LvL UP试验:一项顺序,多重分配,随机对照试验的协议,以评估混合移动生活方式干预措施的有效性
Oscar Castro1, Jacqueline Louise Mair2, Shenglin Zheng2
1Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Contemporary clinical trials
|February 3, 2025
概括
本研究评估了LvL UP,这是一种用于预防非传染性疾病和心理健康问题的移动健康干预措施. 它旨在找到将数字工具与人类支持相结合的最佳方法,以实现可扩展,有效的生活方式变化.
科学领域:
- 公共卫生 公共卫生
- 数字健康数字健康
- 行为科学 行为科学
背景情况:
- 非传染性疾病 (NCD) 和常见的精神疾病 (CMD) 构成了重大的公共卫生挑战.
- 移动健康 (mHealth) 干预,特别是结合自主指导和人类支持的混合方法,显示出预防的希望.
- LvL UP干预方案旨在通过一种新的试验设计来应对这些挑战.
研究的目的:
- 评估LvL UP mHealth生活方式干预措施的有效性和成本效益.
- 在LvL UP中确定最佳的混合方法,平衡个性化支持与可扩展性.
- 研究适应性激励面试 (MI) 对干预结果的影响.
主要方法:
- 一个连续的,多重分配的,随机的试验,涉及年轻和中年新加坡成年人,有NCD/CMD风险.
- 参与者最初被随机分配到"LvL UP"或"比较"组.
- "LvL UP"组中没有反应的人被重新随机选择继续LvL UP或接受LvL UP加自适应性MI.
主要成果:
- 主要结果是心理健康,次要结果包括人体测量,代谢和行为测量.
- 数据收集发生在基线,6个月 (干预后) 和12个月 (后续).
- 分析的重点是比较干预武器之间的结果,并确定影响反应的因素.
结论:
- 该研究将提供关于LvL UP干预措施在预防NCD和CMD方面的有效性的证据.
- 调查结果将为在移动健康干预中优化人力支持的整合提供信息,以提高可扩展性和有效性.
- 这项研究为日益增长的关于预防性护理数字健康策略的证据提供了贡献.
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