Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning.

BACKGROUND: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multidimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome," which is associated with health anxiety, problematic internet use, and obsessive-compulsive symptoms. Cyberchondria is not included in the International Classification of Diseases 11th Revision or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and its defining features, etiological mechanisms, and assessment continue to be debated. OBJECTIVE: This study aims to investigate changes in the severity of cyberchondria during the COVID-19 pandemic and identify the predictors of cyberchondria at this time. METHODS: Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.29, SD 12.88) years, with females constituting the majority (416/725, 57.4%). RESULTS: The results showed that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and compulsion increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). In addition, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. CONCLUSIONS: These findings provide evidence of the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, they are consistent with a theoretical model of cyberchondria during the COVID-19 pandemic proposed in 2020. These findings have implications for the conceptualization and future assessment of cyberchondria.

Smartphone apps for mental health and wellbeing: A usage survey and machine learning analysis of psychological and behavioral predictors.

Objective: Despite the availability of thousands of mental health applications, the extent to which they are used and the factors associated with their use remain largely unknown. The present study aims to (a) assess in a representative US-based population sample the use of smartphone apps for mental health and wellbeing (SAMHW), (b) determine the variables predicting the use of SAMHW, and (c) explore how a set of variables related to mental health, smartphone use, and smartphone "addiction" may be associated with the use of SAMHW. Methods: Data was collected via online questionnaire from 1989 adults. The data gathered included information on smartphone use behavior, mental health, and the use of SAMHW. Latent class analysis was used to categorize participants. Machine learning and logistic regression analyses were used to determine the most important predictors of SAMHW use and associations between predictors and outcome variables. Results: While two-thirds of participants had a statistically high probability for using SAMHW, nearly twice more had high probability for using them to improve wellbeing compared to using them to address mental health problems (43% vs. 18%). In both groups, these participants were more likely to be female and in the younger adult age bracket than male and in the adult or older adult age bracket. According to the machine learning model, the most important predictors for using the relevant smartphone apps were variables associated with smartphone problematic use, COVID-19 impact, and mental health problems. Conclusion: Findings from the present study confirm that the use of SAMHW is growing, particularly among younger adult and female individuals who are negatively impacted by problematic smartphone use, COVID-19, and mental health problems. These individuals tend to bypass traditional care via psychotherapy or psychopharmacology, relying instead on smartphones to address mental health conditions or improve wellbeing. Advising users of these apps to also seek professional help and promoting efforts to prove the efficacy and safety of SAMHW would seem necessary.

Case study: Developing a strategy combining human and empirical interventions to support the resilience of healthcare workers exposed to a pandemic in an academic hospital.

The COVID-19 pandemic has put healthcare workers under important psychological pressure. Concerns have been raised regarding the mental health and psychological status of healthcare workers and have underlined the need for institutions to develop long-term interventions to support their resilience. The current case study presents the way a large university hospital in Brussels, Belgium, has evolved to deal with this health crisis and support its workers. Initiatives were multiple and complementary, as it was decided to combine different forms of clinical interventions that were developed by psychologists, psychiatrists, and human resources, to an empirical approach including a large survey that permitted to reach a much larger audience (the results of the study have been published previously). We describe the initially proposed measures of psychological support, including the creation of a telephone hotline, the presence of psychologists among teams of dedicated COVID-19 units, discussion groups, and individualized follow-ups, and their consequences on healthcare workers. Second, we address how these initial measures of support were modified to tailor in the best way possible the needs of healthcare workers, using a research action project that used a survey to measure and address the psychological distress of healthcare workers. We explain how, through different objectives (screening of distress, adaptation of initial measures based on reported needs, active reinforcement of individual and collective resilience, reminder of availability of help, and normalization of distress), a research action project can be a form of support and is an effective way for an institution to show its pre-occupation for the mental health of its teams. The current case study highlights how an institution can provide support and the importance of the use of a combined strategy to limit the consequences of a major health crisis on the mental health of its healthcare workers. Improving the resilience of healthcare workers both in the short and long term is of the essence to maintain optimal care of patients.

Key implementation factors in telemedicine-delivered medications for opioid use disorder: a scoping review informed by normalisation process theory.

Telemedicine could improve access to medications for opioid use disorder (MOUD). Telemedicine-delivered MOUD (TMOUD) has expanded substantially in response to the restrictions imposed by the COVID-19 pandemic on in-person clinical contact, yet this expansion has not happened consistently across all health systems and countries. This Review aims to understand key factors in TMOUD implementation that might explain variations in uptake. We did a scoping review using three English language databases for articles reporting on the implementation of TMOUD services. 57 peer-reviewed articles were identified, subjected to open coding and thematic analysis, and further interpreted through normalisation process theory (NPT). NPT was originally used to evaluate telehealth innovations and has been applied extensively to describe, assess, and develop the implementation potential of a broad range of complex health-care interventions. By categorising our findings according to the four core NPT constructs of coherence, cognitive participation, collective action, and reflexive monitoring, we aim to rationalise the current evidence base to show the workability of TMOUD in practice. We find that variations in TMOUD models in practice depend on organisations' attitudes towards risk, clinicians' tensions around giving up control over standard practices, organisation-level support in overcoming operational and technological challenges, and evaluation methods that might neglect a potential widening of the digital divide.

Health websites on COVID-19: are they readable and credible enough to help public self-care?

OBJECTIVE: There are concerns about nonscientific and/or unclear information on the coronavirus disease 2019 (COVID-19) that is available on the Internet. Furthermore, people's ability to understand health information varies and depends on their skills in reading and interpreting information. This study aims to evaluate the readability and creditability of websites with COVID-19-related information. METHODS: The search terms "coronavirus," "COVID," and "COVID-19" were input into Google. The websites of the first thirty results for each search term were evaluated in terms of their credibility and readability using the Health On the Net Foundation code of conduct (HONcode) and Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), Gunning Fog, and Flesch Reading Ease Score (FRE) scales, respectively. RESULTS: The readability of COVID-19-related health information on websites was suitable for high school graduates or college students and, thus, was far above the recommended readability level. Most websites that were examined (87.2%) had not been officially certified by HONcode. There was no significant difference in the readability scores of websites with and without HONcode certification. CONCLUSION: These results suggest that organizations should improve the readability of their websites and provide information that more people can understand. This could lead to greater health literacy, less health anxiety, and the provision of better preventive information about the disease.

Multidimensional Assessment of COVID-19-Related Fears (MAC-RF): A Theory-Based Instrument for the Assessment of Clinically Relevant Fears During Pandemics.

In this article, we present the development and psychometric properties of the Multidimensional Assessment of COVID-19--Related Fears (MAC-RF). The MAC-RF is an eight-item, self-report scale that has been developed to assess clinically relevant domains of fear during the COVID-19 pandemic. The MAC-RF is based on a comprehensive theoretical model conceptualizing fears during the pandemics as resulting from an interaction of bodily, interpersonal, cognitive, and behavioral experiences. The MAC-RF was administered to a sample of 623 Italian adults from the community aged between 18 and 76 years old (M= 35.67, SD= 12.93), along with a measure of current clinical symptoms. Item response theory analyses demonstrated that each item of the MAC-RF provided sufficient information about the underlying construct of fear. The statistical fit of the scale was satisfactory. MAC-RF total scores correlated significantly and positively with total scores on the measure of psychopathology and with the clinical symptom domain scores. A ROC (receiver operating characteristic) curve analysis showed that the MAC-RF total score was sufficiently able to identify cases with high levels of current psychopathology, with an area under the curve of.76. These findings suggest that the MAC-RF can be used to assess pathological fear during pandemics. The English, Italian, and French versions of the MAC-RF are annexed to this article for use by clinicians and health services.