基于激励的戒烟干预措施中的生态瞬间评估:预测近期接触和烟雾状况
Vivian Kaufman1, Devin C Tomlinson1, Belle Duguid1
1Addiction Center, Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, MI, USA.
Substance use & misuse
|October 31, 2025
概括
在数字戒烟干预期间的生态瞬间评估 (EMA) 预测了参与和吸烟状态. 这可以帮助量身定制及时适应性干预措施,以获得更好的戒烟结果.
科学领域:
- 公共卫生 公共卫生
- 行为科学 行为科学
- 数字健康数字健康
背景情况:
- 在美国,吸烟仍然是可预防死亡的主要原因之一.
- 医疗补助受益人面临着戒烟的重大障碍.
- 数字干预为戒烟提供了可扩展的解决方案.
研究的目的:
- 在基于数字激励的干预中,确定参与和吸烟状态的预测因素.
- 评估生态瞬间评估 (EMA) 在实时预测中的实用性.
- 为吸烟戒断的适应性干预措施的发展提供信息.
主要方法:
- 37名医疗补助受益人参加了为期4周的数字干预,并获得了金钱奖励.
- 每天收集两次生态瞬间评估 (EMA) 和呼吸样本.
- 混合效应物流回归模型分析了参与和吸烟状态的预测因素.
主要成果:
- 环境和个人因素 (例如,社交吸烟,渴望,压力) 预测参与度较低.
- 对弃权能力的信心预测了更高的参与度.
- 最近的吸烟和压力预测了积极的烟雾状况;信心和动机预测了负面的烟雾状况.
结论:
- 在数字干预期间,EMA有效预测参与和吸烟状态.
- 这些发现支持使用EMA进行实时监测和预测.
- 这可以引导创建恰到好处的适应性干预措施,以改善戒烟.
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