Scholarlink

<p>There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review. Accelerometers were the most common device (<i>n</i> = 115 studies), followed by smartphones (<i>n</i> = 46). The most commonly derived features were sleep duration (<i>n</i> = 50) and time spent sedentary (<i>n</i> = 41). Thirty studies assessed data quality and another 69 applied data quantity thresholds. Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A systematic review of passive data for remote monitoring in psychosis and schizophrenia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月27日

查看全文
  • Siân Bladon,
  • Emily Eisner,
  • Sandra Bucci,
  • Anuoluwapo Oluwatayo,
  • Glen P. Martin,
  • Matthew Sperrin,
  • John Ainsworth,
  • Sophie Faulkner

摘要

There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review. Accelerometers were the most common device (n = 115 studies), followed by smartphones (n = 46). The most commonly derived features were sleep duration (n = 50) and time spent sedentary (n = 41). Thirty studies assessed data quality and another 69 applied data quantity thresholds. Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.