Tag: United Kingdom

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The Performance of Wearable Device–Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis

Background: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression. Objective: This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device–based AI models for detecting depression and predicting depressive episodes and explored factors influencing outcomes. Methods: Following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy) guidelines, the PubMed, Embase, Web of Science, and PsycINFO databases were searched from inception to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) were pooled using a bivariate random effects model. Risk of bias was assessed using Prediction Model Risk of Bias Assessment Tool plus artificial intelligence (PROBAST+ AI), and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. Results: We included 16 studies (32 datasets) with 1189 patients and 13,593 samples. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI 0.83‐0.93) and 0.93 (95% CI 0.87‐0.96), with a diagnostic odds ratio of 110.47 (95% CI 33.33‐366.17) and AUC of 0.96 (95% CI 0.94‐0.98). Random forest models showed the best performance (sensitivity=0.89, specificity=0.91, AUC=0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy (<.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI 0.80‐0.91), and pooled specificity was 0.65 (95% CI 0.59‐0.71). The overall risk of bias was low to moderate, with no evidence of publication bias. Conclusions: Wearable device–based AI models achieved high accuracy for detecting depression and moderate utility in predicting episodes. However, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limited generalizability. Trial Registration: PROSPERO CRD420251070778; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251070778
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[Comment] Lived experience perspectives on the development of a Psychosis Metabolic Risk Calculator (PsyMetRiC)

In this issue of The Lancet Psychiatry, Benjamin Perry and colleagues1 present a collaboratively developed, refined, and externally validated risk prediction tool (the Psychosis Metabolic Risk Calculator [PsyMetRiC]) that is clinically available, and that can separately predict the risk of clinically significant weight gain, metabolic syndrome, and type 2 diabetes in young people with psychosis. Key to the collaborative development of PsyMetRiC has been the involvement of young people with a lived experience of psychosis, supported by the McPin Foundation and Equally Well UK.

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ARIA funding

We’re proud to share that Relatix Bio has applied for funding from the UK’s Advanced Research and Invention Agency (ARIA) under their Trust Everything, Everywhere programme. This initiative explores how trust can be built across the digital and physical worlds, and we believe this conversation must include those whose minds work differently.

Our proposal focuses on one of the most pressing and least understood challenges of the digital age: how people with neurodevelopmental and neurodiverse conditions — including autism, ADHD, schizophrenia, borderline traits, and psychopathy — experience, interact with, and build trust in AI systems. In a world increasingly mediated by algorithms, the ways these systems interpret, respond to, and store our most personal thoughts and data matter profoundly.

Throughout history, individuals living with stigmatised neurocognitive conditions have been marginalised or misrepresented — by institutions, by society, and now, potentially, by AI. Some may over-trust technology that feels neutral or supportive; others may under-trust it due to past harm or bias. We want to ensure that digital systems meet people where they are — building trust rather than eroding it. Protecting privacy, and supporting quality of life, health and wellbeing.

Through our work, Relatix Bio aims to lead the way in ethical and inclusive neuro-AI design: protecting privacy, removing stigma, and defining standards for responsible data handling in the era of AI. Our goal is to make sure that the next generation of AI-driven tools — from chatbots to diagnostics — truly serve everyone, regardless of how their brain is wired.

We know how often in the past things have gone wrong — from chatbots unintentionally encouraging depressive or paranoid thoughts, to credit and gambling platforms optimising for addiction or impulsive behaviour. These systems were built without safeguarding those with neurodevelopmental conditions, who may react differently to AI optimised interactions. Many respond by disengaging digitally, and may be feeling that an AI-driven world is a minefield — because it wasn’t built for them.

Join us in shaping a radically different future where cognitive diversity and digital trust can coexist, and AI tools are built to truly support and facilitate. To learn more about our mission or to collaborate contact our team.