Sports
How to Tell if a Sports Science Study Is Reliable
Dr. Luciana Haddad explains the hierarchy of scientific evidence, bias risks, and how to critically assess sports research.
Learn how the hierarchy of scientific evidence works and how to determine whether a nutrition or training study is truly reliable. Physician and researcher Luciana Haddad breaks down study methodologies, the role of meta-analyses, and why caution is needed with viral online claims.
In the endurance and athletic training space, it has become routine to see social media posts and videos justifying virtually any practice with the phrase "according to a recent study." Whether the topic is carbohydrate intake, carbon-plated shoes, or supplement protocols, the ease of finding scientific papers creates the illusion that every theory enjoys solid backing. In science, however, the publication of a single isolated study rarely settles a debate.
To help athletes and coaches filter what truly holds scientific value, physician, researcher, and USP associate professor Luciana Haddad joined Bruna Mahn on Stemma Performance. With nearly three decades dedicated to academic research, Luciana demystifies reading scientific papers and highlights the essential criteria for separating trustworthy data from biased claims.
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The Evidence Pyramid: From Case Reports to Meta-Analyses
Validating any scientific hypothesis depends directly on the chosen methodological design. At the top of the primary study hierarchy sit randomized controlled trials (RCTs). In this design, researchers isolate as many variables as possible so the only meaningful difference between groups is the tested intervention—such as comparing two models of running shoes or nutrient dosages under identical conditions.
For an RCT to be robust, factors like blinding are essential. When neither the athletes nor the researchers know which group is receiving the active intervention or the placebo (double- or triple-blind), psychological expectation bias is eliminated. Below RCTs are cohort studies, which follow large populations in real-world settings over several years. While they cannot control every variable with laboratory precision, cohort studies provide vital insights into long-term outcomes across broad demographics.
At the apex of the evidence pyramid are systematic reviews with meta-analyses. They pool multiple similar published studies and statistically synthesize their data to reach broader conclusions. However, Luciana warns that a meta-analysis is only as good as its underlying studies: including research with a high risk of bias skews the final results, requiring readers to interpret findings with caution.
Predatory Journals, Peer Review, and AI Risks
Another critical factor in evaluating research is the credibility of the journal in which it appears. In reputable peer-reviewed publications, a manuscript undergoes critical, anonymous evaluation by independent subject-matter experts prior to acceptance. In contrast, the academic landscape has seen a rise in predatory journals—outlets that charge publication fees without enforcing rigorous methodological standards.
The rise of public generative AI tools also warrants extra vigilance. When prompted to support a specific claim, generative AI platforms frequently hallucinate references: fabricating titles, combining real authors from unrelated studies, and generating dead links. Searching for and verifying a study directly in biomedical databases, such as PubMed, remains an indispensable step.
Study Populations, Bias, and the Health vs. High Performance Divide
A frequent interpretation error is extrapolating findings from a narrow sample to the general population. A study conducted exclusively on 30-year-old male professional athletes does not automatically provide actionable answers for 50-year-old recreational runners or female athletes, whose physiological and biomechanical responses can differ substantially.
The debate surrounding polarized topics—such as high carbohydrate intake (90 to 120 grams per hour) versus low-carb diets—illustrates this divide clearly. Protocols designed for elite athletes pushing physical limits to win races should not be confused with guidelines for individuals seeking general health or metabolic fitness. High performance aims for peak competitive output while accepting trade-offs that simply do not apply to recreational athletes.
Supplementation: Balancing Efficacy and Risk
When evaluating a supplement, Luciana Haddad recommends always weighing the risk-to-benefit ratio. Options supported by Level A scientific evidence and established safety profiles—such as creatine, caffeine, and nitrates—offer consistent, albeit modest, performance gains (typically around 2% to 3%), making them ideal for athletes who already have their training, recovery, and nutrition dialed in.
Conversely, interventions that lack regulatory agency oversight (such as unapproved peptides or untested miracle compounds) pose significant health risks in exchange for unproven claims. Athletes should remain focused on foundational lifestyle pillars, using science as a critical guide to make safe, effective choices.
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