Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data.

Stress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives "in the wild" may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress "in the wild", by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4-6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual's self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.

The digital redesign of mental health: leveraging connected digital technologies for agency-driven patient-focused care.

Digital psychiatry could empower individuals to navigate their context-specific experiences outside healthcare visits. This editorial discusses how leveraging digital health technologies could dramatically transform how we conceptualise mental health and the mental health professional's day-day practice, and how patients could be enabled to navigate their mental health with greater agency.

The Post-Roe Political Landscape Demands a Morality of Caution for Women's Health.

The recent Supreme Court decision (ie, Dobbs v. Jackson Women's Health Organization), revoking the constitutional right to abortion in the United States, has the potential to dramatically disrupt progress in women's health research. The typical safeguards to ensure confidentiality and privacy of research participants in studies that collect certain types of personal health information may not hold against criminal investigations surrounding suspected pregnancy terminations. There are additional risks to participants in digital health research studies involving the use of wearable devices capable of tracking physiological measures, such as body temperature and heart rate, as these have shown promise for tracking conception and could be used to identify pregnancy termination signatures. There are strategies researchers can use to protect the safety of participants in health research who could get pregnant, while also maintaining integrity of research methods. The objective of this viewpoint is to discuss potential strategies to protect research participants' privacy that include the minimization of nonessential sensitive personal health information and anonymization protocols in the event of miscarriage or termination of pregnancy. We invite others to join this discussion so as to not let the current political landscape impede progress in women's health and reproductive research, while also protecting research participants.

Correction: An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study.

[This corrects the article DOI: 10.2196/32165.].

Validation of UK Biobank data for mental health outcomes: A pilot study using secondary care electronic health records.

UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.

An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study.

BACKGROUND: Several app-based studies share similar characteristics of a light touch approach that recruit, enroll, and onboard via a smartphone app and attempt to minimize burden through low-friction active study tasks while emphasizing the collection of passive data with minimal human contact. However, engagement is a common challenge across these studies, reporting low retention and adherence. OBJECTIVE: This study aims to describe an alternative to a light touch digital health study that involved a participant-centric design including high friction app-based assessments, semicontinuous passive data from wearable sensors, and a digital engagement strategy centered on providing knowledge and support to participants. METHODS: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study included US frontline health care workers followed between May and November 2020. The study comprised 3 main components: (1) active and passive assessments of stress and symptoms from a smartphone app, (2) objective measured assessments of acute stress from wearable sensors, and (3) a participant codriven engagement strategy that centered on providing knowledge and support to participants. The daily participant time commitment was an average of 10 to 15 minutes. Retention and adherence are described both quantitatively and qualitatively. RESULTS: A total of 365 participants enrolled and started the study, and 81.0% (n=297) of them completed the study for a total study duration of 4 months. Average wearable sensor use was 90.6% days of total study duration. App-based daily, weekly, and every other week surveys were completed on average 69.18%, 68.37%, and 72.86% of the time, respectively. CONCLUSIONS: This study found evidence for the feasibility and acceptability of a participant-centric digital health study approach that involved building trust with participants and providing support through regular phone check-ins. In addition to high retention and adherence, the collection of large volumes of objective measured data alongside contextual self-reported subjective data was able to be collected, which is often missing from light touch digital health studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04713111; https://clinicaltrials.gov/ct2/show/NCT04713111.

Development and validation of a risk calculator for major mood disorders among the offspring of bipolar parents using information collected in routine clinical practice.

BACKGROUND: Family history is a significant risk factor for bipolar disorders (BD), but the magnitude of risk varies considerably between individuals within and across families. Accurate risk estimation may increase motivation to reduce modifiable risk exposures and identify individuals appropriate for monitoring over the peak risk period. Our objective was to develop and independently replicate an individual risk calculator for bipolar spectrum disorders among the offspring of BD parents using data collected in routine clinical practice. METHODS: Data from the longitudinal Canadian High-Risk Offspring cohort study collected from 1996 to 2020 informed the development of a 5 and 10-year risk calculator using parametric time-to-event models with a cure fraction and a generalized gamma distribution. The calculator was then externally validated using data from the Lausanne-Geneva High-Risk Offspring cohort study collected from 1996 to 2020. A time-varying C-index by age in years was used to estimate the probability that the model correctly classified risk. Bias corrected estimates and 95% confidence limits were derived using a jackknife resampling approach. FINDINGS: The primary outcome was age of onset of a major mood disorder. The risk calculator was most accurate at classifying risk in mid to late adolescence in the Canadian cohort (n = 285), and a similar pattern was replicated in the Swiss cohort (n = 128). Specifically, the time-varying C-index indicated that there was approximately a 70% chance that the model would correctly predict which of two 15-year-olds would be more likely to develop the outcome in the future. External validation within a smaller Swiss cohort showed mixed results. INTERPRETATION: Findings suggest that this model may be a useful clinical tool in routine practice for improved individualized risk estimation of bipolar spectrum disorders among the adolescent offspring of a BD parent; however, risk estimation in younger high-risk offspring is less accurate, perhaps reflecting the evolving nature of psychopathology in early childhood. Based on external validation with a Swiss cohort, the risk calculator may not be as predictive in more heterogenous high-risk populations. FUNDING: The Canadian High-Risk Study has been funded by consecutive operating grants from the Canadian Institutes for Health Research, currently CIHR PJT Grant 152796 he Lausanne-Geneva high-risk study was and is supported by five grants from the Swiss National Foundation (#3200-040,677, #32003B-105,969, #32003B-118,326, #3200-049,746 and #3200-061,974), three grants from the Swiss National Foundation for the National Centres of Competence in Research project "The Synaptic Bases of Mental Diseases" (#125,759, #158,776, and #51NF40 - 185,897), and a grant from GlaxoSmithKline Clinical Genetics.

Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach.

BACKGROUND: Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals. RESULTS: Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination. CONCLUSIONS: This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample.

High Blood Pressure and Risk of Dementia: A Two-Sample Mendelian Randomization Study in the UK Biobank.

BACKGROUND: Findings from randomized controlled trials have yielded conflicting results on the association between blood pressure (BP) and dementia traits. We tested the hypothesis that a causal relationship exists between systolic BP (SBP) and/or diastolic BP (DBP) and risk of Alzheimer's disease (AD). METHODS: We performed a generalized summary Mendelian randomization (GSMR) analysis using summary statistics of a genome-wide association study meta-analysis of 299,024 individuals of SBP or DBP as exposure variables against three different outcomes: 1) AD diagnosis (International Genomics of Alzheimer's Project), 2) maternal family history of AD (UK Biobank), and 3) paternal family history of AD (UK Biobank). Finally, a combined meta-analysis of 368,440 individuals that included these three summary statistics was used as final outcome. RESULTS: GSMR applied to the International Genomics of Alzheimer's Project dataset revealed a significant effect of high SBP lowering the risk of AD (ßGSMR = -0.19, p = .04). GSMR applied to the maternal family history of AD UK Biobank dataset (SBP [ßGSMR = -0.12, p = .02], DBP [ßGSMR = -0.10, p = .05]) and to the paternal family history of AD UK Biobank dataset (SBP [ßGSMR = -0.16, p = .02], DBP [ßGSMR = -0.24, p = 7.4 × 10-4]) showed the same effect. A subsequent combined meta-analysis confirmed the overall significant effect for the other SBP analyses (ßGSMR = -0.14, p = .03). The DBP analysis in the combined meta-analysis also confirmed a DBP effect on AD (ßGSMR = -0.14, p = .03). CONCLUSIONS: A causal effect exists between high BP and a reduced late-life risk of AD. The results were obtained through careful consideration of confounding factors and the application of complementary MR methods on independent cohorts.

Stress-related consequences of the coronavirus disease 2019 pandemic on symptoms of Crohn's disease.

OBJECTIVES: A link between stress and Crohn's disease activity suggests an association, but results have been conflicting. The purpose of this study was to assess whether the stress related to the coronavirus disease 2019 (COVID-19) pandemic affected disease activity in patients with Crohn's disease. BASIC METHODS: An anonymous survey was distributed to patients through gastroenterology clinics and networks. Patients were asked to report their Crohn's disease symptoms in the months prior to the COVID-19 pandemic and again during the early stages of the COVID-19 pandemic using the Manitoba inflammatory bowel disease index in addition to questions about stress, perception of reasons for symptom change and personal impact. MAIN RESULTS: Out of 243 individuals with a confirmed diagnosis of Crohn's disease, there was a 24% relative increase in active symptoms between the pre-COVID-19 period to the during-COVID-19 period (P < 0.0001) reflecting an absolute change from 45 to 56%, respectively. The most frequent reported reason for a change in symptoms was 'Increased stress/and or feeling overwhelmed' (118/236), and personal impact of the pandemic was, 'I'm worrying a lot about the future' (113/236), both reported by approximately half of respondents. PRINCIPAL CONCLUSIONS: This study serves as a 'proof of concept' demonstrating the impact of a significant and uniquely uniform stressor as a natural experiment on Crohn's disease activity. The severity of symptoms of Crohn's disease increased during the COVID-19 pandemic. The primary reported reason for symptom change was an increase in stress, not a change in diet, exercise or other lifestyle behaviours, corroborating the hypothesis that stress affects Crohn's disease activity.

Mental health need of students at entry to university: Baseline findings from the U-Flourish Student Well-Being and Academic Success Study.

AIM: Transition to university is associated with unique stressors and coincides with the peak period of risk for onset of mental illness. Our objective in this analysis was to estimate the mental health need of students at entry to a major Canadian university. METHODS: After a student-led engagement campaign, all first year students were sent a mental health survey, which included validated symptom rating scales for common mental disorders. Rates of self-reported lifetime mental illness, current clinically significant symptoms and treatment stratified by gender are reported. The likelihood of not receiving treatment among those symptomatic and/or with lifetime disorders was estimated. RESULTS: Fifty-eight per cent of all first-year students (n = 3029) completed the baseline survey, of which 28% reported a lifetime mental disorder. Moreover, 30% of students screened positive for anxiety symptoms, 28% for depressive symptoms, and 18% for sleep problems with high rates (≅45%) of associated impairment. Only 8.5% of students indicated currently receiving any form of treatment. Females were more likely to report a lifetime diagnosis, anxiety and depressive symptoms, as well as current treatment. Over 25% of students reported lifetime suicidal thoughts and 6% suicide attempt(s). Current weekly binge drinking (25%) and cannabis use (11%) were common, especially in males. CONCLUSIONS: There is limited systematically collected data describing the mental health needs of young people at entry to university. Findings of this study underscore the importance of timely identification of significant mental health problems as part of a proactive system of effective student mental health care.

The True Colours Remote Symptom Monitoring System: A Decade of Evolution.

The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety, substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263 adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.

Investigating the association between anxiety symptoms and mood disorder in high-risk offspring of bipolar parents: a comparison of Joint and Cox models.

BACKGROUND: Anxiety is associated with mood disorders including bipolar disorder. Two statistical modelling frameworks were compared to investigate the longitudinal relationship between repeatedly measured anxiety symptoms and the onset of depression and bipolar disorder in youth at confirmed familial risk. METHODS: Prospectively collected data on 156 offspring of a parent with confirmed bipolar disorder participating in the Canadian Flourish high-risk offspring longitudinal cohort study were used for this analysis. As part of the research protocol at approximately yearly visits, a research psychiatrist completed the HAM-A and a semi-structured diagnostic research interview following KSADS-PL format. Diagnoses using DSM-IV criteria were made on blind consensus review of all available clinical information. We investigated two statistical approaches, Cox model and Joint model, to evaluate the relationship between repeated HAM-A scores and the onset of major depressive or bipolar disorder. The Joint model estimates the trajectory of the longitudinal variable using a longitudinal sub-model and incorporates this estimated trajectory into a Cox sub-model. RESULTS: There was evidence of an increased hazard of major mood disorder for high-risk individuals with higher HAM-A scores under both modelling frameworks. After adjusting for other covariates, a one-unit increase in log-transformed HAM-A score was associated with a hazard ratio of 1.74 (95% CI (1.12, 2.72)) in the Cox model compared to 2.91(95% CI (1.29, 6.52)) in the Joint model. In an exploratory analysis there was no evidence that family clustering substantially affected the conclusions. CONCLUSIONS: Estimated effects from the conventional Cox model, which is often the model of choice, were dramatically lower in this dataset, compared to the Joint model. While the Cox model is often considered the approach of choice for analysis, research has shown that the Joint model may be more efficient and less biased. Our analysis based on a Joint model suggests that the magnitude of association between anxiety and mood disorder in individuals at familial risk of developing bipolar disorder may be stronger than previously reported.

U-Flourish university students well-being and academic success longitudinal study: a study protocol.

INTRODUCTION: Over 30% of Canadians between the ages of 16 and 24 years attend university. This period of life coincides with the onset of common mental illnesses. Yet, data to inform university-based mental health prevention and early intervention initiatives are limited. The U-Flourish longitudinal study based out of Queen's University, Canada and involving Oxford University in the UK, is a student informed study funded by the Canadian Institute for Health Research Strategy for Patient Oriented Research (CIHR-SPOR). The primary goal of U-Flourish research is to examine the contribution of risk and resiliency factors to outcomes of well-being and academic success in first year students transitioning to university. METHODS AND ANALYSIS: The study is a longitudinal survey of all first-year undergraduate students entering Queen's University in the fall term of 2018 (and will launch at Oxford University in fall of 2019). In accordance with the CIHR-SPOR definitions, students represent the target population (ie, patient equivalent). Student peer health educators were recruited to inform the design, content and implementation of the study. Baseline surveys of Queen's first year students were completed in the fall of 2018, and follow-up surveys at the end of first year in the spring of 2019. Extensive student-led engagement campaigns were used to maximise participation rates. The baseline survey included measures of personal factors, family factors, environmental factors, psychological and emotional health, and lifestyle factors. Main outcomes include self-reported indicators of mental health at follow-up and mental health service access, as well as objective measures of academic success through linkage to university administrative and academic databases. A combination of mixed effects regression techniques will be employed to determine associations between baseline predictive factors and mental health and academic outcomes. ETHICS AND DISSEMINATION: Ethical approval was obtained by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (HSREB) (#6023126) at Queen's University. Findings will be disseminated through international and national peer-reviewed scientific articles and other channels including student-driven support and advocacy groups, newsletters and social media.

Epigenetic markers in inflammation-related genes associated with mood disorder: a cross-sectional and longitudinal study in high-risk offspring of bipolar parents.

Bipolar disorder is highly heritable and typically onsets in late adolescence or early adulthood. Evidence suggests that immune activation may be a mediating pathway between genetic predisposition and onset of mood disorders. Building on a prior study of mRNA and protein levels in high-risk offspring published in this Journal, we conducted a preliminary examination of methylation profiles in candidate immune genes from a subsample of well-characterized emergent adult (mean 20 years) offspring of bipolar parents from the Canadian Flourish high-risk cohort. Models were adjusted for variable age at DNA collection, sex and antidepressant and mood stabilizer use. On cross-sectional analysis, there was evidence of higher methylation rates for BDNF-1 in high-risk offspring affected (n = 27) and unaffected (n = 23) for mood disorder compared to controls (n = 24) and higher methylation rates in affected high-risk offspring for NR3C1 compared to controls. Longitudinal analyses (25 to 34 months) provided evidence of steeper decline in methylation rates in controls (n = 24) for NR3C1 compared to affected (n = 15) and unaffected (n = 11) high-risk offspring and for BDNF-2 compared to affected high-risk. There was insufficient evidence that changes in any of the candidate gene methylation rates were associated with illness recurrence in high-risk offspring. While preliminary, findings suggest that longitudinal investigation of epigenetic markers in well-characterized high-risk individuals over the peak period of risk may be informative to understand the emergence of bipolar disorder.

Unlocking stress and forecasting its consequences with digital technology.

Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme heterogeneity by inter and intraindividual characteristics. The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time. In parallel, the advancement and capabilities of artificial intelligence (AI) and machine learning could discern heterogeneous, multidimensional information from individual signs of stress, and possibly inform how these signs forecast the downstream consequences of stress in the form of end-organ damage. The marriage of these tools could dramatically enhance the field of stress research contributing to impactful and empowering interventions for individuals bridging knowledge to practice, and intervention to real-world use. Here we discuss this potential, anticipated challenges, and emerging opportunities.

Coping strategies and self-esteem in the high-risk offspring of bipolar parents.

OBJECTIVES: This study investigated whether there were differences in coping strategies and self-esteem between offspring of parents with bipolar disorder (high-risk) and offspring of unaffected parents (control), and whether these psychological factors predicted the onset and recurrence of mood episodes. METHODS: High-risk and control offspring were followed longitudinally as part of the Flourish Canadian high-risk bipolar offspring cohort study. Offspring were clinically assessed annually by a psychiatrist using semi-structured interviews and completed a measure of coping strategies and self-esteem. RESULTS: In high-risk offspring, avoidant coping strategies significantly increased the hazard of a new onset Diagnostic and Statistical Manual of Mental Disorders, 4th Edition twice revised mood episode or recurrence (hazard ratio: 1.89, p = 0.04), while higher self-esteem significantly decreased this hazard (hazard ratio: 2.50, p < 0.01). Self-esteem and avoidant coping significantly interacted with one another ( p < 0.05), where the risk of a Diagnostic and Statistical Manual of Mental Disorders, 4th Edition twice revised new onset mood episode or recurrence was only significantly increased among high-risk offspring with both high avoidant coping and low self-esteem. CONCLUSION: A reduction of avoidant coping strategies in response to stress and improvement of self-esteem may be useful intervention targets for preventing the new onset or recurrence of a clinically significant mood disorder among individuals at high familial risk.