Reliability of 24-Hour Dietary Recall for Measuring Meal Timing
Principal Investigator
Name
Mary Playdon
Degrees
Ph.D., M.P.H.
Institution
University of Utah
Position Title
Assistant Professor
Email
About this CDAS Project
Study
IDATA
(Learn more about this study)
Project ID
IDATA-40
Initial CDAS Request Approval
Sep 11, 2020
Title
Reliability of 24-Hour Dietary Recall for Measuring Meal Timing
Summary
Emerging research has revealed intermittent fasting, including Time Restricted Eating (TRE), as a compelling strategy for weight management and improved metabolic health. TRE is a form of fasting where food is accessed during a limited window of time in a 24-hour period. TRE has been shown to influence circadian gene transcription and protein expression levels that can modulate numerous metabolic processes. Erratic eating patterns, including the balance of feeding/fasting periods, can disrupt the rhythms of the internal circadian clock, leading to metabolic dysfunction and poor health outcomes.
Although there is growing interest in evaluating the effects of meal timing on health parameters, limited dietary assessment methods capture meal-timing. Traditional food frequency questionnaires (FFQ) capture frequency of consumption of foods and beverages over time, but does not capture individual eating episodes or their timing. The 24-hour dietary recall captures timing of meal, snacks and beverages over the prior 24-hour period. Multiple 24-hour recalls can estimate habitual diet. The few prospective studies that include 24-hour recalls tend to do so for dietary calibration purposes, but often they are limited to a single 24-hour recall. Studies have investigated the reproducibility food, beverage and nutrient intake over time using 24-hour recalls, but none to our knowledge have measured the validity and reliability of measuring habitual meal timing patterns. It is therefore unknown whether a single 24-hour recall is adequate for meal timing measurement. This critical gap must be addressed prior to analyzing associations of meal timing parameters with disease outcomes in epidemiological studies, which we propose to do by leveraging data from the IDATA study.
Methods
We will evaluate the following meal timing variables from the IDATA ASA24.
(i) Duration of overnight fasting: the time between the last meal before the midpoint of time in bed and the first meal following the midpoint time in bed.
(ii) Midpoint of overnight fasting: midpoint of the overnight fasting period.
(iii) 24-hour distribution of caloric intakes: percent of total daily caloric intake in each of the six 4-hour periods spanning from midnight to midnight.
(iv) 24-hour distribution of macronutrient (carbohydrate, protein, and fat) intakes: percent of total macronutrient intake in each of the six 4-hour periods spanning from midnight to midnight.
(v) Ratio of later (15:00-24:00): earlier (06:00-14:00 ) energy intake.
Statistical Analysis
Within and between-person variation in meal timing variables will be measured using the intraclass correlation coefficient (ICC) and Cohen’s kappa statistics. We will conduct mixed effects regression, adjusting for baseline covariates (e.g., age, physical activity, body mass index) to identify predictors of overnight fasting time, midpoint of overnight fasting, and distribution of caloric and macronutrient profiles. Predictors will include demographic (e.g., SES, income, race/ethnicity, age) and lifestyle (physical activity, smoking, alcohol use, dietary pattern).
Although there is growing interest in evaluating the effects of meal timing on health parameters, limited dietary assessment methods capture meal-timing. Traditional food frequency questionnaires (FFQ) capture frequency of consumption of foods and beverages over time, but does not capture individual eating episodes or their timing. The 24-hour dietary recall captures timing of meal, snacks and beverages over the prior 24-hour period. Multiple 24-hour recalls can estimate habitual diet. The few prospective studies that include 24-hour recalls tend to do so for dietary calibration purposes, but often they are limited to a single 24-hour recall. Studies have investigated the reproducibility food, beverage and nutrient intake over time using 24-hour recalls, but none to our knowledge have measured the validity and reliability of measuring habitual meal timing patterns. It is therefore unknown whether a single 24-hour recall is adequate for meal timing measurement. This critical gap must be addressed prior to analyzing associations of meal timing parameters with disease outcomes in epidemiological studies, which we propose to do by leveraging data from the IDATA study.
Methods
We will evaluate the following meal timing variables from the IDATA ASA24.
(i) Duration of overnight fasting: the time between the last meal before the midpoint of time in bed and the first meal following the midpoint time in bed.
(ii) Midpoint of overnight fasting: midpoint of the overnight fasting period.
(iii) 24-hour distribution of caloric intakes: percent of total daily caloric intake in each of the six 4-hour periods spanning from midnight to midnight.
(iv) 24-hour distribution of macronutrient (carbohydrate, protein, and fat) intakes: percent of total macronutrient intake in each of the six 4-hour periods spanning from midnight to midnight.
(v) Ratio of later (15:00-24:00): earlier (06:00-14:00 ) energy intake.
Statistical Analysis
Within and between-person variation in meal timing variables will be measured using the intraclass correlation coefficient (ICC) and Cohen’s kappa statistics. We will conduct mixed effects regression, adjusting for baseline covariates (e.g., age, physical activity, body mass index) to identify predictors of overnight fasting time, midpoint of overnight fasting, and distribution of caloric and macronutrient profiles. Predictors will include demographic (e.g., SES, income, race/ethnicity, age) and lifestyle (physical activity, smoking, alcohol use, dietary pattern).
Aims
Aim 1: Measure within and between-person variability of meal timing variables measured using 24-hour dietary recall.
Aim 2: Measure the correlation of a single 24-hour recall with the average of 2, 3, 4, 5, and 6 measures of meal timing variables.
Aim 3. Identify predictors of overnight fasting time, midpoint of overnight fasting, and distribution of caloric and macronutrient profiles.
Collaborators
Lacie Peterson, Qian Xiao, Tracy Layne, Cici Bauer, Ben Haaland, Mary Playdon