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

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

Synthesis of an ordered mesoporous carbon with graphitic characteristics and its application for dye adsorption

An ordered mesoporous carbon (OMC) was prepared by a chemical vapor deposition technique using liquid petroleum gas (LPG) as the carbon source. During synthesis, LPG was effectively adsorbed in the ordered mesopores of SBA-15 silica and converted to a graphitic carbon at 800 °C. X-ray diffraction and nitrogen adsorption/desorption data and high-resolution transmission electron microscopy (HRTEM) of the OMC confirmed its ordered mesoporous structure. The OMC was utilized as an adsorbent in the removal of dyes from aqueous solution. A commercial powder activated carbon (AC) was also investigated to obtain comparative data. The efficiency of the OMC for dye adsorption was tested using acidic dye acid orange 8 (AO8) and basic dyes methylene blue (MB) and rhodamine B (RB). The results show that adsorption was affected by the molecular size of the dye, the textural properties of carbon adsorbent and surface-dye interactions. The adsorption capacities of the OMC for acid orange 8 (AO8), methylene blue (MB) and rhodamine B (RB) were determined to be 222, 833, and 233 mg/g, respectively. The adsorption capacities of the AC for AO8, MB, and RB were determined to be 141, 313, and 185 mg/ g, respectively. The OMC demonstrated to be an excellent adsorbent for the removal of MB from wastewater.Web of Scienc

Empathy levels among first year Malaysian medical students: an observational study

Brett Williams,1 Sivalal Sadasivan,2 Amudha Kadirvelu,2 Alexander Olaussen11Department of Community Emergency Health and Paramedic Practice, Melbourne, Australia; 2Jeffrey Cheah School of Medicine and Health Sciences, Sunway Campus, Selangor, MalaysiaBackground: The literature indicates that medical practitioners experience declining empathy levels in clinical practice. This highlights the need to educate medical students about empathy as an attribute early in the academic curriculum. The objective of this study was to evaluate year one students' self-reported empathy levels following a 2-hour empathy workshop at a large medical school in Malaysia.Methods: Changes in empathy scores were examined using a paired repeated-measures t-test in this prospective before and after study.Results: Analyzing the matched data, there was a statistically significant difference and moderate effect size between mean empathy scores before and 5 weeks after the workshop (112.08±10.67 versus 117.93±13.13, P<0.0001, d=0.48) using the Jefferson Scale Physician Empathy (Student Version).Conclusion: The results of this observational study indicate improved mean self-reported empathy scores following an empathy workshop.Keywords: empathy, medical students, Malaysi

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

Data-derived wearable digital biomarkers predict Frataxin gene expression levels and longitudinal disease progression in Friedreich’s Ataxia

Friedreich’s ataxia (FA) is caused by repression of the Frataxin gene which impacts patients’ motor behaviour. With current gold-standard clinical scales, it requires 18-24 month-long clinical trials to determine if disease-modifying therapies are beneficial. Our approach captures the full-body movement kinematics from human subjects using a wearable motion-capture-suit. We extracted digital behavioural features from the movement data (eight-meter walk (8MW) and nine-hole peg test (9HPT)) that can distinguish FA patients and controls and then used machine learning to combine these features to longitudinally predict two different ‘gold-standard’ clinical scales (SCAFI34 and SARA). These predictions outperformed predictions from the clinical scales (leave-one-subject-out cross-validated R2 using suit features of 8MW and 9HPT tasks: 0.80 and 0.85 vs R2 of 0.47 using SARA). Unlike the clinical scales, our wearable-derived digital features can accurately cross-sectionally predict for each patient their personal FXN gene expression levels (R2 of suit features of 8MW and 9HPT: 0.59 and 0.53 vs R2 of SARA and SCAFI: 0.01 and 0.01), demonstrating the sensitivity of our approach and the importance of FXN levels in FA. Our work demonstrates that data-derived wearable biomarkers have the potential to substantially reduce clinical trial durations