Scholarlink

<p>Sensor-based data collection of human behaviour (digital phenotyping) enables real-time monitoring of behavioural and physiological markers. This emerging approach offers immense potential to transform mental health research and care by identifying early signs of symptom exacerbation, supporting personalised interventions, and enhancing our understanding of daily lived experiences. However, despite its promise, technical and user-experience challenges limit its effectiveness. This Perspective critically examines these challenges and provides standardisation strategies, including universal protocols and cross-platform interoperability. We propose the development of universal frameworks, adoption of open-source APIs, enhanced cross-platform interoperability, and greater collaboration between academic researchers and industry stakeholders. We also highlight the need for culturally sensitive and user-centred designs to improve equity and engagement. By addressing these gaps, standardisation can enhance data reliability, promote scalability and maximise the potential of digital phenotyping in clinical and research mental health settings.</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

Challenges and standardisation strategies for sensor-based data collection for digital phenotyping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

查看全文
  • Nadia Binte Alam,
  • Mohsin Surani,
  • Chayon Kumar Das,
  • Domenico Giacco,
  • Swaran P. Singh,
  • Sagar Jilka

摘要

Sensor-based data collection of human behaviour (digital phenotyping) enables real-time monitoring of behavioural and physiological markers. This emerging approach offers immense potential to transform mental health research and care by identifying early signs of symptom exacerbation, supporting personalised interventions, and enhancing our understanding of daily lived experiences. However, despite its promise, technical and user-experience challenges limit its effectiveness. This Perspective critically examines these challenges and provides standardisation strategies, including universal protocols and cross-platform interoperability. We propose the development of universal frameworks, adoption of open-source APIs, enhanced cross-platform interoperability, and greater collaboration between academic researchers and industry stakeholders. We also highlight the need for culturally sensitive and user-centred designs to improve equity and engagement. By addressing these gaps, standardisation can enhance data reliability, promote scalability and maximise the potential of digital phenotyping in clinical and research mental health settings.