预测和理解慢性疾病中的不遵守:SPUR 6/24工具的跨队列验证和结构方程建模
Kevin Dolgin1, Reem Kayyali2, Joshua Wells3
1IAE Paris-Sorbonne Business School 8 Bis, Rue de La Croix Jarry, 75013, Paris, France. kevin.dolgin@observia-group.com.
Scientific reports
|September 26, 2025
概括
在慢性疾病中,SPUR 6和SPUR 24工具有效地预测患者不坚持的风险. 社会和心理因素通过理性和使用驱动因素显著影响坚持.
科学领域:
- 健康心理学 心理健康心理学
- 行为科学 行为科学
- 临床研究 临床研究
背景情况:
- 患者不坚持慢性病治疗是一个重大的全球卫生挑战.
- 现有的执法措施往往缺乏对行为驱动因素的全面评估.
- 为了解决这些局限性,开发了SPUR (自我报告的患者理解风险) 工具.
研究的目的:
- 验证和完善SPUR 6和SPUR 24患者报告的遵守措施.
- 评估SPUR工具在不同患者群体和病理方面的预测有效性.
- 通过结构方程建模,研究13个行为驱动因素对非坚持风险的影响.
主要方法:
- 使用SPUR 6和SPUR 24进行早期患者队列的回顾性分析.
- 聚合数据集的分析,结合多个队列,国家和病理.
- 结构方程建模 (SEM) 用于测试结构有效性和驱动因素的影响.
- 使用斯皮尔曼等级相关性,与现有的患者报告的依从度量进行比较.
主要成果:
- 后调和聚合数据分析都支持SPUR 6和SPUR 24,用于评估不合规风险.
- SPUR工具的预测价值与其他广泛使用的指标相当或超过.
- SEM证实,社会和心理驱动因素直接和间接地通过理性/使用驱动因素对不遵守风险产生重大影响.
结论:
- "SPUR 6"和"SPUR 24"是验证的工具,用于预测慢性疾病患者的不服从.
- 行为驱动因素,特别是社会和心理因素,在坚持中起着至关重要的作用,通常由理性和使用因素介导.
- 结果为有针对性的沟通策略提供信息,以改善患者的药物服药.
关键词:
行为驱动因素 行为驱动因素交叉队列分析 交叉队列分析健康行为健康行为药物治疗的坚持 药物治疗的坚持患者报告的遵守措施 (PRAM)患者报告的结果措施 (PROM)这是SPUR工具.结构方程建模 结构方程建模更多相关视频
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