Introduction

In lifestyle interventions where changes in body composition are expected, an accurate assessment of energy expenditure (EE) and energy intake (EI) is needed [1] to better implement optimal strategies to affect one or both sides of the energy balance (EB) equation. The EB equation states that the rate of energy stores (ES) is equal to the rate of energy intake minus the rate of energy expenditure measured in kcal/d (ES = EI – EE). To achieve weight loss (WL), the EI should be less than EE and this inequality should be maintained throughout time, by decreasing EI and/or increasing EE.

The doubly labeled water (DLW) method is the gold standard for measuring EE in any environment [2, 3]. Also, under the condition of neutral EB (where EE equals EI), EI can also be indirectly assessed by this method through the intake-balance method [4]. Specifically, if body composition changes are known, then ES can be calculated and summed with EE assessed by DLW. Despite its accuracy, the DLW assessment is expensive and requires specialized technicians, making it unfeasible for widespread application [5]. As an alternative, accelerometry-based wearable motion devices provide detailed, continuous, and objective measurements of physical activity expenditure (PA) and EE [5, 6].

Measuring EI is also a difficult task for researchers, with self-reported tools, such as food diaries, being known to be inaccurate, mostly by underestimating dietary intake [7, 8]. The conscious or sub-conscious exclusion of foods that were consumed, as well as the lack of literacy regarding portion size, have been pointed out as major concerns, leading to a certain degree of misreporting (mostly underreporting) that has been reported in several studies [1, 3, 9,10,11]. Moreover, the degree of underestimation has been associated with several characteristics, such as sex, perception of body image, restrained eating, sex and specially body composition variables, such as body weight, fat mass (FM) percentage and BMI [1, 5, 10, 12, 13].

As a more valid and scientific approach, EI can be accurately and precisely measured in the inpatient setting or when food is provided in the outpatient condition [8, 14]. Alternatively, and given the relation between EI and EE in the EB equation, it is possible to calculate the EI if the two remaining terms of the equation (ES and EE) are known—the so-called “intake-balance method” [14]. Despite EE being ideally measured by DLW, it has been proven that wearable motion devices can also provide a valid estimate of EE, being considered a valid alternative to the reference method [15]. ES is calculated using the difference quotient: \(9500\frac{\Delta {{\rm{FM}}}}{\Delta t}+1020\frac{\Delta {{\rm{FFM}}}}{\Delta t}\) where FM, FFM and t represent fat mass (kg), fat-free mass (kg) and time in days, respectively, while the ∆ symbol represents the change. Therefore, if FM and FFM are known over a time interval, then ES can be directly calculated and summed with EE to objectively estimate EI [14]. This method provides a more accurate and objective assessment of EI when compared to self-reported instruments [16] (e.g., food diaries).

Therefore, the aim of this study was (1) to compare the measured (intake-balance method) with the self-reported EI (3-day food diaries) in two conditions—under a neutral and a negative EB, and (2) to determine the predictors of misreporting divided by sex, adiposity and BMI category (overweight vs obesity).

Methodology

This study is a secondary analysis of the Champ4life project, a 1-year randomized controlled trial targeting former elite athletes with overweight/obesity. For a more detailed description of the study protocol, including the recruitment procedures, exclusion and inclusion criteria, randomization process, and methods, please see the reference [17].

Shortly, the Champ4life project is a Self-Determination Theory (SDT)-based program aimed to provide a lifestyle intervention targeting former athletes. Participants from the intervention group (IG) [vs a control group (CG)] underwent 4 months of an active WL followed by 8 months of WL maintenance. Nutrition appointments with a certified dietitian were provided for the intervention group to discuss the participant’s eating pattern and create a personalized dietary strategy to promote a moderate caloric deficit (~300–500 kcal/d), according to each participant’s energy requirements and preferences. In addition, they received 12 educational sessions, including educational content and practical application of in-class exercises in the areas of PA and exercise, diet and eating behavior, as well as behavior modification [18]. For the current study, data were collected immediately before and after the 4-month intervention, where the first assessment took place in neutral EB and the second assessment in negative EB.

The Champ4life study was registered at www.clinicaltrials.gov (clinicaltrials.gov ID: NCT03031951), approved by the Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (Lisbon, Portugal) (CEFMH Approval Number: 16/2016), and conducted in accordance with the Declaration of Helsinki for human studies from the World Medical Association [19].

Resting energy expenditure (REE)

REE was measured with the MedGraphics CPX Ultima indirect calorimeter (MedGraphics Corporation, Breezeex Software, Italy) during the morning period (7.00–10.00 am), after an overnight fast. The calorimeter was used to measure breath-by-breath oxygen consumption (V̇O2) and carbon dioxide production (V̇CO2) using a mask placed on the participants’ faces. A pneumotachograph calibrated with a 3-L syringe (Hans Rudolph, inc.TM) was used to measure the flow and volume. The participants were asked to relax, breath normally and not sleep or talk during the test [20].

Body composition

Participants had their weight and height measured with a weight scale (Seca 799, Hamburg, Germany) and a stadiometer (Seca, Hamburg, Germany), respectively.

Dual-energy X-ray absorptiometry (DXA; Hologic Explorer-W, Waltham, USA) was performed to assess the body composition stores, such as total FM and FFM, as described previously [17].

Energy balance (EB) and energy intake (EI)

For each time point, the EB equation was applied to quantify the average rate of change of body ES in kilocalories per day, as denoted by the following equation:

$${\rm{ES}}\left({\rm{kcal}}.{{\rm{d}}}^{-1}\right)={\rm{EI}}-{\rm{EE}}$$

It is recognized that EB (kilocalories per day) is negative when the EE surpasses the EI (energy deficit leading to loss of ES), while EB is positive when EI is larger than EE (energy surplus by increasing ES). In the current study, ES was calculated from the changed body ES from the beginning to the end of the WL intervention. Hence, using the established energy densities for FM [21] and FFM [22], the following equation was applied:

$${\rm{ES}}({\rm{kcal}}.{{\rm{d}}}^{-1})=9500\frac{\Delta {{\rm{FM}}}}{\Delta t}+1020\frac{\Delta {{\rm{FFM}}}}{\Delta t}$$

Where ∆FM and ∆FFM represent the change in kg of FM and FFM from the beginning to end of the intervention and ∆t is the time length of the intervention in days.

For the baseline EI, as participants were weight stable during at least 3 months (inclusion criteria), we considered an EB = 0, and therefore EI = EE. For the baseline EI, as participants were weight stable during at least 3 months (inclusion criteria), we considered an EB = 0, and therefore EI = EE.

In the current study, EI was not directly measured, being estimated by self-report (food records – self-reported EI) and objectively estimated (“intake-balance method” – measured EI), with the former being used as the criteria method [11]. In this sense, and to help differentiate between both EI variables, the different names were used to help differentiate the variables.

For self-reported EI, a 3-day food record (including 2 week and 1 weekend non-consecutive days) was collected to characterize the macronutrient composition of the diet at the 2 assessment times – baseline (0 months) and after the 4-month intervention – using a software package (Food Processor SQL, ESHA Research, Salem, OR, USA).

For measured EI, the “intake-balance method” [23] was used. This method has been previously validated [15, 24] and has been shown to provide a valid estimation of EI through changes in body ES such FM and FFM, together with EE. The following equation was used:

$${{\rm{EI}}}_{({\rm{kcal}}/{\rm{d}})}={{\rm{EE}}}_{({\rm{kcal}}/{\rm{d}})}-{{\rm{ES}}}_{({\rm{kcal}}/{\rm{d}})},$$

Where EE represents the total daily EE (TDEE) measured by accelerometry at each assessment and the ES (calculated as mentioned above). For the baseline EI, as participants were weight stable during at least 3 months (inclusion criteria), we considered an EB = 0, and therefore EI = EE.

This equation was used not only to measure EI at each time point, but also to calculate the degree of energy restriction during the WL phase. A graphical demonstration of the study is presented in Fig. 1.

Fig. 1: Schematic presentation of the study.
figure 1

This sub-analysis of the Champ4Life project considered the baseline assessment— participants in neutral energy balance (both intervention and control groups)—and post-intervention assessment— participants in negative energy balance (intervention group only). EI energy intake, EE energy expenditure.

Full size image

Statistical analysis

The software IBM SPSS statistics version 27.0 (IBM, Chicago, Illinois, USA) and GraphPad Prism, version 5.04 were used to perform the analysis and to create the plots, respectively. The Kolmogorov–Smirnov test was performed to identify if the variables followed a normal distribution. Data were presented as mean (SD).

The Bland–Altman analysis was performed to analyze the agreement between EI measured by the intake-method balance and predicted by food records. A residuals’ plot of calculated EI with weight, FM and FFM as continuous variables were performed to understand if there was any pattern of scatter between EI and these variables. As no pattern was found between them (R2 < 0.1 and slope not different than 0), the assumption of homoscedasticity was supported. The Goldberg ratio was calculated (EI/RMR), where ratios <1.35 suggest a possible underreporting of EI [25]. The ratio EI/EE was also calculated.

Pearson’s correlation was performed to examine the association between the degree of misreporting and age, weight, or body composition variables. Additional analyses were performed by dividing the population in tertiles according to their BMI. Statistical significance was set at a two-sided p < 0.05.

Results

Only participants who had complete information regarding body composition and EI at baseline were included [n = 73, mean (SD): age = 43.7 (9.2) years, BMI = 31.5 (4.5) kg/m2, 37% females]. All participants were overweight or obese (BMI ranged from 25 to 42 kg/m2).

The results of the Champ4life program for all the participants were presented elsewhere [26]. Shortly, participants from the IG lost –4.9 (4.8) kg, corresponding to –5.4% (5.0%) for their initial weight, and –3.4 (3.8) kg of FM, corresponding to –12.5% (11.0%). No differences were found in body composition for the CG.

“Measured” vs self-reported EI

At baseline, the self-reported EI was 2063 (659) kcal/d (no differences between groups, p = 0.948), ~14% different than TDEE, with a Goldberg ratio of 1.27 (0.38). The measured EI was 2441 (368) kcal/d (no differences between groups, p = 0.497), and the Goldberg ratio was 1.51 (0.14). After 4 months, the self-reported EI was 1726 (649) kcal/d [CG: 1826 (758) vs IG: 1600 (467), p = 0.208], ~29% different than TDEE. The measured EI was 2208 (532) kcal/d [CG: 2417 (442) vs IG: 1984 (536), p = 0.002], being only ~7% different from TDEE, with a Goldberg ratio of 1.07 (0.38) and 1.41 (0.20), for self-reported and measured EI, respectively.

Dividing by groups, after 4 months, the measured EI was <1% and ~15% different from TDEE for CG and IG, respectively. Also, the self-reported EI was ~28% different from TDEE for both groups after 4 months. No differences were found between IG and CG for these two ratios except at 4 months for the measured EI [Goldberg Ratio: CG: 1.52 (0.13) vs IG: 1.30 (0.21), p < 0.001; EI/TDEE: CG: 1.00 (0.07) vs IG: 0.85 (0.12), p < 0.001].

The association and the agreement between the measured and self-reported EI are presented in Fig. 2. The EI obtained by food diaries explained 26% of the intake-method balance (p < 0.001), with a standard deviation of the residuals (goodness-of-fit) of 322 kcal/d.

Fig. 2: Association and agreement between measured (intake-balance method) and self-reported EI (food records).
figure 2

A contains a linear regression between measured and self-reported EI. B contains Bland–Altman graphs for the agreement between measured and self-reported EI, where the horizontal solid line represents the mean differences between methods and both upper and lower dashed lines represent 95% limits of agreement (±1.96 SD). EI energy intake, EE energy expenditure.

Full size image

Overall, at baseline mean EI underestimation of food records was 355 kcal/day, while at 4 months underestimation was 570 kcal/day. The 95% limits of agreement for both moments were wide, ranging from –1720 to 1010 kcal/day at baseline and –1918 to 779 kcal/day at 4 months. During both moments, no significant trends in the error were identified (p = 0.315 and p = 0.611, respectively, for baseline and 4 months), meaning that food records’ prediction of EI was not affected by the magnitude of measured EI.

A sub-analysis was performed by dividing the sample in BMI tertiles (Table 1) and by sex (Table 2).

Table 1 Energy intake, Goldberg and EI/TDEE ratio divided by BMI tertiles.
Full size table
Table 2 Energy intake, Goldberg and EI/TDEE ratio divided by sexes.
Full size table

The tertile 3 presented a lower Goldberg ratio than tertile 2 for both self-reported and measured EI (p < 0.05). No differences were found among groups regarding the degree of misreporting.

Dividing by sexes, females showed a lower EI than males for both self-reported (p = 0.002) and measured (p < 0.001). The Goldberg ratio for measured EI was different between sexes (p = 0.039). No differences were found between sexes for the degree of misreporting.

Cross-section associations with degree of misreporting

Small and negative associations were found between age and both measured and self-reported EI (measured: R = –0.220, p = 0.040; self-reported: R = –0.233, p = 0.047).

No correlations were found between the degree of misreporting, the Goldberg ratio nor the EI/TDEE ratio (both for self-reported EI) with BMI, weight, or FM (p > 0.05).

Sex-related

When divided by sex, weight and FM (kg) were predictors of the degree of misreporting (kcal/d) only for females (weight: kcal/d: R2 = 0.393, \(\hat{\beta }=-31.7\), SE = 12.5, p = 0.029; %: R2 = 0.368, \(\hat{\beta }=-1.4\), SE = 0.6, p = 0.037; FM: kcal/d: R2 = 0.384, \(\hat{\beta }=-47.7\), SE = 19.1, p = 0.032; %: R2 = 0.362, \(\hat{\beta }=-2.1\), SE = 0.9, p = 0.039). For males, the BMI was a predictor of the degree of misreporting (kcal/d) (R2 = 0.171, \(\hat{\beta }=-58.2\), SE = 26.2, p = 0.036).

Comparison between under- and overreporters

Tertiles were created according to the degree of misreporting (%) with three groups being formed: (i) underreporters (i.e., self-reported EI is lower than measured EI); (ii) neutrals (i.e., self-reported EI was similar to measured EI); (iii) overreporters (i.e., self-reported EI was higher than measured EI) (Table 3). When dividing according to Goldberg or the EI/TDEE ratio, no differences were found among tertiles regarding weight or body composition variables.

Table 3 Baseline characteristics according to the tertiles division.
Full size table

Longitudinal associations with degree of misreporting

After 4 months, the magnitude of WL and FM loss were negatively associated with the degree of misreporting, i.e., participants who showed a larger amount of WL were those with a smaller discrepancy between measured and reported EI (Fig. 3).

Fig. 3: Associations between the magnitude of weight and fat mass loss and the degree of misreporting at 4 months.
figure 3

The left panel depicts the association between the degree of misreporting (kcal/d) and WL while the right panel depicts the association between the degree of misreporting (kcal/d) and ΔFM, for both intervention (solid points) and control groups (white points). WL weight loss, ΔFM fat mass changes.

Full size image

Discussion

The major finding of this study was that despite finding significant underestimations of EI by self-reported methods during both neutral and negative EB, these errors were not affected by the magnitude of “measured” EI. Also, when considering females only, weight and FM (kg) were predictors of the degree of misreporting (%), with higher values of weight and FM being associated with a larger degree of misreporting.

The association between the degree of misreporting and body composition variables has been reported previously. Some authors showed an association between the degree of underreporting with body composition variables (such as FM), BMI or weight, i.e., people with higher adiposity or heavier are more prone to underestimate their EI [1, 5, 10, 12, 13, 27]. Nevertheless, similarly to our findings, other studies failed to find an association between the degree of misreporting and body composition variables [28,29,30,31]. However, when dividing by sexes, weight and FM were predictors of the degree of misreporting, i.e., women with higher values of weight and FM showed a greater degree of underestimation for EI. A recent systematic review showed that both sexes underestimate EI when using food diaries [32]. Likewise, our study showed that no differences were found between sexes regarding the degree of misreporting. However, it seems that for women, the degree of misreporting is associated with weight and FM, while for males, only BMI emerged as a predictor. Hence, despite no differences being found between sexes regarding the degree of misreporting, body composition and weight tend to better explain this phenomenon in females but not in males.

It is known that an accurate and precise EI measurement is difficult to obtain, especially when this EB component is self-reported [7]. The omission of certain foods/meals and/or the inaccuracy when reporting portion sizes are the two most frequent obstacles that have been reported by several authors [1, 3, 9,10,11]. Regarding the second problem and its association with food literacy, it is expected that people with higher literacy skills about nutrition may be less prone to underreporting, as they are more aware of the portion sizes [5]. As most studies regarding the inaccuracy of food diaries were published before 2000, the literacy levels of the included participants were probably lower when compared with nowadays levels. In fact, the global literacy rate among adults increased from 67% in 1976 to around 86% in 2019 [33], which may in turn influence the accuracy and precision of the EI reporting. Moreover, the general interest about health literacy has increased in the past years [34], which may have also influenced the accuracy of the EI reporting. In addition, the magnitude of “actual” EI could be hypothesized to influence the level of misreporting EI, either under- or overestimation. Nevertheless, as expected, the measured EI provided more plausible values than self-reported, being more similar to TDEE than the EI from food diaries. Similar to Shook et al. study [15], more than half of our participants (~61%) presented a Goldberg ratio <1.35. The Goldberg cut-point evaluates the mean population bias in EI, where values below 1.35 are considered physiologically implausible [35]. However, when using the EI calculated through the EB equation, only ~7% of participants showed a ratio <1.35. However, and despite the significant underestimation of self-reported EI compared to “measured” EI, the difference between methods does not appear to be influenced by the magnitude of “measured” EI, i.e., the degree of misreporting does not seem to be influenced by the value of measured EI, not existing of instance a trend towards higher degrees of underreporting for people with higher measured EI. Also, the Bland–Altman plots allow us to observe the interindividual variability for the between-methods difference, as evidenced by the wide limits of agreement.

When divided by tertiles, no differences in body weight nor BMI were observed but a large variability for weight and BMI was observed within groups. Thus, it may be likely that the reason for the larger EI underestimation is not the body size or composition but probably a mixture of other variables, including psychological factors and health behavior. Therefore, it seems that a single variable may not play a significant effect on the degree of underestimation, with a combination of other factors possibly explaining a higher degree of misreporting.

Another interesting finding of this study is the fact that, from baseline to 4 months, participants who lost larger amounts of weight were those who showed a lower degree of misreporting, i.e., a smaller difference between the self-reported and the measured EI. Moreover, most participants with a smaller degree of underreporting belonged to the IG (vs CG). The Champ4life was an SDT-based program, where participants underwent 12 educational sessions comprising educational content and practical application of in-class exercises in the areas of PA and exercise, diet and eating behavior, as well as behavior modification [18]. More precisely, participants increased their knowledge concerning the nutritional value of food, cooking methods and its influence on energy density and portion size, which may lead to a better EI reporting. Also, participants were taught some strategies to work their relationship with food, avoiding feelings of guilt or shame when eating certain types of food/meals. Therefore, we believe that the Champ4life was an effective program not only in terms of WL and body composition improvement but also in increasing the literacy regarding nutrition, which is associated with a better EI reporting [27, 36].

Alongside understanding who underestimates EI, it is also important to realize how researchers can avoid or at least attenuate the degree of misreporting. Then, some good practices should be considered when using self-reported tools to assess EI. Commonly, respondents are asked to record detailed information about food preparation methods and all ingredients used [37]. The quantities are normally estimated using household measures, but they can also be weighted by the responder or by a member of the research team [38]. Drawings and photographs can also be used to help responders best quantify their dietary intake [7]. Regardless of the method, the more detailed the record is, the better the estimation of EI [38]. Responders should also be provided training to properly complete these questionnaires and enhancing their willingness to record their food. Lastly, upon receiving the food records, a trained interviewer should review the food diaries with the respondent, to clarify any doubts and to examine the possible consumption of forgotten foods [37].

Although this study supports the current literature regarding the use of self-reported methods to determine EI, limitations should be addressed. First, EE was assessed by accelerometry as an alternative to the reference method (DLW), which may influence our EI assessments through the “intake-balance” method. Nevertheless, accelerometry has been proven to be an accurate and precise method [14]. Plus, the models to assess PA energy expenditure (PAEE), and consequently TDEE, by accelerometry include body weight as a predictor, which is already used (indirectly) in the EB equation to assess ES. Therefore, the results should be interpreted carefully. Nevertheless, the use of wearable motion sensors rather than DLW to estimate EE has already been considered in one other study, working as an alternative to the reference method [15]. Also, at baseline, participants did not undergo a period of weight maintenance in order to ensure a neutral EB before starting the intervention. Although being in a neutral EB was an inclusion criterion to participate in Champ4life [17], a period of weight stabilization should have been required before the active WL, to ensure that participants were under a neutral EB. Also, as it was a secondary analysis, the sample size was small, which may have influenced our results. In fact, with a larger sample size, further analyses could have been performed, namely, to explore if there are differences in misreporting between individuals with overweight and those with obesity. Moreover, with a larger sample size a wider range of BMI values might have been achieved as currently our range is small, with the BMI tertiles having very close mean values. Another important limitation is the possible occurrence of the “Hawthorne effect”, defined as a change in one’s behavior as a response to observation and/or assessment [39]. Indeed, evidence shows that the simple action of self-monitoring food intake, such as keeping a daily food record, exerts a positive effect on WL [40]. Then, if participants decreased their EI when filling the 3-day food diaries, they were in fact undereating rather than underestimating their EI. Lastly, this study was performed in former elite athletes with overweight or obesity, which in turn may limit the extrapolation of these results to different populations.

In sum, it seems that understanding why and who underestimates EI is a more complex issue than simply trying to find any associations with body composition variables. As evidenced by our results, the assumption that people with larger body size would underestimate their EI due to their body composition is old-fashioned, outdated and professionals should try and find other possible contributing factors. Also, some tools and recommendations should be given to each participant before filling any self-reported method such as food diaries in order better deal with the degree of misreporting in research.