A5022588452- Mental Health Research Topics
- Digital Mental Health Interventions
- Neonatal and fetal brain pathology
- Functional Brain Connectivity Studies
- Blind Source Separation Techniques
- Health, Environment, Cognitive Aging
- Dementia and Cognitive Impairment Research
- Mobile Health and mHealth Applications
- EEG and Brain-Computer Interfaces
- COVID-19 and Mental Health
- Multiple Sclerosis Research Studies
- Health disparities and outcomes
- Mental Health via Writing
- Sleep and related disorders
- Effects of Vibration on Health
- ECG Monitoring and Analysis
- Non-Invasive Vital Sign Monitoring
- Heart Rate Variability and Autonomic Control
- Circadian rhythm and melatonin
- Impact of Technology on Adolescents
- Sleep and Wakefulness Research
- Sepsis Diagnosis and Treatment
- Treatment of Major Depression
- Eating Disorders and Behaviors
- Tryptophan and brain disorders
King's College London
2021-2025
Janssen (United States)
2018-2025
Johnson & Johnson (United States)
2024-2025
Janssen (Belgium)
2021-2024
Parc Sanitari Sant Joan de Déu
2024
Universitat de Barcelona
2024
Grace (United States)
2021
Centro San Giovanni di Dio Fatebenefratelli
2021
University of Arkansas at Little Rock
2009-2015
IPS Research (United States)
2012-2015
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...
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...
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...
<title>Abstract</title> Recent advancements in Large Language Models (LLMs) present promising opportunities for applying these technologies to aid the detection and monitoring of Major Depression Disorder (MDD). However, demographic biases LLMs may challenges extraction key information. This study evaluates commonly used speech health literature, across a cohort comprised English, Spanish, Dutch speakers with recurrent MDD observe effects different imbalances. Results indicate demographics...
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...
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...
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,...
Identification of risk factors treatment resistance may be useful to guide selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling resistant depression (TRD) via partition data from Sequenced Treatment Alternatives Relieve Depression (STAR*D) cohort into a training testing dataset. also included small yet completely independent RIS-INT-93 as an external test used features enrollment level 1 (up week 2...
Abstract Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) therefore promising to measure cognition needed for IADL early Alzheimer’s disease (AD) both clinic home settings. The primary aim this study was distinguish classify healthy controls (HC) from participants with AD pathology an stage using AR app. secondary aims were test association app clinical cognitive functional tests investigate feasibility...
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...
Major depressive disorder (MDD) is defined by an array of symptoms that make it challenging to understand the condition at a population level. Subtyping offers way unpick this phenotypic diversity for improved characterisation. We aimed identify depression subtypes longitudinally using Inventory Depressive Symptomatology: Self-Report (IDS-SR). A secondary analysis two-year cohort study called Remote Assessment Disease and Relapse in Disorder (RADAR-MDD), which collected data every three...
Abstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection intervention are crucial for enhancing the quality of life patients their families. Remote Monitoring Technologies (RMTs) offer promising solution early by tracking changes in behavioral functions, such as memory, language, problem-solving skills. Timely these symptoms can facilitate intervention, potentially...
Abstract Seasonal and weather changes can significantly impact depression severity, yet findings remain inconsistent across populations. This study explored variations the seasons interplays between changes, physical activity, severity among 428 participants in a real-world longitudinal mobile health study. Clustering analysis identified four participant subgroups with distinct patterns of 1 year. While one subgroup showed stable levels throughout year, others peaked at various seasons. The...
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...
Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) widely used objective measure to evaluate progression MS. Yet, it has limitations such as need for clinical visit proper walkway. The widespread use wearable devices capable depicting patients' activity profiles potential assess level MS-induced disability in free-living conditions. In this work, we...