Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study.

BACKGROUND: Children and young people's mental health is a growing public health concern, which is further exacerbated by the COVID-19 pandemic. Mobile health apps, particularly those using passive smartphone sensor data, present an opportunity to address this issue and support mental well-being. OBJECTIVE: This study aimed to develop and evaluate a mobile mental health platform for children and young people, Mindcraft, which integrates passive sensor data monitoring with active self-reported updates through an engaging user interface to monitor their well-being. METHODS: A user-centered design approach was used to develop Mindcraft, incorporating feedback from potential users. User acceptance testing was conducted with a group of 8 young people aged 15-17 years, followed by a pilot test with 39 secondary school students aged 14-18 years, which was conducted for a 2-week period. RESULTS: Mindcraft showed encouraging user engagement and retention. Users reported that they found the app to be a friendly tool helping them to increase their emotional awareness and gain a better understanding of themselves. Over 90% of users (36/39, 92.5%) answered all active data questions on the days they used the app. Passive data collection facilitated the gathering of a broader range of well-being metrics over time, with minimal user intervention. CONCLUSIONS: The Mindcraft app has shown promising results in monitoring mental health symptoms and promoting user engagement among children and young people during its development and initial testing. The app's user-centered design, the focus on privacy and transparency, and a combination of active and passive data collection strategies have all contributed to its efficacy and receptiveness among the target demographic. By continuing to refine and expand the app, the Mindcraft platform has the potential to contribute meaningfully to the field of mental health care for young people.

Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy.

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy.

A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.

Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics.

Variation in global COVID-19 symptoms by geography and by chronic disease: A global survey using the COVID-19 Symptom Mapper.

Background: COVID-19 is typically characterised by a triad of symptoms: cough, fever and loss of taste and smell, however, this varies globally. This study examines variations in COVID-19 symptom profiles based on underlying chronic disease and geographical location. Methods: Using a global online symptom survey of 78,299 responders in 190 countries between 09/04/2020 and 22/09/2020, we conducted an exploratory study to examine symptom profiles associated with a positive COVID-19 test result by country and underlying chronic disease (single, co- or multi-morbidities) using statistical and machine learning methods. Findings: From the results of 7980 COVID-19 tested positive responders, we find that symptom patterns differ by country. For example, India reported a lower proportion of headache (22.8% vs 47.8%, p<1e-13) and itchy eyes (7.3% vs. 16.5%, p=2e-8) than other countries. As with geographic location, we find people differed in their reported symptoms if they suffered from specific chronic diseases. For example, COVID-19 positive responders with asthma (25.3% vs. 13.7%, p=7e-6) were more likely to report shortness of breath compared to those with no underlying chronic disease. Interpretation: We have identified variation in COVID-19 symptom profiles depending on geographic location and underlying chronic disease. Failure to reflect this symptom variation in public health messaging may contribute to asymptomatic COVID-19 spread and put patients with chronic diseases at a greater risk of infection. Future work should focus on symptom profile variation in the emerging variants of the SARS-CoV-2 virus. This is crucial to speed up clinical diagnosis, predict prognostic outcomes and target treatment. Funding: We acknowledge funding to AAF by a UKRI Turing AI Fellowship and to CEC by a personal NIHR Career Development Fellowship (grant number NIHR-2016-090-015). JKQ has received grants from The Health Foundation, MRC, GSK, Bayer, BI, Asthma UK-British Lung Foundation, IQVIA, Chiesi AZ, and Insmed. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Imperial College London is grateful for the support from the Northwest London NIHR Applied Research Collaboration. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Publisher Correction: Inferring structural connectivity using Ising couplings in models of neuronal networks.

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Inferring structural connectivity using Ising couplings in models of neuronal networks.

Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.