ArticleFrontiers in public health2026
Development of a new segmentation algorithm for "precontemplation" stage in lifestyle change among high-risk populations for lifestyle-related diseases.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Specific health guidance programs provide motivational support to individuals in the precontemplation stage of the Transtheoretical Model (TTM) to encourage behavioral changes. However, this stage may encompass heterogeneous attitudes beyond simple lack of health interest. This study aimed to develop a novel segmentation classification incorporating behavioral economics concepts to better characterize the precontemplation population and validate targeted interventions. Methods: We conducted a two-phase study among Japanese adults aged 40-59 years eligible for specific health guidance. Phase 1 ( Results: Seven distinct segments were identified: 'High health awareness and practices' (18.7%), 'High risk perception and busy' (12.0%), 'High health threat' (21.1%), 'High health anxiety' (11.8%), 'Procrastination and improvement interest' (12.7%), 'Busy and no future image' (19.2%), and 'Procrastination and improvement resistance' (4.5%). A simplified 9-item algorithm achieved 50.7% classification accuracy (κ = 0.42, 95% CI: 0.39-0.45) compared to the original clustering. Message testing revealed significant segment effects ( Discussion: This novel segmentation approach provides deeper understanding of the precontemplation stage by identifying distinct subgroups with unique behavioral economic characteristics including present bias and reference point orientations. Findings suggest that tailored interventions considering these behavioral economic factors may be more effective than traditional stage-based approaches for engaging resistant populations in health behavior change.
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