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

Abstract

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.Digital phenotyping (DP) refers to the moment-by-moment quantification of the individual-level human phenotype using data from personal digital devices such as smartphones and wearables 1 .It involves the collection and analysis of behavioural and physiological data to generate insights into an individual's mental and physical states in real-time [1][2][3] .DP has gained significant interest for use in mental health care 2,4-6 .By leveraging wearable devices and smartphones, DP offers real-time insights into individuals' health, enabling the detection of subtle changes in mental and physical states that were previously difficult to detect [7][8][9] .This technique shows high sensitivity in detecting early signs of mental illness 6 and can help predict relapse using smartphone data days before they become clinically apparent [10][11][12][13][14] .Recent work has even suggested that DP 'could support gold-standard assessment andpredict symptom exacerbations' 6 .This offers particular promise, particularly in mental health care, as early intervention can dramatically improve outcomes 14,15 for conditions such as depression [16][17][18] , anxiety 17,[19][20][21][22][23] , and serious mental illnesses such as psychotic disorders 7,8,[16][17][18][19][20] .Despite its potential, DP faces critical technical challenges and usability barriers that undermines its reliability and scalability 24,25 .These challenges are compounded by the absence of standardisation in methodologies, which results in variability across platforms and studies, limiting the reproducibility and generalisability of findings.In this Perspective, we outline these challenges and propose strategies for developing universal frameworks and protocols, to enable more reliable, scalable and impactful applications of DP.

Topics & Concepts