Kent and Medway Mental Health NHS Trust has a launched a portal to give patients 24/7 online access to their mental health appointments.
The efficacy and safety of transcranial direct current stimulation in patients with ADHD: a systematic review and meta-analysis
ObjectiveThis meta-analysis evaluated the efficacy and safety of transcranial direct current stimulation (tDCS) for treating Attention-Deficit/Hyperactivity Disorder (ADHD).MethodsFollowing PRISMA guidelines, we analyzed 28 randomized controlled trials (RCTs) involving 1,864 participants. Outcomes encompassed core ADHD symptoms, hot and cold executive functions (EFs)—including inhibitory control, working memory, and cognitive flexibility—as well as safety profiles based on adverse events. A multilevel meta-analysis was performed using a random-effects model. Subgroup analyses and meta-regressions were conducted to explore potential moderating factors.ResultsCompared to sham stimulation, tDCS did not significantly improve core ADHD symptoms (standardized mean difference (SMD) = –0.29, 95% CI [–0.59, 0.01], p= 0.05). Similarly, no significant overall effects were observed for cold EFs: inhibitory control (Hedges’ g(g)= –0.11, 95% CI [–0.26, 0.05], p=0.19), working memory (g= 0.13, 95% CI [–0.06, 0.32], p= 0.26), or cognitive flexibility (SMD = –0.42, 95% CI [–1.13, 0.29], p= 0.24). The effect on hot EFs was also non-significant (g = 0.27, 95% CI [–0.14, 0.70], p = 0.19). Exploratory analyses indicated that anode placement at Fp2 was associated with improvement in both inhibitory control (g= –0.52, 95% CI [–0.93, –0.11], p=0.01) and working memory (g = 0.72, 95% CI [0.22, 1.22], p = 0.004), although the overall test for interaction was not significant for inhibitory control (p= 0.19). The most common adverse reactions were mild and transient local skin symptoms, such as itching and redness (RR = 1.42, p=0.04).ConclusiontDCS was well-tolerated but did not demonstrate significant overall efficacy for core ADHD symptoms or executive functions. Anodal stimulation at Fp2 showed potential selective benefits warranting further investigation. tDCS is not currently recommended as a standalone treatment for ADHD. Future research should optimize stimulation protocols and explore combined interventions with behavioral or cognitive therapies.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42024612055.
Speech in noise prediction by use of cortical auditory evoked potentials in normal hearing and sensorineural hearing loss: a systematic review
IntroductionSpeech perception in noise (SPiN) is a critical challenge for individuals with sensorineural hearing loss (SNHL), and current behavioral assessments can be unreliable in populations with language barriers or cognitive impairment. Cortical auditory evoked potentials (CAEPs) can serve as a supplementary measurement as they often show strong correlations with SPiN outcomes across diverse hearing profiles.MethodsFollowing PRISMA and SWiM guidelines, this systematic review includes studies from PubMed, Web of Science, and Scopus databases that examined the relationship between non-task related CAEPs and SPiN outcomes in adults with normal hearing, SNHL, or cochlear implants.ResultsSixteen studies were included, encompassing 238 participants with SNHL and 204 participants with normal hearing. Across studies, N1 latency, P2 latency, and N1-P2 amplitude of the onset CAEP and acoustic change complex (ACC) are most consistently correlated with SPiN performance, particularly in sentence-based tests. The mismatch negativity (MMN) showed limited predictive value, as findings varied by age and hearing status. A meta-analysis was not conducted due to methodological heterogeneity.ConclusionOnset CAEP and ACC N1 and P2 latencies together with N1-P2 amplitudes particularly demonstrate potential as electrophysiological indicators of SPiN performance. Their clinical utility is promising for populations where behavioral testing can be unreliable, such as CI users or individuals with cognitive or language barriers. However, standardization of protocols and further longitudinal research are needed to validate their application in clinical settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023404158, identifier PROSPERO (CRD42023404158).
Digital Health Coffee Time Briefing ☕
Today’s briefing features a free digital platform built for unpaid carers in the UK and an AI-enabled platform for eating disorder services.
Commercial or industrial use of mental health data for research: primer and best-practice guidelines from the DATAMIND patient/public Lived Experience Advisory Group
BackgroundRoutinely collected health data, such as that held by United Kingdom (UK) national health services (NHS), has important research uses. However, its use requires public trust and transparency. Access by commercial/industrial organisations is especially sensitive for the public, as is mental health (MH) data. Although existing MH data science guidelines emphasise patient/public involvement (PPI), they do not cover commercial uses specifically.ObjectivesTo develop patient- and public-led guidelines for the commercial and industrial use of MH data for research. Though UK-focused, their principles may apply internationally.MethodsA PPI Lived Experience Advisory Group (LEAG) was created within DATAMIND, a UK data hub for MH informatics. Initial discussion yielded a requirement for definitions and explanations of concepts relating to MH data research, developed iteratively. Subsequently, the LEAG developed guidelines via a qualitative quasi-Delphi approach. The agreed scope excluded data provided for research with informed consent, data processing arrangements (e.g. companies hosting electronic systems on the instruction of health services), and compliance with legal minimum requirements. The scope included the use of routinely collected MH data for research by commercial/industrial organisations without explicit consent, and aspects of industry-led MH data collection conducted with consent.ResultsAlongside the primer in MH data research concepts, the LEAG provide best-practice guidelines relating to commercial/industrial research use of MH data, for organisations controlling MH data (such as NHS bodies) and for commercial applicants seeking access. Core principles include transparency, patient rights, meaningful PPI, stringent governance, and statistical disclosure control. The guidelines recommend a risk–benefit approach to assessing data access applications, within limits that include avoiding the export of unconsented patient-level data outside NHS-controlled secure data environments, and not providing commercial applicants with access to unconsented free-text MH data. Further recommendations for NHS executive and regulatory bodies relate to public choice and transparency, clarity of guidance to research-active NHS organisations, and support for de-identification.ConclusionsMH data research requires patient/public involvement and understanding. These guidelines reflect the views of people with personal or family experience of mental ill health. We hope they are useful to the MH research community and increase public transparency and trust.
Hertfordshire Community NHS Trust to roll out AI-scribing
Hertfordshire Community NHS Trust is rolling out ambient voice technology following a pilot in community and mental health services.
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
[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.
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.