Impact of risk of generalizability biases in adult obesity interventions: A meta-epidemiological review and meta-analysis

Biases introduced in early-stage studies can lead to inflated early discoveries. The risk of generalizability biases (RGBs) identifies key features of feasibility studies that, when present, lead to reduced impact in a larger trial. This meta-study examined the influence of RGBs in adult obesity interventions. Behavioral interventions with a published feasibility study and a larger scale trial of the same intervention (e.g., pairs) were identified. Each pair was coded for the presence of RGBs. Quantitative outcomes were extracted. Multilevel meta-regression models were used to examine the impact of RGBs on the difference in the effect size (ES, standardized mean difference) from pilot to larger scale trial. A total of 114 pairs, representing 230 studies, were identified. Overall, 75% of the pairs had at least one RGB present. The four most prevalent RGBs were duration (33%), delivery agent (30%), implementation support (23%), and target audience (22%) bias. The largest reductions in the ES were observed in pairs where an RGB was present in the pilot and removed in the larger scale trial (average reduction ES −0.41, range −1.06 to 0.01), compared with pairs without an RGB (average reduction ES −0.15, range −0.18 to −0.14). Eliminating RGBs during early-stage testing may result in improved evidence.