Digital phenotyping and the COVID-19 pandemic: Capturing behavioral change in patients with psychiatric disorders

Jagesar, Raj; Roozen, Mila C.; Heijden, Inge van der; Ikani, Nessa; Tyborowska, Anna; Penninx, Brenda W. J. H.; Ruhé, Henricus G.; Sommer, Iris; Kas, Martien; Vorstman, Jacob

doi:10.1016/j.euroneuro.2020.11.012

Cited by 26 publications

(16 citation statements)

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“…Compared to depression, the increased susceptibility to COVID-19 among patients with anxiety seems to be less attributable to the shared genetic liability, and thereby may be more behavior-or environment-related. Different behavior and mobility patterns have indeed been reported among individuals with depression and anxiety , during COVID-19 pandemic 33,34 . Individuals with depression or negative emotion in response to COVID-19 outbreak tend to implement self-limiting mobility mode (i.e., restricting their daily mobility range) 33,35 , whilst individuals with anxiety were demonstrated to be less resilient to preventive measures (e.g., wearing a face mask, hand washing, social distancing, and self-isolation or quarantine) during the COVID-19 outbreak 36,37 .…”

Section: Discussionmentioning

confidence: 94%

Genetic predisposition to psychiatric disorders and risk of COVID-19

Chen

Zeng

et al. 2021

Preprint

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Background Pre-pandemic psychiatric disorders have been associated with an increased risk of COVID-19. However, the underlying mechanisms remain unknown, e.g. to what extent genetic predisposition to psychiatric disorders contributes to the observed association. Methods The analytic sample consisted of white British participants of UK Biobank registered in England, with available genetic data, and alive on Jan 31, 2020 (i.e., the start of the COVID-19 outbreak in the UK) (n=346,554). We assessed individuals' genetic predisposition to different psychiatric disorders, including substance misuse, depression, anxiety, and psychotic disorder, using polygenic risk score (PRS). Diagnoses of psychiatric disorders were identified through the UK Biobank hospital inpatient data. We performed a GWAS analysis for each psychiatric disorder in a randomly selected half of the study population who were free of COVID-19 (i.e., the base dataset). For the other half (i.e., the target dataset), PRS was calculated for each psychiatric disorder using the discovered genetic variants from the base dataset. We then examined the association between PRS of each psychiatric disorder and risk of COVID-19, or severe COVID-19 (i.e., hospitalization and death), using logistic regression models. The ascertainment of COVID-19 was through the Public Health England dataset, the UK Biobank hospital inpatient data and death registers, updated until July 26, 2020. For validation, we repeated the PRS analyses based on publicly available GWAS summary statistics. Results 155,988 participants (including 1,451 COVID-19 cases), with a mean age of 68.50 years at COVID-19 outbreak, were included for PRS analysis. Higher genetic liability forwards psychiatric disorders was associated with increased risk of both any COVID-19 and severe COVID-19, especially genetic risk for substance misuse and depression. The adjusted odds ratios (ORs) for any COVID-19 were 1.15 (95% confidence interval [CI] 1.02-1.31) and 1.26 (1.11-1.42) among individuals with a high genetic risk (above the upper tertile of PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Largely similar ORs were noted for severe COVID-19 and similar albeit slightly lower estimates using PRSs generated from GWAS summary statistics from independent samples. Conclusion In the UK Biobank, genetic predisposition to psychiatric disorders was associated with an increased risk of COVID-19, including severe course of the disease. These findings suggest the potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19, underscoring the need of increased medical surveillance of for this vulnerable population during the pandemic.

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Section: Discussionmentioning

confidence: 94%

Genetic predisposition to psychiatric disorders and risk of COVID-19

Chen

Zeng

et al. 2021

Preprint

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“…time spent at home) among subjects with psychiatric disorders. 33 Fig. 1 depicts the flow diagram reporting the selection process.…”

Section: Resultsmentioning

confidence: 99%

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

Gianfredi

Provenzano²,

Santangelo³

2021

Public Health

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Objectives At the end of 2019, an acute infectious pneumonia (coronavirus disease 2019 [COVID-19]) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China, and subsequently spread around the world starting a pandemic. Globally, to date, there have been >118 million confirmed cases, including >2 million deaths. In this context, it has been shown that the psychological impact of the pandemic is important and that it can be associated with an increase in internet searches related to fear, anxiety, depression, as well as protective behaviours, health knowledge and even maladaptive behaviours. Study design This is a systematic review. Methods This review aims to collect, analyse and synthesise available evidence on novel data streams for surveillance purposes and/or their potential for capturing the public reaction to epidemic outbreaks, particularly focusing on mental health effects and emotions. Results At the end of the screening process, 19 articles were included in this systematic review. Our results show that the COVID-19 pandemic had a great impact on internet searches for mental health of entire populations, which manifests itself in a significant increase of depressed, anxious and stressed internet users’ emotions. Conclusions Novel data streams can support public health experts and policymakers in establishing priorities and setting up long-term strategies to mitigate symptoms and tackle mental health disorders.

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“…The mean age was 31.2 (range: 20-63) years, and the mean BMI was 24.8 (range: 16.9-47.7) kg/m 2 . The mean (range) of HAM-D total score at screening was 20.4 (17)(18)(19)(20)(21)(22)(23)(24)(25) for the patients, and it was 1.2 (0-3) for the healthy controls. The mean (range) of C-SSRS total score at screening was 32.1 (0-77) for the patients, and it was 1.1 (0-8) for the healthy controls.…”

Section: Subject Demographics and Psychometric Scoresmentioning

confidence: 97%

“…BeHapp aims to provide a "quantified" perspective on human behavior in terms of mobility and social interaction. The service has been applied to research the concept of social functioning in various studies on mental health amongst populations including participants suffering from schizophrenia, Alzheimer's disease and major depression (16)(17)(18). After activation, the BeHapp application remained active in the background of the participant's smartphone.…”

Section: Behappmentioning

confidence: 99%

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

et al. 2021

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Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted.Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression.Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception.Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression.Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

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Digital phenotyping and the COVID-19 pandemic: Capturing behavioral change in patients with psychiatric disorders

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Cited by 26 publications

References 10 publications

Genetic predisposition to psychiatric disorders and risk of COVID-19

Genetic predisposition to psychiatric disorders and risk of COVID-19

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

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