Abstract
Women with previous gestational diabetes mellitus (WPGDM) have high risk for T2D but low rates of engagement in diabetes prevention interventions. We used an informed decision-making strategy with WPGDM to promote participation in behavioral interventions. These included group (National Diabetes Prevention Program (DPP) , web-based DPP, modified DPP for families, WW) ; individual (health coaching, digital WW) ; and dietician consult (passive) . We evaluated 1) intervention preferences, and 2) determinants of intervention choices. We used the CoMac Descriptor™ (CoMac) to assess person-centered determinants of intervention choice. CoMac’s 12-question survey categorizes patients into three engagement categories based on locus of control, emotional state, and agency. Of 116 WPGDM (age 34±7) who participated, 95 chose an intervention. CoMac predicted high (H) , moderate (M) or low (L) engagement with behavioral intervention (63% H; 29% M; 8% L) . Group interventions were chosen by a majority (58%) of participants (53% H, 79% M, 74% L) . Individual interventions were chosen by 34% (33% H, 21% M, 26% L) . Passive interventions were chosen by 8% (13.7% H, 0% M/L) . Participants categorized as H (mean A1C 5.2±.1) chose group interventions just more than half of the time. Participants categorized as M (mean A1C 5.3±.1) and L (mean A1C 5.4±.1) chose group interventions over individual at a higher rate than Hs, nearly three-quarters of the time. Our results show that the majority of WPGDM prefer active over passive behavior interventions. This runs counter to the passive behavioral interventions typically offered in healthcare settings. Results further show that person-centered trait clusters are associated with intervention preference, and CoMac’s patient categorization may help providers to better align interventions with patient preference. Disclosure M.Hume: None. T.S.Hannon: Advisory Panel; Eli Lilly and Company. R.Sandy: None. U.M.Connor: Other Relationship; CoMac Analytics, Inc. Funding Indiana University Grand Challenge Precision Health Initiative
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