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Acceptability of Remote Monitoring Technologies for Early Warning of Major Depression

Year: 2025

Type: article

Abstract: Recent research shows that 16% of individuals aged 16 and older are affected by depression, with Major Depressive Disorder (MDD) severely impairing daily life and potentially leading to suicide. Early intervention is vital, but current early warning methods of depression are time-consuming and not scalable. This study examines the acceptability of smartphone-based early warning systems using machine learning for depression intervention. Interviews were conducted with participants from the RADAR-MDD study, where smartphone sensors collected behavioural data. Three key themes emerged: designing remote monitoring technologies (RMTs) to enhance user engagement and wellbeing, RMTs as tools for comprehensive and empowering mental health support, and ethical and purpose-driven data utilization by RMTs. Whilst participants were open to RMTs, they expressed concerns about data commercialization. The study highlights the importance of prioritizing user experience, ethics, and personalization in designing effective early warning systems.

Source: Journal of Technology in Behavioral Science

DOI: 10.1007/s41347-025-00530-1

Authors: Fintan Haley, Jacob Andrews, Nima Moghaddam

Institutions: University of Nottingham, NIHR MindTech MedTech Co-operative, University of Lincoln

Language: English

Topic: Digital Mental Health Interventions

Subfield: Applied Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,090