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The Journal of Nutrition logoLink to The Journal of Nutrition
. 2018 Apr 11;148(4):658–663. doi: 10.1093/jn/nxy009

Food Photography Is Not an Accurate Measure of Energy Intake in Obese, Pregnant Women

Jasper Most 1, Porsha M Vallo 1, Abby D Altazan 1, Linda Anne Gilmore 1, Elizabeth F Sutton 1, Loren E Cain 1,4, Jeffrey H Burton 2, Corby K Martin 3, Leanne M Redman 1,✉
PMCID: PMC6167554  PMID: 29659958

Abstract

Background

To improve weight management in pregnant women, there is a need to deliver specific, data-based recommendations on energy intake.

Objective

This cross-sectional study evaluated the accuracy of an electronic reporting method to measure daily energy intake in pregnant women compared with total daily energy expenditure (TDEE).

Methods

Twenty-three obese [mean ± SEM body mass index (kg/m2): 36.9 ± 1.3] pregnant women (aged 28.3 ±1.1 y) used a smartphone application to capture images of their food selection and plate waste in free-living conditions for ≥6 d in early (13–16 wk) and late (35–37 wk) pregnancy. Energy intake was evaluated by the smartphone application SmartIntake and compared with simultaneous assessment of TDEE obtained by doubly labeled water. Accuracy was defined as reported energy intake compared with TDEE (percentage of TDEE). Ecological momentary assessment prompts were used to enhance data reporting. Two-one–sided t tests for the 2 methods were used to assess equivalency, which was considered significant when accuracy was >80%.

Results

Energy intake reported by the SmartIntake application was 63.4% ± 2.3% of TDEE measured by doubly labeled water (P = 1.00). Energy intake reported as snacks accounted for 17% ± 2% of reported energy intake. Participants who used their own phones compared with participants who used borrowed phones captured more images (P = 0.04) and had higher accuracy (73% ± 3% compared with 60% ± 3% of TDEE; P = 0.01). Reported energy intake as snacks was significantly associated with the accuracy of SmartIntake (P = 0.03). To improve data quality, excluding erroneous days of likely underreporting (<60% TDEE) improved the accuracy of SmartIntake, yet this was not equivalent to TDEE (–22% ± 1% of TDEE; P = 1.00).

Conclusions

Energy intake in obese, pregnant women obtained with the use of an electronic reporting method (SmartIntake) does not accurately estimate energy intake compared with doubly labeled water. However, accuracy improves by applying criteria to eliminate erroneous data. Further evaluation of electronic reporting in this population is needed to improve compliance, specifically for reporting frequent intake of small meals. This trial was registered at www.clinicaltrials.gov as NCT01954342.

Keywords: energy intake, food photography, pregnancy, doubly labeled water, ecological momentary assessment, SmartIntake

Introduction

In the United States, the number of obese women entering pregnancy has more than tripled in the past 30 y (1). Pregravid obesity and excessive gestational weight gain (GWG) are detrimental to maternal and fetal health, increasing the risks of miscarriage, pre-eclampsia, gestational diabetes, and childhood obesity (2). Achieving an optimal GWG is a challenging task for obese women, as indicated by >50% of women exceeding the 2009 Institute of Medicine recommendations (3–5).

Gilmore et al. (6) identified increased energy intake as the primary driver of excessive GWG and not thrifty energy expenditure. To date, evidence of successful attempts to limit energy intake with improved management of GWG is limited (7). To improve weight management in pregnant women, there is a critical need to assess energy intake objectively and in real time to provide counseling and implement behavior-change strategies.

Common self-report measures of energy intake, such as food intake diaries, are burdensome and are considered to be inaccurate (8). In recent years, technology-based methods have emerged in which participants use a smartphone to capture images of their food selection (9). Although iTunes and Google Play offer ∼350 diet-tracking applications (search terms: “food tracker,” “nutrition,” “calorie counter”), the accuracy of these approaches to assess energy intake is not known.

In contrast to food-intake diaries, 24-h recalls and some smartphone-based applications have indeed shown acceptable accuracy in adults and adolescents [>90% compared with total daily energy expenditure (TDEE)] (10–13). The accuracy of such applications has yet to be studied in pregnant women, because eating patterns likely fluctuate throughout gestation, and current electronic methods originated for a traditional eating schedule may fail to capture all consumed meals.

The goal of this study was to evaluate the validity of an electronic reporting method to measure energy intake in obese, pregnant women compared with TDEE. Toward this end, we evaluated the accuracy of energy intake obtained by a smartphone-based food photography application compared with TDEE obtained by doubly labeled water (DLW) in free-living conditions over a period of ≥6 d. In addition, we aimed to identify factors that contribute to accuracy.

Methods

Study design

The current study was conducted between February 2015 and May 2016 at Pennington Biomedical Research Center, Baton Rouge, Louisiana, and was part of a prospective observational study assessing the determinants of GWG in obese, pregnant women. After the nature of procedures was explained, study participants provided written informed consent, which was approved by the local institutional review board. All procedures were conducted according to the Declaration of Helsinki as revised in 1983. For each participant, we assessed free-living TDEE over a period of ≥6 d by DLW twice in pregnancy: at early (13–16 wk) and late (35–37 wk) gestation. In parallel, we measured energy intake by using the SmartIntake smartphone application. This study is registered at clinicaltrials.gov (NCT01954342).

Study participants

Study participants had a singleton viable pregnancy, were obese [BMI (kg/m2) ≥30], were aged between 18 and 40 y, and were medically cleared for participation by their primary care obstetrician and study medical investigator. Participants were recruited through community and social media advertisements and referrals by local obstetricians (14).

Exclusion criteria included recent history of smoking, alcohol, or drug use; hypertension (i.e., systolic blood pressure >160 mm Hg and diastolic blood pressure >110 mm Hg); pregravid diabetes (glycated hemoglobin ≥6.5%); contraindications to MRI; HIV or AIDS; severe anemia (hemoglobin <8 g/dL or hematocrit <24%); psychological or eating disorders; and contraindicated medications or supplements related to energy intake and energy expenditure. In addition, measurements of TDEE were excluded if high or low values could not be confirmed by measurements of resting metabolic rate (with appropriate adjustment for activity) and accelerometry.

Anthropometric measurements

BMI was calculated by dividing metabolic weight (in kilograms; measured in a fasted state while wearing a clinic gown) at study enrollment (<15 wk of gestation) by height squared (in meters; with head in the Frankfurt plane position). Obesity classes were defined by the following WHO criteria: class I, 30 ≤ BMI < 35; class II, 35 ≤ BMI < 40; class III, BMI ≥40) (15). GWG was calculated as an increase in metabolic weight between early and late gestation (13–16 wk and 35–37 wk, respectively). In addition, scales (BodyTrace, Inc.; accuracy: ±100 g) that automatically transmit measured weights to the investigators were provided to the participants.

Energy intake

The SmartIntake application is based on the remote food photography method (RFPM), which has been published previously (10, 12, 16). Briefly, SmartIntake was introduced by certified study staff, and under supervision, participants demonstrated their ability to successfully use the application to capture food images and record food descriptions. For the next 6–7 d, participants were instructed to take images of all eating occasions as well as the plate (food) waste. A reference card was placed next to the food items photographed at each eating occasion, which allowed for accurate portion-size estimation. The SmartIntake application was either downloaded onto participants’ own iPhones or an iPhone was provided. Participants were also provided with food record forms to record meals and food items that were not captured by images. Images were automatically transmitted via cellular networks or WiFi from the application to the Research Center and were reviewed at the conclusion of the assessment period by the participant and study staff. Food items were categorized as meals (breakfast, lunch, or dinner) or as snacks (example shown in Supplemental Table 1). Energy intake was estimated with the use of the Food Photography application, which is used by trained and certified raters to identify and quantify type of food and portion size. The application simultaneously displays images of the person's food selection, leftovers, and a standard portion for each food consumed. Standard portions are derived from a food image library that contains an archive of >7000 images of different foods, each with different portion sizes of the same food. Raters individually estimated a proportion of each food item on the captured plate in the images compared with a selected standard photo of the food item (9). Energy intakes of food items were determined on the basis of the USDA Food and Nutrient Database for Dietary Studies 2011–2012 (17) and manufacturers’ nutrient information.

Ecological momentary assessment prompts

The SmartIntake application uses ecological momentary assessment (EMA) prompts, which are sent to participants’ phones via e-mail before each anticipated meal and at the end of each day to remind participants to capture foods and meals with the application. These reminders are personalized to the habitual eating pattern for 3 meals (breakfast, lunch, and dinner) and 0–2 snacks. Similar to study 1 in Martin et al. (10), participants were instructed to reply to each EMA prompt (e.g., “Can you remember to take before AND after pictures of Dinner and send them?”) with “Y” to indicate that they have eaten and have sent an image or with “N” in cases when they did not eat, or ate but had forgotten to capture an image. In the latter case, participants were instructed to log their consumed meal on the provided back-up food record form.

Energy expenditure

TDEE was measured with the use of DLW (1.25 g of 10% enriched H218O and 0.10 g of 99.9% enriched 2H2O/ kg body weight) for 7 d (18). Urine samples were collected before consumption of the DLW dose (baseline); at 3, 4.5, and 12 h postdose on site; and on day 6 and day 7 at home at self-reported times. Isotopic enrichments of the postdose urine samples compared with the predose samples were used to calculate elimination rates of hydrogen and oxygen (kH and kO) with the use of linear regression, and initial isotope dilution spaces were calculated by extrapolation to time 0. Carbon dioxide production rate (rCO2) was calculated with the use of the equations of Schoeller et al. (18) and Racette et al. (19): rCO2 = (N/2.078)(1.007kO − 1.041kH) −0.0246rH2Of, where N is the total body water and rH2Of is the rate of fractionated evaporative water loss, which is estimated to be rH2Of = 1.05N(1.007kO − 1.041kH). TDEE was calculated by multiplying rCO2 by the energy equivalent of carbon dioxide for a standardized respiratory quotient of 0.86 (20). In order to be able to pair TDEE and energy intake for an assessment period of 1 wk, we assumed weight maintenance and consequently that energy intake is equal to TDEE.

Statistical analysis

Data are presented as least-square means ± SEMs. Sample size calculations indicated that ≥42 pairs of data (SmartIntake and DLW) were required to detect 10% equivalency between the 2 measurements with 80% power with the use of the two-one–sided t tests method, adjusted with 15% missing data and conservatively assuming no correlation between methods. The accuracy and precision of the SmartIntake to estimate energy intake were evaluated and compared with TDEE with the use of the Bland-Altman method (21). Accuracy was defined as the difference between self-reported energy intake by SmartIntake and TDEE by DLW (percentage of TDEE), and equivalency is conservatively concluded if accuracy is >80% (P < 0.05). To improve data quality (22), 3 independent criteria were applied to the SmartIntake data a priori in an attempt to identify and eliminate erroneous days that were subject to gross underreporting. These criteria were as follows: reported energy intake 1) <60% TDEE, 2) <1000 kcal, or 3) <2 meals (not including snacks) were consumed. Dependent Student's t test and ANOVA were used to assess baseline differences between covariates, including pregnancy stage, own or study phone use, weekdays compared with weekend days, and obesity class. Regression analysis was used to assess relations between covariates (BMI, GWG, parity, age), snacking, and the accuracy after normal distribution was confirmed. All of the statistical tests were performed by a biostatistician (JHB) with the use of SAS/STAT software, version 9.4 of the SAS System for Windows (SAS Institute, Inc.), and significance for equivalency of 20% was defined when P < 0.05.

Results

Participant characteristics

In this study, we studied 46 cases of energy intake paired between SmartIntake and DLW from 23 obese, pregnant women (BMI ≥30). Participant characteristics are summarized in Table 1. The mean TDEE for the 45 assessment periods was 2807 ± 51 kcal/d (2729 ± 80 and 2889 ± 61 kcal/d during early and late pregnancy, respectively; Figure 1). One data pair (energy intake and TDEE during late pregnancy) was excluded because TDEE was substantially high (4849 kcal/d) and could not be confirmed by other assessments, including metabolic chamber or accelerometry (data not shown).

TABLE 1.

Participant characteristics1

Values
Age at enrollment, y 28.3 ± 1.1
Parity, n 0.78 ± 0.23
 Nulliparous 12
 Primiparous 8
 Multiparous 3
Height, m 1.63 ± 0.02
GWG,2 g/wk 323 ± 38
BMI at enrollment, kg/m2 36.9 ± 1.3
BMI, by obesity class, kg/m2
 Obesity class I (n = 10) 31.5 ± 0.3
 Obesity class II (n = 7) 37.7 ± 0.3
 Obesity class III (n = 6) 45.2 ± 1.2
BMI, by race/ethnicity, kg/m2
 African-American (n = 7) 36.3 ± 1.9
 White (n = 12) 37.2 ± 2.2
 Other (n = 4) 37.6 ± 2.6
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1Values are means ± SEMs unless otherwise indicated; n = 23 unless noted otherwise.

2GWG, gestational weight gain. GWG is calculated as metabolic weight gain per week between clinic visits during early and late pregnancy.

3Races/ethnicities defined as “Other” were reported as Hispanic Mexican, Asian–Vietnamese, biracial or Hispanic.

FIGURE 1.

FIGURE 1

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Daily TDEE and reported EI in obese, pregnant women; n = 23. The number of observation periods with EI (n = 45) and energy expenditure (n = 45), respectively, are presented as histograms by using bins of 200 kcal/d. EI, energy intake; TDEE, total daily energy expenditure.

Assessments

From a total of 294 possible assessment days, on which participants were instructed to capture images (21 periods with 6 d each and 24 periods with 7 d each), images were captured and analyzed from 290 d (99%). Considering all 290 assessment days, average participant energy intake with the use of SmartIntake was 1755 ± 59 kcal/d (range of reported energy intake: 804–2729 kcal/d; Figure 1), which were consumed during 2.4 ± 0.04 main meals and 0.9 ± 0.05 snacks/d (example shown in Supplemental Table 1).

Energy intake determined from the application was not significantly different on weekdays than on weekend days (1726 ± 58 compared with 1811 ± 83 kcal/d, respectively; P = 0.22), and accuracy was not significantly different between early and late pregnancy (64.4% ± 3.2% of TDEE compared with 62.2% ± 3.4% of TDEE, respectively; P = 0.65). During 13 of 45 (29%) assessment periods, participants used their own phones, whereas during 32 (71%) assessment periods, participants used phones that were provided. There was a significantly increased level of accuracy when participants used the application on their own phone than when using the application on a phone that was provided (72.6% ± 3.4% compared with 59.6% ± 2.7% of TDEE, respectively; P = 0.01).

Equivalency

Compared with TDEE, SmartIntake captured only 63.4% ± 2.3% of energy intake and was not equivalent to TDEE within a 20% equivalence bound (P = 1.00; Figure 2A). Importantly, there was a significant, systematic reporting bias across the observed range of energy intake (slope: 0.01978; P = 0.02; Figure 2A, Supplemental Figure 1A) that was positively correlated to pregravid BMI (P = 0.07).

FIGURE 2.

FIGURE 2

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Comparison between TDEE and reported EI in obese, pregnant women; n = 23. Values are means compared with the differences between reported EI and TDEE for each assessment period. The measures of accuracy for obesity classes are presented as means ± SEMs (class I, n = 10; class II, n = 7; class III, n = 6). (A) Raw SmartIntake data; (B) only days with >60% TDEE; (C) only days with >1000 kcal/d; (D) only days with >1 meal/d. EI, energy intake; TDEE, total daily energy expenditure.

Interestingly, we found that African-American women (n = 7; 55.4% ± 4.1% of TDEE) reported significantly lower energy intake than did white women (n = 12; 65.8% ± 2.8% of TDEE; P = 0.04) and Asian and biracial women (n = 4; 69.1% ± 6.8% of TDEE; P = 0.08), respectively. Notably, mean pregravid BMI did not significantly differ by race (Table 1). We did not find any correlation between accuracy of energy intake reporting and GWG, age, parity, education, or income (all R2 < 0.05, P > 0.10, Supplemental Table 2).

Data handling rules

By excluding individual days on which <60% of the TDEE was reported (n = 130 d excluded; 45%), the captured energy intake was increased to 2186 ± 49 kcal/d or 78.4% ± 1.4% of TDEE, which did not reach statistical equivalency (P = 0.87; Figure 2B, Supplemental Figure 1B). Application of this rule eliminated the reporting bias across energy intake (slope: –0.0044; P = 0.37) that was observed in the raw data.

After excluding individual days in which the captured energy intake was <60% of TDEE (and elimination of the reporting bias), the reporting accuracy was no longer correlated to BMI (P = 0.13). In addition, no differences were observed for race after using only days that reported >60% of TDEE (all comparisons, P = 1.00). Applying this rule did not affect the significance of the previous correlations with GWG, age, and parity or comparisons between weekdays and weekend days, early and late pregnancy, or the use of participants’ own phones compared with phones provided by the study (83.4% ± 1.9% compared with 76.4% ± 1.7% of TDEE; P = 0.02).

Other data handing rules had no significant effect on accuracy or data quality. The exclusion of all days on which <1000 kcal or <2 meals (excluding snacks) were reported (n = 26 or n = 25 d excluded; both 9%), the accuracy of energy intake reporting increased to 66.7% ± 2.0% and 65.8% ± 2.3% compared with TDEE, respectively, which also was not within a 20% equivalency bound (both, P = 1.00; Figure 2C, D, Supplemental Figure 1C, D).

Snacking

Of the total energy intake that was captured, 83% was reported as breakfast, lunch, and dinner meals and 17% (302 ± 34 kcal/d) was reported as snacks. The amount of energy consumed as snacks as a percentage of the total reported energy intake (“snacking”) correlated positively with reporting accuracy both before (R2 = 0.39, P = 0.005; Figure 3) and after the application of the >60% of TDEE data handling rule (R2 = 0.10, P = 0.018). Similarly, we compared the number of snacks reported by participants with the highest accuracy (upper tertile, reported energy intake >83% of TDEE) with the number of reported snacks by participants with the lowest accuracy (lower tertile, reported energy intake <73% of TDEE) and found that participants who had higher reporting accuracy reported more snacks (1.42 ± 0.07 compared with 0.71 ± 0.06 snacks/d) but a comparable number of meals (2.36 ± 0.1 compared with 2.58 ± 0.06 meals/d). Snacking was not related to BMI, study visit, GWG, age, or parity.

FIGURE 3.

FIGURE 3

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Snacking and the accuracy of SmartIntake. Values are presented as EI reported as snacks (Snacking; percentage of reported EI) compared with the accuracy of reported EI compared with TDEE (percentage of TDEE) for each assessment period. (A) Raw SmartIntake data; (B) only days with >60% TDEE included in analyses. EI, energy intake; TDEE, total daily energy expenditure.

Discussion

The goal of the present study was to assess the accuracy and precision of a technology-based method to estimate food intake in obese, pregnant women. We here report that the SmartIntake application was not able to accurately estimate energy intake compared with TDEE, as assessed by DLW in obese, pregnant women. Interestingly, error decreased among participants with higher levels of energy intake, a finding counter to previous reports (23). Furthermore, the application of a priori data-quality checks (≥60% TDEE, ≥2 meals/d, or ≥1000 kcal/d) did improve the accuracy of the SmartIntake application compared with TDEE, but not toward acceptable accuracy. Interestingly, the amount of reported snacks per day (expressed as a percentage of total energy intake) correlated significantly with the accuracy of the SmartIntake application to TDEE.

Underreporting is an inherent flaw of self-reported assessment methods (8). More advanced technology-based techniques such as the SmartIntake application that are largely reliant on self-report but do not require the user to estimate portion size and feature automated reminders to prompt reporting of food intake have improved the accuracy of self-reported energy intake to >90% compared with direct food weighing and TDEE by DLW in other populations (10–12). Similar accuracy has been reported for food-recall methodologies (13), yet we are not aware of comparable data in pregnant women. Similarly, in studies that assessed the accuracy of food photography methods in healthy children and in nonpregnant, mostly female adults (65% obese), energy intake assessed by the SmartIntake application in the present study was only 66% of TDEE in pregnant women.

A primary step in understanding the lower reporting of energy intake in obese, pregnant women compared with nonpregnant study groups is to confirm that energy intake has been underreported and that participants did not undereat compared with the reference method, DLW. Although we do not have definitive evidence to exclude the possibility of deliberate undereating during the SmartIntake application assessment, we are confident in excluding deliberate undereating as a plausible explanation of our finding for multiple reasons. First, provided scales showed weight gain over the assessment periods (20 ±11 g/wk; range: –1.5 to +2.0 kg/wk; P = 0.04). Second, measured weight changes did not relate to the accuracy of SmartIntake data (R2 < 0.01). We acknowledge that these weights were not measured under controlled conditions, yet a systematic error of overestimating weight change is unlikely. Last, only 2 participants reported excessive nausea or malaise and consuming substantially less food than usual on both maximal 2 assessment days at postmeasurement reviews.

After ruling out the potential for true undereating in our cohort, we need to examine the use of the current RFPM SmartIntake and its application to pregnant women. There were several factors that contributed to increased participant compliance with the SmartIntake application. First, when participants used their own mobile phone to capture food images and to interact with the SmartIntake application, reported energy intake was significantly higher than when participants were provided with an iPhone. Carrying and using this additional phone may increase the burden and therefore likely reduces participant compliance to the food-intake assessment. Incorporation of “plausibility cutoffs” based on predicted or measured energy requirements has been proposed in earlier studies to quantify misreporting of energy intake (22). In the present study, the exclusion of days in which energy intake reported was <60% of the TDEE (without report of deliberate participant undereating) increased the accuracy of SmartIntake by 15%. In addition to the improved accuracy, the reporting bias evident with higher BMI was eliminated, indicating that overall data quality and the generalizability of the method can be improved with a priori data handling methods.

A failure to report consumed meals may be an active decision by the participants to pretend that they are consuming a healthier diet, yet the reported nutrient composition (37% ± 1% of energy as fat) does not suggest preferential reporting of “healthy” foods. Alternatively, participants likely forget to report their meals. This may be an important issue in pregnant women because a more-frequent eating schedule observed during pregnancy (24) requires increased alertness to food-intake recording. Failure to report food intake may be particularly related to energy consumed as snacks. Low reporting of snack foods significantly correlated with low accuracy of energy intake overall, and those who reported energy intake more accurately also reported more snacks but not more meals, suggesting that this is an important factor that needs to be addressed in future smartphone application developments. In line with our findings, others have confirmed that low reporting of snacks caused low accuracy of self-reported energy intake with the use of 24-h recalls (25), FFQs, or dietary records (26). Interestingly, reporting of snacks and accuracy were lower in obese than in nonobese women in this study (25). More severe underreporting for obese participants has been repeatedly shown (23), but our results failed to support this finding. Rather, participants who consumed more energy had lower levels of error, a finding that requires additional research because this is antithetical to previous research. It is therefore possible that the presented method may be more accurate in obese cohorts.

To facilitate timely recording of food-intake information, automated EMA notifications were sent at time points associated with habitual eating patterns, and the accuracy in pregnant women was similar to that in a previous study in which lower-intensity EMA methods were used (10). The inaccuracy of the SmartIntake application may thus relate to the inability of EMA notifications to commit participants to capturing food images. First, EMA notifications relied on e-mail communication, which is used less frequently for synchronous communication. Second, close surveying of the EMA prompts is required, but maintaining this surveillance is burdensome to participants and study staff, resulting in less robust surveillance. Finally, although currently, EMA prompts are tied to a personalized, yet regular eating pattern, we propose that these notifications need to be adjusted to better accommodate varied eating behaviors in pregnant women, possibly learned by observation during an initial ∼2 d and adopted to the subsequent days.

The strengths of this study are the use of the gold-standard technique to assess energy expenditure as an estimate for energy intake, whereas other studies used estimates based on height, weight, and age, or resting metabolic rate (27, 28), and therefore neglect the most variable component of energy requirements—physical activity. However, the lack of objective data on body weight throughout the assessment periods may introduce some error. Nonetheless, data suggest that the reporting accuracy would rather be decreased and not increased when accounting for changes in body weight, which are more likely positive than negative. Last, although this study is powered to detect differences between energy intake estimates by DLW and the SmartIntake application, the differences between groups (e.g., obesity classes or races) may lack sufficient power and are exploratory at best.

To conclude, we found that in its present form the RFPM SmartIntake application does not accurately measure energy intake in free-living pregnant women compared with TDEE by DLW. Smartphone applications have the potential to be a valuable tool to measure food intake, but they must be optimized to the circumstances specific to pregnancy, such as more frequent reporting of foods and meals. Furthermore, modification of the EMA software to send automated reminders via text message rather than e-mail will likely enhance images retrieved by participants in real time and this improvement has recently been made. Until these challenges for food photography have been overcome, accuracy may only be achieved in combination with additional dietary recalls. Data handling rules can be applied a priori to improve reporting accuracy. Beyond energy intake, the current food photography methods can be used to assess macronutrient composition or eating frequency.

Supplementary Material

Supplemental data
Click here for additional data file. (141.6KB, zip)

Acknowledgments

The authors’ responsibilities were as follows—JM, LAG, CKM, and LMR: designed the research; JM, PMV, ADA, LAG, EFS, and LEC: conducted the research; JM, JHB, and LMR: analyzed the data; JM and LMR: wrote the first draft of the manuscript and had primary responsibility for the final content, with contributions from CKM; and all authors: reviewed and commented on subsequent drafts of the manuscript and read and approved the final manuscript.

Notes

Supported by NIH R01DK099175, LA CaTS U54GM104940, and NORC P30DK072476.

Supplemental Tables 1 and 2 and Supplemental Figure 1 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/.

Abbreviations used: DLW, doubly labeled water; EMA, ecological momentary assessment; GWG, gestational weight gain; rCO2, carbon dioxide production rate; RFPM, remote food photography method; TDEE, total daily energy expenditure.

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