解码数据驱动的自我监测中的异质性 数字生活方式干预减肥的坚持轨迹:定性研究
Shiyu Li1, Yan Du2, Christiane Meireles2
1Department of Kinesiology, Pennsylvania State University.
Research square
|February 5, 2024
概括
这项研究探讨了在数字生活方式干预中影响自我监测 (SM) 坚持因素. 提高解决问题的技能和解决技术障碍可以改善中小企业的参与,以获得更好的健康结果.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 行为科学是一种行为科学.
- 定性研究方法的方法.
背景情况:
- 对推动数字生活方式干预中独特的自我监测 (SM) 轨迹的因素的理解有限.
- 在六个月的时间里,对参与者小组进行了定性探索,这些小组的SM坚持程度各不相同.
- 调查不同参与数字健康工具的根本原因.
结论:
- 这些发现补充了定量轨迹建模,强调了需要提高下层SM群的解决问题的技能.
- 解决当前饮食SM工具的技术局限性对于改善坚持至关重要.
- 结果可以为增强中小企业参与未来数字生活方式干预措施的实际策略提供信息.
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