Fouyaxis John, Bidargaddi Niranjan, Du Wei, Looi Jeffrey C L, Lipschitz Jessica
Digital Health Research Lab, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.
Academic Unit of Psychiatry and Addiction Medicine, The Australian National University School of Medicine and Psychology, Garran, ACT, Australia.
Digit Health. 2024 Dec 15;10:20552076241306782. doi: 10.1177/20552076241306782. eCollection 2024 Jan-Dec.
Real-time digital mental health interventions, primarily enabled by smartphone technology offer continuous, personalised support, that adapts in response to the changing needs of individuals. Despite being prominently explored in populations with psychiatric disorders, there remains a notable gap in the systematic analysis of demographic characteristics, as well as the foundational design decisions or rules that underpin the personalisation of these interventions.
(a) Identifying the prevalent design decisions to enable personalisation within real-time digital mental health interventions, (b) the influence of these design decisions on the clinical outcomes of the interventions, and (c) the demographic characteristics of populations with psychiatric disorders targeted by real-time digital health interventions.
Following PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines, a systematic literature review was conducted of peer-reviewed literature focusing on real-time digital interventions in populations with clinically diagnosed psychiatric disorders. We undertook a narrative synthesis to derive the demographics and personalisation design decisions of the interventions and conducted a pooled meta-analysis to evaluate clinical outcomes.
Interventions predominantly targeted female and Caucasian demographics, yielding modest clinical improvements. Our analysis identified nine critical personalisation design decisions concerning measurement, intervention, and interactions with health professional with varying influence on clinical outcomes.
Understanding the complex nuances of design decisions that shape real-time digital health interventions, as well as identifying which patient demographics benefit most, is fundamental for their effective clinical impact and safe use.
PROSPERO CRD42020161663.
主要由智能手机技术实现的实时数字心理健康干预提供持续的个性化支持，可根据个人不断变化的需求进行调整。尽管在患有精神疾病的人群中对其进行了大量研究，但在对人口统计学特征以及这些干预措施个性化的基础设计决策或规则进行系统分析方面，仍存在显著差距。
（a）确定在实时数字心理健康干预中实现个性化的普遍设计决策，（b）这些设计决策对干预临床结果的影响，以及（c）实时数字健康干预所针对的患有精神疾病人群的人口统计学特征。
遵循PRISMA（系统评价和荟萃分析的首选报告项目）指南，对同行评审文献进行系统的文献综述，重点关注临床诊断患有精神疾病人群的实时数字干预。我们进行了叙述性综合分析，以得出干预措施的人口统计学和个性化设计决策，并进行了汇总荟萃分析以评估临床结果。
干预措施主要针对女性和白种人，临床改善效果一般。我们的分析确定了九个关于测量、干预以及与健康专业人员互动的关键个性化设计决策，这些决策对临床结果有不同程度的影响。
了解塑造实时数字健康干预措施的设计决策的复杂细微差别，以及确定哪些患者群体受益最大，对于其有效的临床影响和安全使用至关重要。
PROSPERO注册：PROSPERO CRD42020161663。
