方法对比

	Hill-Bone依从性量表 (HBCS)	药物信念问卷 (BMQ)
领域	药理学	药理学
方法族	Process / pipeline	Process / pipeline
起源年份	1999	1999
提出者	Marjorie T. Kim, Mozella N. Hill, Lisa R. Bone, and Debra M. Levine	Rob Horne, John Weinman, and Michelle Hankins
类型	Self-report	Self-report
开创性文献	Kim, M. T., Hill, M. N., Bone, L. R., & Levine, D. M. (1999). Development and Testing of the Hill-Bone Compliance Scale. Journal of Cardiovascular Nursing, 4(1), 54-59. (Also: Hill, M. N., Bone, L. R., & Kim, M. T. (1996). Perspective on compliance research in hypertension. Journal of Clinical Hypertension, 8(1), 12-17.)	Horne, R., Weinman, J., & Hankins, M. (1999). The Beliefs about Medicines Questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psychology & Health, 14(1), 1-24.
别名	HBCS	BMQ
相关	4	4
摘要	The Hill-Bone Compliance Scale (HBCS) is a brief, disease-specific self-report measure designed to assess medication and lifestyle adherence in hypertension management. Developed by Kim, Hill, Bone, and Levine at Johns Hopkins University in 1999, the HBCS measures three dimensions of hypertension adherence: medication-taking, dietary sodium restriction, and appointment keeping. Unlike generic adherence measures, the HBCS captures the multifaceted nature of hypertension self-management, recognizing that many hypertensive patients struggle equally with medication adherence and behavioral changes (diet, exercise, weight management, stress management). The scale has demonstrated strong reliability and validity in diverse hypertensive populations and remains widely used in hypertension research and clinical management.	The Beliefs about Medicines Questionnaire (BMQ) is an 18-item self-report measure developed by Horne, Weinman, and Hankins in 1999 to assess patients' cognitive beliefs about necessity of medications and concerns about potential adverse effects. It is widely used in clinical research to predict medication adherence, particularly in chronic disease management, and has demonstrated strong predictive validity across diverse populations and disease contexts.