A5100600613- Mental Health Research Topics
- Digital Mental Health Interventions
- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Neural dynamics and brain function
- COVID-19 and Mental Health
- Health disparities and outcomes
- Sleep and Wakefulness Research
- Sleep and related disorders
- Impact of Technology on Adolescents
- Circadian rhythm and melatonin
- Multiple Sclerosis Research Studies
- Elder Abuse and Neglect
- Mental Health via Writing
- Health, Environment, Cognitive Aging
- Advanced MRI Techniques and Applications
- Mobile Health and mHealth Applications
- Gene Regulatory Network Analysis
- Long-Term Effects of COVID-19
- Neurological disorders and treatments
- Neuroscience and Neuropharmacology Research
- Child Abuse and Trauma
- Behavioral Health and Interventions
- Hemodynamic Monitoring and Therapy
King's College London
2020-2025
King's College Hospital
2025
University of Exeter
2019
Engineering and Physical Sciences Research Council
2019
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.
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...
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...
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,...
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...
Abstract Objective The study aim was to compare interictal encephalographic (EEG) functional network topology between people with well‐controlled idiopathic generalized epilepsy (WC‐IGE) and drug‐resistant IGE (DR‐IGE). Methods Nineteen participants WC‐IGE, 18 DR‐IGE, 20 controls underwent a resting state, 64‐channel EEG. An artifact‐free epoch bandpass filtered into the frequency range of high low extended alpha. Weighted connectivity matrices were calculated. Mean degree, degree...
Abstract Zero-phase-delay synchrony between the activity of distant neural populations has been robustly observed. Nevertheless, contemporary electroencephalography and magnetencephalography functional connectivity analyses typically exclude zero-phase-delay connections, assuming that they are predominantly artefactual. However, effects excluding them on performance metrics as potential biomarkers unknown. Here, we showed most cortico-cortical connections occur with zero- or near-zero...
Network models of brain dynamics provide valuable insight into the healthy functioning and how this breaks down in disease. A pertinent example is use network to understand seizure generation (ictogenesis) epilepsy. Recently, computational have emerged aid our understanding seizures predict outcome surgical perturbations networks. Such approaches opportunity quantify effect removing regions tissue from networks thereby search for optimal resection strategy. Here, we elucidate sets nodes...
Background Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration new wearable mobile technologies collect electroencephalographic (EEG) extracerebral signals in a portable system might be solution prospectively identify times seizure occurrence or propensity. performances several detection devices have been assessed by validated studies, patient perspectives on wearables explored better match needs....
Abstract Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations resistance. However, is understood to be network disorder there growing body literature suggesting altered topology large-scale resting networks in compared controls. We hypothesize that...
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...
Abstract Background The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression treatment response. This serves complementary resource to traditional health care settings leveraging mobile technology improve scale lower latency, cost, burden. Objective Smartphones with embedded connected sensors have immense potential for improving various apps (mHealth) platforms. capability...
Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency network apparent in EEGs from people with idiopathic generalized epilepsy both ictal interictal states. Here, we characterize differences the resting-state individuals juvenile myoclonic assess factors influencing heterogeneity features. We collected data 147 participants through Biology Juvenile Myoclonic Epilepsy study. Ninety-five control were acquired two independent studies [Chowdhury et al. (2014) EU-AIMS...
Abstract Most smartphones and wearables are nowadays equipped with location sensing (using Global Positioning System mobile network information) that enable continuous tracking of their users. Several studies have reported the amount time an individual experiencing symptoms Major Depressive Disorder (MDD) spends at home a day (i.e., stay), as well various mobility related metrics, associated symptom severity in MDD. Due to use small homogeneous cohorts participants, it is uncertain whether...
Abstract Hypertension, a prevalent cardiovascular condition, requires effective management of multimodal health risk factors. This study examines the effectiveness digital tool designed for hypertension and explores user perspectives on its utility. We analyse cohort 5,136 participants who used tool, which provides continuous blood pressure monitoring, real-time feedback, personalized recommendations. Our results show that users achieve significant reduction in their values this is...
Abstract Backgroun The Covid Collab study was a citizen science mobile health research project set up in June 2020 to monitor COVID-19 symptoms and mental through questionnaire self-reports passive wearable device data. Methods Using data, we consider whether participant is suffering from long COVID two ways. Firstly, by the has persistent change physiological signal commencing at diagnosis of that last for least twelve weeks. Secondly, self-reported We assess sociodemographic wearable-based...