ABSTRACT
Introduction
Estimating food intake in children is a critical part of obesity prevention and nutrition intervention studies. Accurate and convenient methods to measure food intake among preschool children attending early childcare centers (ECCs) are lacking and digital photography may be a convenient option but needs to be validated in the childcare setting. This pilot study assessed the validity of weighed and digital photography plate waste methods.
Methods
Weighed and digital photography plate waste methods were used to estimate food intake during lunch for three consecutive days. Pre‐consumption weight of each food was measured in grams. Weighed foods were assembled on a tray and photos were taken. Post‐consumption weights and photos were taken for comparison. Two trained research assistants independently estimated food consumption at 10% intervals for the digital photography method; differences in pre‐ and post‐weights were used for weighed method. Food consumption was measured by the difference in grams or estimated percentage in pre‐and post‐meal weights and photos, respectively. Validity was measured using linear mixed models and Bland Altman regressions to assess differences between digital photography and weighed measurements.
Results
Participants were 41 children (mean age 3.86 ± 0.51, 84% Hispanic/Latino, 58.5% females) enrolled in two Head Start centers. Linear mixed model assessment found no significant difference between macro‐ or micronutrients, or food groups between the two methods. Bland Altman regression revealed a high level of agreement between weighed and digital photography methods for macronutrients, micronutrients, and food groups.
Conclusion
Weighed and digital photography plate waste methods yield similar results, and both are valid methods to assess food intake in pre‐school aged children to aid in intervention studies along with evaluating menu quality. Choice of assessment methods depends on budget, time, equipment, expertise, and training.
Keywords: childcare, diet assessment, photography, plate waste, preschool children
Summary
Digital photography plate waste methods are a valid method for assessing dietary intake in preschool children when compared to the weighed plate waste method.
Digital photography plate waste method requires less onsite time, minimizes disruptions during meals, and allows off‐site image analysis, making it a promising method for larger or multi‐site studies.
1. Introduction
Healthy eating habits and good nutrition are critical for preschool‐aged children's development [1]. Healthy eating habits developed during early childhood also reduce the risk of chronic diseases, such as heart disease and diabetes later in adulthood. Nutrition is associated with fine‐ and gross motor skill development as well as cognitive and language development [2, 3, 4]. One key provider of nutrition during early childhood is childcare centers. Approximately 60% of children aged 5 or younger attend early childcare centers (ECCs) in the United States and rates vary by country, with lower income countries typically having lower rates than higher income countries [5]. ECCs provide over two‐thirds of recommended daily dietary energy intake and nutrients for children [6, 7]. Understanding factors influencing children's food consumption is critical to improve nutrition in young children.
A criticism of childhood nutrition studies is the lack of valid measures of food consumption in young children [8]. Common methods to obtain dietary data from food eaten at home are parent‐reported measures like 24‐h dietary recalls, food diaries, and food frequency questionnaires [9]. Assessment of dietary intake at ECCs has been conducted by reviewing menus of food served, reports by staff, direct observational measures like on‐site observation, and plate waste studies, including pre‐ and post‐food weights and digital photography methods [8, 10]. Self‐reported methods rely on caregivers' and parents' reports when used among preschool children, decreasing accuracy and increasing bias compared to direct measurement of food intake. On‐site observation methods have been validated against weighed strategies [11], but require a high ratio of trained staff to children and extensive observer training for portion size estimation and dietary coding [11]. Therefore, on‐site observation methods for research can be challenging in multiple centers with one or more large classes with limited resources. Plate waste studies that use the weighed plate waste method provide the most accurate dietary data by taking the pre‐ and post‐consumption weight [12]. However, plate waste studies can be burdensome to carry out in large‐scale population studies due to the high demands on human resources and time [12].
The digital photography plate waste method is a newer technique that takes advantage of plate waste weighed methods yet employs photography instead of visual or weighed techniques for more efficient data collection and analysis [13]. The digital photography method was first used in cafeteria settings in the early 2000s by Williamson, et al., [14] and has expanded its use in the ECC setting by Nicklas, et al. [15] The potential benefits to utilising digital photography plate waste methods compared to other methods are less interference with the eating environment that may impact eating behaviors, lower research burden by requiring less time and space for onsite data collection, and flexibility and reliability in data analysis [12]. Digital photography plate waste studies have been used primarily in primary and secondary school settings [16, 17, 18, 19] and in college and university dining halls [14] utilising smartphone technologies and remote image processing [20, 21] or have been investigated in a research‐setting [7]. Digital photography plate waste studies have assessed children's food consumption in childcare settings, though, are sparse [7, 22, 23] and validation studies are needed. Digital photography plate waste methods have validated against the weighed method in primarily school [25] and university cafeteria settings [12] and with a small number of pre‐school aged children in a metabolic research unit, for one meal at the ECC, and at home. This study was designed to compare weighed and digital photography plate waste methods to assess food intake among pre‐school children in the ECC setting for multiple meals across multiple days. We hypothesised digital photography plate waste data would have a high level of agreement with weighed plate waste data and therefore be a reliable alternative approach to study children's food consumption behavior in childcare settings.
2. Materials and Methods
2.1. Design
This pilot study was performed in two Head Start Centers in San Antonio, Texas. Head Start is a federally‐funded program that offers full‐time comprehensive childcare, parenting assistance, and social services to qualified low‐income families in the United States [6]. Following federal dietary standards, Head Start provides three meals daily: breakfast, lunch and an afternoon snack. Plate waste data was collected for three consecutive days. A study flyer and informed consent form were sent to children's home in their school folders. Parents signed and returned the consent form to give permission for their children to participate in the study. Forty‐one pre‐school children aged three to four participated in the study after their parents provided informed consent. The study protocol was approved by the Institutional Review Board.
2.2. Procedures
At each measurement session, the lunch was served on a plate to the children who sat around a large dining table. A classroom teacher or assistant supervised the children at their table. Lunch usually included four items: milk, fruit, vegetables, and a main entree. Children were asked not to share their food with other children and were allowed to request additional servings.
Two methods were used: (1) pre‐ and post‐weighed plate waste and (2) digital photography plate waste. Both methods were employed to record the amount of food and beverages consumed by children at lunch following an established protocol (Supporting Information S1: Appendix 1). Researchers were provided with hands‐on training using all equipment before data collection. Weights and photographs were taken at the same time by the same researchers. Trained research assistants observed and documented mealtime spills or other issues and were instructed not to interfere with children's eating behavior. Each child was assigned a unique study identification number. A placemat that included the center name, classroom, table number, and child identification number was placed on the food tray. The placemat had designated spaces for placement of milk or beverage, entrée, vegetable, and fruits. It also had a space where the research assistant documented extra portions of food the child requested. When an additional portion was requested, the researchers weighed the extra portion and added the weight to the data collection sheet and marked the placemat indicating the extra portion. Standardized plates, bowls, and cups were used: a large plate for the entree, a small bowl for fruit, and a transparent cup for milk. This allowed all the food items to be presented uniformly in the photos, therefore, making portion estimation easier for researchers. Standardised scoops and liquid measuring tools were used to serve food in standard portions sizes: ½ cup (#8 scoop), 2/3 cup (#6 scoop), and 6 ounces of beverage.
2.3. Weighed Method
Each food item (milk, fruit, vegetables, and a main entrée) was weighed in grams three times before meal service, once on each of 3 days, in a container (plate, bowls, cups) on a calibrated food scale before consumption. Milk was weighed in the cup, fruit and vegetables were weighed in a bowl and the main entrée was weighed on a plate. Pre‐and post‐consumption weights and additional comments (notes for extra servings, spills, etc.) were documented for each participant. Additional portions were weighed and documented if requested. After the meal was complete, the children were asked not to touch the tray. Research assistants removed the nonfood items, separated the foods, and leftover food was weighed in its original container using a digital scale to obtain the post‐consumption weight in grams. The weight of food consumed was calculated by difference (i.e., using the pre‐consumption weight to subtract the post‐consumption weight).
2.4. Digital Photography Method
The digital photography method was developed based on protocols established by Williamson et. al., [12] and Nikolas et. al., [15] Two lighted photograph cubes contained indicators for uniform tray placement. A tablet computer (iPad) on a tripod was angled at 45° to ground level and was placed one foot away from the photograph cube center. The pre‐consumption photo was taken after the pre‐consumption weights. Additional portions of food or beverage were recorded on the child's placemat. The post‐consumption photo was taken after the leftovers, in their original containers, were repositioned on the placemat like the pre‐consumption photo.
The pre‐and post‐consumption photos were matched based on the study number for side‐by‐side estimation (Figure 1). The researchers used a food portion guide to assist in estimating food consumption as adapted from Bean, et al., who provided reference images of standardized portions for the food served in the study [24]. The food portion guide for this study contained reference pictures of study food items served to the children in varying degrees of consumption from 0% to 100% in 10% intervals. Two independent researcher assistants used the food portion guide to estimate the percentage of food consumed in the after‐consumption photo. A total of 852 food items were assessed, of these, 19.5% of the estimates had a discrepancy of greater than 20%. Estimates with greater than 20% discrepancy were reassessed by both researchers and an independent third researcher and the average of the estimated food consumption was used. The percentage consumed was multiplied by the pre‐weight of the food to calculate the gram weight of food consumed.
Figure 1.
Before and after eating digital images for lunch. Left image is the picture taken premeal service. The picture on the right is taken post‐meal service for the same individual. Meal served: chicken sandwich with cheese, sliced carrots, peaches, and milk.
2.5. Data Management and Statistical Analysis
Researchers entered all consumed weights for both the weighed and digital photography plate waste method in a nutrient analysis software (Nutribase, CyberSoft). Nutrition fact labels for all foods were obtained from the Head Start center and entered into the nutrient analysis software. Measures of macronutrients (calories, protein, carbohydrates, fat, cholesterol, and sugar) and micronutrients (vitamin A and C, calcium), and serving sizes for the food groups (starches, meat, fruit, vegetables, and dairy) were obtained for weighed and digital photography methods. Descriptive statistics for categorical variables are reported as number and percentage, continuous variables are reported as mean and standard deviation. Bland‐Altman plots were constructed for visual examination of agreement between weighed and digital photography measurement methods. Differences between weighed and digital photography methods for each macro and micronutrient were assessed using linear mixed models with results reported as adjusted regression coefficients, 95% confidence intervals (CI), and p values. Linear mixed models were performed for each macro and micronutrient measure as the dependent variable, and each measurement for each method were nested within pairs and pairs nested within subjects. Interactions between measurement day and method were also tested. The day interaction was investigated to account for differences in food items measured and researcher variability. Intraclass correlation coefficient (ICC) was used to assess the level of agreement in nutrient measures by weighed and digital photography methods and are reported as the ICC and 95% CI. Adjusted marginal means from the linear mixed model analyses are reported as means with 95% CI. Descriptive analysis, linear mixed models, and agreement statistics were conducted with IBM SPSS Statistics, version 27 (IBM) and Bland‐Altman plots were generated with GGPLOT2 in R software, version 4.0.2 (R Core Team).
3. Results
A total of 41 children were included in this study (Table 1), each with plate waste measured at three different time periods (measurement day) and 2 methods, yielding 246 observations or 123 pairs of weighed and digital photography data. There were 24 females (58.5%) and 14 males (34.1%), with mean age was 3.8 years, including 23 (56.1%) 3‐year‐olds and 14 (34.1) 4‐year‐olds. Eighty‐four percent of the children were Hispanic. The mean weight was 16.0 kg and the mean height was 99.4 cm. Based on the Centers for Disease Control and Prevention's children growth chart [25], 24.4% of the children were categorized as overweight and 9.8% were categorized as obese.
Table 1.
Descriptive statistics of participant characteristics.
| Characteristic | Statistic |
|---|---|
| N | 41 |
| Age, mean (SD) | 3.8 (0.5) |
| 3‐year‐olds, n (%) | 23 (56.1) |
| 4‐year‐olds, n (%) | 14 (34.1) |
| Missing, n (%) | 4 (9.8) |
| Gender, n (%) | |
| Female | 24 (58.5) |
| Male | 14 (34.1) |
| Missing | 3 (7.3) |
| Weight, kg, mean (SD) | 16.0 (2.2) |
| Missing, n (%) | 5 (12.2) |
| Height, cm, mean (SD) | 99.4 (4.4) |
| Missing, n (%) | 5 (12.2) |
| BMI Category, n (%) | |
| Underweight | 0 (0.0) |
| Normal weight | 30 (73.2) |
| Overweight | 10 (24.4) |
| Obese | 4 (9.8) |
Bland‐Altman plots for macronutrients showed a high level of agreement between weighed and digital photography methods across kilocalories and each macronutrient (Figure 2). A high level of agreement was also observed for micronutrients and food groups, with only a few extreme observations observed, most notably for vitamin A (Figure 3). One extreme value for vitamin A was an outlier and its matched digital photography measurement was missing, so this observation was excluded from subsequent multivariable analyses.
Figure 2.
Bland‐Altman plots by macronutrient (A) Kilocalories, (B) Protein, (C) Carbohydrate, (D) Fat, (E) Cholesterol, and (F) Sugar.
Figure 3.
Bland‐Altman plots by micronutrient and food group. (A) Vitamin A, (B) Vitamin C, (C) Calcium Food Groups: (D) Starch, (E) Meat, (F) Vegetable, (G) Fruit, and (H) Milk.
Results of multivariable adjusted linear mixed models comparing means between weighed and digital photography measurement methods indicate that there were no significant differences in the mean values for any of the macronutrients or micronutrients (Table 2). This suggests that the weighed and digital photography methods produce similar measurement distributions for each nutrient. The interaction between measurement day and method was tested but not significant for any of the nutrients measured. Additionally, agreement statistics based on the intraclass correlation coefficient ranged from 0.71 (95% CI: 0.54, 0.83; p < 0.001) for calcium to 0.98 (95% CI: 0.98, 0.99; p < 0.001) for vitamin C, indicating a high level of agreement between weighed and digital photography methods across all nutrients measured (Table 2). The adjusted marginal means from linear mixed models were consistent between weighed and digital photography methods for each nutrient assessed (Table 3). The largest mean difference between weighed and digital photography methods was for vitamin A, with a mean difference of 4.79 (95% CI: −27.80, 37.38; p = 0.77) IU (Digital: 107.3 [95% CI: 83.6, 131.0] vs. Weighed: 102.5 [95% CI: 79.0, 126.1]; p = 0.77), but it was not statistically or clinically significant.
Table 2.
Results of linear mixed model regression analyses* for difference in means between weighed and digital photography methods and agreement statistics.
| Measure Digital vs. weighed | Coefficient (95% CI); p value | Intraclass correlation coefficient (95% CI); p value |
|---|---|---|
| Macronutrients | ||
| Calories (kcal) | 6.12 (−22.24, 34.48); 0.67 | 0.91 (0.86, 0.95); < 0.001 |
| Carbohydrate (g) | 0.97 (−3.04, 4.98); 0.63 | 0.90 (0.84, 0.94); < 0.001 |
| Fat (g) | 0.21 (−0.86, 1.29); 0.70 | 0.95 (0.91, 0.97); < 0.001 |
| Protein (g) | 0.04 (−1.54, 1.64); 0.96 | 0.94 (0.90, 0.96); < 0.001 |
| Cholesterol (mg) | −0.29 (−3.45, 2.88); 0.86 | 0.96 (0.93, 0.97); < 0.001 |
| Sugar (g) | 0.20 (−1.17, 1.57); 0.77 | 0.87 (0.79, 0.92); < 0.001 |
| Micronutrients | ||
| Calcium (mg) | −0.19 (−7.65, 7.28); 0.96 | 0.71 (0.54, 0.83); < 0.001 |
| Vitamin A (IU)** | 4.79 (−27.80, 37.38); 0.77 | 0.98 (0.98, 0.99); < 0.001 |
| Vitamin C (mg) | −0.61 (−2.72, 1.50); 0.57 | 0.86 (0.77, 0.91); < 0.001 |
| Fruit (cups) | 0.01 (−0.05, 0.06); 0.84 | 0.91 (0.85, 0.94); < 0.001 |
| Meat (1 oz equivalent) | −0.03 (−0.21, 0.15); 0.75 | 0.91 (0.85, 0.94); < 0.001 |
| Milk (cups) | 0.01 (−0.09, 0.09); 0.99 | 0.92 (0.87, 0.95); < 0.001 |
| Starch (1 oz equivalent) | 0.18 (−0.09, 0.45); 0.19 | 0.78 (0.64, 0.87); < 0.001 |
| Vegetable (cups) | 0.01 (−0.07, 0.10); 0.73 | 0.79 (0.66, 0.87); < 0.001 |
Abbreviations: g, grams, IU, international units; kcal, kilocalories; mg, milligrams; oz, ounce.
Models adjusted for age, sex, weight, height, and measurement day.
One vitamin A weighed observation was excluded from analysis because it was an extreme outlier and its paired digital observation had a missing value.
Table 3.
Adjusted marginal means by nutrient type and measurement method, mean (95% confidence interval).*
| Measures | Digital mean (95% CI) | Weighed mean (95% CI) |
|---|---|---|
| Macronutrients | ||
| Calories (kcal) | 218.2 (197.6, 238.8) | 212.1 (191.8, 232.4) |
| Carbohydrate (g) | 27.1 (24.2, 30.0) | 26.1 (23.2, 29.0) |
| Fat (g) | 7.2 (6.4, 8.0) | 7.0 (6.2, 7.8) |
| Protein (g) | 11.7 (10.6, 12.9) | 11.7 (10.5, 12.8) |
| Cholesterol (mg) | 19.6 (17.3, 21.9) | 19.9 (17.6, 22.1) |
| Sugar (g) | 7.5 (6.5, 8.5) | 7.3 (6.3, 8.2) |
| Micronutrients | ||
| Calcium (mg) | 24.1 (18.7, 29.5) | 24.3 (18.9, 29.6) |
| Vitamin A (IU)** | 107.3 (83.6, 131.0) | 102.5 (79.0, 126.1) |
| Vitamin C (mg) | 8.3 (6.8, 9.9) | 8.9 (7.4, 10.5) |
| Fruit (cups) | 0.20 (0.17, 0.24) | 0.20 (0.16, 0.23) |
| Meat (1 oz equivalent) | 0.89 (0.76, 1.02) | 0.92 (0.79, 1.05) |
| Milk (cups) | 0.38 (0.32, 0.44) | 0.38 (0.32, 0.44) |
| Starch (1 oz equivalent) | 0.92 (0.72, 1.11) | 0.74 (0.54, 0.93) |
| Vegetable (cups) | 0.20 (0.14, 0.26) | 0.18 (0.13, 0.24) |
Abbreviations: g, grams; IU, international units; kcal, kilocalories; mg, milligrams; oz, ounce.
Models adjusted for age, sex, weight, height, and measurement day.
One vitamin A weighed observation was excluded from analysis because it was an extreme outlier and its paired digital observation had a missing value.
4. Discussion
This pilot study aimed to compare digital photography and weighed plate waste methods to assess food intake among Head Start children. As hypothesised, the analysis demonstrated a high level of agreement for kilocalories, macronutrients, and micronutrients between the weighed and digital photography plate waste methods indicating the digital photography method is an alternative to the weighed method that can be employed in childcare settings. While previous studies have demonstrated visual observations as an acceptable measure of food consumption in childcare settings [11, 26], the digital photography plate waste is still in its infancy [13, 27] with a limited number of studies showing promising validity in childcare settings [12, 14].
The weighed method does not require specialized, expensive equipment to implement and much of the equipment may be already required at childcare centers [12, 14]. Standardized scoops and measuring cups, calibrated food scales, and standardised serving dishes and paper and pencil for recording weights are required. The research team provided the scoops and scales and utilised the standardised serving dishes that were used across all centers. Conversely, the digital photography method requires specialised equipment for recording images [14]. In this study, a photo cube and tablet, along with specialised placemats were needed for data collection. This can incur a large start‐up cost, although electronic devices with high quality photo capabilities may be common and readily available. The digital photography method does allow for expedited data collection on‐site since analysing images occurs off‐site. Less time at the site collecting data can ease the research burden at childcare centers and may cause less disruption in daily activities. The weighing method requires more time on‐site to portion and weigh each food three times before and after eating, which may cause delays in meals and other center activities. The weighed method requires less specialised training, while the digital photography method does require technical knowledge to manage the device and photo storage. Both methods consume space to collect data at the site, and this can be an issue for smaller childcare centers. It is important to choose data collection areas that will not cause disruptions during meals that may affect food consumption. All data collection areas were outside the eating areas so as not to disrupt meals times and influence eating behaviors [10].
Strengths of this study include the collection of data over 3 days for both methods simultaneously allowing for direct comparison of results. This study also provided a larger sample size of 123 paired comparisons for multiple meals over 3 days. The protocol allowed children to request additional servings and asked children not to share food. Adjustments were made for additional servings in the analysis [11].
There were several limitations to the study. One primary limitation was pre‐portioning of foods and not served family style as in many Head Start and childcare centers [28]. Typically, Head Start centers present food to the children in large serving containers and family style service is used in which the children serve themselves the foods and portion sizes they want to eat. Proportioning all foods on the plate and not allowing children to determine their portion size could impact food intake [7], especially for children who are likely to serve themselves larger portions [29, 30]. Many factors impact the food intake of school‐aged children including, but not limited to, adult and peer modeling [31, 32], repeated exposure to familiar foods [33], and portion sizes served [30]. To limit the impact of this plate waste study on food intake, the normal mealtime schedule and activities like teachers modeling healthy eating with the children and second portions were continued. The interactions between the research staff and children (i.e., child requesting and research staff serving additional servings) during mealtimes might have impacted children's food consumption. The findings should be considered within the study limitations [9].
5. Conclusion
The findings from this pilot study support the limited research on the validity of the digital photography method for plate waste compared to the weighed plate waste method, which is considered the gold standard in field‐based dietary assessment [34]. Researchers should take into consideration resources, space and equipment availability, and staff expertise while realis the challenges in evaluating the impact of nutrition in childhood obesity in different types of studies [27, 35]. Policy makers and practitioners need to acknowledge the utility of various methods within the constraints of each center. The digital photography method provides an alternative to time‐consuming data collection methods and childcare providers and their staff may be able to collect data at mealtimes with adequate training. Additional studies are needed to address the feasibility of the digital photography method in the childcare setting. Furthermore, digital photography used in assessment of food intake may be further expanded upon with increased use of artificial intelligence (AI) in research to reduce data analysis time. AI combined with the digital photography plate waste method could be a beneficial tool to investigate in future studies [36, 37].
Author Contributions
Conceptualization and funding acquisition: Zenong Yin and Deborah Parra‐Medina. Analysis: Yuanyuan Liang and Jeffrey T. Howard. Data curation: Yuanyuan Liang, Jeffrey T. Howard, and Sarah L. Ullevig. Writing – original draft preparation: Sarah L. Ullevig, Jeffrey T. Howard, Erica T. Sosa, Kelsey Doolittle. Writing – review and editing: all authors; Project administration/data acquisition: Sarah L. Ullevig, Kelsey Doolittle, and Zenong Yin. All authors have read and agreed to the published version of the manuscript.
Ethics Statement
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Institutional Review Board of the University of Texas at San Antonio (IRB# 18–187). Written informed consent was obtained from all subjects' legal guardian.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Digital plate waste procedures.
Acknowledgements
The authors would like to thank the Head Start personnel, parents, and children for participating in this study and to the graduate and undergraduate students for their dedication in assisting with implementing the data‐collection protocol.
Ullevig S. L., Sosa E. T., Howard J. T., et al., “Validity of Weighed and Digital Photography Diet Estimation Study in a Childcare Setting,” Journal of Human Nutrition and Dietetics 38 (2025): 1‐9, 10.1111/jhn.70160.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Digital plate waste procedures.