Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic.

BACKGROUND AND OBJECTIVE: Due to the constraints of the COVID-19 pandemic, healthcare workers have reported acting in ways that are contrary to their moral values, and this may result in moral distress. This paper proposes the novel digital phenotype profile (DPP) tool, developed specifically to evaluate stress experiences within participants. The DPP tool was evaluated using the COVID-19 VR Healthcare Simulation of Stress Experience (HSSE) dataset (NCT05001542), which is composed of passive physiological signals and active mental health questionnaires. The DPP tool focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with moral injury outcome scale (Brief MIOS). METHODS: Data-driven techniques are encompassed to develop a tool for robust evaluation of distress among participants. To accomplish this, we applied pre-processing techniques which involved normalization, data sanitation, segmentation, and windowing. During feature analysis, we extracted domain-specific features, followed by feature selection techniques to rank the importance of the feature set. Prior to classification, we employed k-means clustering to group the Brief MIOS scores to low, moderate, and high moral distress as the Brief MIOS lacks established severity cut-off scores. Support vector machine and decision tree models were used to create machine learning models to predict moral distress severities. RESULTS: Weighted support vector machine with leave-one-subject-out-cross-validation evaluated the separation of the Brief MIOS scores and achieved an average accuracy, precision, sensitivity, and F1 of 98.67%, 98.83%, 99.44%, and 99.13%, respectively. Various machine learning ablation tests were performed to support our results and further enhance the understanding of the predictive model. CONCLUSION: Our findings demonstrate the feasibility to develop a DPP tool to predict distress experiences using a combination of mental health questionnaires and passive signals. The DPP tool is the first of its kind developed from the analysis of the HSSE dataset. Additional validation is needed for the DPP tool through replication in larger sample sizes.

Striking a match between FHIR-based patient data and FHIR-based eligibility criteria

Inputs and Outputs: The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point of-care recognition of patient-specific clinical decision support.Specifications: The eligibility criteria are defined in HL7 FHIR (version R5) Evidence Variable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.Application: The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.

OBJECTIVE: The COVID-19 pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500,000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. MATERIALS AND METHODS: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHR) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. RESULTS: We identified 502,505 participants (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. DISCUSSION AND CONCLUSION: Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.

mHealth and social mediation: Mobile support among stigmatized people living with HIV and substance use disorder

The social mediation role of mobile technology is typified by mHealth apps designed to connect individuals to others and support substance use disorder (SUD) recovery. In this study, we examined the use and utility of one such app designed to support people living with HIV (PLWH) and SUD. Drawing on Ling's emphasis on reciprocity and micro-coordination in mobile telephony as a social mediation technology, we gathered digital trace data from app logs to construct two metrics, initiation (i.e. whether a particular feature is engaged on a given day) and intensity (i.e. degree of involvement in the activity when engaged on that day), at three levels of communication—networked (one-to-many), dyadic (one-to-one), and intraindividual (self-to-self). We consider these affordances alongside use of information resources, games and relaxation links, a meeting and events calendar, and support tools to address use urges. We found few differences in patterns of use by race, sex, and age, though African Americans were less likely to engage in intraindividual expression, whereas women and older users were more likely to make use of this affordance. The initiation and intensity of network and dyadic reception, as well as the intensity of network expression, predicts recovery outcomes as measured on a weekly "check-in” survey, suggesting the utility of mobile log data for digital phenotyping in mHealth. By implementing this app during the COVID-19 pandemic, the study also found the disruption caused by national lockdown was negatively related to the app use. © The Author(s) 2023.

Digital phenotyping for classification of anxiety severity during COVID-19.

COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77 ± 0.13 % and 97.35 ± 0.11 % for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping.

Microbiome in the Framework of Predictive, Preventive and Personalised Medicine

The human body is inhabited by trillions of diverse microorganisms collectively called "microbiome" or "microbiota". Microbiota consists of bacteria, viruses, fungi, protozoa, and archaea. Microbiome demonstrates multi-faceted effects on human physical and mental health. Per evidence there is a multi-functional interplay between the whole-body microbiome composition on the epithelial surfaces including skin, nasal and oral cavities, airway, gastro-intestinal and urogenital tracts on one hand and on the other hand, the individual health status. Microbiota composition as well as an option to modulate it - together create a highly attractive operation area for the translational bio/medical research with multi-professional expertise and healthcare-relevant output in the framework of predictive, preventive and personalised medicine (PPPM/3 PM). Advanced PPPM strategies implemented in the microbiome area are expected to significantly improve individual outcomes and overall cost-efficacy of healthcare. According to the accumulated research data, corresponding diagnostic and treatment approaches are applicable to primary care (health risk assessment in individuals with sub-optimal health conditions and prevention of a disease development), secondary care (personalised treatment of clinically manifested disorders preventing a disease progression) and tertiary care (making palliation to an optimal management of non-curable diseases). In the current book, we do highlight the implementation potential of the microbiome-relevant research in the framework of predictive diagnostics, targeted prevention and treatments tailored to the individualised patient profile.Copyright © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Immune cell landscape in symptomatic and asymptomatic SARS-CoV-2 infected adults and children in urban Dhaka, Bangladesh.

OBJECTIVES: The study of cellular immunity to SARS-CoV-2 is crucial for evaluating the course of the COVID-19 disease and for improving vaccine development. We aimed to assess the phenotypic landscape of circulating lymphocytes and mononuclear cells in adults and children who were seropositive to SARS-CoV-2 in the past 6 months. METHODS: Blood samples (n = 350) were collected in a cross-sectional study in Dhaka, Bangladesh (Oct 2020-Feb 2021). Plasma antibody responses to SARS-CoV-2 were determined by an electrochemiluminescence immunoassay while lymphocyte and monocyte responses were assessed using flow cytometry including dimensionality reduction and clustering algorithms. RESULTS: SARS-CoV-2 seropositivity was observed in 52% of adults (18-65 years) and 56% of children (10-17 years). Seropositivity was associated with reduced CD3+T cells in both adults (beta(ß) = -2.86; 95% Confidence Interval (CI) = -5.98, 0.27) and children (ß = -8.78; 95% CI = -13.8, -3.78). The frequencies of T helper effector (CD4+TEFF) and effector memory cells (CD4+TEM) were increased in seropositive compared to seronegative children. In adults, seropositivity was associated with an elevated proportion of cytotoxic T central memory cells (CD8+TCM). Overall, diverse manifestations of immune cell dysregulations were more prominent in seropositive children compared to adults, who previously had COVID-like symptoms. These changes involved reduced frequencies of CD4+TEFF cells and CD163+CD64+ classical monocytes, but increased levels of intermediate or non-classical monocytes, as well as CD8+TEM cells in symptomatic children. CONCLUSION: Seropositive individuals in convalescence showed increased central and effector memory T cell phenotypes and pro-resolving/healing monocyte phenotypes compared to seronegative subjects. However, seropositive children with a previous history of COVID-like symptoms, displayed an ongoing innate inflammatory trait.

A third SARS-CoV-2 mRNA vaccine dose in people receiving hemodialysis overcomes B cell defects but elicits a skewed CD4+ T cell profile.

Cellular immune defects associated with suboptimal responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccination in people receiving hemodialysis (HD) are poorly understood. We longitudinally analyze antibody, B cell, CD4+, and CD8+ T cell vaccine responses in 27 HD patients and 26 low-risk control individuals (CIs). The first two doses elicit weaker B cell and CD8+ T cell responses in HD than in CI, while CD4+ T cell responses are quantitatively similar. In HD, a third dose robustly boosts B cell responses, leads to convergent CD8+ T cell responses, and enhances comparatively more T helper (TH) immunity. Unsupervised clustering of single-cell features reveals phenotypic and functional shifts over time and between cohorts. The third dose attenuates some features of TH cells in HD (tumor necrosis factor alpha [TNFα]/interleukin [IL]-2 skewing), while others (CCR6, CXCR6, programmed cell death protein 1 [PD-1], and HLA-DR overexpression) persist. Therefore, a third vaccine dose is critical to achieving robust multifaceted immunity in hemodialysis patients, although some distinct TH characteristics endure.

Atypical Inflammatory Syndrome Triggered by SARS-CoV-2 in Infants With Down Syndrome

PURPOSE OF THE STUDY: The aim of this study was to identify unique clinical features and immune markers in infants with Down Syndrome (DS) affected by multisystem inflammatory syndrome (MIS-C). STUDY POPULATION: Cases were 2 unrelated infant girls with DS ages 6 months (P1) and 8 months (P2) admitted to the hospital for MIS-C illness for over 4 months (n = 2). The first control group included infants without DS with MIS-C illness (n = 2) from an outpatient setting. The second control group included 10 children with DS unaffected by MIS-C illness from an outpatient setting. METHODS: This was a case-control study that compared the clinical characteristics including immune phenotyping between infants with Down Syndrome (DS) affected by MIS-C and age-matched controls with or without DS. Clinical characteristics were collected from P1 and P2 by chart review, and literature review was done for clinical characteristics of children with MIS-C without DS. Samples of blood were collected from the 2 cases and controls. Subsequently, both mass cytometry and multiplex cytokine analysis were performed. Unpaired t-tests were used to assess the significances of differences in quantitative variables between 2 groups. RESULTS: Both patients with DS and MIS-C had significant neutrophilia and profound B-cell lymphopenia when compared with children with DS without MIS-C (P = .008). Specifically, both patients had decreased memory and plasma B cell subsets, whereas naïve B cells were increased. Control patients with acute MIS-C without DS had normal B cell counts. Activated CD4 T cells were decreased in both patients. P1's neutrophils and monocytes had markedly increased intracellular interleukein-8 and interleukin-1β, but this was not seen in P2. Both patients had markedly elevated inflammatory and immune activation markers. CONCLUSIONS: Children with Down Syndrome affected by MIS-C can have an atypical and severe presentation compared with children affected by MIS-C without DS, hallmarked by significant B cell depletion, younger age of onset, prolonged hospital stay, and refractoriness to treatment.

Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.

PURPOSE: The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction. METHODS: Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs. RESULTS: In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280). CONCLUSIONS: Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

An Interactive Interface for Patient Diagnosis using Machine Learning Model

Hospitals are the most common option for health checks, illness diagnosis, and treatment for sick people. This practice is followed by almost everyone in the world. But there is a drawback with this method of getting diagnosed. There are a lot of patients with various diseases/viruses which have a potential to spread in the hospital premises. People never considered the diseases/viruses present in the hospital atmosphere. People are aware of the risk of viral transmissions in hospital environments, post COVID era. Getting diagnosed and going through the reports with an efficient accuracy takes time and some people in emergency may not have enough time to perform the conventional procedures. Users have a necessity of an online website which can help them diagnose their health problems at the comfort of their homes. This would benefit people as they don't have to travel to the hospitals and reduce their risks of transmitting hospital acquired infections. This paper presents an interactive interface that functions as a virtual therapist which accepts input in the form of text, voice, or video. Data is pushed into the machine learning pipeline that generates results. The end result of this model is a report containing root cause of the disease, a tentative prescription, and any estimated treatment expenses. This model helps to prevent hospital-acquired infections, reduces the costs of treatment as users would be able to diagnose earlier and would prefer frequent testing, reducing surgeries and also reduces the tasks of doctors. © 2022 IEEE.

Phenotyping of Acute and Persistent Coronavirus Disease 2019 Features in the Outpatient Setting: Exploratory Analysis of an International Cross-sectional Online Survey.

BACKGROUND: Long COVID, defined as the presence of coronavirus disease 2019 (COVID-19) symptoms ≥28 days after clinical onset, is an emerging challenge to healthcare systems. The objective of the current study was to explore recovery phenotypes in nonhospitalized individuals with COVID-19. METHODS: A dual cohort, online survey study was conducted between September 2020 and July 2021 in the neighboring European regions Tyrol (TY; Austria, n = 1157) and South Tyrol (STY; Italy, n = 893). Data were collected on demographics, comorbid conditions, COVID-19 symptoms, and recovery in adult outpatients. Phenotypes of acute COVID-19, postacute sequelae, and risk of protracted recovery were explored using semi-supervised clustering and multiparameter least absolute shrinkage and selection operator (LASSO) modeling. RESULTS: Participants in the study cohorts were predominantly working age (median age [interquartile range], 43 [31-53] years] for TY and 45 [35-55] years] for STY) and female (65.1% in TY and 68.3% in STY). Nearly half (47.6% in TY and 49.3% in STY) reported symptom persistence beyond 28 days. Two acute COVID-19 phenotypes were discerned: the nonspecific infection phenotype and the multiorgan phenotype (MOP). Acute MOP symptoms encompassing multiple neurological, cardiopulmonary, gastrointestinal, and dermatological symptoms were linked to elevated risk of protracted recovery. The major subset of individuals with long COVID (49.3% in TY; 55.6% in STY) displayed no persistent hyposmia or hypogeusia but high counts of postacute MOP symptoms and poor self-reported physical recovery. CONCLUSIONS: The results of our 2-cohort analysis delineated phenotypic diversity of acute and postacute COVID-19 manifestations in home-isolated patients, which must be considered in predicting protracted convalescence and allocating medical resources.

Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

BACKGROUND: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). OBJECTIVE: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic. METHODS: First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. RESULTS: Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F1-score: 0.84). CONCLUSIONS: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Smartphone Ownership, Smartphone Utilization, and Interest in Using Mental Health Apps to Address Substance Use Disorders: Literature Review and Cross-sectional Survey Study Across Two Sites.

BACKGROUND: In recent years, there has been increasing interest in implementing digital technologies to diagnose, monitor, and intervene in substance use disorders. Smartphones are now a vehicle for facilitating telepsychiatry visits, measuring health metrics, and communicating with health care professionals. In light of the COVID-19 pandemic and the movement toward web-based and hybrid clinic visits and meetings, it has become especially salient to assess phone ownership among individuals with substance use disorders and their comfort in navigating phone functionality and using phones for mental health purposes. OBJECTIVE: The aims of this study were to summarize the current literature around smartphone ownership, smartphone utilization, and the acceptability of using smartphones for mental health purposes and assess these variables across two disparate substance use treatment sites. METHODS: We performed a focused literature review via a search of two academic databases (PubMed and Google Scholar) for publications since 2007 on the topics of smartphone ownership, smartphone utilization, and the acceptability of using mobile apps for mental health purposes among the substance use population. Additionally, we conducted a cross-sectional survey study that included 51 participants across two sites in New England-an inpatient detoxification unit that predominantly treats patients with alcohol use disorder and an outpatient methadone maintenance treatment clinic. RESULTS: Prior studies indicated that mobile phone ownership among the substance use population between 2013 and 2019 ranged from 83% to 94%, while smartphone ownership ranged from 57% to 94%. The results from our study across the two sites indicated 96% (49/51) mobile phone ownership and 92% (47/51) smartphone ownership among the substance use population. Although most (43/49, 88%) patients across both sites reported currently using apps on their phone, a minority (19/48, 40%) reported previously using any apps for mental health purposes. More than half of the participants reported feeling at least neutrally comfortable with a mental health app gathering information regarding appointment reminders (32/48, 67%), medication reminders (33/48, 69%), and symptom surveys (26/45, 58%). Most patients were concerned about privacy (34/51, 67%) and felt uncomfortable with an app gathering location (29/47, 62%) and social (27/47, 57%) information for health care purposes. CONCLUSIONS: The majority of respondents reported owning a mobile phone (49/51, 96%) and smartphone (47/51, 92%), consistent with prior studies. Many respondents felt comfortable with mental health apps gathering most forms of personal information and with communicating with their clinician about their mental health. The differential results from the two sites, namely greater concerns about the cost of mental health apps among the methadone maintenance treatment cohort and less experience with downloading apps among the older inpatient detoxification cohort, may indicate that clinicians should tailor technological interventions based on local demographics and practice sites and that there is likely not a one-size-fits-all digital psychiatry solution.

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period. © 2022 Owner/Author.

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study

Background: Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes. This study is novel as it adds to the field by conducting a trial using private and nonintrusive sensors that can help detect and monitor depression in a continuous, passive manner. Objective: This study demonstrates a novel mental behavioral profiling metric (the Mental Health Similarity Score), derived from analyzing passively monitored, private, and nonintrusive smartphone use data, to identify and track depressive behavior and its progression. Methods: Smartphone data sets and self-reported Patient Health Questionnaire-9 (PHQ-9) depression assessments were collected from 558 smartphone users on the Android operating system in an observational study over an average of 10.7 (SD 23.7) days. We quantified 37 digital behavioral markers from the passive smartphone data set and explored the relationship between the digital behavioral markers and depression using correlation coefficients and random forest models. We leveraged 4 supervised machine learning classification algorithms to predict depression and its severity using PHQ-9 scores as the ground truth. We also quantified an additional 3 digital markers from gyroscope sensors and explored their feasibility in improving the model’s accuracy in detecting depression. Results: The PHQ-9 2-class model (none vs severe) achieved the following metrics: precision of 85% to 89%, recall of 85% to 89%, F1 of 87%, and accuracy of 87%. The PHQ-9 3-class model (none vs mild vs severe) achieved the following metrics: precision of 74% to 86%, recall of 76% to 83%, F1 of 75% to 84%, and accuracy of 78%. A significant positive Pearson correlation was found between PHQ-9 questions 2, 6, and 9 within the severely depressed users and the mental behavioral profiling metric (r=0.73). The PHQ-9 question-specific model achieved the following metrics: precision of 76% to 80%, recall of 75% to 81%, F1 of 78% to 89%, and accuracy of 78%. When a gyroscope sensor was added as a feature, the Pearson correlation among questions 2, 6, and 9 decreased from 0.73 to 0.46. The PHQ-9 2-class model+gyro features achieved the following metrics: precision of 74% to 78%, recall of 67% to 83%, F1 of 72% to 78%, and accuracy of 76%. Conclusions: Our results demonstrate that the Mental Health Similarity Score can be used to identify and track depressive behavior and its progression with high accuracy.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps;mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

Identification of Genetic Risk Factors of Severe COVID-19 Using Extensive Phenotypic Data: A Proof-of-Concept Study in a Cohort of Russian Patients.

The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in MYH14p = 1.4 × 10-7), rs11243705 in SETX (p = 8.2 × 10-6), and rs16885 in ATXN1 (p = 1.3 × 10-5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data (rs33985936 in SCN11A, rs16885 in ATXN1, and rs4747194 in CDH23). Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World.

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.