Implausible results from the use of invalid methods

Meat consumption & positive mental health: A scoping review

The objective of this scoping review was to examine the breadth of the existing literature on the relation between meat consumption or meat abstention and positive psychological functioning. In April 2022, we conducted a systematic search of online databases (PubMed, PsycINFO, CINAHL Plus, Medline, Cochrane Library, and Web of Science) for primary research examining positive psychological functioning in meat consumers and those who abstain from meat. Thirteen studies met the inclusion/exclusion criteria, representing 89,138 participants (54,413 females and 33,863 males) with 78,562 meat consumers and 10,148 meat abstainers (13-102 years) from multiple geographic regions. The primary outcomes were life satisfaction, "positive mental health", self-esteem, and vigor. The secondary outcomes were "meaning in life", optimism, positive emotions, and psychological well-being. Eight of the 13 studies demonstrated no differences between the groups on positive psychological functioning, three studies showed mixed results, and two studies showed that compared to meat abstainers, meat consumers had greater self-esteem, "positive mental health", and "meaning in life". Studies varied substantially in methods and outcomes. Although a small minority of studies showed that meat consumers had more positive psychological functioning, no studies suggested that meat abstainers did. There was mixed evidence for temporal relations, but study designs precluded causal inferences. Our review demonstrates the need for future research given the equivocal nature of the extant literature on the relation between meat consumption and meat abstention and positive psychological functioning.

Controversy and debate: Memory-Based Methods Paper 1: the fatal flaws of food frequency questionnaires and other memory-based dietary assessment methods

There is an escalating debate over the value and validity of self-reported dietary intake as estimated by Food Frequency Questionnaires and other forms of memory-based dietary assessment methods. Proponents argue that despite limitations, memory-based methods provide valid and valuable information about consumed foods and beverages and therefore can be used to assess diet-disease relations. In fact, over the past 60 years, thousands of memory-based dietary research reports were used to inform public policy and establish the Dietary Guidelines for Americans. Yet, despite this impressive history, our position is that memory-based dietary assessment methods are invalid and inadmissible for scientific research and therefore cannot be used in evidence-based policy making. Herein, we present the empirical evidence and theoretic and philosophic perspectives that render data derived from memory-based methods both fatally flawed and pseudoscientific. First, the use of memory-based methods is founded upon two inter-related logical fallacies: a category error and reification. Second, human memory and recall are not valid instruments for scientific data collection. Third, in standard epidemiologic contexts, the measurement errors associated with self-reported data are nonfalsifiable because there is no way to ascertain if the reported foods and beverages match the respondent's actual consumption. Fourth, the assignment of nutrient and energy values to self-reported intake (i.e., the pseudoquantification of anecdotal data) is impermissible and violates the foundational tenets of measurement theory. Fifth, the proxy estimates created via pseudoquantification are often physiologically implausible and have little relation to actual nutrient and energy consumption. Finally, investigators engendered a fictional discourse on the health effects of dietary sugar, salt, fat and cholesterol when they failed to cite contrary evidence or address decades of research demonstrating the fatal measurement, analytic, and inferential flaws of memory-based dietary assessment methods.

Empirically Derived Dietary Patterns in UK Adults Are Associated with Sociodemographic Characteristics, Lifestyle, and Diet Quality

The aim of this study was to examine empirical dietary patterns in UK adults and their association with sociodemographic characteristics, lifestyle factors, self-reported nutrient intake, nutrient biomarkers, and the Nutrient-based Diet Quality Score (NDQS) using National Diet and Nutrition Survey data 2008–2012 (n = 2083; mean age 49 years; 43.3% male). Four patterns explained 13.6% of the total variance: ‘Snacks, fast food, fizzy drinks’ (SFFFD), ‘Fruit, vegetables, oily fish’ (FVOF), ‘Meat, potatoes, beer’ (MPB), and ‘Sugary foods, dairy’ (SFD). ‘SFFFD’ was associated positively with: being male; smoking; body mass index (BMI); urinary sodium; intake of non-milk extrinsic sugars (NMES), fat and starch; and negatively with: age; plasma carotenoids; and NDQS. ‘FVOF’ was associated positively with: being non-white; age; income; socioeconomic classification (National Statistics Socio-economic Classifications; NSSEC); plasma carotenoids; intake of non-starch polysaccharides and polyunsaturated fatty acids. It was negatively associated with: being male, smoking, BMI, urinary sodium, intake of saturated fat; and NMES and NDQS. Whilst the patterns explained only 13.6% of the total variance, they were associated with self-reported nutrient intake, biomarkers of nutrient intake, sociodemographic and lifestyle variables, and the NDQS. These findings provide support for dietary patterns analyses as a means of exploring dietary intake in the UK population to inform public health nutrition policy and guidance.

Common scientific and statistical errors in obesity research

This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and "P-value hacking," 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician.