Uncovering the Truth About Your Diet: Objective Strategies for Dietary Assessment

What we eat matters! But how do we know what people are really eating? Most nutrition studies rely on dietary assessment methods such as food frequency questionnaires (FFQs), 24-hour recalls, and food diaries. All these methods depend on people reporting what they eat. But obtaining accurate data is not easy. Misreporting food intake is a big problem. In this article, we will explore how people misreport and how to improve the accuracy of dietary data.

Problem #1 – Estimating the Correct Portion Size

One reason for inaccurate reporting is estimating the correct portion size. Ideally, the most accurate method would be to weigh and keep track of every food item in real time. Not only the serving size, but also any leftovers, should be recorded in a food diary.

Weighed food diaries are the gold standard method for dietary assessment in clinical trials. Yet, tracking every sip and bite in real time for more than 3 – 4 days in a row is burdensome and time-consuming. That is why retrospective methods like 24-hour-recalls or Food Frequency Questionnaires (FFQs) are more common (1).

Although frequently used, retrospective methods rely on our memory. This can be problematic as our memory is prone to forgetting key details like the type and quantity of food consumed. Foods and drinks habitually consumed might be easier to recall. But what about seasonal foods and foods consumed not so often? The recorded intakes may be less accurate. Moreover, small, medium, and large are not standard measures everywhere in the world. For example, in Italy a small cappuccino is around 150 ml, whereas in the US a small cappuccino is around 360 ml.

The more accurate the estimation of portion size, the better the dietary analysis. But how do we achieve more accuracy? Weighing foods is too cumbersome and using small, medium, and large measurements is too imprecise. A better approach would be to use image-assisted methods (2).

Solution #1 – Image-Assisted Dietary Assessment Methods

To further reduce the burden of analysis, fully automated image-based approaches have been developed. The captured food images are sent to a central server for analysis. Instead of a trained human being, software compares the captured food images with images in the database (3). These methods are still in their infancy and need further testing and validation.

The image-assisted and image-based methods offer an improvement in the accuracy of portion size estimation. Yet, they are not perfect. Apps can remind users, but they cannot ensure that all food and drinks are captured throughout the day. Therefore, even if accuracy is improved, the reporting might still be incomplete.

Problem #2 – Unintentional and Intentional Misreporting

Studies have indicated that the more days captured, the higher the burden and likelihood of misreporting (4). Single food items like snacks, calorie-containing beverages or even whole meals might be forgotten. Unhealthy foods, high in fat or sugar, might be underreported. Whereas, healthy foods, like fresh fruits and vegetables, might be overreported.

Other factors, including body image, body weight, age, and gender can also affect how accurately people report their food intake (5). For example, studies have indicated that women tend to underreport more often than men (6). Greater underreporting has been observed in overweight or obese individuals, exhibiting a social desirability bias (7). In these cases, underreporting food or drink intake may be a way to avoid criticism and adhere to perceived social norms.

Furthermore, people might change their eating habits while reporting. Why? Because they aim to simplify the reporting process or become more mindful when preparing and eating foods. This effect is known as the observer effect or reactivity. Once the reporting phase has ended, these new habits may not be maintained, rendering the reported information unreliable.

Solution #2 – Wearable Devices

Wearable devices are also in their infancy and need further testing and evaluation. But as technology advances, new tools might emerge. There might be swallowable cameras or hand-to-mouth movement trackers in the future. Let’s keep an eye on promising technological advancements!

Meanwhile, there are other options to improve the reporting of food intake. We can use a more integrated approach. We can complement self-report techniques with nutritional biomarkers, for example (8,9).

An Integrated Approach

It is possible to measure thousands of metabolites simultaneously, and in different samples, including erythrocytes, urine, serum, salvia, and hair. These new techniques not only allow the identification of new metabolites, but can provide new insights into the complex diet-disease interactions (10).

Recent studies have identified multi-metabolite profiles related to specific diets like the Mediterranean and Western diet. These profiles can then be used to unravel why and how a healthy plant-based diet can lower the risk of type 2 diabetes, for example (11). Additionally, we could use these profiles to assess how strictly people adhere to a specific diet.

Quiz Questions

Good luck!

For more information on measuring food intake, have a look at lesson 3 of the PAN Academy module 3 at PAN Academy Module 3. It’s packed with the ins and outs of all dietary assessment methods described in this post.

Are you interested in how many metabolites have been identified already? Then check out the FoodDB. You can browse either foods or compounds.

For the definitions of important terms, go to our Glossary.