Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.

BACKGROUND: 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 in speech recordings collected from clinical samples. METHODS: The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. RESULTS: From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LIMITATIONS: Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. CONCLUSION: This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.

Interventions targeting social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews.

Globally, mental disorders account for almost 20% of disease burden and there is growing evidence that mental disorders are socially determined. Tackling the United Nations Sustainable Development Goals (UN SDGs), which address social determinants of mental disorders, may be an effective way to reduce the global burden of mental disorders. We conducted a systematic review of reviews to examine the evidence base for interventions that map onto the UN SDGs and seek to improve mental health through targeting known social determinants of mental disorders. We included 101 reviews in the final review, covering demographic, economic, environmental events, neighborhood, and sociocultural domains. This review presents interventions with the strongest evidence base for the prevention of mental disorders and highlights synergies where addressing the UN SDGs can be beneficial for mental health.

Etiological Factors and Symptom Triggers in Functional Motor Symptoms and Functional Seizures: A Pilot Investigation.

OBJECTIVE: This study examined etiological factors and symptom triggers of functional motor symptoms (FMS) or functional seizures (FS) and assessed potential relationships with relevant clinical features (i.e., functional symptoms, quality of life, and general functioning). METHODS: Seventeen participants with FMS or FS and 17 healthy control participants underwent an in-depth clinical interview and completed questionnaires assessing adverse life events, psychological and physical symptoms, alexithymia, autistic traits, illness perceptions, health-related quality of life (HRQoL), and work and social functioning. RESULTS: Participants with FMS or FS perceived various causes of the disorder, including physical symptoms (65%), emotional problems (53%), adverse life events (47%), and work-related factors (29%). Triggers of FMS and FS included physical activity or exertion (59%), stress and emotions (59%), sensory experiences (47%), and fatigue (41%). Compared with healthy control participants, participants with FMS or FS reported more adverse events during adolescence and higher levels of alexithymia, somatoform dissociation, psychological dissociation (disengagement, depersonalization, and derealization), anxiety, depression, and physical symptoms. Participants with FMS or FS had worse HRQoL than healthy control participants and impaired work and social functioning. There were inverse associations between HRQoL scores and somatoform dissociation, anxiety, and adverse life events. CONCLUSIONS: Participants with FMS or FS reported diverse biopsychosocial etiological factors and symptom triggers. Ongoing psychological symptoms and lifetime adverse experiences were associated with worse HRQoL. Future studies will examine these factors in larger samples of individuals with FMS or FS to better understand their shared and distinct etiological underpinnings.

Improving our understanding of the social determinants of mental health: a data linkage study of mental health records and the 2011 UK census.

OBJECTIVES: To address the lack of individual-level socioeconomic information in electronic healthcare records, we linked the 2011 census of England and Wales to patient records from a large mental healthcare provider. This paper describes the linkage process and methods for mitigating bias due to non-matching. SETTING: South London and Maudsley NHS Foundation Trust (SLaM), a mental healthcare provider in Southeast London. DESIGN: Clinical records from SLaM were supplied to the Office of National Statistics for linkage to the census through a deterministic matching algorithm. We examined clinical (International Classification of Disease-10 diagnosis, history of hospitalisation, frequency of service contact) and socio-demographic (age, gender, ethnicity, deprivation) information recorded in Clinical Record Interactive Search (CRIS) as predictors of linkage success with the 2011 census. To assess and adjust for potential biases caused by non-matching, we evaluated inverse probability weighting for mortality associations. PARTICIPANTS: Individuals of all ages in contact with SLaM up until December 2019 (N=459 374). OUTCOME MEASURES: Likelihood of mental health records' linkage to census. RESULTS: 220 864 (50.4%) records from CRIS linked to the 2011 census. Young adults (prevalence ratio (PR) 0.80, 95% CI 0.80 to 0.81), individuals living in more deprived areas (PR 0.78, 95% CI 0.78 to 0.79) and minority ethnic groups (eg, Black African, PR 0.67, 0.66 to 0.68) were less likely to match to census. After implementing inverse probability weighting, we observed little change in the strength of association between clinical/demographic characteristics and mortality (eg, presence of any psychiatric disorder: unweighted PR 2.66, 95% CI 2.52 to 2.80; weighted PR 2.70, 95% CI 2.56 to 2.84). CONCLUSIONS: Lower response rates to the 2011 census among people with psychiatric disorders may have contributed to lower match rates, a potential concern as the census informs service planning and allocation of resources. Due to its size and unique characteristics, the linked data set will enable novel investigations into the relationship between socioeconomic factors and psychiatric disorders.

Association of coexistence of frailty and depressive symptoms with mortality in community-dwelling older adults: Kashiwa Cohort Study.

OBJECTIVES: This study aimed to determine the longitudinal associations of the coexistence of frailty and depressive symptoms with mortality among older adults. METHODS: The study participants were community-dwelling older adults aged ≥65 years who participated in the baseline survey of the Kashiwa Cohort Study in Japan in 2012. We used Fried's frailty phenotype criteria to classify participants as non-frail (score = 0), pre-frail (1 or 2), or frail (≥3). Depressive symptoms were assessed using the GDS-15 (≥6 points). Cox proportional hazards models were used to evaluate the association of co-occurring frailty and depressive symptoms with all-cause mortality, after adjusting for sociodemographic and clinical characteristics. RESULTS: The study included 1920 participants, including 810 non-frail, 921 pre-frail, and 189 frail older adults, of which 9.0 %, 15.7 %, and 36.0 %, respectively, had depressive symptoms. Ninety-one (4.7 %) participants died during the average follow-up period of 4.8 years. Compared with non-frail participants without depressive symptoms, frail participants had greater adjusted hazard ratios for mortality: 2.47 (95 % CI, 1.16 to 5.25) for frail participants without depressive symptoms and 4.34 (95 % CI, 1.95 to 9.65) for frail participants with depressive symptoms. However, no statistically significant associations were observed in non-frail or pre-frail participants irrespective of depressive symptoms. CONCLUSION: Frail older adults with depressive symptoms have a substantially greater risk of mortality. Screening for depressive symptoms and frailty in older adults should be incorporated into health checkups and clinical practice to identify high-risk populations.

Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial.

BACKGROUND: Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD. OBJECTIVE: This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement. METHODS: A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness). RESULTS: The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI -9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19), and system usability (2.29, 95% CI -5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion. CONCLUSIONS: The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32653.

A wearable device perspective on the standard definitions of disability progression in multiple sclerosis.

INTRODUCTION: Multiple sclerosis (MS) is a leading cause of disability among young adults, but standard clinical scales may not accurately detect subtle changes in disability occurring between visits. This study aims to explore whether wearable device data provides more granular and objective measures of disability progression in MS. METHODS: Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) is a longitudinal multicenter observational study in which 400 MS patients have been recruited since June 2018 and prospectively followed up for 24 months. Monitoring of patients included standard clinical visits with assessment of disability through use of the Expanded Disability Status Scale (EDSS), 6-minute walking test (6MWT) and timed 25-foot walk (T25FW), as well as remote monitoring through the use of a Fitbit. RESULTS: Among the 306 patients who completed the study (mean age, 45.6 years; females 67%), confirmed disability progression defined by the EDSS was observed in 74 patients, who had approximately 1392 fewer daily steps than patients without disability progression. However, the decrease in the number of steps experienced over time by patients with EDSS progression and stable patients was not significantly different. Similar results were obtained with disability progression defined by the 6MWT and the T25FW. CONCLUSION: The use of continuous activity monitoring holds great promise as a sensitive and ecologically valid measure of disability progression in MS.

Unravelling the influence of affective stimulation on functional neurological symptoms: a pilot experiment examining potential mechanisms.

BACKGROUND: Differences in affective processing have previously been shown in functional neurological disorder (FND); however, the mechanistic relevance is uncertain. We tested the hypotheses that highly arousing affective stimulation would result in elevated subjective functional neurological symptoms (FNS), and this would be associated with elevated autonomic reactivity. The possible influence of cognitive detachment was also explored. METHOD: Individuals diagnosed with FND (motor symptoms/seizures; n=14) and healthy controls (n=14) viewed Positive, Negative and Neutral images in blocks, while passively observing the stimuli ('Watch') or detaching themselves ('Distance'). The FND group rated their primary FNS, and all participants rated subjective physical (arousal, pain, fatigue) and psychological states (positive/negative affect, dissociation), immediately after each block. Skin conductance (SC) and heart rate (HR) were monitored continuously. RESULTS: FNS ratings were higher after Negative compared with Positive and Neutral blocks in the FND group (p=0.002, ηp 2=0.386); however, this effect was diminished in the Distance condition relative to the Watch condition (p=0.018, ηp 2=0.267). SC and/or HR correlated with FNS ratings in the Negative-Watch and Neutral-Distance conditions (r values=0.527-0.672, p values=0.006-0.035). The groups did not differ in subjective affect or perceived arousal (p values=0.541-0.919, ηp 2=<0.001-0.015). CONCLUSIONS: Emotionally significant events may exert an influence on FNS which is related to autonomic activation rather than altered subjective affect or perceived arousal. This influence may be modulated by cognitive detachment. Further work is needed to determine the relevance and neural bases of these processes in specific FND phenotypes.

Frequency of Consumption of Food Groups and Cardio-Metabolic Risk Factors: A Genetically Informative Twin Study in Sri Lanka.

Low- and middle-income countries (LMICs) globally have undergone rapid urbanisation, and changes in demography and health behaviours. In Sri Lanka, cardio-vascular disease and diabetes are now leading causes of mortality. High prevalence of their risk factors, including hypertension, dysglycaemia and obesity have also been observed. Diet is a key modifiable risk factor for both cardio-vascular disease and diabetes as well as their risk factors. Although typically thought of as an environmental risk factor, dietary choice has been shown to be genetically influenced, and genes associated with this behaviour correlate with metabolic risk indicators. We used Structural Equation Model fitting to investigate the aetiology of dietary choices and cardio-metabolic phenotypes in COTASS, a population-based twin and singleton sample in Colombo, Sri Lanka. Participants completed a Food Frequency Questionnaire (N = 3934) which assessed frequency of intake of 14 food groups including meat, vegetables and dessert or sweet snacks. Anthropometric (N = 3675) and cardio-metabolic (N = 3477) phenotypes were also collected including weight, blood pressure, cholesterol, fasting plasma glucose and triglycerides. Frequency of consumption of most food items was found to be largely environmental in origin with both the shared and non-shared environmental influences indicated. Modest genetic influences were observed for some food groups (e.g. fruits and leafy greens). Cardio-metabolic phenotypes showed moderate genetic influences with some shared environmental influence for Body Mass Index, blood pressure and triglycerides. Overall, it seemed that shared environmental effects were more important for both dietary choices and cardio-metabolic phenotypes compared to populations in the Global North.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.

Background: Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators. Setting: EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs. Objectives: To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning. Methods: The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2). Findings: n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram). Conclusion: EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

Severe mental illness, race/ethnicity, multimorbidity and mortality following COVID-19 infection: nationally representative cohort study.

BACKGROUND: The association of COVID-19 with death in people with severe mental illness (SMI), and associations with multimorbidity and ethnicity, are unclear. AIMS: To determine all-cause mortality in people with SMI following COVID-19 infection, and assess whether excess mortality is affected by multimorbidity or ethnicity. METHOD: This was a retrospective cohort study using primary care data from the Clinical Practice Research Database, from February 2020 to April 2021. Cox proportional hazards regression was used to estimate the effect of SMI on all-cause mortality during the first two waves of the COVID-19 pandemic. RESULTS: Among 7146 people with SMI (56% female), there was a higher prevalence of multimorbidity compared with the non-SMI control group (n = 653 024, 55% female). Following COVID-19 infection, the SMI group experienced a greater risk of death compared with controls (adjusted hazard ratio (aHR) 1.53, 95% CI 1.39-1.68). Black Caribbean/Black African people were more likely to die from COVID-19 compared with White people (aHR = 1.22, 95% CI 1.12-1.34), with similar associations in the SMI group and non-SMI group (P for interaction = 0.73). Following infection with COVID-19, for every additional multimorbidity condition, the aHR for death was 1.06 (95% CI 1.01-1.10) in the SMI stratum and 1.16 (95% CI 1.15-1.17) in the non-SMI stratum (P for interaction = 0.001). CONCLUSIONS: Following COVID-19 infection, patients with SMI were at an elevated risk of death, further magnified by multimorbidity. Black Caribbean/Black African people had a higher risk of death from COVID-19 than White people, and this inequity was similar for the SMI group and the control group.

Twins Early Development Study (TEDS): A genetically sensitive investigation of mental health outcomes in the mid-twenties.

The Twins Early Development Study (TEDS) is a longitudinal study following a cohort of twins born 1994-1996 in England and Wales. Of the 13,759 families who originally consented to take part, over 10,000 families remain enrolled in the study. The current focus of TEDS is on mental health in the mid-twenties. Making use of over 25 years of genetically sensitive data, TEDS is uniquely placed to explore the longitudinal genetic and environmental influences on common mental health disorders in early adulthood. This paper outlines recent data collection efforts supporting this work, including a cohort-wide mental health assessment at age 26 and a multi-phase Covid-19 study. It will also provide an update on data linkage efforts and the Children of TEDS (CoTEDS) project.

Objective and subjective neurocognitive functioning in functional motor symptoms and functional seizures: preliminary findings.

INTRODUCTION: This study aimed to provide a preliminary assessment of objective and subjective neurocognitive functioning in individuals with functional motor symptoms (FMS) and/or functional seizures (FS). We tested the hypotheses that the FMS/FS group would display poorer objective attentional and executive functioning, altered social cognition, and reduced metacognitive accuracy. METHOD: Individuals with FMS/FS (n = 16) and healthy controls (HCs, n = 17) completed an abbreviated CANTAB battery, and measures of intellectual functioning, subjective cognitive complaints, performance validity, and comorbid symptoms. Subjective performance ratings were obtained to assess local metacognitive accuracy. RESULTS: The groups were comparable in age (p = 0.45), sex (p = 0.62), IQ (p = 0.57), and performance validity (p-values = 0.10-0.91). We observed no impairment on any CANTAB test in this FMS/FS sample compared to HCs, although the FMS/FS group displayed shorter reaction times on the Emotional Bias task (anger) (p = 0.01, np2 = 0.20). The groups did not differ in subjective performance ratings (p-values 0.15). Whilst CANTAB attentional set-shifting performance (total trials/errors) correlated with subjective performance ratings in HCs (p-values<0.005, rs = -0.85), these correlations were non-significant in the FMS/FS sample (p-values = 0.10-0.13, rs-values = -0.46-0.50). The FMS/FS group reported more daily cognitive complaints than HCs (p = 0.006, g = 0.92), which were associated with subjective performance ratings on CANTAB sustained attention (p = 0.001, rs = -0.74) and working memory tests (p < 0.001, rs = -0.75), and with depression (p = 0.003, rs = 0.70), and somatoform (p = 0.003, rs = 0.70) and psychological dissociation (p-values<0.005, rs-values = 0.67-0.85). CONCLUSIONS: These results suggest a discordance between objective and subjective neurocognitive functioning in this FMS/FS sample, reflecting intact test performance alongside poorer subjective cognitive functioning. Further investigation of neurocognitive functioning in FND subgroups is necessary.

Changes to healthcare utilisation and symptoms for common mental health problems over the first 21 months of the COVID-19 pandemic: parallel analyses of electronic health records and survey data in England.

Background: Few studies have investigated the effect of the COVID-19 pandemic on mental health beyond 2020. This study quantifies changes to healthcare utilisation and symptoms for common mental health problems over the pandemic's first 21 months. Methods: Parallel cohort studies using primary care database and survey data for adults (≥16 years) in England from January 2015 to December 2021: 16,551,842 from the Clinical Practice Research Datalink (CPRD) and 40,699 from the UK Household Longitudinal Survey (UKHLS). Interrupted time-series models estimated changes in monthly prevalence of presentations and prescribed medications for anxiety and depression (CPRD); and self-reported psychological distress (UKHLS). The pandemic period was divided into five phases: 1st Wave (April-May 2020); post-1st Wave (June-September 2020); 2nd Wave (October 2020-February 2021); post 2nd Wave (March-May 2021); 3rd Wave (June-December 2021). Findings: Primary care presentations for depression or anxiety dropped during the first wave (4.6 fewer monthly appointments per 1000 patients, 4.4-4.8) and remained lower than expected throughout follow-up. Self-reported psychological distress exceeded expected levels during the first (Prevalence Ratio = 1.378, 95% CI 1.289-1.459) and second waves (PR = 1.285, 1.189-1.377), returning towards expected levels during the third wave (PR = 1.038, 0.929-1.154). Increases in psychological distress and declines in presentations were greater for women. The decrease in primary care presentations for depression and anxiety exceeded that for physical health conditions (rheumatoid arthritis, diabetes, urinary tract infections). Anxiety and depression prescriptions returned to pre-pandemic levels during the second wave due to increased repeat prescriptions. Interpretation: Despite periods of distress during the pandemic, we did not find an enduring effect on common mental health problems. The fall in primary care presentations for anxiety or depression suggests changing healthcare utilisation for mental distress and a potential treatment gap. Funding: National Institute for Health and Care Research (NIHR).

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. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. METHODS: We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. RESULTS: Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. LIMITATIONS: Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. CONCLUSIONS: Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.

Associations between air pollution and mental health service use in dementia: a retrospective cohort study.

BACKGROUND: Little is known about the role of air pollution in how people with dementia use mental health services. OBJECTIVE: We examined longitudinal associations between air pollution exposure and mental health service use in people with dementia. METHODS: In 5024 people aged 65 years or older with dementia in South London, high resolution estimates of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. Associations between air pollution and Community Mental Health Team (CMHT) events (recorded over 9 years) were examined using negative binomial regression models. Cognitive function was measured using the Mini Mental State Examination (MMSE) and health and social functioning was measured using the Health of the Nation Outcomes Scale (HoNOS65+). Associations between air pollution and both MMSE and HoNOS65+ scores were assessed using linear regression models. FINDINGS: In the first year of follow-up, increased exposure to all air pollutants was associated with an increase in the use of CMHTs in a dose-response manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) (eg, NO2: adjusted incidence rate ratio (aIRR) 1.27, 95% CI 1.11 to 1.45, p<0.001). Dose-response patterns between PM2.5 and CMHT events remained at 5 and 9 years. Associations were strongest for patients with vascular dementia. NO2 levels were linked with poor functional status, but not cognitive function. CONCLUSIONS: Residential air pollution exposure is associated with increased CMHT usage among people with dementia. CLINICAL IMPLICATIONS: Efforts to reduce pollutant exposures in urban settings might reduce the use of mental health services in people with dementia, freeing up resources in already considerably stretched psychiatric services.

The United Kingdom Eating Disorders Genetics Initiative.

OBJECTIVE: The United Kingdom Eating Disorders Genetics Initiative (EDGI UK), part of the National Institute for Health and Care Research (NIHR) Mental Health BioResource, aims to deepen our understanding of the environmental and genetic etiology of eating disorders. EDGI UK launched in February 2020 and is partnered with the UK eating disorders charity, Beat. Multiple EDGI branches exist worldwide. This article serves the dual function of providing an in-depth description of our study protocol and of describing our initial sample including demographics, diagnoses, and physical and psychiatric comorbidities. METHOD: EDGI UK recruits via media and clinical services. Anyone living in England, at least 16 years old, with a lifetime probable or clinical eating disorder is eligible to sign up online: edgiuk.org. Participants complete online questionnaires, donate a saliva sample for genetic analysis, and consent to medical record linkage and recontact for future studies. RESULTS: As of September 2022, EDGI UK recruited 7435 survey participants: 98% female, 93.1% white, 97.8% cisgender, 65.9% heterosexual, and 52.1% have a university degree. Over half (57.8%) of these participants have returned their saliva DNA kit. The most common diagnoses are anorexia nervosa (48.3%), purging disorder (37.8%), bulimia nervosa (37.5%), binge-eating disorder (15.8%), and atypical anorexia nervosa (7.8%). CONCLUSION: EDGI UK is the largest UK eating disorders study and efforts to increase its diversity are underway. It offers a unique opportunity to accelerate eating disorder research. Researchers and participants with lived experience can collaborate on projects with unparalleled sample size. PUBLIC SIGNIFICANCE STATEMENT: Eating disorders are debilitating and costly for society but are under-researched due to underfunding. EDGI UK is one of the largest eating disorder studies worldwide with ongoing recruitment. The collected data constitute a resource for secondary analysis. We will combine data from all international EDGI branches and the NIHR BioResource to facilitate research that improves our understanding of eating disorders and their comorbidities.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.

BACKGROUND: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE: We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

Associations between socioeconomic factors and depression in Sri Lanka: The role of gene-environment interplay.

BACKGROUND: Low socioeconomic status is a risk factor for depression. The nature and magnitude of associations can differ cross-culturally and is influenced by a range of contextual factors. We examined the aetiology of socioeconomic indicators and depression symptoms and investigated whether socioeconomic indicators moderate genetic and environmental influences on depression symptoms in a Sri Lankan population. METHODS: Data were from a population-based sample of twins (N = 2934) and singletons (N = 1035) in Colombo, Sri Lanka. Standard of living, educational attainment, and financial strain were used to index socioeconomic status. Depression symptoms were assessed using the Revised Beck Depression Inventory. Structural equation modelling explored genetic and environmental influences on socioeconomic indicators and depression symptoms and moderation of aetiological influences on depression symptoms by socioeconomic status. RESULTS: Depression symptoms were associated with lower standard of living, lower educational attainment, and financial strain. Sex differences were evident in the aetiology of standard of living, with a small contribution of genetic influences in females. Educational attainment was moderately heritable in both males and females. Total variance in depression was greater among less socioeconomically advantaged individuals. Modest evidence of moderation of the aetiology of depression by standard of living and education was observed. LIMITATIONS: While the sample is representative of individuals living in Colombo District, it may not be representative of different regions of Sri Lanka. CONCLUSIONS: The aetiology of depression varies across socioeconomic contexts, suggesting a potential mechanism through which socioeconomic disadvantage increases the risk for depression in Sri Lanka. Findings have implications for cross-cultural investigations of the role of socioeconomic factors in depression and for identifying targets for social interventions.