在数字减肥干预 (Spark) 中优化自我监测:因数随机试验的协议
Michele L Patel1, Abby C King1,2, Lisa G Rosas2,3
1Department of Medicine, Stanford Prevention Research Center, School of Medicine, Stanford University, Palo Alto, CA, United States.
JMIR research protocols
|September 23, 2025
概括
本研究确定了数字减肥干预措施的有效自我监测策略. 通过精确确定关键组件,研究结果将优化患者的努力,并最大限度地提高减肥结果.
科学领域:
- 行为科学 行为科学
- 数字健康数字健康
- 肥胖治疗方法 肥胖治疗方法
背景情况:
- 自我监测对于肥胖治疗至关重要,包括跟踪饮食,活动和体重.
- 为了最大限度地减肥,自我监测策略的最佳组合仍然没有确定.
- 多阶段优化战略框架确定了有效的干预组件,并最大限度地减少了患者的负担.
研究的目的:
- 检查跟踪饮食摄入量,步骤和体重对减肥的独特和综合影响.
- 在数字减肥干预中确定最有效的自我监测策略.
主要方法:
- 一个优化随机临床试验 (Spark) 具有2x2x2的全因数设计,涉及176名超重或肥胖的美国成年人.
- 参与者在使用商业工具的6个月数字干预中接受了0-3个自我监测策略 (饮食,步骤,体重).
- 主要结局:从基线到6个月的体重变化;次要结局包括BMI,热量摄入量,饮食质量,体力活动和生活质量.
主要成果:
- 招聘发生在2023年9月至2024年11月;数据收集于2025年6月结束.
- 目前正在进行数据分析.
- 结果将阐明个人和联合自我监测策略对减肥的影响.
结论:
- 这项试验将确定数字减肥干预措施中自我监测的"活性成分".
- 研究结果将为优化干预提供信息,最大限度地减肥并最大限度地降低患者负担.
- 探索特定策略对子组的益处将使治疗方法个性化.
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