Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Absorption of Nutrients01:19

Absorption of Nutrients

3.5K
Absorption refers to taking dietary nutrients from the intestinal lumen for transportation throughout the body. After digestion in the small intestine, carbohydrates, proteins, and fats are broken down into simpler forms. These essential macronutrients and other vital substances, such as vitamins, minerals, and water, are then prepared for absorption into the bloodstream.
Enterocytes, which are specialized polar epithelial cells, line the mucosa of the small intestinal walls. These cells...
3.5K
Regulation of Water Intake01:25

Regulation of Water Intake

2.7K
Osmolality refers to the number of solute particles per kilogram of solvent in a solution. Plasma osmolality specifically indicates the total number of solute particles per kilogram of water in blood plasma. This value reflects the body's hydration status and is tightly regulated through mechanisms controlling water intake and output. While water consumption is a conscious decision, the body has intrinsic regulatory systems to maintain fluid balance. Dehydration, a state of water deficit...
2.7K
Regulation of Food Intake01:30

Regulation of Food Intake

2.8K
Short-term regulation of food intake primarily involves neural signals from the gastrointestinal (GI) tract, blood nutrient levels, and GI tract hormones. Communication between the gut and brain via vagal nerve fibers plays a significant role in evaluating the contents of the gut. Clinical studies have shown that protein ingestion produces a more prolonged response in these nerve fibers compared to an equivalent amount of glucose. Additionally, the activation of stretch receptors caused by GI...
2.8K
Sample Handling01:02

Sample Handling

2.7K
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
2.7K
What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

232
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
232

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interindividual Variation in Adult Gut Microbiome Composition in Two Rural Communities in Japan: Associations With Energy and Nutrient Intakes.

American journal of human biology : the official journal of the Human Biology Council·2026
Same author

Impact of Initial Lip Asymmetry on Long-Term Lip Symmetry in Unilateral Cleft Lip and Alveolus Patients: An Orthodontic Perspective.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2026
Same author

A Case Report of Valacyclovir-Associated Neurotoxicity.

Microorganisms·2026
Same author

Evidence of anti-corticotroph autoantibodies in Down syndrome with isolated adrenocorticotropic hormone deficiency: findings from a single case.

Endocrine·2026
Same author

Ifinatamab deruxtecan, a B7-H3-directed antibody-drug conjugate, in patients with advanced solid tumours (IDeate-PanTumor01): dose-escalation results from a phase 1/2 trial.

The Lancet. Oncology·2026
Same author

Transient adrenocorticotropic hormone elevation with a disproportionate cortisol response prior to the onset of immune checkpoint inhibitor-related hypophysitis.

Hormones (Athens, Greece)·2026

Related Experiment Video

Updated: Jan 28, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.9K

Handling missing data in an FFQ: multiple imputation and nutrient intake estimates.

Mari Ichikawa1, Akihiro Hosono1, Yuya Tamai1

  • 11Department of Public Health,Nagoya City University Graduate School of Medical Sciences,1 Kawasumi, Mizuho-cho, Mizuho-ku,Nagoya 467-8601,Japan.

Public Health Nutrition
|February 27, 2019
PubMed
Summary

Multiple imputation is a better method than zero imputation for handling missing dietary data in food frequency questionnaires (FFQs). This approach improves the accuracy of estimating nutrient intake when missing data is minimal.

Keywords:
FFQItem non-responseMissing dataMultiple imputation

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.8K

Related Experiment Videos

Last Updated: Jan 28, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.8K

Area of Science:

  • Nutritional Epidemiology
  • Biostatistics

Background:

  • Dietary assessment using food frequency questionnaires (FFQs) often involves missing data.
  • Accurate estimation of dietary intake is crucial for epidemiological studies.

Purpose of the Study:

  • To examine missing data patterns in FFQs.
  • To compare the effects of multiple imputation and zero imputation on estimating dietary intake.

Main Methods:

  • Utilized data from the Okazaki Japan Multi-Institutional Collaborative Cohort (J-MICC) study.
  • Analyzed data from 5120 participants with FFQ data at baseline (FFQ1) and 5-year follow-up (FFQ2).
  • Compared missing value imputation methods: using FFQ1 values, multiple imputation, and zero imputation.

Main Results:

  • The proportion of missing data in FFQ2 was 3.7%.
  • Missing food items often represented zero intake.
  • Multiple imputation showed smaller differences in total energy and nutrient estimates compared to zero imputation, except for alcohol.

Conclusions:

  • Missing data in FFQs, particularly when representing zero intake, can be reasonably predicted.
  • Multiple imputation is a more effective method than zero imputation for estimating dietary intake from FFQ data, especially when missingness is low.