Minimizing Omissions in 24-Hour Dietary Recalls: Strategies for Enhanced Data Accuracy in Biomedical Research

Abstract

This article provides a comprehensive guide for researchers and drug development professionals on addressing the critical challenge of omitted foods in 24-hour dietary recalls (24HR). Omissions, such as condiments, vegetables in mixed dishes, and additions like fats and sugars, introduce significant measurement error, potentially biasing study outcomes in clinical and epidemiological research. We explore the cognitive and methodological foundations of recall bias, detail advanced data collection techniques like the Automated Multiple-Pass Method (AMPM) and image-assisted recalls, and present strategies for optimizing training and technological tools. Furthermore, the article reviews validation methodologies, including recovery biomarkers and comparison with weighed food records, to assess and improve data quality. By synthesizing current evidence and emerging technologies, this resource aims to empower researchers to enhance the validity and reliability of dietary intake data, thereby strengthening the evidence base for diet-disease relationships and nutritional interventions.

Understanding the Problem: The Science Behind Food Omissions in Dietary Recall

Troubleshooting Guide: Addressing Common Memory-Related Recall Errors

This guide helps researchers identify and mitigate specific memory failures that lead to omitted foods in 24-hour dietary recalls (24HR).

Memory Error	Impact on Dietary Recall	Evidence & Mechanism	Mitigation Strategy
Transience [1] [2]	Forgetting consumed foods over time; rapid initial memory decay [1].	Quantitative: Memory quality deteriorates from specific to general over time [1].	Use multiple 24HRs on non-consecutive days to capture usual intake and counter single-day forgetting [3] [4].
Absent-Mindedness [1] [2]	Failing to encode a memory due to divided attention during meal (e.g., eating while working, watching TV) [5].	Physiological: Divided attention reduces activity in brain regions critical for memory encoding (left frontal lobe, hippocampus) [5] [1].	Use meal context probes: Ask about simultaneous activities (e.g., "Were you working or watching TV while eating?") to trigger associative memory [6].
Blocking [1] [2]	Temporary retrieval failure; food item feels "on the tip of the tongue" [2].	Cognitive: Cue available, but information retrieval fails. Occurs more with age; weaker links for unusual food names [2].	Provide specific food cues: Use visual aids, food models, or category-specific checklists (e.g., "common snack foods," "condiments") to unblock retrieval [6].
Source Confusion [2]	Misattributing a memory; recalling a food from a different day or confusing a imagined food for consumed one [2].	Experimental: Imagination can inflate confidence that an event occurred [2].	In multiple-pass method, use distinct temporal and event-based passes to anchor memories (e.g., "Walk me through your day from waking up") [5].
Schematic Errors [2]	Recalling a "typical" meal rather than the actual meal, omitting atypical items [2].	Cognitive: Reliance on mental scripts (e.g., "I usually have a salad for lunch") fills memory gaps with generic information [2].	Use item-specific probing: Ask "Was there anything different or unusual about this meal?" to break through schema and recall actual items [6].

Frequently Asked Questions (FAQs) for Researchers

Q1: What are the most robust cognitive predictors of measurement error in self-administered 24HRs?

Research indicates that visual attention and executive functioning are strong predictors. A 2025 controlled feeding study found that longer completion times on the Trail Making Test (a measure of visual attention and executive function) were significantly associated with greater error in energy intake estimation using automated self-administered tools (ASA24 and Intake24). Regression models showed that cognitive scores explained 13.6% to 15.8% of the variance in energy estimation error [5]. In contrast, interviewer-administered recalls can help compensate for these individual cognitive differences [5].

Q2: How do recall strategies differ by food type, and how can we use this to reduce omissions?

Participants use distinct recall strategies for different food categories [6]. leveraging these patterns with targeted probes can reduce omissions.

• Routine-Based Foods (e.g., breakfast coffee, daily snacks): Probe for habits: "What do you typically have for your morning beverage?" [6].
• Foods Eaten in Social Settings (e.g., chips at a party): Use event-based cues: "Did you eat anything while visiting with friends or family?" [6].
• Foods with "Rules" (e.g., "I never eat dessert"): Gently challenge the rule: "Did you have any dessert or sweets yesterday, even if it's not something you usually have?" [6].

Q3: Beyond memory, what other factors should I consider that contribute to food omissions?

Memory is only one part of the puzzle. A comprehensive troubleshooting approach should also consider [3] [7] [4]:

• Social Desirability: Participants may omit foods they perceive as "unhealthy" [5] [7].
• Participant Burden: Long or complex assessment tools lead to fatigue and incomplete data [3] [7].
• Literacy and Cognitive Load: Tools requiring high literacy or numeracy can overwhelm participants, leading to omissions [3] [7].

Experimental Protocols for Investigating Omissions

Protocol 1: Validating Recall Against Controlled Feeding

This "gold standard" protocol measures the true extent and nature of omissions [5] [7].

• Objective: To quantitatively assess omission rates and identify which food types and characteristics (e.g., energy density, being a snack) are most frequently omitted.
• Design: A controlled crossover study where participants consume provided meals for 3 non-consecutive days [5].
• Procedure:
  • Feeding Day: Provide participants with all meals and snacks in a controlled setting. Weigh and record all items served [7].
  • Recall Day: The following day, administer the 24HR (e.g., ASA24, Intake24, or interviewer-administered) [5].
  • Data Analysis: Calculate the percentage error between reported and true energy and nutrient intakes. Categorize omissions by food type (e.g., condiments, snacks, beverages) and eating occasion [5].

Protocol 2: Linking Cognitive Function to Omission Rates

This protocol isolates the cognitive components that contribute to omissions [5] [7].

• Objective: To determine if specific neurocognitive deficits (e.g., in working memory, attention) predict the likelihood of omitting foods.
• Design: Cross-sectional or cohort study where participants complete cognitive tests and dietary recalls.
• Cognitive Assessments (to be administered prior to recalls) [5]:
  • Trail Making Test: Measures visual attention and executive function. The outcome is time to completion; longer times predict greater error [5].
  • Visual Digit Span (Forwards/Backwards): Measures working memory capacity. The outcome is the longest correctly recalled digit sequence [5].
  • Wisconsin Card Sorting Test: Measures cognitive flexibility. The outcome is the percentage of correct trials, indicating the ability to adapt to new rules [5].
• Analysis: Use linear regression to assess the association between cognitive task scores and the rate of food omissions or total energy estimation error [5].

The Researcher's Toolkit: Key Reagents & Materials

Research Workflow for Investigating Dietary Recall Omissions

Tool Category	Specific Tool	Function in Research
Cognitive Assessments [5]	Trail Making Test	Quantifies visual attention and executive function; longer completion times predict greater recall error [5].
	Wisconsin Card Sorting Test (WCST)	Measures cognitive flexibility (ability to switch thinking); scored by percent correct trials [5].
	Visual Digit Span	Assesses working memory capacity; scored by longest correctly recalled digit sequence [5].
Dietary Recall Platforms [5] [3]	ASA24 (Automated Self-Administered)	Automated 24HR system; reduces interviewer cost but susceptible to visual attention errors [5] [3].
	Intake24	Another self-administered system; useful for large-scale studies [5].
	Interviewer-Administered 24HR (IA-24HR)	An interviewer uses the multiple-pass method with probes; can compensate for low participant cognitive scores [5].
Validation Methods [5] [4]	Controlled Feeding Study	The "gold standard" for measuring true intake and quantifying omissions/errors [5].
	Recovery Biomarkers (e.g., Doubly Labeled Water)	Objective measures of energy expenditure to identify under-reporting [3] [4].

Troubleshooting Guides and FAQs

Frequently Asked Questions

Q1: What types of foods are most commonly omitted in 24-hour dietary recalls? Research indicates that certain food categories are systematically more prone to being forgotten by respondents. The foods most subject to recall bias include:

• Beverages
• Unhealthy snacks
• Fruits
• Condiments and additions (e.g., sauces, butter, spices added after preparation) [3] [8].

The omission of these items is not random and can lead to a systematic underestimation of energy and specific nutrient intakes.

Q2: What is the quantitative impact of these omissions on dietary data? Omissions lead to significant underreporting of energy and nutrient intake. When common omitted items are added back to dietary records using recall aids, studies report statistically significant changes in most dietary outcomes [8]. The extent of underreporting varies by instrument:

• 24-hour recalls: Underreport energy by 15-17% on average [9].
• Food Frequency Questionnaires (FFQs): Underreport energy by 29-34% on average [9].

This underreporting is greater for energy than for other nutrients and is more prevalent among obese individuals [9].

Q3: What methodological approaches can minimize omission errors? Implementing a standardized multiple-pass 24-hour recall protocol is crucial. This method structures the interview into distinct phases to enhance memory retrieval [10] [11]. Additionally, using pictorial recall aids (e.g., photo albums of foods) has been shown to help respondents remember and report foods they would otherwise forget, significantly modifying dietary intake estimates [8].

Q4: How can researchers validate the completeness of their dietary data? Using objective recovery biomarkers is the gold standard for detecting systematic errors like underreporting:

• Doubly Labeled Water (DLW): Measures total energy expenditure to validate reported energy intake [10] [12] [9].
• Urinary Biomarkers: Nitrogen for protein intake, potassium and sodium for their respective intakes [10] [9].

Comparing 24-hour recall intakes with same-day weighed food records can also help identify inaccuracies in portion size estimation and omissions [10].

Troubleshooting Common Experimental Issues

Problem: Suspected widespread underreporting in your dataset.

• Solution: Incorporate a validation substudy using recovery biomarkers like doubly labeled water. If this is not feasible, statistically compare reported energy intake to predicted basal metabolic rate using established cut-offs [11]. For specific nutrients, consider 24-hour urine collections [9].

Problem: Respondents consistently forget certain food items.

• Solution: Integrate pictorial recall aids into your protocol. Provide respondents with visual prompts of commonly forgotten foods (beverages, snacks, fruits) during the interview [8]. The use of such aids has been shown to significantly increase the reported consumption of these items.

Problem: Data shows high within-person variation, obscuring usual intake.

• Solution: Collect multiple non-consecutive 24-hour recalls per participant. The number of repeats depends on the nutrient of interest and study objectives. For population-level estimates, collect repeats on a random subset (≥30-40 individuals) to model within-person variance [10]. Also, ensure recalls are proportionately collected across all days of the week and seasons to account for "nuisance effects" [10].

Problem: Need to improve accuracy without the budget for extensive biomarkers.

• Solution: Utilize a supervised machine learning approach. Train a model (e.g., Random Forest classifier) on data from participants deemed highly reliable (e.g., based on objective health markers) to predict likely food consumption frequencies. This model can then identify and help correct for potential underreporting in the broader dataset [13].

Quantitative Data on Omissions and Their Impact

TABLE 1: Commonly Omitted Food Categories and Their Impact

Commonly Omitted Food Category	Documented Impact on Data Integrity	Supporting Research Context
Beverages	Leads to underestimation of total fluid intake, calories from sugary drinks, and certain micronutrients.	Identified as most subject to recall bias in studies using pictorial aids [8].
Unhealthy Snacks	Causes significant underestimation of total energy, fat, sugar, and sodium intake.	A key category where recall aids revealed substantial omissions [8].
Fruits	Results in underestimation of vitamin, mineral, and fiber intake.	Commonly forgotten, leading to misclassification of diet quality [8].
Condiments & Added Fats	Impacts accuracy of fat, salt, and calorie data (e.g., butter on bread, sauces).	Probing questions about additions made after preparation are critical [3].

TABLE 2: Magnitude of Underreporting by Dietary Assessment Tool

Dietary Assessment Tool	Average Underreporting of Energy	Key Limitations
Food Frequency Questionnaire (FFQ)	29-34% [9]	Less suitable for estimating absolute intake; greater bias among obese individuals [9].
Single 24-Hour Recall	~15-17% (when extrapolated from multi-day average) [9]	High day-to-day variability; cannot estimate usual intake without statistical adjustment [10] [3].
Multiple Automated 24-Hour Recalls (ASA24)	15-17% [9]	Provides better estimate of absolute intake than FFQ; requires multiple administrations [9].

Detailed Experimental Protocols

Protocol 1: The Multiple-Pass 24-Hour Recall Method

This standardized interview protocol is designed to minimize memory error and is a best-practice standard [10] [11].

• Quick List: The respondent is asked to recall all foods and beverages consumed in the preceding 24 hours, without interruption, creating a "quick list."
• Detailed Probing: The interviewer systematically probes for forgotten details about each food item, including:
  • Food preparation and cooking methods.
  • Additions (e.g., condiments, spreads, sauces).
  • Time and name of the eating occasion.
  • Portion sizes, using visual aids (e.g., glasses, bowls, shapes) for quantification.
• Forgotten Foods: The interviewer directly asks about categories of foods commonly omitted (e.g., "Did you have any sugary drinks, candy, or snacks between meals?").
• Final Review: A final review pass is conducted for the respondent to add any other items not yet reported [11].

Protocol 2: Validating Data with Pictorial Recall Aids

This protocol supplements the standard 24-hour recall to specifically address omission errors [8].

• Initial Recall: Conduct a standard multiple-pass 24-hour recall without visual aids.
• Introduction of Recall Aid: Present the respondent with a pictorial aid. This should be a photo album or catalog containing images of foods and beverages that are commonly omitted in the specific study context (e.g., local snacks, fruits, fried foods, beverages).
• Aided Recall Prompt: Ask the respondent to look through the pictorial aid and identify any items they consumed in the last 24 hours but forgot to mention during the initial recall.
• Data Integration: For any new items identified, conduct a detailed probe to ascertain the portion size and other relevant details, exactly as in the main protocol.
• Data Analysis: Integrate the omitted items into the final dietary record. Analyze the dietary outcomes (e.g., energy, nutrient intake) both with and without the omitted items to quantify the impact of recall bias.

The Scientist's Toolkit: Research Reagent Solutions

TABLE 3: Essential Materials and Tools for Mitigating Omissions

Tool / Solution	Function	Example / Note
Standardized Multiple-Pass Software	Provides a structured, consistent interview framework to minimize random error and interviewer bias.	GloboDiet, ASA24 (Automated Self-Administered 24-h recall) [10] [3] [9].
Pictorial Recall Aids	Visual prompts to stimulate memory and reduce the omission of commonly forgotten foods and beverages.	Customizable photo albums of local snacks, fruits, and drinks [8].
Portion Size Estimation Aids	Helps respondents convert their memory of food consumed into quantitative estimates.	Standard shapes, household measures, food models, or food atlases [3].
Recovery Biomarkers	Objective, biological measurements used to validate self-reported intake and quantify systematic error.	Doubly Labeled Water (energy), Urinary Nitrogen (protein), Urinary Sodium/Potassium [10] [12] [9].
Statistical Modeling Software	To adjust data for within-person variation and estimate "usual intake" from short-term tools.	The National Cancer Institute (NCI) method requires software like SAS or R [10] [3].
Machine Learning Algorithms	To identify and correct for patterns of misreporting within existing datasets.	Random Forest classifiers can be trained to flag likely under-reported entries [13].

Troubleshooting Guides

Guide 1: Identifying and Resolving Systematic Errors in 24-Hour Dietary Recalls

Problem: Data collection yields consistent inaccuracies (bias) that skew results in a specific direction, threatening the validity of your findings on diet-disease relationships.

Solution: Implement a multi-faceted approach to identify, quantify, and correct for systematic biases.

• Step 1: Check for Instrument Calibration (Your Protocol)

  • Action: Regularly validate your dietary assessment tool against an objective reference method.
  • Example: Compare energy intake from 24-hour recalls against total energy expenditure measured by Doubly Labeled Water (DLW), the gold standard reference measure [10] [14]. Significant under-reporting indicates a systematic error.
  • Protocol: In a subsample of participants, collect self-reported intake via 24-hour recall and simultaneously measure TEE using DLW over 7-14 days. Analyze the difference between reported EI and TEE to quantify the bias [14].

• Step 2: Review Data Collection Procedures for Interviewer Bias

  • Action: Standardize interviewer behavior and use automated, standardized probing.
  • Example: Implement an Automated Multiple-Pass Method (AMPM), as used in NHANES, to systematically prompt for forgotten foods (e.g., condiments, additions to main dishes) and standardize detail collection [15]. This reduces variability introduced by different interviewers' techniques.

• Step 3: Analyze Data for Participant-Related Biases

  • Action: Actively check for and account for under-reporting and social desirability bias.
  • Example: Calculate the ratio of reported energy intake (EI) to estimated basal metabolic rate (BMR). A ratio below a plausible threshold (e.g., 1.1 for sedentary populations) suggests systematic under-reporting [14]. This data can then be statistically adjusted.

Guide 2: Mitigating Random Errors in 24-Hour Dietary Recalls

Problem: Data exhibits unpredictable variability or "noise," reducing the precision of your measurements and obscuring true effects or relationships.

Solution: Reduce variability through study design and statistical techniques.

• Step 1: Increase the Number of Repeat Measurements

  • Action: Collect multiple 24-hour recalls per participant to account for day-to-day variation in intake.
  • Protocol: The number of repeat days required can be calculated using the formula: d = [r²/(1 - r²)] * (σw / σb), where d is the number of days, r is the expected correlation between observed and usual intake, and σ_w / σ_b is the ratio of within-person to between-person variance [16]. Fewer days are needed for energy (lower variability) compared to nutrients like Vitamin A (higher variability) [16].

• Step 2: Increase Sample Size

  • Action: Ensure your study is powered to account for random variability.
  • Protocol: In large samples, random errors in different directions tend to cancel each other out, providing a more reliable estimate of the group mean [17] [18]. A larger sample size increases statistical power and precision.

• Step 3: Control Extraneous Variables

  • Action: Design your study to minimize unintended variability.
  • Example: Account for "nuisance effects" like day of the week and season by proportionally distributing recalls across all days and seasons [10]. Use standardized portion size aids (e.g., image aids, food models) for all participants to reduce random misestimation [15].

FAQs

FAQ 1: Which is worse for my research: random or systematic error?

Systematic error is generally considered more problematic [17] [18]. While random error reduces precision and makes it harder to detect a true effect, it is often predictable and can be reduced by increasing sample size or measurement days. Systematic error, or bias, compromises the accuracy of your data consistently, leading to false conclusions about relationships between variables (e.g., between a nutrient and a health outcome) [17]. Even with a large sample, systematic error will not cancel out and can invalidate your findings [18].

FAQ 2: How can I detect an error if I don't know the "true" intake of my participants?

You can use internal and external strategies.

• Internally: Compare your data to expected physiological principles. For example, use the EI:BMR ratio to identify likely under-reporters [14].
• Externally: Incorporate a reference method in a validation sub-study. The best practice is to use recovery biomarkers like Doubly Labeled Water for energy and urinary nitrogen for protein, which are independent of self-report errors [10] [14]. Alternatively, in a controlled setting, compare 24-hour recalls to same-day weighed food records [10].

FAQ 3: Are certain types of foods more prone to being omitted in 24-hour recalls?

Yes, the tendency to omit items is not uniform across food groups. A systematic review of direct observation studies found that omissions are highly variable but follow some patterns [19].

• Frequently Omitted: Vegetables, condiments, and additions to main dishes (e.g., cheese in sandwiches, mayonnaise) are often forgotten [19] [15].
• Less Frequently Omitted: Beverages are typically omitted less often than solid foods [19].

The table below summarizes quantitative data on omission rates from studies comparing self-report to observed intake:

Table 1: Omission Rates of Selected Food Items in 24-Hour Recalls

Food Item	Omission Rate Range	Citation
Tomatoes	42% (ASA24)	[15]
Mustard	17% (ASA24 & AMPM)	[15]
Green/Red Pepper	16-19%	[15]
Cheddar Cheese	14-18%	[15]
Lettuce	12-17%	[15]
Vegetables (general)	2% - 85%	[19]
Condiments (general)	1% - 80%	[19]
Beverages	0% - 32%	[19]

FAQ 4: What is the single most effective step to improve the accuracy of my 24-hour recall data?

There is no single silver bullet, but the most impactful strategy is to use a standardized, multi-pass interview method (e.g., AMPM, GloboDiet) [10] [15]. This method is specifically designed to aid memory and reduce both omissions and intrusions through a structured series of passes and standardized probes for commonly forgotten foods.

Conceptual Diagrams and Workflows

Error Impact on Data

24-Hour Recall Error Mitigation Workflow

Research Reagent Solutions

Table 2: Essential Tools and Methods for Dietary Assessment Research

Item/Method	Function in Research	Example/Note
Doubly Labeled Water (DLW)	Gold-standard reference method for validating energy intake by measuring total energy expenditure [10] [14].	Requires specialized equipment for isotope analysis; high cost.
Automated Multiple-Pass Method (AMPM)	Structured interview protocol to enhance memory and reduce omissions in 24-hour recalls [10] [15].	Used in US NHANES; available in interviewer-administered format.
GloboDiet (formerly EPIC-Soft)	Computer-assisted 24-hour recall software standardized for international studies to minimize systematic error [10] [15].	Adapted for use in multiple European countries and other contexts.
ASA24 (Automated Self-Administered 24hr Recall)	Self-administered, web-based tool automating the multiple-pass method to reduce interviewer bias and cost [15].	Developed by the NCI; allows for efficient large-scale data collection.
Urinary Nitrogen	Recovery biomarker used as a reference method to validate protein intake estimates [10] [14].	Provides an objective measure independent of self-report.
Statistical Modeling (e.g., MSM, SPADE)	Methods to adjust intake distributions for within-person variation and estimate "usual intake" from short-term data [16].	Corrects for random error; crucial for assessing nutrient adequacy.

Quantitative Data on Reporting Errors for Vulnerable Food Categories

The table below summarizes data on the frequency of omissions and portion misestimation for food categories often missed in 24-hour dietary recalls.

TABLE 1: Error Rates for Vulnerable Food Categories in Self-Reports

Food Category	Omission Rate Range	Primary Error Type	Key Characteristics
Condiments	1% - 80% [19]	Omission [19]	Often additions to main foods (e.g., mustard, mayonnaise) [15]
Vegetables	2% - 85% [19]	Omission [19]	Frequently ingredients in multicomponent foods (e.g., in salads, sandwiches) [15]
Beverages	0% - 32% [19]	Omission [19]	—
Cheese	14% - 18% [15]	Omission [15]	Ingredient in complex dishes [15]
Sweets & Snacks	—	Portion Misestimation [19]	Portion misestimation can account for ~99% of energy intake error [19]

Experimental Protocols for Validation Studies

Q1: What are the key methodologies for validating the accuracy of dietary recalls? The gold standard for validating self-reported dietary intake involves comparing reported data against a known reference. Two primary experimental protocols are used [19]:

• Controlled Feeding Studies: Participants consume foods provided by researchers, with all items and their weights meticulously recorded. Their self-reports are later compared against this known intake [19].
• Direct Observation: Trained researchers discreetly record all foods and beverages consumed by participants in a naturalistic setting (e.g., a cafeteria). This record serves as the objective benchmark for comparing subsequent self-reports [19]. In some studies, video recording is used as an indirect form of observation [19].

Q2: How are specific reporting errors quantified in these studies? When self-reported data is compared to observed intake, errors are categorized and measured as follows [19]:

• Omissions: Counting the number of food items that were consumed but not reported.
• Intrusions: Counting the number of food items that were reported but not consumed.
• Misclassifications: Identifying items that were reported with incorrect details (e.g., reporting "whole milk" instead of the consumed "2% milk").
• Portion Misestimation: Calculating the difference (in grams or as a percentage) between the consumed and reported weight of a food item.

Cognitive Process and Error Pathways in Dietary Recall

The following diagram illustrates the cognitive pathway a participant follows when reporting dietary intake, and where errors commonly occur.

The Scientist's Toolkit: Research Reagent Solutions

TABLE 2: Essential Tools for Dietary Recall Validation Research

Tool / Method	Function in Research
Automated Multiple-Pass Method (AMPM)	A structured interview protocol that uses probing questions and memory aids to minimize the omission of forgotten foods and standardize detail collection [15].
Automated Self-Administered 24-Hour Recall (ASA24)	A self-administered, web-based tool that adapts the AMPM methodology for automated data collection, facilitating implementation in large-scale studies [15].
GloboDiet (formerly EPIC-SOFT)	Interviewer-led software used to standardize the collection of 24-hour recall data across different countries and cultures [15].
Direct Observation Protocol	Provides an objective benchmark of true food consumption against which self-reported data can be validated [19].
Controlled Feeding Study Design	Provides data on "true" intake with known food weights and items, allowing for precise quantification of self-reporting errors [19].

Advanced Data Collection Techniques to Capture the Full Diet

Leveraging the Automated Multiple-Pass Method (AMPM) for Structured Probing

Troubleshooting Guides

Guide 1: Addressing Incomplete Food Lists in the Quick List

• Problem: Respondent provides an incomplete or sparse "Quick List" of foods consumed.
• Explanation: The initial Quick List is an unstructured pass where the respondent rapidly recalls all foods and beverages. An incomplete list here can lead to missed items in subsequent passes [20].
• Solution: Do not interrupt or probe for details during the Quick List pass. The structured probing in later passes is designed to recover these omissions. Proceed methodically through the entire AMPM protocol [21].

Guide 2: Managing Inaccurate Portion Size Estimation

• Problem: Respondent struggles to estimate the quantity of food consumed.
• Explanation: Accurate portion size estimation is a major source of error in self-reported dietary data. The AMPM uses aids and detailed questioning to improve accuracy, but it remains cognitively challenging [10].
• Solution: Utilize standardized, validated portion size visualization aids. Train interviewers to use neutral, non-leading probing questions to help respondents describe the amount consumed without influencing their response [10].

Guide 3: Correcting for Systematic Under-Reporting

• Problem: Data suggests a systematic pattern of under-reporting energy intake, particularly for certain food groups.
• Explanation: Under-reporting is a common systematic error in self-reported dietary recalls. It can be influenced by factors like social desirability, obesity, and the interview setting [10].
• Solution:
  • Study Design: Collect recalls across all days of the week and different seasons to account for "nuisance effects" [10].
  • Validation: Where feasible, incorporate objective reference measures like Doubly Labeled Water (DLW) to quantify and correct for energy under-reporting [10].
  • Interviewer Training: Ensure interviewers are trained to create a non-judgmental environment to reduce social desirability bias.

Frequently Asked Questions (FAQs)

What is the AMPM and how does its structure help prevent omitted foods?

The USDA Automated Multiple-Pass Method (AMPM) is a computerized, interviewer-administered method for collecting 24-hour dietary recalls. It uses a structured five-pass approach designed specifically to enhance memory retrieval and reduce the omission of foods commonly forgotten in a single-pass recall [21]. The multiple steps provide several opportunities for a respondent to remember and report foods.

In what step of the AMPM are foods most commonly recalled?

Research on food reporting patterns shows that foods are recalled throughout the multiple steps of the AMPM interview. The initial Quick List captures the first wave of memories, but a significant number of foods are recalled during the subsequent structured passes and the Final Probe, which uses additional memory cues. The pattern of recall varies by demographic factors [20].

What are the main types of measurement error in 24-hour recalls, and how does AMPM address them?

The main types of error are random error (which reduces precision) and systematic error (or bias, which reduces accuracy) [10].

• AMPM and Random Error: AMPM's standardized protocol helps reduce random measurement error. To further mitigate random day-to-day variation, researchers should collect more than one 24-hour recall per person [10].
• AMPM and Systematic Error: While AMPM's structure can help reduce some systematic errors like omission bias, other errors like general under-reporting are not fully eliminated by the method alone and require study design solutions (e.g., scheduling, reference measures) [10] [22].

Can the AMPM be effectively administered over the telephone?

Yes, the AMPM can be administered both in-person and by telephone. Studies have validated telephone-administered multiple-pass 24-hour recalls against objective measures like Doubly Labeled Water, confirming their effectiveness [11].

Experimental Protocols & Data

Quantitative Data on Food Reporting Patterns

The following table summarizes findings from an analysis of food reporting patterns in the AMPM, based on data from the 2007-2008 "What We Eat in America" NHANES [20].

TABLE 1: Factors Influencing AMPM Reporting Score

Factor	Impact on Reporting Score
Day of Interview	Reporting scores showed significant variation depending on the day of the week the recall was conducted [20].
Gender	Significant differences in reporting scores were observed between males and females [20].
Age	Reporting scores varied significantly across different age groups of respondents (12 years and older) [20].
Race/Ethnicity	Significant differences in reporting scores were identified between different racial and ethnic groups [20].

Detailed Methodology: The 5-Pass AMPM Protocol

The following workflow details the sequence of steps in the AMPM interview [20] [21] [11].

AMPM 5-Pass Workflow

• Pass 1: Quick List. The respondent gives an uninterrupted, unstructured list of all foods and beverages consumed the previous day [20] [11].
• Pass 2: Forgotten Foods Probe. The interviewer uses a structured list of common food categories and specific memory cues to help the respondent recall frequently omitted items (e.g., sweets, beverages, snacks) [11].
• Pass 3: Time and Occasion. For each food reported, the interviewer asks about the time of consumption and the eating occasion (e.g., breakfast, lunch), helping to create a chronological framework [11].
• Pass 4: Detail Cycle. The interviewer collects detailed information about each food, including the description, preparation method, portion size, and any additions (e.g., sauces, fats) [11].
• Pass 5: Final Review. A final, unstructured probe gives the respondent one last opportunity to report any additional foods, often using further memory cues [20].

The Scientist's Toolkit

TABLE 2: Essential Research Reagents for AMPM Implementation

Item	Function
Standardized AMPM Interview Protocol	The core script and procedure ensuring consistent, interviewer-administered recalls that minimize random error and enhance complete food reporting [10] [21].
Portion Size Visualization Aids	Tools (e.g., graduated models, photographs, household measures) to help respondents accurately estimate and report the quantity of food consumed [10].
Food Composition Database	A comprehensive database used to convert reported food intake data into estimated nutrient intakes. The quality of this database directly impacts the accuracy of the final nutrient analysis [10].
Quality Control (QC) Procedures	Standardized procedures for training interviewers, monitoring interview quality, and data processing to maintain data integrity and reduce random measurement error throughout the study [10].
Reference Measure (e.g., Doubly Labeled Water)	An objective, biological method used in validation sub-studies to detect and correct for systematic errors like energy under-reporting in the 24-hour recall data [10].

Frequently Asked Questions (FAQs)

Q1: What is the primary advantage of using a food atlas or portion size images over traditional 24-hour dietary recall? The primary advantage is the significant improvement in accuracy and the reduction of food item omissions. Traditional 24-hour recall relies on participant memory and is prone to errors, especially for condiments, oils, and complex dishes. Using standardized visual aids helps participants and researchers estimate portion sizes more consistently and objectively, leading to more reliable nutrient intake calculations [23] [24].

Q2: Our study involves foods not found in existing food atlases. How should we handle this? For foods not listed in your atlas, the recommended protocol is to replace them with the most visually similar food item available in the atlas. Detailed documentation of the substitution should be made. For long-term studies, consider developing and validating new, culturally specific image-series to fill these gaps, ensuring the new images follow established development criteria for portion size increments and presentation [23] [24].

Q3: We are noticing consistent underestimation of certain food groups, like vegetables. Is this a known issue and how can it be mitigated? Yes, this is a documented issue. Validation studies have shown that vegetable intake can be significantly underestimated using visual methods [23]. To mitigate this, ensure your food atlas includes a wide variety of vegetable preparation types (chopped, whole, cooked, raw) and uses high-contrast place settings (e.g., a dark plate for light-colored vegetables) to make the items more discernible. Providing specific training to interviewers on estimating these problematic food groups is also crucial.

Q4: How many portion size images should an ideal image-series contain? Validation research indicates that a higher number of images leads to more accurate portion size estimation. Image-series containing seven portion size images have been shown to provide satisfactory estimation accuracy and are recommended for use in digital dietary assessment tools [24].

Q5: Are digital images as effective as printed food atlases? Studies comparing digital and printed images have reported no statistical difference in estimation accuracy between the two formats [24]. The choice can therefore be based on practicality; digital images offer greater convenience for web-based or mobile dietary assessment tools.

Troubleshooting Guides

Problem: Low Correlation for Oils, Fats, and Condiments

Description: When validating your method, you find low correlation coefficients for food groups like oils, fats, condiments, and spices.

Possible Cause	Solution
Low visual salience: These items are often added in small quantities or integrated into dishes, making them difficult to visualize.	Use specialized image-series that show these items measured in spoons, cups, or on standardized food items (e.g., butter on a piece of bread).
Lack of proxy images: The food atlas lacks images for commonly used condiments.	Expand the food atlas to include a comprehensive list of condiments and fats, depicting them in common serving vessels.

Experimental Protocol for Validation: To identify such issues, conduct a validation study comparing your visual aid method against the weighed food record (WFR) for a range of food groups. Calculate Spearman’s correlation coefficients for each group; coefficients for oils and condiments will likely be lower than for other groups, which is a known challenge [23].

Problem: Inaccurate Portion Size Estimation by Participant Demographics

Description: Data analysis reveals systematic estimation errors linked to participant characteristics like sex.

Possible Cause	Solution
Sex-based differences: Validation studies have shown that female participants may estimate portion sizes more accurately than males [24].	Ensure your interviewer training includes techniques to assist all participants. Consider this a potential variable during data analysis.
Lack of familiarity: Participants with little cooking experience may have a poorer innate sense of food weights.	The visual aids themselves help overcome this, but pre-survey familiarization with the image-series can improve performance [23].

Experimental Protocol for Validation: During your tool's validation, use Mann-Whitney U tests to explore if estimation accuracy differs significantly across sample characteristics like sex, education level, or age. This will help you identify and account for biases in your methodology [24].

Key Experimental Protocols and Data

Protocol 1: Validating a Visual Aid Method against Weighed Food Records

This protocol is adapted from a study validating the 24hR-camera method [23].

• Participant Recruitment: Recruit a sample of participants representative of your target population (e.g., 30 Japanese males, aged 31-58, who rarely cook).
• Dietitian Training: Divide registered dietitians (RDs) into groups: one to administer the WFR and another to administer the visual aid method (24hR-camera), ensuring blinding.
• Simultaneous Data Collection: On the test day, both methods are used simultaneously for the same meals.
  • WFR Method: A trained RD weighs all pre-cooked ingredients, cooked meals, and leftovers to calculate actual intake.
  • 24hR-Camera Method: Participants photograph all food and drink before and after consumption. The following day, a different RD interviews the participant, using the photos and a food atlas to estimate food weight and intake.
• Data Analysis:
  • Calculate nutrient intake from both methods using a standard food composition database.
  • Use Spearman’s correlation coefficient to assess the relationship between the two methods for energy, macronutrients, and food groups.
  • Use Bland-Altman plots to assess agreement and identify any systematic biases.

Protocol 2: Developing and Validating New Portion Size Image-Series

This protocol is based on the development of image-series for a Norwegian dietary tool [24].

• Item Selection: Identify foods for new image-series based on dietary surveys, cultural frequency of consumption, and their potential to act as a proxy for other similar foods.
• Define Portion Sizes: Create seven images with increasing portion sizes for each food.
  • Use standard serving sizes as the middle images (images 3-5).
  • Use fixed percentage weight increments (e.g., 50%) between images to ensure visible differences.
• Photography: Photograph food items naturally placed on plates, using kitchen scales to ensure accuracy. Use a consistent, neutral background.
• Validation Study: Present participants with 46 pre-weighed food portions and have them select the matching image from the series.
  • Analysis: Classify estimations as correct, adjacent, or misclassified. Calculate the mean weight discrepancy between the chosen and correct image. Use Mann-Whitney U tests to check for differences in accuracy by demographics.

Table 1: Validation of the 24hR-Camera Method vs. Weighed Food Records (WFR) for Select Nutrients [23]

Nutrient	Correlation Coefficient (vs. WFR)	Conclusion
Energy	0.774	High correlation
Protein	0.855	High correlation
Lipids (Fats)	0.769	High correlation
Carbohydrates	0.763	High correlation
Salt Equivalents	0.583	Moderate correlation
Potassium	0.560	Moderate correlation

Table 2: Performance of Newly Developed Portion Size Image-Series in a Validation Study [24]

Metric	Result
Total number of image-series validated	23
Number of food items presented for estimation	46
Average correct or adjacent classification rate	98% (for 38 out of 46 items)
Mean weight discrepancy	2.5%
Significant difference in accuracy by sex	Yes (Females more accurate)

Research Reagent Solutions

Table 3: Essential Materials for Implementing Visual Aid-Based Dietary Assessment

Item	Function in Research	Example / Specification
Standardized Food Atlas	A visual library with photographs of foods in multiple portion sizes; used as a reference during interviews to estimate intake.	Manual with full-scale portion size photos; can be digital or printed. Example: A Japanese food atlas [23].
Portion Size Image-Series	A set of images (e.g., 7 images) for a specific food showing increasing portion sizes; integrated into digital recall tools.	PNG files with transparent backgrounds. Example: The ASA24 database contains over 17,000 such images [25].
High-Contrast Tableware	Plates and cups that create a strong visual contrast with the food to improve visibility and estimation accuracy, especially for pureed or similarly colored foods.	Red or blue tableware for high contrast against common foods. Proven to increase intake in patients with visual impairments [26] [27].
Digital Camera / Smartphone	Allows participants or researchers to capture images of consumed meals for later analysis, reducing reliance on memory.	Basic model capable of capturing clear, well-lit images. A card with a color reference or grid mat can be included for scale [23].
Food Composition Database (FCDB)	A database linking foods to their nutritional content; essential for converting estimated food weights into nutrient intake data.	Standardized national databases, e.g., Standard Tables of Food Composition in Japan [23] or the Norwegian Food Composition Database [24].

Experimental Workflow and Signaling Pathways

The following diagram illustrates the typical workflow for implementing and validating a visual aid-based dietary recall method, highlighting the role of food atlases and portion size images in reducing the omission of foods.

Visual Aid Integration Workflow

FAQs: Troubleshooting Camera and Smartphone Issues for Research

Q1: Why does my smartphone camera show a black screen or fail to open?

This is often a software glitch rather than a hardware failure. First, try restarting your smartphone, as this can resolve temporary bugs affecting the camera [28]. If the problem persists, check that the camera app has the necessary permissions. Go to your phone's Settings > Apps > Camera, and ensure that Camera, Microphone, and Location permissions are allowed [28]. Another common fix is to clear the camera app's cache and data (Settings > Apps > Camera > Storage > Clear Cache/Clear Data), which resets the app without deleting your photos [29] [28].

Q2: How can I fix consistently blurry images in my research documentation?

Blurry images can stem from technique or camera issues. To diagnose, first place the camera on a stable surface or tripod and take a picture of a high-detail, stationary object. If the image remains blurred, there may be a hardware problem [30]. If it is sharp, the issue is likely your technique.

• Ensure adequate lighting: Low light can cause slow shutter speeds, leading to motion blur [30].
• Stabilize your device: Use a tripod or steady your hands against a surface [29].
• Check the lens: Clean the camera lens gently with a soft, lint-free cloth to remove smudges or dirt [30].
• Verify focus mode: Ensure you are not too close for the standard focus range or using macro mode for a distant subject [30].

Q3: What should I do if my camera app crashes or freezes repeatedly?

Application crashes are frequently resolved by force-quitting the app and reopening it. On Android, press and hold the camera app icon, tap the "i" button, and select "Force Stop" [28]. The next step is to update your software. Check for updates to both your phone's operating system and the camera app itself, as these updates often contain bug fixes [29] [28]. If crashes continue, free up storage space on your device, as insufficient space can prevent the app from functioning correctly [28].

Q4: Why are my photos overexposed (too bright) or underexposed (too dark)?

Improper exposure can affect the legibility of research data. For smartphone cameras:

• Adjust exposure compensation: Many camera apps allow you to manually increase or decrease the exposure; look for a +/- icon or slider [29].
• Use HDR mode: In high-contrast scenes, HDR (High Dynamic Range) can help balance the light and dark areas of a photo [29].
• Check flash usage: The built-in flash on small cameras is only effective over a short distance (e.g., up to 10 feet). If your subject is too close, the image may be overexposed; if too far, it may be underexposed [30].

Troubleshooting Guide: Common Camera Problems and Solutions

The table below summarizes frequent issues and their solutions, tailored for a research environment.

Table 1: Troubleshooting Guide for Common Camera Problems in Research Settings

Problem	Possible Causes	Immediate Solutions	Preventive Measures for Long-Term Studies
Black Screen / Non-responsive Camera [29] [28]	Software bug, insufficient permissions, faulty app cache.	Restart device, check app permissions, force stop app, clear app cache/data.	Keep operating system and camera app updated to the latest version.
Blurry or Out-of-Focus Images [29] [30]	Camera shake, dirty lens, poor lighting, incorrect focus mode.	Clean lens, use a tripod, ensure good lighting, check focus mode (macro vs. normal).	Standardize shooting protocols with fixed camera stands and controlled lighting for consistent image capture.
Camera App Crashes or Freezes [29] [28]	App conflict, corrupted temporary files, low storage, outdated software.	Force quit the app, clear app cache, free up device storage, update software.	Use a dedicated device for research photography with minimal other apps installed.
Overexposed or Underexposed Images [29] [30]	Incorrect exposure settings, improper flash use, challenging lighting.	Manually adjust exposure, use HDR mode, review and adjust flash settings.	Use a color calibration card in a test shot to ensure accurate color and exposure reproduction in your specific environment.
Photos Are Grainy (Noisy) [30]	High ISO setting (in low light), underexposure, sensor overheating.	Shoot in brighter light, use a lower ISO setting, ensure correct exposure.	Control the ambient temperature where cameras are stored and used to prevent sensor heat buildup.

Experimental Protocols: Integrating Camera Technology in 24-Hour Dietary Recalls

The following workflow diagram illustrates how digital cameras are integrated into a modern, image-assisted 24-hour dietary recall (24HR) protocol, which helps mitigate the omission of foods.

Figure 1: Workflow for an image-assisted 24-hour dietary recall method.

Detailed Methodology for Image-Assisted 24HR

The protocol is designed to maximize accuracy and minimize the systematic error of food omission by leveraging digital imagery.

• Image Capture Protocol:

  • Equipment: Participants use a smartphone or portable camera. A fiducial marker (an object of known size, shape, and color) is included in the frame to aid in subsequent portion size estimation [31].
  • Procedure: Participants are instructed to capture "before" and "after" images of all eating occasions, ensuring that all food and beverage items are visible and in focus [31]. This visual record serves as an objective memory aid that is less susceptible to the biases of traditional recall.

• Image Review and Analysis:

  • Automated Processing: Uploaded images are processed using computer vision and machine learning techniques for initial food identification [31].
  • Trained Analyst Verification: A human analyst reviews the automated results. This step is critical for verifying food types, identifying specific brands or preparation methods, and using the fiducial marker to estimate portion sizes accurately [31]. This hybrid approach balances scalability with precision.

• Structured Recall Interview:

  • Unlike traditional recalls that rely solely on memory, the interviewer uses the analyst-verified images as a foundational prompt at the start of the interview [31]. This novel approach helps to:
    • Reduce Omissions: The images provide a concrete starting point, cueing the participant's memory for items they may have forgotten.
    • Improve Detail: The interviewer can ask specific, probing questions about items visible in the images (e.g., "I see a white sauce on the pasta; can you tell me what it was?") [32].
    • Enhance Portion Size Estimation: The analyst's preliminary portion estimates from the images can be confirmed or adjusted by the participant during the interview.

Quantitative Comparison of Technology-Assisted 24HR Methods

Research is ongoing to evaluate the accuracy and cost-effectiveness of different technology-assisted methods. The following table summarizes key features of several automated and image-assisted systems.

Table 2: Comparison of Technology-Assisted 24-Hour Dietary Recall Methods

Method Name	Primary Mode	Key Features	Reported Advantages	Considerations for Research Use
ASA24 [33] [31]	Automated Web-Based Self-Administered Recall	Adapted from the interviewer-led AMPM; uses multiple passes and standard food images for portion estimation.	Structured, thorough probing; reduces interviewer costs.	May generate a higher number of perceived user problems compared to other self-administered tools [33].
INTAKE24 [33] [31]	Automated Web-Based Self-Administered Recall	Developed through multiple user-testing cycles; simplified interface.	High user preference and fewer perceived problems [33].	Well-suited for large-scale population surveillance.
Image-Assisted mFR24 [31]	Image-Assisted Mobile Food Record	Uses before/after photos with a fiducial marker; image review initiates the recall interview.	Objectively captures data, reduces reliance on memory, potential for highly accurate portion sizing.	Requires participant compliance in taking clear, complete images.

The Scientist's Toolkit: Essential Research Reagents & Materials

This table details key materials and digital tools required for implementing camera-based dietary assessment protocols.

Table 3: Essential Research Reagents and Solutions for Digital Dietary Assessment

Item / Tool	Function / Purpose	Application in Research Protocol
Fiducial Marker	An object of known dimensions placed in food photos to provide a scale reference.	Crucial for calibrating image analysis software to estimate portion sizes of consumed foods accurately [31].
Standardized Color Calibration Card	Ensures consistent and accurate color reproduction across different cameras and lighting conditions.	Used to correct color balance in food images during analysis, improving food identification accuracy (e.g., distinguishing between types of cooked meat).
ASA24 & INTAKE24	Automated, self-administered 24-hour dietary recall systems.	Enable cost-effective, large-scale dietary data collection with integrated nutrient databases, reducing researcher coding burden [33] [31].
Structured Interview Protocol with Documentation Checklist	A standardized list of probes and checks for interviewers.	Ensures all relevant details (e.g., cooking methods, additions like salt or sauces) are consistently queried across all participants, reducing systematic error [32].

Culturally and Linguistically Adapted Food Lists for Diverse Populations

The Problem: Why Standard Food Lists Fall Short in Diverse Populations

In dietary research, 24-hour recalls are a foundational method for assessing intake. However, when conducted with ethnically diverse populations using non-adapted tools, a critical issue arises: the omission of culturally specific foods. Standard food lists, often developed for majority populations, fail to capture unique foods, preparation methods, and eating patterns of minority ethnic groups [34]. This leads to systematic measurement error, undermining data quality and the validity of research linking diet to health outcomes [10]. This guide provides troubleshooting strategies to identify and correct these omissions, ensuring your data accurately reflects the true dietary intake of all population groups.

Key Terminology

• Culturally Adapted Food Lists: Food lists and databases specifically modified to include foods, portion sizes, and preparation methods common in a particular cultural or ethnic group [35].
• Omitted Foods: Food items that are consumed by a population but are missing from the dietary assessment tool, leading to under-reporting [10] [34].
• Portion-Size Estimation Element (PSEE): Any tool (e.g., food models, household utensils, photos, diagrams) used to help respondents quantify the amount of food consumed [34].
• Usual Intake: The long-term average consumption of a food or nutrient by an individual, which requires multiple dietary assessments to estimate accurately [10] [36].

Troubleshooting Guides

Problem: Suspected Omission of Culturally Specific Foods

Symptoms: Data shows inexplicably low energy or nutrient intakes for a subgroup; participants frequently add foods in "other" categories; focus group feedback indicates common foods are missing from the list.

Step	Action	Rationale & Details
1	Conduct Preliminary Qualitative Research	Hold focus groups or key informant interviews with members of the target community to identify frequently consumed foods that are absent from your standard instrument [37] [35].
2	Analyze Single 24-Hour Recalls	Review completed recalls for foods that were manually written in or difficult for participants to categorize. This is a primary source for identifying omitted items [34].
3	Pilot a Modified Food List	Integrate the newly identified foods into your food list or FFQ. Test the modified tool in a small sample from the target population to ensure comprehension and completeness [35].
4	Validate with Biomarkers (If Feasible)	Use objective measures like doubly labeled water (for energy) or urinary nitrogen (for protein) to detect and quantify systematic under-reporting that may be due to omissions [10].

Problem: Inaccurate Portion Size Estimation

Symptoms: High within-person variation for amorphous foods (e.g., stews, rice); participants struggle to estimate volumes using standard cups and spoons; nutrient data is inconsistent.

Step	Action	Rationale & Details
1	Identify Culturally Appropriate PSEEs	Determine the most relevant household utensils, common serving vessels, or market units used by the population (e.g., a specific type of bowl or spoon) [34].
2	Develop and Validate Photo Aids	Create photographic aids depicting a range of portion sizes for culturally specific foods, using the identified household utensils. Where possible, validate the perceived portion sizes against weighed amounts [34].
3	Combine Multiple PSEEs	Use a combination of tools (e.g., photos, food models, and household measures) during the 24-hour recall interview to improve accuracy, especially for foods with irregular shapes [34].
4	Train Interviewers Thoroughly	Ensure interviewers are proficient in using the PSEEs and understand the cultural context of food consumption, such as practices of eating from shared dishes [34] [38].

Problem: High Day-to-Day Variability Obscuring Usual Intake

Symptoms: A single 24-hour recall per person provides a "noisy" and unreliable estimate of habitual diet; prevalence of nutrient inadequacy shifts dramatically when more recalls are collected [36].

Step	Action	Rationale & Details
1	Implement Multiple 24-Hour Recalls	Collect at least 2-3 non-consecutive 24-hour recalls per person, as this significantly improves the accuracy of estimating usual intake distributions [10] [36].
2	Use Statistical Adjustment	Apply specialized software (e.g., PC-SIDE, the National Cancer Institute's method) to adjust intake distributions for within-person variation and estimate usual intake [10] [36].
3	Strategize Recall Days	Spread recalls across all days of the week and different seasons to account for cyclical variations in diet, especially in populations affected by food insecurity or seasonal availability [10].

Frequently Asked Questions (FAQs)

Q1: What is the minimum number of 24-hour recalls needed to estimate usual intake in a diverse population? While a single recall can describe group-level mean intake, estimating the distribution of usual intake for nutrients with high day-to-day variability (e.g., vitamin A) requires multiple recalls. Research in an urban Mexican population found that three 24-hour recalls significantly improved the estimates of energy and nutrient intakes and the prevalence of inadequacy compared to a single recall [36]. For some nutrients, the estimated prevalence of inadequacy changed by over 25 percentage points when using three recalls instead of one [36].

Q2: How can we adapt a food list for a population with limited literacy or language barriers? The strategy involves:

• Linguistic Translation: Forward- and back-translate all materials by professional, bilingual translators.
• Cultural Adaptation: Go beyond literal translation. Incorporate colloquial food names and consider culturally specific combination foods [35].
• Visual Aids: Heavily rely on images of foods and portion sizes to bypass literacy requirements [34] [38].
• Interviewer-Administered Recalls: Use trained, bilingual interviewers who can build rapport and use probes appropriate to the cultural context [37] [11].

Q3: Are there validated, ready-to-use culturally adapted food lists? While some studies have published their methodologies for creating ethnic-specific Food Frequency Questionnaires (FFQs) [35], there is no universal repository. The development is often specific to the population and country context. The best practice is to follow a documented methodology, like that of the HELIUS study, which developed FFQs for Surinamese, Turkish, Moroccan, and ethnic Dutch populations by using 24-hour recall data to select foods that contributed most to nutrient intake in each group [35].

Q4: How do cultural values impact the response to dietary assessment tools? Cultural values can significantly influence how participants perceive and respond to dietary interventions and assessments. For example, research adapting a text-message intervention for Hispanic adults found that cultural beliefs such as familism (prioritizing family) and fatalism/destiny could predict interest in the program [37]. Higher beliefs in destiny were associated with lower interest and perceived efficacy [37]. Tailoring communication to resonate with cultural values like familism can improve engagement and accuracy.

Experimental Protocols & Workflows

Detailed Protocol: Developing an Ethnic-Specific Food List

This protocol is adapted from the HELIUS study and other cited sources [35] [37].

Objective: To expand an existing food list or create a new one that adequately captures the habitual diet of a specific ethnic or cultural group.

Materials:

• Recording equipment for focus groups (audio, video).
• Existing food list or FFQ (as a base).
• Dietary assessment software or nutrient database.
• Resources for chemical analysis of ethnic-specific foods (if not in existing databases).

Procedure:

• Formative Research: Conduct focus groups and interviews with community members and key leaders. Discuss common foods, preparation methods, typical meals, and snacking patterns.
• Compile a Master Food List: Combine foods from the existing base tool with all unique foods identified in Step 1.
• Collect Quantitative Data: Administer a single 24-hour recall to a representative sample of the target population (n > 100). Record all foods consumed, including brand names and recipes.
• Food Item Selection:
  • For each food in the master list, calculate its percentage contribution to the total intake of key nutrients (e.g., energy, fat, protein, micronutrients of interest) in the target group.
  • Also calculate the percentage contribution to the between-person variance of nutrient intake.
  • Select food items that collectively explain >90% of the intake and variance for the key nutrients.
• Construct the Nutrient Database: For each selected food, assign a nutrient composition.
  • Use a standard national food composition table for generic foods.
  • For unique ethnic foods not in the database, use data from:
    • Chemical analysis (gold standard).
    • International food composition tables from the food's country of origin.
    • Standardized recipes.
• Pilot and Validate: Administer the new food list (e.g., as an FFQ) and validate it against multiple 24-hour recalls or biomarkers in a subsample.

Workflow Diagram: The Cultural Adaptation Process for Dietary Assessment Tools

The following diagram visualizes the multi-stage, iterative process of culturally adapting a dietary assessment tool, synthesizing methodologies from the search results.

The Scientist's Toolkit: Research Reagent Solutions

Table: Key Materials for Developing Culturally Adapted Dietary Tools

Item / Solution	Function in Research	Specification & Best Practices
Multiple-Pass 24-Hour Recall Protocol	A structured interview technique to minimize forgotten foods. It is the gold standard for dietary intake data collection and the basis for validating new tools [11] [38].	Uses multiple "passes": a quick list, detailed probing about forgotten foods, and a final review. Should be administered by a trained interviewer [10] [11].
Culturally Relevant Portion-Size Estimation Aids (PSEEs)	To help respondents accurately quantify the amount of food consumed, which is a major source of error in recalls [34].	Can include food photographs, household utensils (e.g., specific bowls/spoons), food models, or dimensional models (width/length). Must be validated for the target population [34].
Ethnic-Specific Nutrient Database	To convert reported food consumption into nutrient intake data. Standard databases often lack ethnic-specific foods [35].	Construct by supplementing a national database (e.g., USDA, UK) with data from chemical analyses of ethnic foods or international food composition tables [35].
Digital Dietary Assessment Platforms	To automate the 24-hour recall process, reduce coding burden, and potentially allow for self-administration [38].	Platforms (e.g., myfood24) should have a large, customizable food database, support multiple languages, and include image-based portion size aids [38].
Validation Biomarkers	Objective measures to detect and correct for systematic errors like under-reporting, which can be exacerbated by omitted foods [10].	Doubly Labeled Water (DLW): For total energy expenditure. Urinary Nitrogen: For protein intake. Use in a subsample to calibrate self-reported data [10].

Optimizing Protocols and Training to Mitigate Recall Bias

Effective Interviewer and Staff Training Models for Enhanced Probing

FAQs: Troubleshooting Interviewer Performance in Dietary Recalls

FAQ 1: What are the most common causes of omitted foods in 24-hour dietary recalls, and how can we mitigate them?

Omitted foods are a major source of measurement error, often stemming from forgotten items, misjudged portion sizes, or unstructured eating occasions [10]. To mitigate this:

• Use a Multiple-Pass Method: Implement a structured interview protocol like the Automated Multiple-Pass Method (AMPM) [10] [39]. This approach uses several distinct "passes" to help respondents gradually remember and report all consumed foods and beverages, significantly reducing omissions.
• Employ Enhanced Probing Techniques: Train interviewers to use neutral, open-ended probing questions. For example, if a respondent mentions "a sandwich," the interviewer should probe for details: "What type of bread was the sandwich on?" and "What was inside the sandwich?" [39]. This moves the respondent from a generic memory to a specific one.
• Leverage Visual Aids: Provide food models, pictures, and other visual aids to help respondents judge and report portion sizes more accurately, which can also trigger memory of forgotten items [39].

FAQ 2: Our interviewers are inconsistent in their probing techniques. How can we standardize their approach?

Inconsistency often arises from a lack of formal interviewing knowledge and unstructured "conversational" interviews [40]. The solution is to implement a structured training program:

• Structured Interview Protocols: Develop and enforce the use of a standardized 24-hour recall protocol across all interviewers [10]. This ensures every respondent is asked the same core questions in the same way.
• Practical Workshops with Role-Playing: Conduct regular mock interviews and role-playing exercises where interviewers can practice their probing skills in a safe environment [41] [40]. Use real-life and challenging scenarios, such as a respondent who has difficulty remembering snacks.
• Structured Feedback and Coaching: Establish a feedback loop using tools that record interviews (with consent) and provide data on talk-to-listen ratios and questioning techniques [40]. Offer ongoing coaching with specific examples from these recordings to help interviewers improve [41] [40].

FAQ 3: How can we validate the accuracy of our 24-hour recall data and identify systematic errors like underreporting?

Validation is crucial for assessing data quality. While random error can be reduced by collecting multiple recalls per person, detecting systematic error (bias) requires a reference measure [10].

• Use a Reference Method: Compare 24-hour recall intakes with data from a same-day weighed food record, which is considered a more objective measure [10]. For energy intake, the gold standard is the doubly labeled water (DLW) method, which measures energy expenditure and can identify under-reporting [10].
• Analyze for Expected Patterns: Check your data for known confounding factors. Systematic errors can be introduced if data collection does not account for the day of the week, season, or cultural feast days [10]. Design your protocol to proportionately include all days and seasons.

Experimental Protocols for Enhanced Probing

Protocol 1: Implementing the Multiple-Pass Method in a Field Study

This methodology is designed to minimize random error and forgotten food items [10] [39].

• Interviewer Training: Train all interviewers on the standardized multiple-pass protocol. Training should include active listening, neutral probing, and the use of portion size aids [10] [41].
• The Five Passes:
  • Pass 1 (Quick List): The respondent provides a quick, uninterrupted list of all foods and beverages consumed in the past 24 hours.
  • Pass 2 (Forgotten Foods): The interviewer uses neutral probes to prompt for commonly forgotten items (e.g., "Did you have any fruits or vegetables as a snack?" "Did you add any fats or oils in cooking?").
  • Pass 3 (Time and Occasion): The interviewer collects detailed information about the time and eating occasion for each food/beverage.
  • Pass 4 (Detail Cycle): The interviewer collects detailed descriptions and portion sizes for each item using visual aids.
  • Pass 5 (Final Review): The interviewer reads back the entire list for the respondent to confirm or make final corrections.
• Quality Control: Implement a system where a random subset of interviews is reviewed by a senior researcher to ensure protocol adherence [10].

Protocol 2: A Study to Validate Probing Techniques Using the STAR Framework

This protocol adapts the STAR (Situation, Task, Action, Result) method from behavioral interviewing to train and test dietary recall probing techniques [42].

• Define Competencies: Define the key competencies for effective interviewers: Active Listening, Emotional Intelligence, and Clear Communication [41].
• Develop Scenarios: Create realistic scenarios where a respondent provides a vague answer. For example:
  • Situation: "I had a busy day and just ate whatever was around."
  • Task (Interviewer's): To obtain a complete and accurate list of foods.
  • Action (Trained Probe): "Let's walk through your day from morning to night. What was the first thing you had to eat or drink after you woke up?"
  • Result: A detailed, chronological list of intake is obtained.
• Run Practical Workshops: Interviewers participate in mock interviews using these scenarios. Peers and trainers evaluate their use of STAR-based probing to move from surface-level to impactful, detailed answers [41] [42].
• Measure Success: Success is measured by the completeness of the resulting dietary recall and a reduction in vague or generic food descriptions.

Data Presentation: Validation of 24-Hour Recall

The following table summarizes quantitative data on the validity of the 24-hour dietary recall from a study comparing recalled intake to observed intake, highlighting areas where probing and training can have the most impact [43].

TABLE 1: Validity of 24-Hour Dietary Recall vs. Observed Intake

Nutrient/Food Item	Mean Difference (Recalled - Observed)	Product-Moment Correlation Coefficient	Key Insight for Interviewer Training
Sucrose	-20%	0.58 - 0.74	High omission rate for sugary items; probe specifically for added sugars, sweetened drinks, and desserts.
Vitamin C	-16%	0.58 - 0.74	Fruits and vegetables are commonly omitted; use a "forgotten foods" pass focused on these items.
Cooked Vegetables	Omission Rate: 50%	Not Reported	A high-risk category for omission. Probe for side dishes, ingredients in mixed dishes, and cooking methods.
Fish	Omission Rate: 4%	Not Reported	Less frequently omitted, indicating some food types are recalled more reliably.
All Nutrients (ex. Sucrose/Vit C)	-6% to +11%	0.58 - 0.74	Validity is more satisfactory for estimating group means than individual intake.

The Scientist's Toolkit: Research Reagent Solutions

TABLE 2: Essential Materials for Dietary Recall Research and Training

Item	Function in Research
Structured Interview Protocol (e.g., AMPM)	Provides a standardized, multi-step framework for conducting recalls, minimizing interviewer variability and reducing forgotten foods [10] [39].
Visual Portion Size Aids	Food models, photographs, or digital tools that help respondents convert consumed foods into quantitative amounts, improving accuracy of portion size estimation [39].
Audio Recording & Transcription Tools	Allows for post-interview review and analysis of interviewer performance, including probing technique, talk-to-listen ratio, and adherence to protocol [40].
Food Composition Database	A database used to convert reported food intake into estimated nutrient intake. The choice of database is critical for the accuracy of the final data [10] [39].
Quality Control Checklists	Standardized forms used by senior staff to monitor a subset of interviews for consistency, protocol adherence, and proper probing technique [10] [41].
Mock Interview Scenarios	Realistic scripts used in role-playing exercises to train interviewers on handling challenging situations, such as vague respondents or complex mixed dishes [41] [40].

Visualization: Workflow for a Structured Dietary Recall Interview

The following diagram illustrates the logical workflow of a structured dietary recall interview, incorporating elements of the multiple-pass method and continuous quality control.

Standardizing Procedures Across Research Sites and Groups

Troubleshooting Guide: Common Data Quality Issues

This guide addresses frequent problems encountered during the collection and processing of 24-hour dietary recall data, with a specific focus on identifying and handling omitted foods.

Problem Description	Root Cause	Impact on Data	Solution Protocol	Preventive Measures
Incomplete Dietary Recall	Participant forgets to report foods consumed, especially snacks or condiments [44]	Under-reporting of energy/nutrient intake; compromises dataset validity	Implement the Automated Multiple-Pass Method (AMPM) [44]. Cross-check with a food frequency questionnaire if available [44].	Use a validated interview method; train interviewers to use neutral prompts and memory cues.
Unreliable Recall Status	Participant recall is incomplete or deemed unreliable by interviewer [44]	Data record may be excluded from analysis, reducing sample size	Check the Dietary Recall Status (DR1DRSTZ/DR2DRSTZ) variable [44]. Filter for records with status=1 (reliable and complete).	Standardize interviewer training on criteria for determining recall reliability.
Misclassified Foods	Food item reported is vague or incorrectly matched to a database food code [44]	Introduces error in nutrient calculations; reduces data precision	Consult the Food Code Description File (DRXFCD) [44] for accurate code mapping. Use the long description (DRXFDLD) for verification.	Utilize a standardized food dictionary and maintain a site-specific glossary for common local foods.
Inconsistent Unit Conversion	Participant reports consumed amount in household measures not converted to grams [44]	Invalidates nutrient calculations derived from gram-weight [44]	Apply standardized conversion factors. Verify the DR1IGRMS (Food Gram Weight) [44] variable is correctly populated for all foods.	Provide interviewers with visual aids (photo albums, measuring guides) to improve portion size estimation.
Missing Secondary Day Recall	Participant fails to complete the second 24-hour recall [44]	Limits ability to model usual intake distributions for the population	Use appropriate statistical methods for single-day intakes. Apply the WTDRD1 dietary weight for First Day analysis [44].	Motivate participants by explaining the importance of the second day for research accuracy.

Frequently Asked Questions (FAQs)

1. How does the NHANES database structure support the identification of incomplete records? The NHANES dietary data is structured to flag recall completeness explicitly. The Total Nutrient Intakes (TOT) files contain records for all participants, including those with incomplete or unreliable recalls (marked with DR1DRSTZ=2 or 5). The Individual Foods (IFF) files, however, only contain records for participants with complete and reliable intakes (DR1DRSTZ=1). This structure allows researchers to easily filter and identify which records are suitable for analysis [44].

2. What is the first variable I should check to assess data quality in NHANES dietary datasets? The primary variable for initial data quality assessment is the Dietary Recall Status code (DR1DRSTZ for Day 1, DR2DRSTZ for Day 2). A value of 1 indicates a reliable and complete recall. Other values signify various states of incompleteness or unreliability, allowing you to quickly filter your dataset to include only valid records [44].

3. A participant recalls eating a food but cannot describe it in detail. How should this be handled? This is a common challenge. The protocol involves:

• Recording Available Information: Document all details the participant can provide (e.g., "a red sauce," "a crunchy snack").
• Using Standardized Probes: Interviewers should use neutral prompts from a standardized method like the AMPM to jog memory without leading the participant.
• Coding to the Best Fit: Use the Food Code Description File (DRXFCD) to find the best-matching code, noting any uncertainty. It is more conservative to use a generic code than to omit the item entirely [44].

4. Our multi-site study is seeing variability in food coding. How can we standardize this? Standardization is critical for multi-site studies [45] [46]. Implement a centralized quality control protocol including:

• Shared Codebook: Maintain a project-specific codebook that maps common ambiguous food responses to specific DRXFDCD codes.
• Regular Coder Calibration: Hold frequent meetings where coders from all sites practice coding the same difficult responses and discuss discrepancies.
• Centralized Audit: Have a lead coder periodically audit a random sample of coded records from each site to ensure consistency [45].

5. Why is the sample size in my Individual Foods File analysis different from the Total Nutrient Intakes File? This is expected. The Individual Foods Files (DR1IFF_E, DR2IFF_E) only include records for participants with complete and reliable intakes. The Total Nutrient Intakes Files (DR1TOT_E, DR2TOT_E) include records for all participants, even those who did not participate in the dietary recall at all or had unreliable recalls. Always confirm your filtering criteria based on the DR1DRSTZ variable [44].

Experimental Protocol: Handling Omitted Foods in 24-Hour Recalls

Objective

To systematically identify, classify, and implement a statistical adjustment protocol for foods omitted during 24-hour dietary recall interviews, thereby improving the accuracy of usual intake estimates.

Methodology

Step 1: Detection and Flagging

• Data Source: Begin with the Individual Foods Files (DR1IFF_E, DR2IFF_E) [44].
• Key Variables: Utilize the DR1DRSTZ/DR2DRSTZ variable to exclude records deemed incomplete from the outset [44].
• Cross-Reference: For studies with additional data (e.g., food frequency questionnaires), cross-reference the food lists to identify items commonly reported in one instrument but absent in the 24-hour recall.

Step 2: Classification of Omissions

Categorize suspected omissions to understand the nature of the missing data:

• Category A (Forgotten Items): Snacks, beverages, condiments.
• Category B (Social Desirability Bias): High-sugar, high-fat, or alcoholic items.
• Category C (Misclassification): Items described too vaguely for accurate coding.

Step 3: Data Imputation and Adjustment Workflow

The following diagram outlines the logical decision process for handling suspected omitted foods.

Step 4: Integration into Usual Intake Modeling

• After applying the above corrections, utilize the day-one dietary sample weight (WTDRD1) to generate population-level estimates that account for the complex survey design of NHANES [44].
• Employ specialized statistical software and methods (e.g., the National Cancer Institute method) to estimate usual intake distributions that account for within-person variation, now using the adjusted intake data.

Item Name	Function in Analysis	Specification / Notes
NHANES Dietary Data Files (IFF & TOT) [44]	Primary source of 24-hour recall data. IFF files contain per-food data; TOT files contain per-person daily totals.	Files are distinguished by day (First vs. Second) and type. Always use the corresponding sample weight (WTDRD1, WTDRD2).
Food Code Description File (DRXFCD) [44]	Master dictionary for converting food codes into meaningful descriptions.	Contains short (DRXFCSD) and long (DRXFDLD) descriptions. Essential for verifying and correcting food item classification.
Dietary Recall Status Code (DR1DRSTZ) [44]	The essential filter for data quality. Identifies complete/reliable recalls for analysis.	Code '1' = Complete/reliable. Code '2' = Not complete/not reliable. Code '4' = Breast-fed infant. Code '5' = Non-response.
Dietary Sample Weights (WTDRD1, WTDRD2) [44]	Enables calculation of population-representative estimates from the sample data.	WTDRD1 is for Day 1 analysis. WTDRD2 is for Day 2 analysis. Must be used for any summary statistics.
Automated Multiple-Pass Method (AMPM) [44]	The validated interview methodology used to collect recalls, minimizing omission.	Understanding this method is crucial for correctly interpreting data structure and potential sources of bias.

Within the framework of research on 24-hour dietary recalls, addressing the problem of omitted foods is paramount for data accuracy. Omitted foods, a form of recall bias, occur when participants fail to report items they consumed, leading to significant underestimation of energy and nutrient intake [10] [15]. This technical support center outlines the technology-driven methodologies and tools designed to mitigate this issue, providing researchers with troubleshooting guides and FAQs to enhance their experimental protocols.

Understanding the Problem of Omitted Foods

The omission of foods is a major source of measurement error in 24-hour dietary recalls. The cognitive process of recalling dietary intake is complex, and items are frequently forgotten [15]. Research has identified that omissions are not random; certain types of foods are more likely to be omitted than others. These are often additions to main dishes or ingredients in complex, multi-component foods [15]. The table below summarizes common omitted food items and their rates of omission from validation studies.

TABLE: Common Omitted Food Items in 24-Hour Recalls

Food Item	Context of Omission	Reported Omission Rate
Tomatoes	Ingredient in salads/sandwiches	42% (ASA24), 26% (AMPM) [15]
Mustard	Condiment	17% (ASA24), 17% (AMPM) [15]
Green/Red Pepper	Ingredient in salads/sandwiches	16% (ASA24), 19% (AMPM) [15]
Cucumber	Ingredient in salads/sandwiches	15% (ASA24), 14% (AMPM) [15]
Cheddar Cheese	Ingredient in salads/sandwiches	14% (ASA24), 18% (AMPM) [15]
Lettuce	Ingredient in salads/sandwiches	12% (ASA24), 17% (AMPM) [15]
Mayonnaise	Condiment	9% (ASA24), 12% (AMPM) [15]
Cooked Vegetables	Side dish or ingredient	Up to 50% of times eaten [43]
Salad Dressings	Addition to foods	Historically high rate of being forgotten [15]

Technological Solutions & Experimental Protocols

To combat omissions, automated multiple-pass methods have been developed. These systems structure the recall interview into several distinct "passes" to systematically jog the participant's memory and standardize data collection [10] [11].

Core Protocol: The Automated Multiple-Pass Method

The following workflow is encoded in tools like ASA24, NDSR, and GloboDiet. The diagram below illustrates the logical sequence of this protocol.

Detailed Methodology for Key Passes:

• Pass 1: The Quick List. The respondent is asked to provide a rapid, uninterrupted list of all foods and beverages consumed in the preceding 24 hours. This captures the most easily remembered items [11].
• Pass 2: Detailed Probing. The interviewer (or software) goes back through the quick list, gathering detailed information for each item. This includes:
  • Time and eating occasion (e.g., breakfast, lunch).
  • Detailed description (e.g., type of bread, brand of cereal).
  • Preparation methods and cooking practices.
  • Additions and condiments (e.g., sugar in coffee, ketchup on fries) [10] [11].
• Pass 3: Forgotten Foods List. The respondent is prompted with a list of food categories commonly forgotten. This is a critical technological feature for directly addressing omissions. Prompts may include categories like "sweets and snacks," "beverages," "fruits," and "vegetables" [47] [15] [11].
• Pass 4: Final Review. A final opportunity is provided for the respondent to remember and report any additional foods not yet mentioned. The interviewer may review the entire day's intake chronologically [11].

The Scientist's Toolkit: Research Reagent Solutions

The following table details key tools and their functions in implementing technology-driven dietary assessment.

TABLE: Essential Tools for Digital Dietary Assessment

Tool Name	Type	Primary Function	Key Feature
ASA24 (Automated Self-Administered 24-hr recall) [47]	Web-based/ Mobile Tool	Self-administered 24-hour recalls and food records.	Automatically codes food intake into nutrient and food group data using a multiple-pass approach.
NDSR (Nutrition Data System for Research) [48] [49]	Software & Service	Interviewer-administered 24-hour recalls and food records.	Provides immediate nutrient calculation and offers a service for outsourced, unannounced telephone recalls.
GloboDiet (formerly EPIC-SOFT) [10] [15]	Software Platform	Standardized, interviewer-administered 24-hour recalls in international settings.	Designed for pan-European and international adaptation, with standardized probing questions.
AMP (Automated Multiple-Pass Method) [10]	Methodological Protocol	A structured interview technique to enhance recall completeness.	The foundational methodology implemented in ASA24, NDSR, and used in NHANES.

Troubleshooting Guides and FAQs

FAQ 1: How can we adapt web-based tools like ASA24 for low-literacy or low-income populations?

• Challenge: The tool may be inappropriate for those with low literacy or limited experience with computers [47].
• Solution: Pilot testing with the target population is essential. Researchers should not assume usability. Consider using interviewer-administered recalls (e.g., via NDSR services) for these subgroups. Some studies have successfully used tablet-based versions in low-income settings with limited connectivity [15].

FAQ 2: Our data shows systematic under-reporting of energy. How can we validate and correct for this?

• Challenge: Under-reporting, especially of energy, is a pervasive systematic error that cannot be fixed by more recalls alone [10].
• Solution: Incorporate objective reference measures into a validation sub-study. The gold standard for energy intake validation is the Doubly Labeled Water (DLW) method to measure energy expenditure [10]. Alternatively, use biomarkers like urinary nitrogen for protein intake or 24-hour urinary sodium/potassium for those nutrients. In controlled settings, same-day weighed food records can also serve as a reference [10].

FAQ 3: What is the optimal number of 24-hour recalls to collect per participant to account for day-to-day variation and random omissions?

• Challenge: A single day of intake is not representative of "usual intake" due to large within-person variation [10].
• Solution: The number of repeats depends on the study objective and the nutrient of interest. For estimating population-level usual intake, collecting at least two non-consecutive 24-hour recalls on a random subset of the population (e.g., 30-40 individuals per life-stage group) is recommended. This allows for statistical adjustment of within-person variance [10].

FAQ 4: How do we handle seasonal variations in food intake in our study design?

• Challenge: Dietary patterns, especially in low-income countries, can fluctuate dramatically with seasons [10].
• Solution: Account for season as a "nuisance effect" in the study design. Administer the survey over a longer period (e.g., a full year) and include randomly selected days representative of all seasons. This ensures the data captures the full range of habitual intake [10].

Tailoring Recall Timeframes and Prompts to Participant Lifestyles

Frequently Asked Questions: Troubleshooting Omitted Foods

Q1: Our data shows high within-person variation, leading to potentially omitted foods. How many 24-hour recalls are needed for a reliable estimate? The number of recalls depends on your study's objective and the nutrient of interest. A single recall is insufficient as it captures only a single day's intake and is subject to high random error. Collecting multiple non-consecutive 24-hour recalls per participant allows for statistical adjustment to estimate "usual intake" and mitigate the effect of day-to-day variation [10]. Evidence from an urban Mexican population showed that using three 24-hour recalls, as opposed to one, significantly improved the estimates of energy and nutrient intakes and resulted in substantial differences in the calculated prevalence of inadequacy [36]. For some nutrients, the variance of the usual intake distribution was smaller with three days of data [36].

Q2: How can we design a 24-hour recall protocol to minimize systematic errors like seasonality or day-of-the-week effects? These "nuisance effects" can be controlled through careful study design [10].

• Day of the Week: Proportionately represent all days of the week in your data collection, including weekends [10].
• Seasonality: Administer the survey over a longer period and include randomly selected days that are representative of all seasons [10].
• Special Days: Avoid conducting recalls on feast days, as dietary practices are often unusual [10].

Q3: What are the best methods to validate our 24-hour recall data and check for systematic underreporting? The most robust method is to use a reference measure that is free from error [10]. Suitable reference measures include:

• Doubly Labeled Water (DLW): To assess energy expenditure and identify underreporting of energy intake [10].
• Urinary Biomarkers: Urinary nitrogen for protein intake, and urinary potassium and sodium for their respective intakes [10]. As a more accessible alternative, you can compare 24-hour recall intakes with same-day weighed food records to identify biases [10].

Q4: How can we adapt recall prompts for participants with low literacy or numeracy? The 24-hour recall method is often chosen for LICs because it can be designed to be culturally sensitive and cognitively undemanding [10]. Using a multiple-pass 24-hour recall software can help minimize forgotten food items. This method involves several steps (passes) designed to guide the participant through the previous day without requiring high cognitive load or numeracy skills [10].

Protocol: Multiple-Pass 24-Hour Recall This method is designed to enhance memory recall and reduce omissions [10].

• Quick List: The respondent freely recalls all foods and beverages consumed in the preceding 24-hour period.
• Forgotten Foods: The interviewer uses specific prompts (e.g., "Any fruits or vegetables? Any snacks?") to probe for commonly omitted items.
• Time and Occasion: The respondent clarifies the time and eating occasion for each food item.
• Detail Cycle: For each food, detailed descriptions are collected, including preparation methods, brand names, and recipes.
• Final Review: A final probe is used to capture any additional items.

Protocol: Validating against Doubly Labeled Water This protocol assesses the accuracy of energy intake reporting [10].

• Administer 24HR: Conduct a 24-hour dietary recall with the participant.
• Dose with DLW: Administer a dose of doubly labeled water (²H₂¹⁸O) to the participant.
• Collect Urine Samples: Obtain urine samples from the participant over a period of 10-14 days.
• Analyze Samples: Use isotope ratio mass spectrometry to analyze the urine samples for the elimination rates of ²H and ¹⁸O.
• Calculate Energy Expenditure: Calculate the carbon dioxide production rate and total energy expenditure.
• Compare Data: Statistically compare the reported energy intake from the 24-hour recall with the measured energy expenditure from DLW to identify under-reporting.

Table 1: Impact of Repeated 24-Hour Recalls on Prevalence of Inadequacy Data from an urban Mexican population shows how increasing from one to three recalls changes prevalence estimates [36].

Nutrient	Age Group	Prevalence of Inadequacy (1 recall)	Prevalence of Inadequacy (3 recalls)
Folate	Preschool Children	30.0%	3.7%
Calcium	Preschool Children	43.0%	4.6%

Table 2: Comparison of Validation Methods for Systematic Error A summary of reference measures used to detect biases like underreporting in 24-hour recalls [10].

Validation Method	Nutrient/Focus	Principle	Key Advantage
Doubly Labeled Water (DLW)	Energy	Compares reported energy intake to measured energy expenditure.	Considered the gold standard for validating energy intake.
Urinary Nitrogen	Protein	Compares reported protein intake to urinary nitrogen excretion.	Objective biomarker for protein intake.
Same-Day Weighed Record	Energy & Nutrients	Compares recall data to a detailed, weighed record of all food consumed on the same day.	Does not require complex laboratory analysis.

The Scientist's Toolkit: Key Research Reagents & Materials

Table 3: Essential Materials for Dietary Recall Validation Studies A list of key reagents and tools used in advanced dietary assessment protocols.

Item	Function in Research
Doubly Labeled Water (²H₂¹⁸O)	A gold-standard reference measure for validating total energy expenditure and, by extension, energy intake reported in dietary recalls [10].
Isotope Ratio Mass Spectrometer	The analytical instrument used to measure the isotopic enrichment of ²H and ¹⁸O in urine samples following DLW administration [10].
Multiple-Pass 24-Hour Recall Software (e.g., GloboDiet)	Computer-assisted interview software that structures the 24-hour recall into multiple passes to enhance completeness and standardize data collection across researchers [10].
Food Composition Database	A detailed nutritional table used to convert reported food consumption data from 24-hour recalls into estimated nutrient intakes [10].

Methodological Workflows

Research Workflow for Usual Intake

Measuring Success: Validation Strategies and Comparative Methodologies

Frequently Asked Questions (FAQs)

General Validation Concepts

Q1: Why is validation against both weighed food records and biomarkers considered a "gold standard" approach?

Validation against weighed food records (WFR) offers a high-quality reference for self-reported intake, while biomarkers provide an objective, non-self-reported measure of consumption. Using both creates a robust framework for identifying and quantifying measurement error. WFR are considered a "reference" method because they record intake as it occurs, reducing recall bias [50]. Biomarkers are "recovery" biomarkers that objectively measure nutrient intake or its metabolic consequences, independent of self-report [51] [52]. This dual approach is powerful because it can reveal different types of error; for instance, a method might show good agreement with WFR but still systematically underestimate true intake, a error that can only be detected with objective biomarkers [53].

Q2: What are the most common biomarkers used for validating energy and nutrient intake?

The table below summarizes key biomarkers used in dietary assessment validation studies.

Table 1: Common Biomarkers for Dietary Validation Studies

Biomarker	Measured In	Reflects Intake of	Key Characteristics
Doubly Labeled Water (DLW)	Urine	Total Energy Expenditure (proxy for Energy Intake) [54]	Considered the gold standard for energy expenditure under energy balance conditions [54].
Urinary Nitrogen	Urine	Protein [54] [52]	A recovery biomarker; excellent for validating protein intake estimates [51].
Urinary Potassium	Urine	Potassium [51] [53]	A recovery biomarker for potassium intake [51].
Urinary Sodium	Urine	Sodium [53]	A recovery biomarker for sodium intake [53].
Plasma Alkylresorcinols (AR)	Blood (Plasma)	Whole grain wheat and rye [55]	A concentration biomarker; specific to whole grains versus refined grains [55].
Serum Carotenoids	Blood (Serum)	Fruits and vegetables [54] [55]	A concentration biomarker; reflects intake of carotenoid-rich produce [55].
Plasma Fatty Acids	Blood (Plasma)	Fat quality & specific fats (e.g., Linoleic acid for margarine/oil; EPA/DHA for seafood) [55]	Pattern of fatty acids reflects overall dietary fat composition and specific fat sources [55].
Flavanols Metabolites (gVLMB, SREMB)	Urine	Flavanols (general and (-)-epicatechin specific) [52]	Used to assess background diet and adherence in nutritional trials [52].

Troubleshooting Validation Experiments

Q3: Our dietary assessment tool shows good correlation with weighed records but consistently shows poor agreement with biomarkers. What could be the cause?

This discrepancy often indicates a systematic bias that affects both your tool and the weighed records. A classic example is energy underreporting, which is common in self-reported methods. Participants may systematically omit foods, underestimate portion sizes, or change their diet during recording for both the tool and the WFR [50]. Biomarkers like doubly labeled water can uncover this systemic underreporting that would be missed when comparing only to another self-report method [50] [52]. To investigate, check if the under-reporting is selective for certain food groups (e.g., snacks, sugary drinks) by using food-specific biomarkers like plasma alkylresorcinols for whole grains or urinary sucrose for total sugar intake [51] [55].

Q4: In a controlled feeding trial, how can I objectively confirm participant adherence to the intervention diet?

Self-reported adherence, such as pill counts or questionnaires, can be unreliable [52]. The solution is to use nutritional biomarkers specific to the intervention. For example:

• In a cocoa flavanol trial, measure urinary flavanol metabolites (gVLMB and SREMB) to confirm that the intervention group has significantly higher levels than the control group [52].
• In a diet rich in specific foods, use a panel of biomarkers: plasma alkylresorcinols for whole grains, serum carotenoids for fruits/vegetables, and plasma EPA/DHA for seafood intake [55]. This biomarker-based approach provides an objective measure of compliance, moving beyond participant self-report [55] [52].

Q5: How many repeated administrations of a 24-hour recall or food record are needed for reliable validation?

A single day of intake is not representative of habitual intake due to large day-to-day variation. The required number of repeats depends on the nutrient and study purpose.

• For estimating a group's usual intake, research suggests at least two non-consecutive 24-hour recalls are needed to allow for correction of within-person variability [51] [50].
• For classifying individuals according to their intake, more repeats are needed. Performance improves incrementally with the mean of more measures [51]. For energy intake, one study calculated that nearly 5 days of food records are needed to estimate a person's true mean intake within 20%, 95% of the time [50].

Experimental Protocols for Key Validation Analyses

Protocol 1: Validating a Digital Dietary Tool Against Biomarkers

This protocol is adapted from large-scale validation studies of tools like the Oxford WebQ and myfood24 [51] [53].

Objective: To assess the validity of a self-administered online 24-hour dietary recall tool by comparing its estimates of nutrient intake against objective biomarker measures.

Workflow Overview: The following diagram illustrates the multi-stage workflow for this validation protocol.

Step-by-Step Methodology:

• Participant Recruitment:

  • Recruit metabolically stable adults (e.g., no significant weight change in past 3 months) [53] [54].
  • Target a sample size of at least 100 participants to achieve adequate power for correlation analyses [54].
  • Obtain ethical approval and informed consent.

• Study Design & Data Collection:

  • Use a repeated-measures design. Collect data at 3 non-consecutive time points, separated by approximately 2 weeks, to approximate longer-term habitual intake [51] [53].
  • Biomarker Collection: At each time point, collect spot or 24-hour urine samples for biomarkers of protein (nitrogen), potassium, and total sugars. Optionally, use doubly labeled water to estimate total energy expenditure [51] [53] [54].
  • Dietary Assessment: Within a few days of each biomarker collection, participants complete the online 24-hour dietary recall tool (e.g., Oxford WebQ, myfood24, ASA24) for the same period the biomarker reflects [51] [53]. Randomize the order of administration if comparing multiple tools.

• Data Analysis:

  • Calculate nutrient intakes from the dietary tool.
  • Use a measurement error model (e.g., method of triads) to compare the dietary tool, a traditional interviewer-based recall, and the biomarker data simultaneously [51].
  • Calculate attenuation factors (how much a diet-disease odds ratio is weakened by measurement error) and correlation coefficients (how well the tool ranks individuals by intake) between the tool and the biomarkers [51] [53]. Expect correlation coefficients in the range of 0.3-0.5 for key nutrients against recovery biomarkers [51] [53].

Protocol 2: Using a Biomarker Panel to Verify Compliance in an Intervention Trial

This protocol is based on the approach used in the ADIRA trial and research on flavanol biomarkers [55] [52].

Objective: To use a suite of objective biomarkers to verify participant adherence to specific dietary instructions in a controlled intervention trial.

Step-by-Step Methodology:

• Define Biomarker Targets: Align biomarkers with key intervention components.

  • Example: For an "anti-inflammatory" diet intervention rich in whole grains, fruits, vegetables, and seafood, the target biomarkers would be:
    • Plasma Alkylresorcinols (AR): For whole grain wheat/rye intake.
    • Serum Carotenoids: For fruit and vegetable intake.
    • Plasma Linoleic Acid (LA) & Alpha-Linolenic Acid (ALA): For use of specific margarines/oils.
    • Plasma EPA & DHA: For seafood intake [55].

• Sample Collection:

  • Collect fasting blood samples at baseline and at the end of each intervention period.
  • Process samples (e.g., centrifuge to isolate plasma/serum) and store at -80°C until analysis.

• Laboratory Analysis:

  • Analyze biomarkers using established techniques, typically liquid chromatography-mass spectrometry (LC-MS) for compounds like alkylresorcinols and carotenoids, and gas chromatography for fatty acids [55] [52].

• Data Interpretation:

  • Compare post-intervention biomarker levels between the intervention and control groups using paired t-tests or similar statistics.
  • Compliance is strongly supported if the intervention group shows significantly higher levels of AR, LA, EPA, and DHA after the intervention diet period compared to the control period, confirming increased intake of the target foods [55].

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials and Tools for Dietary Validation Studies

Tool / Reagent	Function / Application	Example Products / Mentions
Automated 24-hr Recall Tools	Self-administered, low-cost dietary assessment for large-scale studies; reduces interviewer burden.	Oxford WebQ [51], ASA24 [47], myfood24 [53], INTAKE24 [53]
Recovery Biomarkers	Objective validation of energy and specific nutrient intake, independent of self-report.	Doubly Labeled Water (Energy) [54], Urinary Nitrogen (Protein) [51] [54], Urinary Potassium [51]
Concentration Biomarkers	Validate intake of specific foods or food groups; reflect medium-term intake.	Plasma Alkylresorcinols (Whole Grains) [55], Serum Carotenoids (Fruits & Vegetables) [55], Plasma Fatty Acids (Fat Quality & Seafood) [55]
Controlled Feeding Diets	Provides known, fixed intake for method validation or biomarker discovery in a highly controlled setting.	Dietary Biomarkers Development Consortium (DBDC) feeding studies [56]
Metabolomics Platforms	Discovery and analysis of small-molecule metabolites (metabolomics) for novel biomarker identification.	Liquid Chromatography-Mass Spectrometry (LC-MS) [56] [52]
Accelerometers	Provide an objective measure of physical activity and energy expenditure to help identify misreporting of energy intake.	Piezo-electric uniaxial accelerometers (e.g., CSA model) [50]

Statistical Approaches for Assessing and Correcting for Measurement Error

Troubleshooting Guides

Troubleshooting Guide: Identifying Measurement Error

Problem Symptom	Potential Cause	Diagnostic Check	Solution Pathways
Attenuated effect estimates (bias towards null)	Classical measurement error in exposure [57]	Compare effect size from naive model to estimates from validation studies; assess attenuation factor.	Apply Regression Calibration (RC) or Simulation-Extrapolation (SIMEX) [58] [59] [60].
Bias in any direction (away from null)	Differential measurement error; error correlated with outcome [58]	Check if error structure differs between cases/controls or exposed/unexposed.	Use Multiple Imputation for Measurement Error (MIME) or Moment Reconstruction (MR) [59] [57].
Confounder measurement error leading to residual confounding	Error in a covariate [57]	Evaluate if adjusting for the mismeasured confounder changes the exposure effect estimate unexpectedly.	Extend regression calibration to multi-variable setting; correct for error in all mismeasured variables [59].
Dietary patterns are unstable or hard to interpret	Systematic or random errors in food group intake data [61]	Conduct sensitivity analysis by adding simulated noise to food groups and re-deriving patterns.	Use dietary patterns derived by Principal Component Factor Analysis (PCFA), which are more robust to measurement error than K-means Cluster Analysis (KCA) [61].

Troubleshooting Guide: Correcting Measurement Error

Problem	Required Data	Method & Experimental Protocol	Key Assumptions & Limitations
Classical error in a continuous exposure (e.g., nutrient intake) [59]	A main study with a mismeasured exposure (W~i1~) and a validation subsample with replicates (W~i2~) or a gold standard (X~i~).	Protocol for Regression Calibration (RC): 1. In the validation sample, fit a model: E(X~i~ | W~i1~, W~i2~, Z~i~). 2. Use this model to predict calibrated exposure (X̂~i~) for everyone in the main study. 3. Fit the outcome model using X̂~i~ instead of W~i1~ [59] [60].	Assumes non-differential error. Is a gold standard available? If using replicates, assumes error is random [57].
Differential error or complex error structures not meeting classical assumptions [58]	Internal validation data where the true exposure (or a superior measure) is observed for a subset.	Protocol for Multiple Imputation for Measurement Error (MIME): 1. In the validation sample, model the relationship between true exposure (X) and mismeasured exposure (W). 2. For each individual in the main study, create multiple imputed values for X based on their W and the model from step 1. 3. Analyze each imputed dataset and combine the results [58] [59].	Computationally intensive. Requires correct specification of the measurement error model.
Systematic error in 24-hour recalls (e.g., under-reporting) [10] [62]	A reference instrument such as doubly labeled water for energy or 24-hour urinary excretion for sodium/potassium.	Protocol for Quantitative Bias Analysis: 1. In a validation study, measure intake using both the 24HR and the reference instrument. 2. Quantify the mean bias (e.g., 24HR minus reference). 3. Adjust the intake values in the main study by subtracting the mean bias [10] [62].	Assumes the bias is constant across individuals. Requires a high-quality, objective reference measure.
Error in a time-to-event outcome (e.g., real-world progression-free survival) [63]	An internal validation sample where both the "true" (gold standard) and mismeasured (real-world) event times are available.	Protocol for Survival Regression Calibration (SRC): 1. In the validation sample, fit separate Weibull regression models for the true and mismeasured times. 2. Estimate the bias in the scale and shape parameters of the Weibull model. 3. Calibrate the mismeasured event times in the full study based on the estimated parameter bias [63].	More suitable for time-to-event data than standard RC, which can produce negative event times. Relies on the Weibull model assumption.

Frequently Asked Questions (FAQs)

Q1: What is the most critical first step in dealing with measurement error? The most critical first step is to formally consider the measurement error mechanism using a causal framework, such as directed acyclic graphs (DAGs). This helps determine if the error is differential or non-differential, classical or Berkson, and independent or dependent. This diagnosis is essential for selecting the correct correction method [58].

Q2: Why is it insufficient to rely on a tool's reliability (repeatability) to assume it is valid? High reliability means a tool gives consistent results, but it does not guarantee it measures the true underlying construct. A measure can be highly repeatable yet systematically biased. Validity pertains to whether the instrument measures what it purports to measure, which is a distinct property from reliability [58].

Q3: We have no validation data. Should we just ignore measurement error? No. A lack of validation data is not an excuse to ignore the problem. You can conduct sensitivity analyses to evaluate the potential impact of measurement error. This involves modeling how your results would change under different plausible scenarios of error magnitude and structure [58] [59].

Q4: In the context of 24-hour dietary recalls (24HR), what are the main strategies to mitigate random within-person variation? The primary strategy is to collect multiple 24HR recalls on non-consecutive days for each participant. The number of repeats needed depends on the study objective and the nutrient of interest. For estimating usual intake in a population, repeats on a representative subset of 30-40 individuals can be sufficient to model and adjust for within-person variation [10].

Q5: How does measurement error specifically affect dietary pattern analysis? Simulation studies show that both systematic and random measurement errors can distort derived dietary patterns, making them less consistent with true patterns. Furthermore, measurement error almost always attenuates (weakens) the estimated association between a dietary pattern and a health outcome, potentially masking real effects [61].

Q6: What is a practical method to correct for measurement error when I have two repeated measures of my exposure? Regression Calibration (RC) is a widely accessible and commonly used method for this situation. It uses the repeated measures to estimate the true exposure and then uses this calibrated value in the outcome model. It performs well under classical measurement error assumptions [59] [60] [57].

Data Presentation

Table 1. Quantitative Impact of Measurement Error in 24-Hour Dietary Recalls (24HR) vs. Urinary Biomarkers

This table presents validation data from NHANES 2014, comparing sodium and potassium intake from a 24HR to the objective gold standard of 24-hour urinary excretion (24HUE) [62].

Nutrient	Mean Bias (24HR - 24HUE)	Correlation with Gold Standard (Single 24HR)	Attenuation Factor (Single 24HR)
Sodium	-452 mg (CI: -646, -259)	0.27 (CI: 0.16, 0.37)	0.16 (CI: 0.09, 0.21)
Potassium	-315 mg (CI: -450, -179)	0.35 (CI: 0.26, 0.55)	0.25 (CI: 0.16, 0.36)
Sodium-to-Potassium Ratio	-0.04 (CI: -0.15, 0.07)	0.27 (CI: 0.13, 0.32)	0.20 (CI: 0.10, 0.25)

Interpretation: The 24HR significantly underestimates mean intake of sodium and potassium (negative bias). The low attenuation factors indicate that a study using a single 24HR to measure sodium intake would observe only about 16% of the true strength of its association with a health outcome, a severe bias towards the null.

Table 2. Comparison of Key Measurement Error Correction Methods

This table summarizes the core features of several correction methods discussed in the technical literature [58] [59] [57].

Method	Best for Error Type	Data Requirements	Key Advantage	Key Limitation
Regression Calibration (RC)	Classical, non-differential	Replicates or internal/external validation sample	Simple intuition, widely implemented in software [60].	Biased under differential error [59].
Simulation-Extrapolation (SIMEX)	Classical, non-differential	Replicates or known error variance	Intuitive graphical presentation; does not require a model for the true exposure.	Computationally intensive; requires correct extrapolation function [58] [64].
Multiple Imputation for Measurement Error (MIME)	Complex, including differential error	Internal validation sample	Flexible; can handle differential and dependent error [58] [59].	Computationally intensive; requires specifying correct imputation model.
Moment Reconstruction (MR)	Differential error	Internal validation sample	Designed specifically for differential error; can be used with standard software after reconstruction [59] [57].	Less established than RC or SIMEX; may be less efficient.

Experimental Protocols

Detailed Protocol 1: Implementing Regression Calibration with Replicate Measurements

This protocol is adapted for a setting where the true long-term average exposure (X) is unobserved, but two replicate measurements (W~1~, W~2~) are available for a subset, assuming classical measurement error [59] [60].

1. Study Design and Data Collection:

• Main Study: Collect data on the outcome (Y), the mismeasured exposure (W~1~), and accurately measured covariates (Z) for all participants.
• Reliability Substudy: Select a random subset of the main study participants. From each, collect a second, independent measurement of the exposure (W~2~). The error in W~2~ should have the same distribution as the error in W~1~.

2. Calibration Model Estimation: In the reliability substudy, fit the following linear model to estimate the relationship between the replicates: E(W~i2~ | W~i1~, Z~i~) = α₀ + α₁W~i1~ + αᵗ₂Z~i~ This model leverages the fact that, under classical assumptions, the best linear predictor of one replicate given the other and covariates provides an unbiased estimate of the true exposure.

3. Prediction of Calibrated Exposure: Using the coefficients (α̂₀, α̂₁, α̂₂) from the calibration model, compute a calibrated exposure value for every participant in the main study: X̂~i~ = α̂₀ + α̂₁W~i1~ + αᵗ₂Z~i~

4. Outcome Model Analysis: Fit the final outcome model of interest (e.g., logistic regression for a binary disease outcome) using the calibrated exposure X̂~i~ in place of the naive measurement W~i1~. logit(P(Y~i~=1)) = β₀ + βₓX̂~i~ + βᵗ₂Z~i~ The coefficient β̂ₓ is the measurement error-corrected estimate of the exposure-disease association.

Detailed Protocol 2: Validation of 24HR Using Biomarkers

This protocol outlines how to use recovery biomarkers, like doubly labeled water (for energy) or 24-hour urinary excretion (for sodium/potassium), to quantify systematic error in 24HRs [10] [62].

1. Validation Study Recruitment: Recruit a representative subsample from your cohort or target population. The sample size should provide sufficient power to detect meaningful biases.

2. Concurrent Data Collection:

• Administer the 24HR interview to the participant.
• Simultaneously, collect the biomarker measurement over the exact same time period:
  • For energy intake: Use the doubly labeled water method to measure total energy expenditure.
  • For sodium/potassium intake: Collect a complete 24-hour urine sample. Ensure completeness using urinary markers like para-aminobenzoic acid (PABA).

3. Data Analysis and Bias Quantification:

• For each participant (i), calculate the difference: Difference~i~ = 24HR~i~ - Biomarker~i~.
• Calculate the mean bias for the group as the average of these differences. A significant negative mean bias indicates under-reporting.
• Calculate the correlation between the 24HR and the biomarker to assess the instrument's validity.
• Estimate the attenuation factor (λ), which describes how much the 24HR dilutes a true association, using specialized measurement error models (e.g., the Kipnis model) [62].

4. Application to Main Study: The estimated mean bias can be used to adjust intake values in the main study upward. The attenuation factor can be used to de-attenuate (strengthen) observed effect estimates in diet-disease analyses.

Diagrams and Workflows

Measurement Error Correction Decision Pathway

Relationship Between True and Mismeasured Intake in 24HR

The Scientist's Toolkit: Research Reagent Solutions

Item Category	Specific Example	Function in Measurement Error Research
Gold Standard Reference Instrument	Doubly Labeled Water (DLW)	An objective recovery biomarker used to validate self-reported energy intake by measuring total energy expenditure [10] [57].
Gold Standard Reference Instrument	24-Hour Urinary Collection	Used as an objective biomarker to validate intake of sodium, potassium, and protein (via urinary nitrogen) [62] [57].
Alloyed Gold Standard Instrument	Multiple-Pass 24-Hour Recall	A structured interview protocol (e.g., USDA method, GloboDiet) designed to minimize memory lapse and improve portion size estimation, often used as a superior reference against FFQs [10] [57].
Alloyed Gold Standard Instrument	Weighed Food Record	A prospective method where participants weigh and record all consumed foods, considered more accurate than FFQs and often used as a reference in calibration studies [10] [57].
Statistical Software Package	SAS, R, Stata	Platforms with dedicated macros and packages (e.g., simex in R, rc_regress in Stata) for implementing correction methods like RC and SIMEX [60] [64].
Measurement Error Model	Kipnis Model	A joint mixed-effects model used specifically in nutritional epidemiology to estimate attenuation factors and correlations between FFQs/24HRs and true intake, accounting for within-person variation [62].

Comparative Analysis of Omission Rates Across Different Recall Methodologies

FAQ: Understanding and Troubleshooting Omission Rates

Q1: What are the primary factors that contribute to food item omissions in 24-hour dietary recalls?

Research indicates that omissions are one of the most frequently reported contributors to error in self-reported dietary intake [65]. The major factors include:

• Food Type: Certain food groups are more susceptible to being forgotten. Beverages are omitted less frequently, while vegetables and condiments show the highest and most variable omission rates [65].
• Recall Methodology: The design of the recall (e.g., open vs. list-based, interviewer-led vs. self-administered) significantly influences what is reported [66].
• Social Desirability Bias: Caregivers or participants may underreport consumption of foods perceived as unhealthy, such as salty or fried snacks [66].
• Memory and Cognitive Effort: The ability to remember foods is enhanced when respondents perform effortful memory tasks at the time of eating and when the recall method provides support for retrieval [67].

Q2: How does an open 24-hour recall differ from a list-based recall, and how does this affect omissions?

The choice between these methods represents a key trade-off, as they can yield different prevalence estimates for the same population.

• Open 24HR: The interviewer uses standard probing questions to help the respondent recall all foods consumed. Responsibility for correctly classifying foods into groups lies with the trained enumerator. It may be more susceptible to participants forgetting items, especially foods consumed in small amounts or outside of meals [66].
• List-Based 24HR: The interviewer reads a list of specific food groups, and the respondent indicates what was consumed. This places the classification burden on the respondent but can serve as a prompt for memory. Studies have shown that the list-based method can detect a significantly higher percentage of children consuming sweet foods (61.6%) compared to the open method (43.8%) [66].

Q3: What quality control procedures can be implemented to minimize omissions during data collection?

Implementing rigorous Quality Control (QC) procedures is essential for preventing, detecting, and correcting errors. Proven methods include:

• Interviewer Training and Certification: Intensive training for dietary interviewers is critical [32].
• Structured Interview Protocols: Using a standardized multiple-pass method with specific steps (quick list, forgotten foods list, time and place, detail probing, final review) helps systematically prompt memory [68] [32].
• Real-time Interview Monitoring: Randomly evaluating taped interviews using a structured checklist (e.g., probing technique, use of memory aids, review process) ensures ongoing interviewer quality [32].

Quantitative Data: Omission Rates Across Food Groups and Methods

The following tables synthesize quantitative findings on omission rates from controlled studies and systematic reviews.

Table 1: Omission Rates by Food Group from a Systematic Review [65]

Food Group	Range of Omission Rates	Notes
Beverages	0% - 32%	Less frequently omitted than other food groups.
Vegetables	2% - 85%	Shows one of the highest and most variable omission rates.
Condiments	1% - 80%	Highly susceptible to being forgotten.

Source: A systematic review of 29 studies corresponding to 2964 participants across 15 countries, which examined contributors to misestimation based on short-term self-report dietary assessment instruments.

Table 2: Comparative Omission/Detection Rates by Recall Methodology

Study & Methodology	Key Finding on Detection/Omission	Statistical Significance
Cambodia (Peri-urban); IYC [66]	The list-based 24HR detected a higher prevalence of sweet food consumption (61.6%) compared to the open 24HR (43.8%).	P = 0.012
Fully Controlled Feeding Study; R24W (Web-Based) [69]	Participants reported 89.3% of food items they received. The most frequently omitted categories were vegetables in recipes (40.0%) and side vegetables (20.0%).	Not Provided

Experimental Protocols for Key Cited Studies

Protocol 1: Comparison of Open vs. List-Based 24HR in Cambodia [66]

• Objective: To compare the estimated consumption of unhealthy foods using open versus list-based 24-hour dietary recalls among young children and explore the effect of social desirability bias.
• Design: Secondary analysis of a longitudinal cohort study.
• Population: 567 children aged 10–13.9 months at baseline in a rural/peri-urban district of Cambodia.
• Procedure:
  • For five months, data were collected monthly via an open 24HR. Interviewers used probing to facilitate recall of all items.
  • At the 6th month, half the children were randomly assigned to also receive a list-based 24HR, where caregivers were directly asked about consumption of sentinel sweet and salty/fried foods.
  • A 13-question social desirability scale was administered to caregivers at the final timepoint.
• Analysis: Compared prevalence estimates between the two methods and explored the relationship with social desirability scores.

Protocol 2: Validation of a Web-Based 24HR (R24W) Using Controlled Feeding [69]

• Objective: To validate the R24W by comparing self-reported intake to actual known intake in a controlled setting.
• Design: Validation study within fully controlled feeding studies.
• Population: 62 adults enrolled in metabolic studies where all meals were provided.
• Procedure:
  • Participants received all meals prepared by the research team, with the exact type and weight of each food item recorded.
  • Participants completed the self-administered R24W twice on different days while still in the controlled feeding phase.
  • The R24W used a meal-based approach, memory cues, and portion size images.
• Analysis:
  • Calculated the proportion of adequately reported food items.
  • Assessed correlation and agreement between offered and reported portion sizes using correlation coefficients and Bland-Altman plots.

Visual Workflow: The Multiple-Pass Method for Minimizing Omissions

The following diagram illustrates the Automated Multiple-Pass Method (AMPM) used in systems like ASA24 and adapted in other tools. This structured workflow is designed specifically to mitigate memory lapse and reduce omission rates [68].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools and Methods for Dietary Recall Research

Item / Solution	Function in Dietary Assessment	Example / Note
Standardized 24HR Protocol	Provides a consistent framework for data collection to minimize random error and improve comparability.	The Automated Multiple-Pass Method (AMPM) is a widely used standard [10] [68].
Food Composition Database	Converts reported food consumption into estimated nutrient intakes.	The USDA Food and Nutrient Database for Dietary Studies (FNDDS) is used in the U.S. [70].
Portion Size Estimation Aids	Helps respondents visualize and report the amount of food consumed more accurately.	Food models, photographs, and standard household measures can improve accuracy [32] [69].
Social Desirability Scale	A questionnaire module used to quantify and control for the bias of respondents reporting socially desirable answers.	Adapted short forms of the Marlow-Crowne scale can be used [66].
Quality Control Checklist	A tool for monitoring interviewer performance to ensure protocol adherence and data quality.	Can include criteria on probing, objectivity, use of memory aids, and review [32].

Emerging Biomarkers and 'Omics Technologies for Objective Intake Assessment

Frequently Asked Questions (FAQs)

• FAQ 1: Why are self-reported dietary methods like 24-hour recalls insufficient on their own? Self-reported methods are subject to significant measurement errors, including the omission of foods (forgetfulness), misestimation of portion sizes, and systematic underreporting of intake, particularly for foods with high social desirability bias. These limitations can obscure true diet-health relationships [10] [71]. Biomarkers provide an objective, complementary measure to mitigate these biases.

• FAQ 2: What is the difference between a biomarker of intake and a biomarker of effect? A biomarker of intake (or exposure) indicates the consumption of a specific food or nutrient (e.g., alkylresorcinols for whole-grain intake). A biomarker of effect provides information on the biological response or physiological state resulting from dietary intake (e.g., homocysteine levels for folate status) [71].

• FAQ 3: My metabolomics data is complex. How can I identify true dietary biomarkers from background noise? Leveraging multi-omics approaches is key. Correlating metabolomics data with genomic, proteomic, and clinical data can help pinpoint specific signals. Using controlled feeding studies is the gold standard for discovery, as it provides a known intake level against which to compare biomarker levels [72]. Advanced AI and machine learning tools are also essential for identifying hidden patterns in these complex datasets [73] [74].

• FAQ 4: What are the biggest challenges in validating a new dietary biomarker? Key challenges include a lack of standardized analytical protocols, the need for comprehensive food composition databases, limited access to chemical standards for a broad range of food constituents, and the requirement for robust statistical procedures to confirm the biomarker's sensitivity and specificity in diverse populations [72].

• FAQ 5: How can multi-omics approaches improve dietary biomarker discovery? Multi-omics integrates data from genomics, transcriptomics, proteomics, and metabolomics to provide a systems-level view of how diet influences biology. This integration helps move beyond single biomarkers to identify biomarker panels or signatures that more accurately reflect the intake of complex dietary patterns and their subsequent metabolic effects [74] [71].

Troubleshooting Guides

Guide 1: Addressing Low Biomarker Sensitivity and Specificity

Problem	Possible Cause	Solution
Low Sensitivity (fails to detect true consumers)	Rapid metabolism/short half-life of the biomarker; low bioavailability of the food component.	Test in controlled feeding studies (CFS) to confirm kinetics. Explore timed sample collection or measure a stable metabolite [72].
Low Specificity (falsely identifies non-consumers)	The biomarker is present in multiple foods or is influenced by non-dietary factors (e.g., gut microbiome, host metabolism).	Use multi-analyte panels instead of single biomarkers. Employ network integration to map biomarkers onto shared biochemical pathways for better mechanistic understanding [74].
High Inter-individual Variability	Genetic polymorphisms (e.g., in taste receptors or metabolizing enzymes), differences in gut microbiota composition.	Collect genomic and microbiome data alongside the biomarker measurement to stratify participants and account for this variability [71].

Guide 2: Managing Complex Multi-Omics Data

Problem	Possible Cause	Solution
Data Harmonization Issues	Data from multiple cohorts or omics layers have different formats, scales, and biological contexts.	Implement data harmonization techniques and advanced computational methods to unify disparate datasets into a cohesive dataset for higher-level analysis [74].
Inability to Correlate Data	Analyzing omics datasets individually (in silos) and only correlating results afterward.	Adopt an integrated multi-omics approach where data signals from each omics layer are combined prior to processing. This maximizes information content and statistical power [74].
Lack of Actionable Insights	The analytical pipeline is designed for a single data type and cannot handle multi-modal data.	Utilize purpose-built analysis tools and AI specifically designed to ingest, interrogate, and integrate a variety of omics data types simultaneously [74].

Experimental Protocols for Key Methodologies

Protocol 1: Discovery of Novel Dietary Biomarkers Using Controlled Feeding and Metabolomics

This protocol outlines a robust methodology for identifying and validating biomarkers of food intake, as recommended by an NIH workshop on dietary biomarkers [72].

1. Study Design:

• Population: Recruit a cohort of participants representative of the target population. Account for factors like age, sex, genetics, and gut microbiome.
• Intervention: Implement a controlled feeding study (CFS). Participants consume a defined diet, first a run-in period without the food of interest, followed by a period with the food of interest introduced.
• Control: The study should include a control group or a control period to account for background metabolic noise.

2. Sample Collection:

• Collect bio-samples (e.g., blood, urine, feces) at baseline and at regular intervals during the feeding intervention.
• Immediately process samples (e.g., centrifugation for plasma/serum) and store at -80°C to prevent metabolite degradation.

3. Metabolomic Analysis:

• Employ high-throughput mass spectrometry (MS) for broad, untargeted metabolomic profiling.
• Use complementary techniques like liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) to cover a wide range of metabolites.
• Incorporate stable isotope-labeled compounds from the food of interest, if available, to track the specific metabolic fate of food constituents.

4. Data Processing and Biomarker Identification:

• Process raw MS data using bioinformatic tools for peak picking, alignment, and normalization.
• Use multivariate statistical analysis (e.g., PCA, OPLS-DA) to identify metabolites that significantly differ between the control and intervention phases.
• Confirm the identity of candidate biomarkers by comparing their MS spectra and retention times with authentic chemical standards.

5. Validation:

• Validate the candidate biomarker in a free-living population using self-reported dietary assessment (e.g., 24HR) and confirm the correlation between reported intake and biomarker concentration.
• Assess the biomarker's sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve.

Protocol 2: Validating Biomarker Panels for Dietary Patterns

This protocol uses a multi-omics approach to move beyond single foods to assess overall dietary patterns [71].

1. Cohort Selection:

• Utilize a large, well-characterized prospective cohort study where dietary data (multiple 24HRs or food frequency questionnaires) and biospecimens have been collected.

2. Multi-Omics Profiling:

• Perform untargeted metabolomics on baseline plasma or serum samples.
• Integrate with other omics data if available (e.g., proteomics, microbiome data) to build a more comprehensive model.

3. Statistical Integration and Machine Learning:

• Use dietary data to classify participants into distinct dietary patterns (e.g., "Western," "Mediterranean").
• Apply machine learning algorithms (e.g., random forest, neural networks) to the multi-omics data to identify a panel of biomarkers that best predicts the dietary pattern classification.
• Use network integration to map these biomarkers onto shared biochemical networks to improve mechanistic understanding [74].

4. Validation and Replication:

• Split the cohort into discovery and validation sets to test the predictive accuracy of the biomarker panel.
• Replicate the findings in an independent cohort to ensure generalizability.

Signaling Pathways and Workflows

Multi-Omics Integration in Nutrition Research

The Scientist's Toolkit: Research Reagent Solutions

The following table details key reagents, technologies, and platforms essential for research in dietary biomarkers and omics technologies, based on current trends and innovations in the field [73] [74] [75].

Item Name	Type	Function/Benefit in Dietary Biomarker Research
High-Throughput Mass Spectrometry	Analytical Instrument	Enables broad, untargeted metabolomic profiling for discovery of novel biomarkers in bio-fluids; high sensitivity and resolution [72] [71].
Next-Generation Sequencing (NGS)	Technology Platform	Provides comprehensive genomic, transcriptomic, and epigenomic data to understand genetic influences on dietary response and biomarker metabolism [73] [74].
Automated Multiple-Pass 24HR	Software/Interview Method	Standardizes dietary intake interviews to improve completeness and reduce omission of foods, providing higher-quality data for biomarker validation [39] [10].
Liquid Biopsy Assays	Diagnostic Tool	Allows non-invasive collection of biomarkers from blood (e.g., ctDNA, proteins, metabolites); emerging for nutrition (e.g., analyzing cfDNA/RNA) [73] [74].
Stable Isotope-Labeled Compounds	Research Reagent	Used as internal standards in MS for precise quantification and to track the metabolic fate of specific nutrients in controlled studies [72].
AI/Machine Learning Platforms	Software/Bioinformatics	Essential for integrating and analyzing complex multi-omics datasets to identify subtle biomarker patterns and build predictive models of intake [73] [74] [75].
Single-Cell & Spatial Omics	Technology Platform	Reveals cellular heterogeneity and tissue context of dietary responses, moving beyond bulk tissue analysis for greater biological resolution [75] [76].

Conclusion

Addressing food omissions in 24-hour dietary recalls is not merely a methodological refinement but a fundamental requirement for generating robust evidence in biomedical and clinical research. A multi-faceted approach—combining a deep understanding of cognitive psychology, the rigorous application of structured methods like the AMPM, strategic integration of technology, and comprehensive staff training—is essential to mitigate recall bias. The future of dietary assessment lies in the continued development and validation of hybrid tools that leverage digital imagery, artificial intelligence, and objective biomarkers to cross-validate self-reported data. For researchers in drug development and public health, prioritizing these strategies will enhance the accuracy of dietary exposure measurement, leading to more reliable evaluations of diet-disease relationships and more effective, evidence-based nutritional interventions. Future efforts must focus on creating adaptive, personalized assessment tools that are accessible and valid across diverse global populations.

References

• 1. The Seven Sins of Memory: An Update - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8285452/]
• 2. Memory error [https://en.wikipedia.org/wiki/Memory_error]
• 3. Overview of Dietary Assessment Methods for Measuring ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8338737/]
• 4. Measurement Errors in Dietary Assessment Using Self- ... [https://www.sciencedirect.com/science/article/pii/S2161831322008274]
• 5. The association of cognitive task scores with energy intake ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12169953/]
• 6. Recall Strategies Used by Respondents to Complete a ... [https://www.sciencedirect.com/science/article/abs/pii/S0002822305021036]
• 7. CONSIDERATIONS IN SELECTION OF DIET ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5800305/]
• 8. The Use of Pictorial Recall Aids Modifies Dietary ... [https://www.sciencedirect.com/science/article/pii/S2475299124023862]
• 9. Comparison of self-reported dietary intakes from the ... [https://www.sciencedirect.com/science/article/pii/S0002916522027459]
• 10. Measurement Errors in Dietary Assessment Using Self- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5683000/]
• 11. Multiple Interviewer Administered 24hr Recalls [https://cancercontrol.cancer.gov/brp/research/group-evaluated-measures/adopt/24hour-recalls]
• 12. Food Frequency Questionnaire versus 24-Hour Diet Recalls [https://pmc.ncbi.nlm.nih.gov/articles/PMC4581885/]
• 13. Mitigating underreported error in food frequency questionnaire ... [https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02262-9]
• 14. Validity of Dietary Assessment Methods When Compared ... [https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2019.00850/full]
• 15. Measurement Error in Dietary assessment [https://nutritionalassessment.org/errors/]
• 16. Handling random errors and biases in methods used for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4211566/]
• 17. . Random | Definition & Examples vs Systematic Error [https://www.scribbr.com/methodology/random-vs-systematic-error/]
• 18. - Statistics By Jim Random Error vs Systematic Error [https://statisticsbyjim.com/basics/random-error-vs-systematic-error/]
• 19. A Systematic Review Examining Contributors to Misestimation ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9776649/]
• 20. Food Reporting Patterns in the USDA Automated Multiple- ... [https://www.sciencedirect.com/science/article/pii/S2211601X13000230]
• 21. AMPM - USDA Automated Multiple-Pass Method [https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/ampm-usda-automated-multiple-pass-method/]
• 22. Improving dietary data collection tools for better nutritional ... [https://www.sciencedirect.com/science/article/pii/S2666990022000180]
• 23. Validation of improved 24-hour dietary recall using a ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8283912/]
• 24. Development and evaluation of image-series for portion size ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8057404/]
• 25. ASA24® Portion Size Image Database | EGRP/DCCPS/NCI/NIH [https://epi.grants.cancer.gov/asa24/resources/portionsize.html]
• 26. Visual contrast enhances food and liquid intake in ... - PubMed [https://pubmed.ncbi.nlm.nih.gov/15297089/]
• 27. Visual contrast enhances food and liquid intake in ... [https://www.sciencedirect.com/science/article/abs/pii/S0261561403002103]
• 28. How to fix an Android camera that's not working [https://www.asurion.com/connect/tech-tips/android-camera-not-working/]
• 29. Troubleshooting Camera Problems with a Smartphone or ... [https://digimob.com.au/troubleshooting-camera-problems-with-a-smartphone-or-tablet-a-comprehensive-guide/]
• 30. Common Camera Problems: How to Troubleshoot Your ... [https://www.adorama.com/alc/troubleshooting-your-digital-point-and-shoot-camera/]
• 31. Accuracy and Cost-effectiveness of Technology-Assisted ... [https://www.researchprotocols.org/2021/12/e32891/]
• 32. Attainment of precision in implementation of 24 h dietary recalls [https://pmc.ncbi.nlm.nih.gov/articles/PMC6660155/]
• 33. Predicting Perceived Problems in Self-Administered 24- ... [https://www.mdpi.com/2072-6643/14/20/4281]
• 34. Dietary assessment in minority ethnic groups - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC5410991/]
• 35. ethnic-specific questionnaires to assess the diet of a ... [https://pubmed.ncbi.nlm.nih.gov/25226823/]
• 36. Three 24-Hour Recalls in Comparison with One Improve ... [https://www.sciencedirect.com/science/article/pii/S0022316623006065]
• 37. Cultural and Linguistic Adaptation of a Healthy Diet Text ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5555404/]
• 38. Dietary Assessment Methods: What is a 24 hour recall? [https://www.myfood24.org/blog/dietary-assessment-methods-what-is-a-24hr-recall/]
• 39. 24-hour Dietary Recall (24HR) At a Glance [https://dietassessmentprimer.cancer.gov/profiles/recall/]
• 40. Interviewer Training : a Starter Guide [https://www.noota.io/en/interviewer-training-guide]
• 41. 5 Steps to Effective Interviewer Training [https://www.evidenced.app/blog/5-steps-to-effective-interviewer-training]
• 42. Effective Probing Techniques | CodeSignal Learn [https://codesignal.com/learn/courses/conducting-effective-sales-behavioral-interviews/lessons/effective-probing-techniques]
• 43. Validity of the 24-hour dietary recall [https://www.sciencedirect.com/science/article/pii/S0002822321038189]
• 44. NHANES Dietary Analyses Module [https://wwwn.cdc.gov/nchs/nhanes/tutorials/dietaryanalyses.aspx]
• 45. Fifth Annual Empirical Research Conference on Standardization [https://www.law.northwestern.edu/research-faculty/clbe/events/standardization/]
• 46. Fifth Annual Northwestern Empirical Research Conference ... [https://www.ssrn.com/index.cfm/en/janda/announcement/?id=17118]
• 47. ASA24® Dietary Assessment Tool | EGRP/DCCPS/NCI/NIH [https://epi.grants.cancer.gov/asa24/]
• 48. 24-hr Dietary Recall Collection [https://www.ncc.umn.edu/24-hr-dietary-recall-collection/]
• 49. NDSR Software [https://www.ncc.umn.edu/products/]
• 50. Repeated 24-hour recalls versus dietary records for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3215303/]
• 51. of the Oxford WebQ Online Validation - 24 Questionnaire... Hour Dietary [https://pubmed.ncbi.nlm.nih.gov/31318012/]
• 52. The overlooked impact of background diet and adherence in ... [https://pubs.rsc.org/en/content/articlehtml/2025/fo/d5fo01134e]
• 53. Validity of an online 24 -h recall tool (myfood 24 ) for dietary assessment... [https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1113-8]
• 54. Protocol of the validation of the experience sampling-based ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12587619/]
• 55. Dietary biomarkers and food records indicate compliance ... [https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2023.1209787/full]
• 56. The Dietary Biomarkers Development Consortium: An ... [https://www.sciencedirect.com/science/article/pii/S2475299125028951]
• 57. Systematic review of statistical approaches to quantify, or ... [https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-017-0421-6]
• 58. The Measurement Error Elephant in the Room [https://pmc.ncbi.nlm.nih.gov/articles/PMC9005058/]
• 59. A toolkit for measurement error correction, with a focus on ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4285313/]
• 60. Regression calibration method for correcting measurement ... [https://www.sciencedirect.com/science/article/pii/S0002916523177331]
• 61. The impacts of measurement errors on a dietary pattern ... [https://www.sciencedirect.com/science/article/pii/S0002916522000569]
• 62. Evaluation of measurement error in 24-hour dietary recall ... [https://www.sciencedirect.com/science/article/pii/S0002916522032257]
• 63. mitigating bias due to measurement error in real-world ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12464481/]
• 64. Measurement error corrected stochastic frontier analysis [https://link.springer.com/article/10.1007/s11123-025-00785-z]
• 65. A Systematic Review Examining Contributors to ... [https://www.sciencedirect.com/science/article/pii/S2161831323000947]
• 66. Comparison of an Open versus List-Based Dietary Recall ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12125694/]
• 67. Using Encoding and Retrieval Strategies to Improve 24 ... [https://www.sciencedirect.com/science/article/abs/pii/S0002822398002272]
• 68. ASA24® Respondent Website Methodology [https://epi.grants.cancer.gov/asa24/respondent/methodology.html]
• 69. Validation of a newly automated web-based 24-hour dietary ... [https://bmcnutr.biomedcentral.com/articles/10.1186/s40795-017-0153-3]
• 70. Data Analysis for the 2025 Guidelines Committee Report [https://www.dietaryguidelines.gov/2025-advisory-committee-report/data-analysis]
• 71. Biomarkers of Nutrition and Health: New Tools for New ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6567133/]
• 72. Perspective: Dietary Biomarkers of Intake and Exposure— ... [https://www.sciencedirect.com/science/article/pii/S2161831322002514]
• 73. Cancer Biomarkers: Reflection on Recent Progress, Emerging ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12468363/]
• 74. 2025 Trends: Multiomics [https://www.genengnews.com/topics/omics/2025-trends-multiomics/]
• 75. Insights from Biomarkers & Precision Medicine 2025 [https://www.signifyresearch.net/insights/biomarkers-billions-and-breakthroughs-insights-from-biomarkers-precision-medicine-2025/]
• 76. NextGen Omics & Spatial Biology US 2025 [https://oxfordglobal.com/precision-medicine/events/nextgen-omics-us-2025/archive]