Remote measurement technologies for depression in young people: A realist review with meaningful lived experience involvement and recommendations for future research and practice

Background Remote measurement technologies (RMT), such as smartphones and wearables, allow data collection from an individual in real-time during their day-to-day life, from which their mood, physiology, behaviour, and environment can be inferred. As such, RMT could monitor and detect changes relevant to depression for objective screening, symptom management, relapse-prevention, and personalised interventions. Whilst RMT for depression in young people has been previously reviewed, technological capability and digital mental health literature steeply increase each year but with limited scrutiny of the realist and ethical considerations likely to impact the benefits, implementation, and overall potential of RMT in the real-world. Methods A realist review of RMT for depression in young people aged 14 – 24 years was conducted in collaboration with two young, lived experience co-researchers from The McPin Foundation Young People’s Network (YPN) and in accordance with the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) for quality and publication. Iterative searches across 10 electronic databases and 7 sources of grey literature, fine-tuning of selection-criteria, data extraction and evidence synthesis with insights from the wider YPN members allowed gradual refinement of an initial framework into a realist intervention theory. Results Of 6118 records identified, 104 were included in evidence synthesis. What does and does not work? Smartphones were most preferred, with both passive and active data collection for a holistic approach but a balance between data quality, intrusiveness, and data privacy. From the evidence currently available, depression was best detected by changes in sleep, mobility, smartphone use, social communication, and self- or- parent-reported mood. This had some uses in screening, self-monitoring, and feedback to the healthcare professional but not in relapse-prevention and personalised interventions, where significantly more research is required. How and why? The impact of RMT as an intervention itself on depression outcomes remained unclear but self-monitoring and feedback improved emotional self-awareness, therapeutic relationship, and help-seeking behaviours. For whom? With limited standardisation and investigation of the impact of depression on adherence rates, there may be an overestimation of how much young people are likely to use RMT in the real-world. However, they were most likely to benefit those interested in and motivated by the data-driven nature, who have lower depression severity, no co-morbidities where self-monitoring could cause harm, and the presence of changeable behaviours. In what contexts? RMT facilitated monitoring during transition to university, known to be associated with worsening depression in young people; however, there were significant challenges in health care and school settings. Adaptability was important, such that RMT were culturally compelling and accurate for the local context. Overall, there were many gaps in the evidence and common methodological issues across the literature. Conclusions From the evidence base and lived experience insights, realist and ethical considerations were highlighted, as well as the remaining gaps in evidence and methodological issues common across the literature. For RMT to be the scalable solution for depression in young people rather than a case of overplayed potential, several important recommendations for future research and practice were made. ### Competing Interest Statement AW receives funds from The McPin Foundation in her role as a lived experience panel member on an unrelated project. AvH developed the Electronic Behaviour Monitoring app (EBM version 2.0) as part of the StandStrong platform for passive sensing in maternal depression in low resource settings. GN, TS, MM, ZZ and VM declare no conflicts of interest. ### Funding Statement This work was funded by a Wellcome Trust Mental Health Active Ingredients commission awarded to AW at KCL. We would like to thank The McPin Foundation and their Young Peoples Network for their significant contributions to the project, manuscript, and dissemination. VM and AW are also supported by a MQ Brighter Futures grant [MQBF/1 IDEA], and VM by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London (NIHR Maudsley BRC). The views expressed are those of the authors and not necessarily those of the Wellcome Trust, MQ, NHS, NIHR, Department of Health and Social Care, or Kings College London. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript. * EMA : ecological momentary assessment LMICs : low-to-middle income countries RMT : remote measurement technologies YPN : Young People’s Network