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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319862

ABSTRACT

SummaryBackground: Public health measures to curb SARS-CoV-2 transmission rates may have negative psychosocial consequences in youth. Digital interventions may help to mitigate these effects. We investigated the associations between social isolation, cognitive preoccupation, worries, and anxiety, objective social risk indicators, psychological distress as well as use of, and attitude towards, mobile health (mHealth) interventions in youth during the COVID-19 pandemic. Methods: Data were collected as part of the ‘Mental Health And Innovation During COVID-19 Survey’ —a cross-sectional panel study including a representative sample of individuals aged 16 to 25 years (N=666;Mage 21·3) (assessment period: 07.05.-16.05.2020). Outcomes: Overall, 38% of youth met criteria for moderate psychological distress and 30% felt ‘often’ or ‘very often’ socially isolated, even after most restrictive infection control measures had been lifted. Social isolation, COVID-19-related worries and anxiety, and objective risk indicators were associated with psychological distress, with evidence of dose-response relationships for some of these associations. For instance, psychological distress was progressively more likely to occur as levels of social isolation increased (reporting ‘never’ as reference group: ‘occasionally’: adjusted odds ratio [aOR] 9·1, 95% confidence interval [CI] 4·3 – 19·1, p<0·001;‘often’: aOR 22·2, CI 9·8 – 50·2, p<0·001;’very often’: aOR 42·3, CI 14·1 – 126·8, p<0·001). There was evidence that psychological distress, worries, and anxiety were associated with a positive attitude towards using digital interventions, whereas high levels of psychological distress, worries, and anxiety were associated with actual use.Interpretation: Public health measures during pandemics may be associated with poor mental health in youth. Digital interventions may help mitigate the negative psychosocial impact given there is an objective need and subjective demand.

2.
PLoS One ; 16(11): e0259499, 2021.
Article in English | MEDLINE | ID: covidwho-1506558

ABSTRACT

BACKGROUND: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. METHODS: We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. DISCUSSION: We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).


Subject(s)
Artificial Intelligence , Cross-Sectional Studies , Depression , Social Media
3.
Eur Psychiatry ; 64(1): e20, 2021 03 09.
Article in English | MEDLINE | ID: covidwho-1123674

ABSTRACT

BACKGROUND: Public health measures to curb SARS-CoV-2 transmission rates may have negative psychosocial consequences in youth. Digital interventions may help to mitigate these effects. We investigated the associations between social isolation, COVID-19-related cognitive preoccupation, worries, and anxiety, objective social risk indicators, and psychological distress, as well as use of, and attitude toward, mobile health (mHealth) interventions in youth. METHODS: Data were collected as part of the "Mental Health And Innovation During COVID-19 Survey"-a cross-sectional panel study including a representative sample of individuals aged 16-25 years (N = 666; Mage = 21.3; assessment period: May 5, 2020 to May 16, 2020). RESULTS: Overall, 38% of youth met criteria for moderate or severe psychological distress. Social isolation worries and anxiety, and objective risk indicators were associated with psychological distress, with evidence of dose-response relationships for some of these associations. For instance, psychological distress was progressively more likely to occur as levels of social isolation increased (reporting "never" as reference group: "occasionally": adjusted odds ratio [aOR] 9.1, 95% confidence interval [CI] 4.3-19.1, p < 0.001; "often": aOR 22.2, CI 9.8-50.2, p < 0.001; "very often": aOR 42.3, CI 14.1-126.8, p < 0.001). There was evidence that psychological distress, worries, and anxiety were associated with a positive attitude toward using mHealth interventions, whereas psychological distress, worries, and anxiety were associated with actual use. CONCLUSIONS: Public health measures during pandemics may be associated with poor mental health outcomes in youth. Evidence-based digital interventions may help mitigate the negative psychosocial impact without risk of viral infection given there is an objective need and subjective demand.


Subject(s)
COVID-19 , Internet-Based Intervention/statistics & numerical data , Mental Health , Quarantine , Social Isolation/psychology , Stress, Psychological , Anxiety/prevention & control , Anxiety/psychology , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/psychology , Cross-Sectional Studies , Female , Germany/epidemiology , Humans , Male , Quarantine/methods , Quarantine/psychology , SARS-CoV-2 , Stress, Psychological/etiology , Stress, Psychological/prevention & control , Telemedicine/methods , Young Adult
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