A5061293835- Digital Mental Health Interventions
- Mental Health Research Topics
- COVID-19 and Mental Health
- Mobile Health and mHealth Applications
- Biomedical Text Mining and Ontologies
- Impact of Technology on Adolescents
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Long-Term Effects of COVID-19
- Functional Brain Connectivity Studies
- Sleep and related disorders
- EEG and Brain-Computer Interfaces
- Machine Learning in Healthcare
- Data-Driven Disease Surveillance
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Attention Deficit Hyperactivity Disorder
- Health disparities and outcomes
- Electronic Health Records Systems
- COVID-19 Clinical Research Studies
- Eating Disorders and Behaviors
- Emergency and Acute Care Studies
- IoT and Edge/Fog Computing
- Disaster Response and Management
- Mental Health via Writing
- Heart Rate Variability and Autonomic Control
- Telemedicine and Telehealth Implementation
South London and Maudsley NHS Foundation Trust
2014-2025
University College London
2016-2025
King's College London
2016-2025
Health Data Research UK
2019-2025
UCL Biomedical Research Centre
2020-2025
University College London Hospitals NHS Foundation Trust
2024-2025
University of London
2019-2024
NIHR Maudsley Dementia Biomedical Research Unit
2016-2024
NIHR Maudsley Biomedical Research Centre
2024
King's College Hospital NHS Foundation Trust
2024
Background In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective quantitative means passively monitoring impact response these at local level is needed. Objective We aim to explore utility recently developed open-source mobile health platform Remote Assessment Disease Relapse (RADAR)–base toolbox rapidly test effect NPIs intended limit spread COVID-19. Methods...
Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources secure, highly scalable, extensible platform is high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment Disease Relapse-Central Nervous System (RADAR-CNS) program an exemplary project with requirements support collection high-resolution at scale; as such, Relapse...
The SARS-CoV-2 virus binds to the angiotensin-converting enzyme 2 (ACE2) receptor for cell entry. It has been suggested that inhibitors (ACEi) and angiotensin II blockers (ARB), which are commonly used in patients with hypertension or diabetes may raise tissue ACE2 levels, could increase risk of severe COVID-19 infection.
Abstract Background The National Early Warning Score (NEWS2) is currently recommended in the UK for risk stratification of COVID-19 patients, but little known about its ability to detect severe cases. We aimed evaluate NEWS2 prediction outcome and identify validate a set blood physiological parameters routinely collected at hospital admission improve upon use alone medium-term stratification. Methods Training cohorts comprised 1276 patients admitted King’s College Hospital Health Service...
Abstract Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response treatment identify early indicators relapse. Remote Measurement Technologies (RMT) provide an opportunity transform the measurement management MDD, via data collected from inbuilt smartphone sensors wearable devices alongside app-based questionnaires tasks. A key question for field extent which participants can adhere research protocols completeness...
There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which be biased by dysfunctional perceptions current symptom severity. Predictors relapse include disrupted sleep, reduced sociability, physical activity, changes mood, prosody cognitive function, are all amenable to via RMT. This study aims to: 1) determine...
Abstract Objective Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has potential to provide a step change in available for secondary research use, generation actionable medical insights, hospital management, trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search analytics tool EHRs. Methods SemEHR implements generic information extraction (IE) retrieval infrastructure by identifying...
Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as significance modern economy expands in scope and permeates healthcare domain, there is an increasing urgency for organisations offer that address expectations clinicians, researchers business intelligence community alike. Amongst other emergent requirements, principal unmet need might be defined 3R principle (right data, right place, time) deficiencies...
Abstract Aims The SARS-Cov2 virus binds to the ACE2 receptor for cell entry. It has been suggested that ACE-inhibitors (ACEi) and Angiotensin-2 Blockers (ARB), which are commonly used in patients with hypertension or diabetes may raise levels, could increase risk of severe COVID19 infection. Methods Results We evaluated this hypothesis a consecutive cohort 1200 acute inpatients at two hospitals multi-ethnic catchment population London (UK). mean age was 68±17 years (57% male) 74% had least 1...
Research in mental health has implicated sleep pathologies with depression. However, the gold standard for assessment, polysomnography, is not suitable long-term, continuous, monitoring of daily sleep, and methods such as diaries rely on subjective recall, which qualitative inaccurate. Wearable devices, other hand, provide a low-cost convenient means to monitor home settings. The main aim this study was devise extract features, from data collected using wearable device, analyse their...
The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction relationships) between depressive symptom severity and phone-measured have yet fully explored.
Abstract Recent growth in digital technologies has enabled the recruitment and monitoring of large diverse populations remote health studies. However, generalizability inference drawn from remotely collected data could be severely impacted by uneven participant engagement attrition over course study. We report findings on long-term retention patterns a multinational observational study for depression containing active (surveys) passive sensor via Android smartphones, Fitbit devices 614...
Abstract Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from RATE-AF trial wearables study, which was designed compare heart rate in older, multimorbid patients with permanent atrial fibrillation failure who were randomized treatment either digoxin or beta-blockers. Heart ( n = 143,379,796) physical activity 23,704,307) intervals obtained 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a...
The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity guidance for best practice in conducting such analyses. For the results study to be valid and replicable, multiple biases must addressed course data preparation analysis. In addition, standardizing methods across small, independent would increase comparability potential effective meta-analysis. This article provides discussion important aspects quality control,...
Background Research in mental health has found associations between depression and individuals’ behaviors statuses, such as social connections interactions, working status, mobility, isolation loneliness. These statuses can be approximated by the nearby Bluetooth device count (NBDC) detected sensors mobile phones. Objective This study aimed to explore value of NBDC data predicting depressive symptom severity measured via 8-item Patient Health Questionnaire (PHQ-8). Methods The used this...
The implementation of governmental Non-Pharmaceutical Interventions (NPIs) has been the primary means controlling spread COVID-19 disease. One intended effects these NPIs to reduce population mobility. Due huge costs implementing NPIs, it is essential have a good understanding their efficacy. Using aggregated mobility data per country, released by Apple and Google we investigated proportional contribution magnitude rate changes at multi-national level. with greatest impact on change were...
Background Most smartphones and wearables are currently equipped with location sensing (using GPS mobile network information), which enables continuous tracking of their users. Several studies have reported that various mobility metrics, as well home stay, is, the amount time an individual spends at in a day, associated symptom severity people major depressive disorder (MDD). Owing to use small homogeneous cohorts participants, it is uncertain whether findings those generalize broader...
Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study individuals with a history recurrent major depressive disorder (MDD) explored the intra-individual variations HR parameters and their relationship severity.Data from 510 participants (Number observations = 6666) were collected three centres Netherlands, Spain, UK, as part remote assessment disease relapse-MDD study. We analysed between severity, assessed every 2...
A number of challenges exist for the analysis mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold missing data; distinguishing between cross-sectional longitudinal relationships different features to determine their utility in tracking within-individual variation or screening individuals at high risk; heterogeneity with which depression manifests itself behavioral patterns quantified by passive...
Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis COVID-19, although evidence is not conclusive. However, effects COVID-19 vaccine on this association, or effect other subtypes, are clear. We aimed to investigate association between and 2, 1, gestational non-specific diabetes, COVID- 19 vaccination, up 52 weeks diagnosis.
Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics speech recordings collected from clinical samples. The data included 3919 English free-response via smartphones 265 participants a depression history. We transcribed automatic recognition (Whisper tool, OpenAI) identified principal...