Verify, Offload, Extend & Recommend: Selective Complementarity in AI Support for Physical Activity Planning with Longitudinal Patient Data

2026-09-16

AI Summary

研究探讨了AI在心血管疾病患者体力活动规划中的辅助作用，通过对比有无AI支持下的计划制定，发现AI作为专业技能的选择性补充，其有效性依赖于临床背景和专家知识。

Abstract

Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.

Problem

• Clinical Decision Support
• Physical Activity Planning
• Patient-Generated Health Data
• Artificial Intelligence
• Cardiovascular Disease

Innovation

• Generative AI
• Clinical Decision Support System
• Physical-activity Planning
• Visualization Literacy
• Selective Complementarity