提高实时数字心理健康干预的关键设计决策和用户人口统计:系统审查
John Fouyaxis1, Niranjan Bidargaddi1, Wei Du1
1Digital Health Research Lab, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.
Digital health
|December 17, 2024
概括
实时数字心理健康干预提供个性化的支持. 这次审查确定了影响精神病患者群体结果的关键设计选择和人口因素,突出了改善临床影响的领域.
科学领域:
- 数字健康数字健康
- 心理健康干预措施 心理健康干预
- 精神疾病 精神疾病
背景情况:
- 实时数字心理健康干预利用智能手机技术提供持续的适应性支持.
- 在理解这些针对精神病患者群体的个性化干预措施的设计原则和人口定向方面存在差距.
研究的目的:
- 在实时数字心理健康干预中识别个性化设计决策.
- 评估这些设计决定对临床结果的影响.
- 描述这些干预措施所针对的精神病患者群体的人口结构.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 人口统计和个性化设计的叙事合成.
- 聚合的元分析用于评估临床结果.
主要成果:
- 干预主要针对女性和白人人口统计学,显示了适度的临床改善.
- 在测量,干预和专业互动方面确定了9个关键的个性化设计决策.
- 这些设计决定对临床结果的影响各不相同.
结论:
- 了解个性化设计和人口定向对于数字心理健康工具的有效和安全的临床实施至关重要.
- 进一步的研究可以优化针对特定患者群体的干预措施.
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