Decoding heterogeneity in data-driven self-monitoring adherence trajectories in digital lifestyle interventions for weight loss: a qualitative study

Abstract

Abstract Background Data-driven trajectory modeling approaches have been used to identify participant subgroups with differing responses to digital lifestyle interventions. Identifying contributing factors to different participant subgroups can inform tailored strategies to early “rescue” intervention non-responders. Self-monitoring (SM) is a central mechanism in lifestyle interventions for driving behavior change and can serve as an early indicator for later intervention response. This qualitative study aimed to compare SM experiences between intervention response subgroups and to identify contributing factors to intervention response subgroups in a 6-month digital lifestyle intervention for adults with overweight or obesity. Results Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Responder Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Non-responder Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions Our study indicates that qualitative analysis is valuable for translating data-driven findings to actionable intervention improvement strategies. Our findings may inform the development of practical SM improvement strategies in future digital lifestyle interventions for weight loss. Notably, building problem solving skills emerge as a key approach to prevent potential non-responders from intervention disengagement.

Keywords

• Data science
• Qualitative research
• Lifestyle interventions
• Obesity
• Self-monitoring
• Adherence