人口,临床和语言特征与参与严重精神疾病的基于信息的干预相关
Justin Tauscher1, Anna Larsen1, Trevor Cohen1
1Department of Psychiatry and Behavioral Sciences, Behavioral Research in Technology and Engineering Center, University of Washington.
患者的人口统计学,临床状况和信息语言预测了参与严重精神疾病的数字干预. 识别这些因素可以个性化治疗,降低学率.
科学领域:
- 数字心理健康干预数字心理健康干预
- 严重的精神疾病 (SMI)
- 患者参与度分析
背景情况:
- 基于信息的干预措施对严重精神疾病 (SMI) 有希望.
- 了解影响患者参与的因素对于干预成功至关重要.
- 预测和提高参与度可以降低数字心理健康中学率.
研究的目的:
- 检查患者人口统计学,临床状况和短信语言特征之间的关联,参与基于消息的SMI干预.
- 确定预测参与的特征,并为干预量身定制提供信息.
主要方法:
- 基于信息的心理健康干预随机对照试验的数据分析.
- 订阅操作化为每天发送的文本和脱离订阅的日.
- 语言查询和字数 (LIWC) 用于分析消息内容;用于统计分析的概括估计方程.
主要成果:
- 人口统计学 (种族,教育) 和诊断 (精神分裂症) 与参与有关.
- 黑人参与者和受过大学教育的人发送的短信更多;精神分裂症患者有更多的不参与的日子.
- 信息内容 (焦虑,友,认知过程,常用动词) 与参与相关,特别是预测未来的参与.
结论:
- 人口,临床和语言因素与参与基于信息的SMI干预有很大关系.
- 根据这些已识别的特征量身定制干预措施可以提高患者的参与度.
- 个性化的数字心理健康策略可以改善治疗坚持和减少学.
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