Bladon Siân, Eisner Emily, Bucci Sandra, Oluwatayo Anuoluwapo, Martin Glen P, Sperrin Matthew, Ainsworth John, Faulkner Sophie
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.
Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, M13 9PL, UK.
NPJ Digit Med. 2025 Jan 27;8(1):62. doi: 10.1038/s41746-025-01451-2.
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.
越来越多地使用数字工具对患有精神病和精神分裂症的患者进行远程监测,但使用这类数据具有挑战性。本系统评价旨在总结研究如何处理和分析通过数字设备收集的数据。该评价总共纳入了203篇从患有精神病或精神分裂症的参与者通过智能手机或可穿戴设备收集被动数据的文章。加速度计是最常用的设备(n = 115项研究),其次是智能手机(n = 46)。最常得出的特征是睡眠时间(n = 50)和久坐时间(n = 41)。30项研究评估了数据质量,另有69项应用了数据量阈值。21项研究使用了混合效应模型,18项研究使用了时间序列和机器学习方法。处理和分析数据的方法报告不一致,突出表明需要改进该研究领域方法和报告的标准化。