Dataset Information

Different dietary assessment methods, similar conclusions? Comparison of a country's adherence to food-based dietary guidelines as depicted in two population-based surveys using different dietary assessment methods.

ABSTRACT:

Objective

Different methods of dietary intake assessment are frequently used to assess a population's diet. In this study, we aimed to compare the adherence to Swiss food-based dietary guidelines as depicted in two Swiss population-based surveys using different methods of dietary assessment.

Design

Two population-based, cross-sectional surveys were compared. In the Swiss Health Survey (SHS), diet was assessed via a short set of questions on specific food groups, while in menuCH by two non-consecutive 24-h dietary recall interviews.

Setting

To compare the diet depicted in these surveys, we used the Swiss food-based dietary guidelines on vegetable, fruit, dairy product, meat and meat product, fish and alcohol. The weighted proportion of responders meeting these guidelines was calculated for both surveys and was compared overall and by selected characteristics.

Participants

Residents of Switzerland, selected from a stratified random sample of the non-institutionalised residents, who agreed to participate in the respective survey. To ensure comparability between the surveys, the age of the study populations was restricted to 18-75 years.

Results

In menuCH, approximately 2 % of responders met ≥4 of the selected Swiss food-based dietary guidelines. In the SHS, using a cruder dietary assessment, the corresponding percentage was 20 %. In both surveys, more women and never smokers were meeting ≥4 food-based dietary guidelines compared to men and current or former smokers, respectively.

Conclusions

Our study comparing the diet in two population-based, representative surveys detected large variations in guideline adherence depending on the dietary assessment method used.

SUBMITTER: Karavasiloglou N

PROVIDER: S-EPMC9991603 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Different dietary assessment methods, similar conclusions? Comparison of a country's adherence to food-based dietary guidelines as depicted in two population-based surveys using different dietary assessment methods.

Karavasiloglou Nena N Pestoni Giulia G Dehler Anna A Sych Janice J Faeh David D Rohrmann Sabine S

Public health nutrition 20220321 9

<h4>Objective</h4>Different methods of dietary intake assessment are frequently used to assess a population's diet. In this study, we aimed to compare the adherence to Swiss food-based dietary guidelines as depicted in two Swiss population-based surveys using different methods of dietary assessment.<h4>Design</h4>Two population-based, cross-sectional surveys were compared. In the Swiss Health Survey (SHS), diet was assessed via a short set of questions on specific food groups, while in menuCH by ...[more]

PMID: 35307049

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Project description:BACKGROUND:Under-reporting because of the limitations of human memory is one of the key challenges in dietary assessment surveys that use the multiple-pass 24-hour recall. Research indicates that shortening a retention interval (ie, the time between the eating event and recall) reduces the burden on memory and may increase the accuracy of the assessment. OBJECTIVE:This study aimed to explore the accuracy and acceptability of Web-based dietary assessment surveys based on a progressive recall, where a respondent is asked to record multiple recalls throughout a 24-hour period using the multiple-pass protocol and portion size estimation methods of the 24-hour recall. METHODS:The experiment was conducted with a dietary assessment system, Intake24, that typically implements the multiple-pass 24-hour recall method where respondents record all meals they had for the previous day on a single occasion. We modified the system to allow respondents to add multiple recalls throughout the day using the multiple-pass protocol and portion size estimation methods of the 24-hour recall (progressive recall). We conducted a dietary assessment survey with 33 participants, where they were asked to record dietary intake using both 24-hour and progressive recall methods for weekdays only. We compared mean retention intervals (ie, the time between eating event and recall) for the 2 methods. To examine accuracy, we compared mean energy estimates and the mean number of reported foods. Of these participants, 23 were interviewed to examine the acceptability of the progressive recall. RESULTS:Retention intervals were found to be, on average, 15.2 hours (SD 7.8) shorter during progressive recalls than those during 24-hour recalls. We found that the mean number of foods reported for evening meals for progressive recalls (5.2 foods) was significantly higher (P=.001) than that for 24-hour recalls (4.2 foods). The number of foods and the amount of energy reported for other meals remained similar across the 2 methods. In interviews, 65% (15/23) of participants said that the 24-hour recall is more convenient in terms of fitting in with their daily lifestyles, and 65% (15/23) of respondents indicated that they remembered meal content and portion sizes better with the progressive recall. CONCLUSIONS:The analysis of interviews and data from our study indicate that progressive recalls provide minor improvements to the accuracy of dietary assessment in Intake24. Additional work is needed to improve the acceptability of progressive recalls in this system.

Project description:ObjectiveHuman error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water).Materials and methodsLiterature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool.ResultsDatabase and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods.ConclusionsRelative errors for volume and calorie estimations suggest that AI methods align with - and have the potential to exceed - accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations.

Dataset Information

Different dietary assessment methods, similar conclusions? Comparison of a country's adherence to food-based dietary guidelines as depicted in two population-based surveys using different dietary assessment methods.

Objective

Design

Setting

Participants

Results

Conclusions

Publications

Different dietary assessment methods, similar conclusions? Comparison of a country's adherence to food-based dietary guidelines as depicted in two population-based surveys using different dietary assessment methods.

Similar Datasets

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