Table 1 Summary of qualitative analysis, including examples of analytical categories identified
Examples | Description | Analytical Categories |
“Cox-regression analysis of pooled (intervention and control group) data, showed that participants who achieved ≥5% weight loss at year one had 64% lower T2D incidence….” (Penn et al. 2013:5 and Fig. 4) “Results of these analyses showed that a greater increase in fitness was associated with greater weight loss with DSE [Diabetes support and Education] and ILI [Intensive Lifestyle Intervention] combined…” (Jakicic et al. 2013: 1301 and Fig. 3) “There was a significant reduction in waist circumference at 3 months (− 2.5 cm; 95% CI -3.8 to − 1.3) and 1 year (− 2.8 cm;95% CI -4.4 to − 1.1) in the intervention group” (Juul et al. 2016;116) “With LTPA treated as a continuous variable, there was a significant association (p = 0.02) between change in LTPA and change in weight after adjustment for baseline LTPA, clinic, treatment, clinic*treatment, and baseline weight. This association remained significant when data were analyzed separately for ILI (p = 0.02) but not for DSE. With change in LTPA treated as a continuous variable and with adjustment for selected covariates, there was no significant association observed between HbA1c and LTPA”. (Jakicic et al. 2013;1301) “The initially large effects in HRQOL that diminish over time are consistent with the larger weight loss and greater compliance with the intervention seen in the first year of Look AHEAD followed by weight regain and diminished compliance in later years” (Zhang et al. 2016; 862) “Another possibility is that a sustained weight loss of more than that achieved in the intervention group may be required to reduce the risk of cardiovascular disease” (Wing et al. 2013; 151) “These new findings advance our knowledge that community delivery of the DPP intervention in a lower-cost format by the YMCA can achieve meaningful weight loss among broader segments of the US population. This has important implications for ongoing diabetes prevention” (Ackermann 2015; 2333) “Weight loss is assumed to be the predominant factor for preventing type 2 diabetes in high risk groups” (Juul et al. 2016; 118) “Analysis showed the preventive effect of 5% weight loss, especially if maintained long term, which has utility for intervention monitoring” (Penn et al. 2013) “Most importantly, we found that this relatively modest intervention could produce beneficial effects on the incidence of type 2 diabetes during a 3-year period. The halving (51%) of the relative risk for overall subjects through this intervention is not negligible, even though it did not reach a statistically significant level”. (Sakane et al. 2011; 6) | Pooling of intervention and control group data to test for association between weight loss and outcomes Within group differences reported before or instead of between group differences Prioritizing positive secondary outcomes over non-significant primary outcomes Significant change in secondary outcomes presented as certain to produce reduction in diabetes incidence or complications Weight loss presented as an objective measure of lifestyle adherence, particularly in the context of lifestyle behavioural data being unreliable Varying BMI cut-points used for inclusion criteria in addition to measures of glucose metabolism Weight loss, regardless of intervention group or the inclusion of primary outcomes, was presented as certain to reduce risk of diabetes and its complications | Pooling data Within group difference Promote positive secondary outcome Downplay negative primary outcome Order of presentation Alignment of published outcomes with trial registry Omission of weight loss as outcome in registry Caloric restriction Dietary data not available/unreliable BMI as inclusion criteria Weight as adherence Weight loss as equivalent to diabetes risk reduction |