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自动化消息与减肥行为治疗一起提供:定性研究
Michael Berry1, Lauren Taylor1,2, Zhuoran Huang2
1Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States.
JMIR formative research
|November 6, 2023
概括
在减肥干预中,自动化消息得到了适度的接受,但往往无法捕捉用户的个人经验,从而限制了其有效性. 未来的干预需要更多的量身定制的信息来提高参与度.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 肥胖问题研究研究
背景情况:
- 自动化消息传递在移动健康 (mHealth) 中常见,用于减肥.
- 目前的mHealth干预措施显示体重减轻的有效性有限.
- 有限的数据存在于用户与自动化消息的体验,特别是定制反系统.
研究的目的:
- 在减肥试验中使用自动化消息探索超重或肥胖个人的主观经验.
- 评估用户对自动化消息接收的反,并确定需要改进的领域.
主要方法:
- 40名超重/肥胖的参与者在6-12个月内收到自动化消息.
- 参与者提供了关于消息可接受性的利克特级别评分.
- 39名参与者完成了通过专题分析分析的定性访谈.
主要成果:
- 消息的接受度是中等的 (2.67/5).
- 消息对总结目标进展最有用,对新策略最不有用.
- 参与者觉得信息没有捕捉到他们生活的减肥经历,尽管提醒他们目标.
结论:
- 自动化消息被认为不足以适应个人减肥旅程.
- 未来的研究应侧重于通过增加参与者的投入和互动性来加强信息定制.
- 改进的定制可能会增加数字减肥干预措施的参与度和有效性.
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