A5073465017- Digital Mental Health Interventions
- Mental Health Research Topics
- COVID-19 and Mental Health
- Mobile Health and mHealth Applications
- Impact of Technology on Adolescents
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Functional Brain Connectivity Studies
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- EEG and Brain-Computer Interfaces
- IoT and Edge/Fog Computing
- Long-Term Effects of COVID-19
- Health disparities and outcomes
- Attention Deficit Hyperactivity Disorder
- Wireless Body Area Networks
- Context-Aware Activity Recognition Systems
- Health, Environment, Cognitive Aging
- Non-Invasive Vital Sign Monitoring
- Sleep and related disorders
- Circadian rhythm and melatonin
- COVID-19 diagnosis using AI
- Mental Health via Writing
- Multiple Sclerosis Research Studies
- Sleep and Wakefulness Research
- Behavioral Health and Interventions
- COVID-19 Clinical Research Studies
King's College London
2018-2025
Janssen (United States)
2022
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...
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...
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...
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...
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...
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...
Speech contains neuromuscular, physiological and cognitive components, so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate pausing are associated with major depressive disorder (MDD). However, results inconclusive as many small underpowered do not include clinical samples. These have also been unilingual use speech collected in controlled settings. If markers to help understand the onset progress MDD, we need uncover robust language establish...
Background Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations real-world settings. Objective This study aims to explore associations severity wearable-measured rhythms while accounting for impacts. Methods Data were sourced from a large longitudinal mHealth study,...
<sec> <title>BACKGROUND</title> Research priorities for autistic people include developing effective interventions the numerous challenges affecting their daily living, e.g., mental health problems, sleep difficulties, and social wellbeing. However, clinical research progress is limited by a lack of validated objective measures that represent target outcomes improvement. Digital technologies, including wearable devices smartphone applications, provide opportunities to develop novel may...
Patients with chronic respiratory diseases and those in the postdischarge period following hospitalization because of COVID-19 are particularly vulnerable, little is known about changes their symptoms physiological parameters. Continuous remote monitoring parameters symptom offers potential for timely intervention, improved patient outcomes, reduced health care costs.This study investigated whether a real-time multimodal program using commercially available wearable technology, home-based...
Abstract Background The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had major impact on mental health globally. Those diagnosed with depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings loneliness or lack access care. This study seeks assess 1st lockdown – pre-, during and post in adults recent history MDD across multiple centres. Methods is secondary analysis...
We assessed the feasibility and validity of remote researcher-led administration self-administration modified versions two cognitive tasks sensitive to ADHD, a four-choice reaction time task (Fast task) combined Continuous Performance Test/Go No-Go (CPT/GNG), through new measurement technology system.
Background Gait is an essential manifestation of depression. However, the gait characteristics daily walking and their relationships with depression have yet to be fully explored. Objective The aim this study was explore associations between symptom severity daily-life derived from acceleration signals in real-world settings. Methods We used two ambulatory data sets (N=71 N=215) collected by wearable devices mobile phones, respectively. extracted 12 features describe distribution variance...
Chronic lung disorders like chronic obstructive pulmonary disease (COPD) and idiopathic fibrosis (IPF) are characterized by exacerbations. They unpleasant for patients sometimes severe enough to cause hospital admission death. Moreover, due the COVID-19 pandemic, vulnerable populations with these at high risk, their routine care cannot be done properly. Remote monitoring offers a low cost safe solution gaining visibility into health of people in daily lives, making it useful populations.