Maternal expressions of warmth and negativity and adolescent mental health: using longitudinal monozygotic twin-difference analyses to approach causal inference.

Background: Emotions that mothers express about their offspring are associated with offspring mental health during childhood, but little research has explored whether this extends into adolescence. We investigated associations between maternal warmth and negativity towards twin offspring at age 10, and subsequent mental health outcomes in early and late adolescence.

Methods: The Environmental Risk (E-Risk) Longitudinal Twin Study is a population-based cohort of 2,232 same-sex twins born in 1994-1995 across England and Wales.

Collection and Analysis of Repeated Speech Samples: Methodological Framework and Example Protocol.

Background: Speech and language biomarkers have the potential to provide regular, objective assessments of symptom severity in several neurological and mental health conditions, both in the clinic and remotely. However, speech and language characteristics within an individual are influenced by multiple variables that can make findings highly dependent on the chosen methodology and study cohort. These characteristics are often not reported adequately in studies investigating speech-based health assessment, which (1) hinders the progress of methodological speech research, (2) prevents replication, and (3) makes the definitive identification of robust biomarkers problematic.

Assessing seasonal and weather effects on depression and physical activity using mobile health data.

Seasonal and weather changes can significantly impact depression severity, yet findings remain inconsistent across populations. This study explored depression variations across the seasons and the interplays between weather changes, physical activity, and depression severity among 428 participants in a real-world longitudinal mobile health study. Clustering analysis identified four participant subgroups with distinct patterns of depression severity variations in 1 year.

"There's a lot that's different, but it's still me": exploring podcast narratives of emotional adjustment after encephalitis.

Background: Encephalitis can lead to acquired brain injury (ABI) with neuropsychiatric consequences. Emotional adjustment is important for ensuring positive, long-term outcomes. The transdiagnostic cognitive behavioral therapy (T-CBT) model offers a way of understanding adjustment post-encephalitis but has not yet been tested.

Exploring the dynamic relationships between nocturnal heart rate, sleep disruptions, anxiety levels, and depression severity over time in recurrent major depressive disorder.

Background: Elevated night resting heart rate (HR) has been associated with increased depression severity, yet the underlying mechanisms remain elusive. This study aimed to investigate the mediating role of sleep disturbance and the influence of anxiety on the relationship between night resting HR and depression severity.

Methods: This is a secondary data analysis of data collected in the Remote Assessment of Disease and Relapse (RADAR) Major Depressive Disorder (MDD) longitudinal mobile health study, encompassing 461 participants (1774 observations) across three national centers (Netherlands, Spain, and the UK).

Identifying depression subtypes and investigating their consistency and transitions in a 1-year cohort analysis.

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 a way to unpick this phenotypic diversity for improved disorder characterisation. We aimed to identify depression subtypes longitudinally using the Inventory of Depressive Symptomatology: Self-Report (IDS-SR).

Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability.

Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations.

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

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 in real-world settings.

Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol.

Background: Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls.

Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities.

Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events.

Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures.

Automatically extracted measures of speech constitute a promising marker of psychosis as disorganized speech is associated with psychotic symptoms and predictive of psychosis-onset. The potential of speech markers is, however, hampered by (i) lengthy assessments in laboratory settings and (ii) manual transcriptions. We investigated whether a short, scalable data collection (online) and processing (automated transcription) procedure would provide data of sufficient quality to extract previously validated speech measures.

Automated analysis of speech as a marker of sub-clinical psychotic experiences.

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications.

Multilingual markers of depression in remotely collected speech samples: A preliminary analysis.

Background: Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples.

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis.

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired.

Digital endpoints in clinical trials of Alzheimer's disease and other neurodegenerative diseases: challenges and opportunities.

Alzheimer's disease (AD) and other neurodegenerative diseases such as Parkinson's disease (PD) and Huntington's disease (HD) are associated with progressive cognitive, motor, affective and consequently functional decline considerably affecting Activities of Daily Living (ADL) and quality of life. Standard assessments, such as questionnaires and interviews, cognitive testing, and mobility assessments, lack sensitivity, especially in early stages of neurodegenerative diseases and in the disease progression, and have therefore a limited utility as outcome measurements in clinical trials. Major advances in the last decade in digital technologies have opened a window of opportunity to introduce digital endpoints into clinical trials that can reform the assessment and tracking of neurodegenerative symptoms.

Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study.

Background: Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.

Objective: This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis.

The aim of this study was to evaluate the association between changes in the autonomic control of cardiorespiratory system induced by walk tests and outcome measures in people with Multiple Sclerosis (pwMS). Electrocardiogram (ECG) recordings of 148 people with Relapsing-Remitting MS (RRMS) and 58 with Secondary Progressive MS (SPMS) were acquired using a wearable device before, during, and after walk test performance from a total of 386 periodical clinical visits. A subset of 90 participants repeated a walk test at home.

Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety.

Background: Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated.

Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years.