Scholarlink

<p>Digital phenotyping offers a promising but heterogeneous approach for assessing schizophrenia-spectrum disorders (SSD). This systematic review, the first of its kind, comprehensively analyzes the diagnostic and predictive utility of digital phenotyping in SSD. Following PRISMA guidelines, we synthesized data from 142 peer-reviewed studies (2004–2024; <i>n</i> = 6294 participants). Results show a predominance of smartphone and wearable-based approaches, with only ~20% of studies combining active and passive methods. Among six symptom domains, cognitive performance yielded the largest pooled effect size (Hedges’ <i>g</i> ≈ 1.20) for differentiating individuals with SSD from controls, followed by behavior and activity (<i>g</i> ≈ 0.62). However, both domains exhibited very high heterogeneity (I² &gt; 70%). Correlations with the PANSS scale were scarce (&lt;5% of studies), with coefficients reaching 0.6. Relapse prediction models showed promise, with some AUC values reaching 0.8, but lacked methodological standardization. This review highlights the potential of specific digital measures while underscoring the urgent need for improved reporting, multimodal data integration, and longitudinal studies with diverse populations to enhance diagnostic and predictive power in SSD.</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

Diagnostic digital phenotyping in schizophrenia-spectrum disorders: a systematic review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

查看全文
  • Ivan Vecchio,
  • Lucas Mifsud,
  • Sofia Castro e Almeida,
  • Johannes Passecker

摘要

Digital phenotyping offers a promising but heterogeneous approach for assessing schizophrenia-spectrum disorders (SSD). This systematic review, the first of its kind, comprehensively analyzes the diagnostic and predictive utility of digital phenotyping in SSD. Following PRISMA guidelines, we synthesized data from 142 peer-reviewed studies (2004–2024; n = 6294 participants). Results show a predominance of smartphone and wearable-based approaches, with only ~20% of studies combining active and passive methods. Among six symptom domains, cognitive performance yielded the largest pooled effect size (Hedges’ g ≈ 1.20) for differentiating individuals with SSD from controls, followed by behavior and activity (g ≈ 0.62). However, both domains exhibited very high heterogeneity (I² > 70%). Correlations with the PANSS scale were scarce (<5% of studies), with coefficients reaching 0.6. Relapse prediction models showed promise, with some AUC values reaching 0.8, but lacked methodological standardization. This review highlights the potential of specific digital measures while underscoring the urgent need for improved reporting, multimodal data integration, and longitudinal studies with diverse populations to enhance diagnostic and predictive power in SSD.