at a gross level and better interpret the data and results provided.

High Protein Intake Is Associated With Histological Disease Activity in Patients With NAFLD

“…(23) Energy misreporting is a very frequently observed issue in self-reported dietary assessment and is considered to be unavoidable. (24,25) However, excluding misreporters leads to a loss of statistical power and may bias estimates of associations. (25) As an alternative approach, we calculated the ratio between energy intake (EI) and the basal metabolic rate (BMR) (EI:BMR ratio).…”

“…(24,25) However, excluding misreporters leads to a loss of statistical power and may bias estimates of associations. (25) As an alternative approach, we calculated the ratio between energy intake (EI) and the basal metabolic rate (BMR) (EI:BMR ratio). Thirty-six percent of the cohort were definite energy misreporters (EI:BMR < 1).…”

“…To account for energy misreporting in our study, we adjusted our multivariate analyses additionally for the EI:BMR ratio in similarity to other publications. (25,26) gut BaCteRial seQuenCing…”

Improvement in dietary intake estimates through the combined use of different approaches

“…Food Frequency Questionnaires (FFQ) are commonly used for the assessment of usual dietary intake in epidemiological studies, particularly prospective studies, and it is considered an important tool for the investigation of the relationship between diet and the etiology, prevention, and treatment of diseases. Nevertheless, as all methods used in dietary intake assessment, it has measurement errors that affect risk and association estimates [1][2][3][4][5][6][7][8][9].…”

“…Considering that such errors are inherent to the method and aiming to minimize them, techniques have been developed to approximate the estimates of the FFQ to those of reference methods [2][3][4][5][6][7][8][9]. In addition to the instrument validation, calibration can also be used.…”