People are bad at reporting what they eat. That's a problem for dietary research
Nutrition research that relies on people self-reporting what they eat is widely seen as unreliable, because most individuals misremember, misestimate portions, or selectively omit foods in ways that aren’t uniform across groups. Commenters argue this makes many epidemiological diet studies noisy and confounded by lifestyle and socioeconomic factors, fueling contradictory headlines about “good” and “bad” foods. Others focus on practical workarounds — from food scales, apps, and wearables to controlled feeding trials — while noting how hard it remains both to do rigorous long-term studies and to track one’s own intake accurately in everyday life.
Limits of Dietary Research & Confounding
- Many commenters argue most nutrition studies are near-impossible to do well: too many confounders (activity, income, culture, health-consciousness, sleep, genetics, microbiome).
- Strong skepticism that “large N” averages these out, since diet often correlates with lifestyle (e.g., people who eat more vegetables also exercise more and see doctors).
- Meta-analyses aggregating many weak, questionnaire-based studies are seen as especially unreliable, generating flip‑flopping claims (coffee, wine, meat, sweeteners, etc.).
Self‑Reporting Inaccuracy
- Consensus that people misreport food, alcohol, smoking, exercise, and sex, often systematically:
- Underreport foods seen as “bad”; overreport “good” foods.
- Different groups misreport in different directions (e.g., “frat vs. Mormon” example).
- Even trained professionals are said to mis-estimate portion sizes badly; visual estimates off by ~40–50% in one cited CVPR paper.
- Time-scale issue: short food logs vs. long-term outcomes (decades) further weakens causal inference.
Calorie Tracking & Personal Practice
- Many describe weighing ingredients and using apps (MyFitnessPal, Cronometer, Carb Manager, etc.), often only for weeks or months to “calibrate” portion intuition.
- Common strategy: precisely track calorie‑dense items (oil, cheese, nuts, meats, sauces) and ignore low-calorie vegetables/spices.
- Eating out is repeatedly called the biggest source of uncertainty; users resort to aggressive overestimation or avoiding restaurant food when cutting.
- Several note you don’t need precision; consistency plus feedback via body weight lets you adjust.
Tech & Automation Ideas
- Proposed solutions: AI + cameras, LiDAR portion estimation, barcodes, connected kitchen scales, continuous glucose monitors, wearable chewing detectors.
- Current food‑photo apps are viewed as better than recall surveys but still quite inaccurate, especially on portion size and mixed dishes.
Controlled Feeding & Ethics
- Some argue only tightly controlled feeding trials (hospital, prison, remote “boot camp” settings) yield rigorous data, but these are expensive, invasive, and may not generalize to normal life.
- Using prisoners is raised and criticized on ethical and ecological‑validity grounds.
Broader Debates
- Calories‑in/calories‑out (CICO) is defended as physically true but admitted to be hard to measure and implement; others say it’s descriptively true but not a useful planning tool.
- Strong emphasis on satiety, ultra‑palatable foods, and psychology: tracking works partly by forcing mindfulness, not mathematical accuracy.
- Some commenters see nutrition science as “deeply unserious” given reliance on self-report; others argue imperfect methods are still better than abandoning the field.