Scientists have two main ways to study how food affects our health. Traditional studies randomly assign people to different groups and carefully track results—this is considered the gold standard but is expensive and time-consuming. A newer approach uses medical records from hospitals and clinics to study large groups of real people in everyday life. This review explores how both methods work, their strengths and weaknesses, and when each one is most useful for nutrition research. The answer isn’t simple: sometimes we need the careful control of traditional studies, and sometimes the real-world data from medical records tells us more useful information.

The Quick Take

  • What they studied: How two different research methods compare for studying nutrition and health: traditional controlled experiments versus using existing medical records from hospitals and clinics.
  • Who participated: This is a review article that examines research methods rather than studying actual people. The authors analyzed how both approaches work in nutrition science.
  • Key finding: Neither method is perfect for all situations. Traditional controlled studies are best for proving cause-and-effect, but medical records can provide faster, cheaper answers about how nutrition changes affect large groups of real people.
  • What it means for you: Nutrition research you read about may come from either method. Understanding which type of study it is helps you know how confident you should be in the results. Medical record studies are good for spotting patterns, while controlled studies are better for proving something actually works.

The Research Details

This is a review article, not a study with participants. The authors examined and compared two major research approaches used in nutrition science. The first approach is the randomized controlled trial (RCT), where researchers recruit similar people, randomly split them into groups, give some a treatment and others a placebo, and carefully measure the results. The second approach uses electronic health records (EHRs)—the medical information already collected by hospitals and clinics—to study how nutrition affects large populations over time.

The authors looked at the advantages and disadvantages of each method. RCTs are considered the ‘gold standard’ because randomization helps ensure fair comparisons and proves cause-and-effect. However, they’re expensive, hard to recruit for, and people often drop out. EHRs use data that’s already been collected, making them faster and cheaper, but they have their own problems like missing information and data quality issues.

The review examined when each method works best. For example, if you want to study how a new tax on sugary drinks affects a whole country’s health, medical records are ideal. But if you want to prove that a specific vitamin supplement actually prevents disease, a controlled trial is more reliable.

Understanding research methods matters because it affects how much you should trust nutrition news. Different study types answer different questions and have different levels of reliability. This review helps readers understand that ‘best’ research depends on the question being asked, not on one method being universally superior.

This is a peer-reviewed review article published in a nutrition journal, meaning experts checked the authors’ work. However, as a review rather than original research, it doesn’t present new data—it synthesizes existing knowledge. The authors appear balanced, acknowledging strengths and weaknesses of both approaches rather than favoring one. The publication date is recent (2026), so it reflects current thinking about research methods.

What the Results Show

The review identifies that randomized controlled trials excel at proving cause-and-effect relationships. When researchers randomly assign people to groups and control conditions carefully, they can definitively say whether a nutrition intervention caused a health outcome. This is why RCTs are considered the gold standard for proving something works.

However, RCTs have significant practical limitations. They’re expensive to run, often costing millions of dollars. Finding enough willing participants takes time, and many people drop out before the study ends. Additionally, nutrition studies can’t always be ‘blinded’—participants know whether they’re eating a special diet or not, which can affect results. RCT findings also may not apply to everyone; a study of healthy young adults might not tell us what happens in elderly people or those with diseases.

Electronic health records offer a different advantage: they provide real-world data from actual patients in real settings. Because the data is already collected, studies using EHRs are faster and cheaper. They can include millions of people, giving results that apply to whole populations. Researchers can follow people over many years, seeing long-term effects. When a government makes a nutrition policy change—like adding folic acid to flour—EHRs can quickly show whether it improved population health.

The review highlights that EHRs have their own challenges. Medical records weren’t created for research, so important information might be missing. Data quality varies between hospitals. Privacy concerns mean some patients don’t want their records used for research, which can bias results. Additionally, using medical records makes it harder to prove cause-and-effect because researchers can’t control all the variables like they do in RCTs.

The authors note that the best approach often combines both methods. RCTs can answer ‘does this work?’ while EHRs can answer ‘what happens in real life?’ Using both together gives a complete picture. For example, an RCT might prove a supplement helps in controlled conditions, while EHR data shows whether people actually benefit when taking it at home with their regular diet.

This review reflects a growing shift in nutrition science. Historically, RCTs were considered the only truly reliable research method. However, as costs have risen and technology has improved, researchers increasingly recognize that EHRs provide valuable information RCTs cannot. The review positions this not as EHRs replacing RCTs, but as complementary approaches. This represents current scientific consensus that multiple research methods have value depending on the question.

As a review article, this work doesn’t present new experimental data, so readers should understand it reflects the authors’ interpretation of existing research. The review focuses on nutrition science specifically, so conclusions may not apply equally to other medical fields. The authors acknowledge that EHR infrastructure varies globally, meaning some countries can use this approach better than others. Additionally, the review was published in early 2026, so very recent developments in data technology or privacy regulations may not be included.

The Bottom Line

When reading nutrition research, consider the study type. If researchers claim something ‘causes’ a health effect, check whether it’s from a randomized controlled trial—this type of evidence is most reliable for cause-and-effect claims (high confidence). If research shows a pattern across large populations, it may come from medical records, which is good for spotting trends but less certain about cause-and-effect (moderate confidence). The strongest evidence comes when both types of studies reach similar conclusions (high confidence).

Everyone reading nutrition news should understand these research methods. Healthcare providers use this information to decide which studies to trust when making recommendations. Policymakers deciding on nutrition programs should understand that EHR data is excellent for evaluating real-world impact. Nutrition researchers need to know when each method is appropriate. People with specific health conditions should be cautious about applying RCT results if the study didn’t include people like them.

Research timelines vary dramatically by method. RCTs typically take 2-5 years to complete and publish results. EHR studies can sometimes provide answers in months because data is already collected. However, longer-term health effects may take years to appear in either type of study. Don’t expect immediate answers to complex nutrition questions.

Want to Apply This Research?

  • Track which type of nutrition research you encounter: note whether articles mention ‘randomized controlled trial,’ ‘RCT,’ ‘medical records,’ or ‘observational study.’ Over a month, track how many of each type you read. This builds awareness of research quality and helps you evaluate nutrition claims more critically.
  • When you read a nutrition headline, use the app to log the study type before deciding whether to change your habits. Ask yourself: ‘Is this from a controlled experiment or real-world data?’ This simple pause helps prevent chasing every nutrition trend. Use the app’s research method guide to understand what the study actually proves.
  • Create a personal ’nutrition claims tracker’ in the app. When you encounter nutrition advice, log it with the study type that supports it. Over months, you’ll see patterns in which types of research support which claims. This long-term tracking helps you develop skepticism about unsupported claims and confidence in well-researched recommendations.

This article reviews research methods rather than providing medical advice. Different types of nutrition research have different strengths and limitations. Before making significant changes to your diet or nutrition habits based on any research, consult with a healthcare provider or registered dietitian who can consider your individual health status, medications, and needs. This review does not constitute medical advice and should not replace professional medical guidance.