This study seeks to investigate the levels of common psychopathology symptoms (i.e., depression and generalized anxiety) in a general population during the strict social distancing government-initiated non-pharmacological interventions (NPI's) related to the COVID-19 pandemic. The study also seeks to examine the predictors of generalized anxiety and depressive symptoms, as well as predictors of adherence rates to government-initiated non-pharmacological epidemiological interventions (NPI's). The aim of the project is to: * Inform the policymakers, the general public, scientists, and health practitioners about the psychological associations of the COVID-19-related government-initiated measures. * Provide a foundation for policymakers and health-care professionals to employ interventions that protect the general public against possibly increased psychological stressors, suffering and dysfunction during society's handling of the pandemic. * Help policymakers better understand the associations of demographic variables and psychological symptoms with adherence, providing an initial understanding of adherence rates, which may be used to help society fight against the COVID-19-virus from an epidemiological perspective by promoting factors that increase adherence.
This study seeks to investigate the levels of common psychopathology symptoms (i.e., depression and generalized anxiety) in a general population during the strict social distancing government-initiated non-pharmacological interventions (NPI's) related to the COVID-19 pandemic. The study also seeks to examine the predictors of generalized anxiety and depressive symptoms, as well as predictors of adherence rates to government-initiated non-pharmacological epidemiological interventions (NPI's). The aim of the project is to: * Inform the policymakers, the general public, scientists, and health practitioners about the psychological associations of the COVID-19-related government-initiated measures. * Provide a foundation for policymakers and health-care professionals to employ interventions that protect the general public against possibly increased psychological stressors, suffering and dysfunction during society's handling of the pandemic. * Help policymakers better understand the associations of demographic variables and psychological symptoms with adherence, providing an initial understanding of adherence rates, which may be used to help society fight against the COVID-19-virus from an epidemiological perspective by promoting factors that increase adherence. Hypotheses related to general psychopathology - Depressive and anxiety symptom levels 1. H1: Social distancing measures related to the pandemic (NPI's) are associated with increased depressive and generalized anxiety symptoms in the general population, as revealed through comparisons with prevalence rates of GAD and depression during from other benchmark studies with similar sample during non-pandemic periods. Exploratory: Investigate the differences in levels of depressive and generalized anxiety symptoms across different demographic subgroups in the sample. 2. H2: The demographic variables gender, age, relationship status and education level will be significant predictors of psychopathology (i.e., depression and anxiety), with women revealing higher symptoms, younger adults and single individuals revealing more severe symptoms, and those with higher education levels revealing less severe symptoms of anxiety and depression. Presence of self-reported psychological diagnosis will be a significant predictor of depression and anxiety symptoms, with those reporting having a diagnosis predicted to reveal higher symptoms of anxiety and depression. 3. H3: Involuntary job loss and autonomy frustration will significantly predict higher psychopathology levels, with those loosing their job involuntary and those who feel a lack of autonomy reporting higher levels of anxiety and depression. 4. H4: Perceived competence, sufficient information access, and stress concerning longer isolation period will significantly predict psychopathology, with the former two being associated with lower symptom levels, and the latter associated with larger symptoms. 5. H5: Higher levels of coping behaviors such as doing positive things not done otherwise; experiencing nature; as well as being engaged in physical activity will be associated with lower levels of anxiety and depressive symptoms. Hypotheses related to adherence to government-initiated non-pharmacological interventions 1. H1: Given previous factors related to adherence in previous pandemics, such as access to information and trust in one's government, the investigators predict high rates of adherence to government-initiated non-pharmacological measures (i.e., most of the respondents will adhere to NPI's nearly every day), in Norway, where both public resources and governmental trust has been revealed as high. 2. H2: The demographic variables gender, age, education level, and employment status will be significant predictors of adherence to NPI's, with women, older adults and those with higher education levels revealing higher rates of adherence, whereas those who are currently employed will reveal lower rates of adherence. 3. H3: Self-chosen vs. instructed social distancing and altruism will be significant predictors of adherence to NPI's, with the those self-choosing to distance themselves revealing lower adherence rates, and those with high scores on altruism revealing higher adherences levels. 4. H4: The situational variables sufficient access to information and difficulty to work from home will be significant predictors of adherence, with the first being associated with higher rates of adherence, and the latter with lower rates of adherence. 5. H5: Health anxiety, fear of significant others being infected, and fear of infecting others will be associated with higher rates of adherence. Exploratory: Additionally, the investigators will exploratory investigate whether the following variables are related to adherences rates, namely; depression levels, GAD levels, whether one suspects being infected the by COVID-19-virus, and worry about extended duration of NPI's. Multiple linear regression will be used to assess theorized predictors in three multiple regression analysis with: 1) Depressive symptom levels as the DV; 2) Generalized anxiety symptoms as the DV; and the last multiple regression analysis with 3) levels of adherence as the DV. Specific predictors of increased depressive levels and specific hypotheses for the multiple regression: 1. Gender: Women will report higher depressive levels, as consistently found in the psychopathology literature (Nolen-Hoeksema, 2001; Thayer et al., 2003). 2. Age: Younger subjects will report poorer mental health symptoms than older subjects, as previously found by a large epidemiological Scandinavian study (e.g., Molarius et al., 2009). 3. Relationship/Marital Status: Individuals in a relationship will experience less depressive symptoms as highlighted by previous studies (e.g., LaPierre, 2009; Zhang \& Li, 2010). Being married versus single has been reported as a reduced risk for most mental disorders in both genders (e.g., Scott et al., 2010). 4. Education Level: Lower education levels is associated with increased depressive symptoms (Lorant et al., 2003; Molarius \& Granström, 2018). 5. Involuntary job loss related to the COVID-19 pandemic and whether one is currently employed or not: Involuntary job loss will be associated with higher levels of depressive symptoms, as highlighted in other studies (e.g., Gallo et al., 2006; Kim \& Knesebeck, 2016). This is further based on The Economic Stress hypothesis, where job loss has been related to increased symptoms of psychological disorders (e.g., Castalano \& Dooley, 1983). 6. Lack of autonomy: Based on Deci and Ryan's Self-Determination Theory, the investigators hypothesize that Autonomy Frustration will significantly predict depressive symptoms, with higher reports of Autonomy Frustration being associated with higher levels of depressive symptoms. Thus, those who socially distance themselves voluntary will experience less adverse psychological symptoms. 7. Perceived Competence: Based on associations competence and symptoms of depression and anxiety (e.g., Tindall \& Curtis, 2019), the investigators hypothesize that self-perceived competence will significantly predict depressive levels, with higher levels of perceived competence being associated with lower levels of depressive symptoms. 8. Sufficient access to information: In line with previous findings (e.g., Brooks et al., 2020), the investigators hypothesize sufficient access to information to be an important protector against psychological distress, predicting that sufficient access to information will be associated with lower levels of depressive symptoms. 9. Stressors connected to the duration of the non-pharmacological pandemic measures: As stress about longer isolation and quarantine periods have been associated with poorer mental health (e.g., Brooks et al., 2020), the investigators hypothesize this variable as a significant predictor of depressive levels, where increased stress concerning longer social distancing period will be associated with higher levels of depressive symptoms. 10. Psychological diagnosis: As the nature of receiving a diagnosis according to the DSM, involves the co-occurrence of multiple symptoms within a symptom-cluster (i.e., disorder), the investigators wish to control for psychological diagnosis in the multiple regression, where the investigators expect to see psychological diagnosis as a significant predictor of depressive levels, with those reporting a diagnosis revealing larger levels of depressive symptoms. The following established three protective variables will also be included in the multiple regression analysis: 11. Physical acitvity 12. Experiencing nature 13. Doing positive things not done otherwise during social distancing period. Specific predictors of increased generalized anxiety levels and specific hypotheses for the multiple regression: 1. Gender: the investigators hypothesized that gender will be a significant predictor of GAD, with women being associated with higher scores of GAD symptoms, as highlighted in other epidemiological studies (e.g., Grant et al., 2007; Hunt et al., 2002). 2. Age: Younger subjects will report poorer mental health symptoms than older subjects, as previously found by similar large epidemiological Scandinavian studies (e.g., Molarius et al., 2009). 3. Relationship/Marital Status: the investigators hypothesize that being in a relationship is associated with decreases in generalized anxiety symptoms, as found elsewhere (e.g., Scott et al., 2010). 4. Education Level: The investigators hypothesize education to be a significant predictor of generalized anxiety symptoms, with higher education levels is associated with decreased GAD symptoms, as found in other epidemiological studies in Norway (Bjelland et al., 2008). 5. Involuntary job loss related to the COVID-19 pandemic whether one is currently employed or not: Involuntary job loss leads to increased GAD symptoms, based on previous findings on unemployment and anxiety symptoms (e.g., Westman, 2004) and based on The Economic Stress hypothesis, where job loss has been related to increased symptoms of psychological disorders (e.g., Castalano \& Dooley, 1983). 6. Lack of autonomy: Based on Deci and Ryan's Self-Determination Theory, the investigators hypothesize that Autonomy Frustration will significantly predict depressive symptoms, with higher reports of Autonomy Frustration being associated with higher levels of generalized anxiety symptoms. Thus, those who socially distance themselves voluntary will experience less adverse psychological symptoms. 7. Perceived Competence: Based on associations competence and symptoms of depression and anxiety (e.g., Tindall \& Curtis, 2019), the investigators hypothesize that self-perceived competence will significantly predict generalized anxiety levels, with higher levels of perceived competence being associated with lower levels of generalized anxiety symptoms. 8. Sufficient access to information: In line with previous findings (e.g., Brooks et al., 2020), the investigators hypothesize sufficient access to information to be an important protector against psychological distress, predicting that sufficient access to information will be associated with lower levels of depressive symptoms. 9. Stressors connected to the duration of the non-pharmacological pandemic measures: As stress about longer isolation and quarantine periods have been associated with poorer mental health (e.g., Brooks et al., 2020), the investigators hypothesize this variable as a significant predictor of generalized anxiety levels, where increased stress concerning longer social distancing period will be associated with higher levels of generalized symptoms. 10. Psychological diagnosis: As the nature of receiving a diagnosis according to the DSM, involves the co-occurrence of multiple symptoms within a symptom-cluster (i.e., disorder), the investigators wish to control for psychological diagnosis in the multiple regression, where the investigators expect to see psychological diagnosis as a significant predictor of generalized anxiety levels, with those reporting a diagnosis revealing larger levels of generalized anxiety symptoms. The following established three protective variables will also be included in the multiple regression analysis: 11. Physical acitvity 12. Experiencing nature 13. Doing positive things not done otherwise during social distancing period. Specific predictors of adherence and specific hypotheses for the multiple regression: 1. Gender: Consistent with previous findings revealing gender differences in compliance and conformity to rules (e.g., Cooper, 1979; Costa et al., 2001; Weisberg et al., 2011), the investigators hypothesize that compliance rates will be higher for women as compared to men. There has been mixed evidence in terms of demographic and employment characteristics with regards to adherence (Webster et al., 2020). 2. Age: the investigators hypothesize that older adults will reveal higher rates of compliance, as consistent with previous findings on general risk perception and age-related differences in risk-taking (e.g., Rolison et al., 2013). 3. Educational level: In accordance with previous studies (e.g., Hakes \& Viscusi, 2004) finding that higher levels of education is associated with more accurate risk perception, the investigators hypothesize that those with higher education in the sample are to be associated with higher adherence rates. 4. Altruism: Altruism has been theorized as a potentially important variable in relation to increase of adherence (e.g., Brooks et al., 2020; Webster et al., 2020). The investigators hypothesize altruism to be a significant predictor of adherence, with higher levels of altruism being associated with higher levels of adherence. 5. Self-chosen versus instructed social distancing: The investigators hypothesize that those who chose themselves to distance themselves in contrast to those instructed to be in quarantine or isolation have lower levels of adherence, as reported in a previous study related to SARS (e.g., DiGiovanni et al., 2004). 6. Employment status: In accordance with previous findings from the SARS pandemic reported by Porten et al. (2006), the investigators hypothesize current employment status (whether the individuals work or not) to be related to adherence, with those in work revealing less adherence. 7. Sufficient access to information: The investigators hypothesize that reports of sufficient information access will be a predictor of compliance, with more higher levels of reported access to information associated with higher levels of compliance, as found in other studies (Braunack-Mayer et al., 2013; Hsu et al., 2006; Webster et al., 2020). 8. Stressors connected to the duration of the non-pharmacological pandemic measures: Exploratory. The investigators will exploratory investigate how stress about longer duration of pandemic measures is related to adherence levels. 9. Difficulty to work from home: The investigators hypothesize that those finding it difficult to work from home will reveal lower levels of adherence. 10. Transmitting others: The belief of transmitting others has previously been found to increase adherence rates (DiGiovanni et al., 2004). The investigators hypothesize that this belief will be a significant predictor of adherence, with higher degrees of belief associated with higher degrees of compliance. 11. Fear of significant other being infected by the virus: The investigators hypothesize that fear of significant other being infected by the virus will be related to higher adherence levels, based on evolutionary kin selection theory (Hamilton, 1964) which includes the desire to protect and ensure the survival of ones nearest family and relatives. 12. Health anxiety: The investigators hypothesize that those with health anxiety symptoms will report higher rates of compliance. 13. Depression: Exploratory. The investigators aim to exploratory investigate GAD levels and their relationship to adherence in the sample. 14. GAD: Exploratory. The investigators aim to exploratory investigate depressive levels and their relationship to adherence in the sample. 15. Suspect Covid: Some studies (e.g., Teh et al., 2012) have found that individuals br eak quarantine to seek medical attention, given that they suspect an infectious disease. It could also be possible that those suspecting COVID will stay home. The investigators thus have no specific hypothesis in terms of direction, but only that Suspecting COVID will be a significant predictor of adherence. Participants were asked to fill out a set of validated questionnaires including demographic variables, psychological symptoms, situational factors related to the consequences of the COVID-19 virus, personality-trait and psychological needs variables, beliefs and fears related to COVID-19, as well as adherence to government-initiated non-pharmacological epidemiological measures (NPI's), in a random order. Some questionnaires are given as a whole, whereas other questions includes theoretically-driven selections of items from validated questionnaires, with the goal of avoiding topological overlap. This study is part of a 'The Norwegian COVID-19, Mental Health and Adherence Project' involving multiple studies. In order to not overwhelm and burden the participants with long questionnaire, and due to the mentioned empirical concerns of topological overlap (i.e., overlap in item content) between similar items (for network analysis purposes in some of the studies in the mentioned large-scale project), in some scales involving large item-content overlap, single items where chosen in a theory-driven manner by three independent clinical psychologists and clinical specialists in adult psychopathology. Data collection started during the time-period with the strictest and equal number of government-initiated non-pharmacological interventions (NPI's) in Norway, and data collection was stopped once these NPI's were modified or new information about NPI's were added. The data include one directly identifiable variable (contact information) for participants in accordance to the General Data Protection Regulation (GDPR) law in EU, which is to give the participants the opportunity to have their data deleted upon request. Data are thus kept on a safe server belonging to the University of Oslo and will be accessed first following de-identification. Stopping rule for data collection: Stopping rule: 1) At once if government-initiated NPI's are modified or novel information are given about NPIs (to control for cognitive variables) and/or 2) once the study reached enough participants given the power analysis (10000 participants). Measures (as mentioned above under specific hypotheses): PHQ-9; GAD-7; Adherence to the eight government-initiated NPI's in Norway; demographic variables (gender; age; relationship/marital status; employment status; education level); situational variables related to Covid-19 (Whether respondent Lost Job Because of Covid-19 consequences; sufficient access to information; stress/worry about NPI's duration lasting longer; whether it is self-choice to stay at home/socially isolate or one has been instructed to be in quarantine/isolation; whether one suspects having COVID; fear of transmitting others; fear of significant others being infected by COVID; difficulty to work from home); person-trait variables (Autonomy frustration; Perceived Competence); and whether one has a psychological diagnosis or not; protective factors (Physical activity; doing positive things not done otherwise; experiencing nature). The outcome variables are PHQ-9; GAD-7; and total score of the adherence levels to the NPI's. The other variables are predictors of these three outcome variables. Kindly see above (hypotheses section) for exactly which predictors that belong to which outcome variable. Indices: Given Acceptable Cronbach's alpha (Cronbach's alpha \>= 0.7), the investigators will combine four variables that measure health anxiety and fear of death related to COVID-19. Inference criteria Given the large sample size in this study, the investigators pre-define their significance level: p \< 0.001 to determine significant. Sample size estimation: The mentioned 'Norwegian COVID-19, Mental Health and Adherence Project' involves multiple studies, where some involve a Complex Systems (Network analysis) approach. These mutlivariate analyses require large samples and power analysis was conducted accordingly. Following power analysis guidelines by Fried \& Cramer (2017), it is recommended that the number of participants be at the very least three times larger than the number of estimated parameters. However, more conservative recommendations by Roscoe (1975) for multivariate research, recommends sample size that is ten times larger than the number of estimated parameters. Thus, with the more conservative estimates by Roscoe, an optimal sample size included slightly above 10000 individuals. According to the stopping rule mentioned above, due to the importance of keeping the NPI variable constant, the investigators would stop data collection even if the investigators did not obtain the target N. Fortunately, sufficient sample was reached during a period with identical NPIs across the country. How the participants were reached: Participants include The general population of adults (Age \>= 18) from all regions (i.e., counties) of Norway, having equal opportunity and the probability of partaking in the study. Given the time-sensitivty of the project and the strict and time-consuming process of getting approval to access registry data, the investigators did not apply for access to registry data (e.g., address, phone or e-mails of the general population), as such data access is highly strict and regulated in Norway and the time-frame of such an application could have encompassed variation in an important variable the investigators wished to hold constant (namely identifical NPIs (non-pharmacological interventions) employed over the time-frame of data collection). Thus, the investigators did not apply for registry data, but still attempted to obtain a probability sample through the means elaborated below. The investigators reached out to the general Norwegian population systematically in the following six ways, with the goal of providing the entire adult population an equal opportunity to be exposed to the survey: 1. Through broadcasting on the main national news channel of Norway which had nearly 1.1 million viewers at the time of broadcast. 2. Using Facebook Business Advertisement where the investigators exposed all adult Norwegian Facebook users (3.6 million; 85% of the Norwegian adult population) with an equal opportunity of being exposed to the survey in a random manner. The survey reached a random selection of nearly 180 000 of the adult population. 3. Broadcasting the survey on national and region radio stations across the country 4. Broadcasting about the survey on local radio stations across the country 5. Using national newspaper to reach out to participants about the survey 6. Using regional and local newspapers to reach out to participants across all regions and counties in Norway. Only the Facebook-adverts alone reached out to a random sample of 85% of Norways adult population (a population of 3.6 million on Facebook out of 4.2 million adults in the country). The investigators argue that the probability is high that the survey reached out to the residual 15% not on facebook, through the 5 other channels - including the national news channel of Norway with 1.1 million viewers at the time of broadcast, as well as regional and local newspapers. As at least one of the present outreach methods includes reaching out randomly to participants across the adult population of Norway, the investigators judge the sampling technique to be equal to that of that of access to registry data, which would simply involve doing the same (selecting a random set of participants) with the difference being that participants receive the voluntary survey in their physical mail-box rather than digitally. Both the presently used digital method and the more commonly used physical technique (registry data) involve voluntarily participation, thus not necessarily involving different response rates. Consequently, both involving techniques involve a random reach out to participants, and the investigators judge the present sampling technique to be comparable to that of random sampling with registry data. Statistical models: Three multiple regression analyses will be conducted; one with PHQ-9; the second with GAD-7; and the third total score of adherence to NPI's as the dependent variable. Specific predictors of each of these three multiple regression analysis are listed above (hypothesis section). Multicollinearity and other assumptions will be checked; if the multicollinearity assumption is violated (if VIF \> 5 and Tolerance \< 0.2; Hocking, 2003; O'Brian, 2007), for example due to variables that may or may not overlap due to potential topological overlap (e.g., Health anxiety and GAD symptoms), the investigators will remove health anxiety from the model. However, as the investigators have approached the problem of topological overlap and handled it in a theory-driven manner in the planning of the study through a discussion panel with clinical experts, the investigators do not expect to see such problems with multicollinearity. Descriptive statistics with frequency tables including N, means and SDs and other standard descriptive statistics will examine the hypothesis concerning general levels of depressive- and anxiety symptoms, as well as adherence rates. The hypothesis concerning higher levels of psychopathology (i.e., depressive- and anxiety symptoms) during the COVID-19 pandemic will be checked by comparing the proportion that meets the cut-offs for PHQ-9 and GAD-7 (each cut-off = 10, as found in other studies) with benchmark studies from similar populations during non-pandemic periods. GAD-7 has been validated in Norway, where the cut-off has been revealed lower than cut-off found in other countries (i.e., GAD-7 cut-off = 8 in Norway, whereas it is 10 in other countries; (Johnson, Ulvenes, Øktedalen \& Hoffart, 2019). Consequently, as validated benchmark studies exist within our population, the investigators will utilize the Norwegian cut-off for GAD-7, but will also report the common cut-off proportion results (i.e., cut-off = 10). Transformations Depending on degree of skewness compared to theoretical possibilities and interpretations, variables will be assessed in their original and validated format as is recommended practice, as long as this is possible. As this study examines psychopathology levels amongst a general population (and not a clinical population), the investigators do expect a skewed data on depressive and anxiety levels (with most individuals reporting low levels of anxiety and depression). Given the validation of these questionnaires (e.g., PHQ-9 and GAD-7) which are theoretically and empirically (i.e., validation studies) supposed to yield accurate depressive and anxiety levels, it may not theoretically nor empirically make sense to transform the data, although the investigators expect a skew. Similarly, due to mentioned factors such as resources to disseminate information as well as governmental trust in Norway (see above in hypothesis section), the investigators also expect skew in total score of adherence, with most individuals revealing high adherence. This skew is expected. Consequently, the investigators will attempt to assess these variables in their original and validated format as is recommended practice, as long as this is possible with regards to statistical assumptions. However, if this is not possible with regards to the statistical assumptions behind the analyses, transformation (e.g., square root or log-transformations) may be needed to apply interval-based methods. In any case, even though the parametric tests planned in this study are robust against skewness, the investigators will examine the degree of skewness and evaluate this against the assumptions and analyses before choosing the appropriate analysis. The pre-registered and planned analyses include multiple regression, as long as assumptions are met. Exploratory analyses: The investigators will exploratory examine the differences in levels of depression and generalized anxiety symptoms across different demographic subgroups in our sample. The investigators will exploratory investigate whether the following variables are related to adherences rates, namely; depression levels, GAD levels, whether one suspects being infected the by COVID-19-virus, and worry about being longer duration of NPI's. The investigators will also exploratory assess the correlation between Perceived Competence in dealing with the COVID-19 crisis with the variable Sufficient Access to information. The investigators may also do other exploratory analysis that the investigators have not yet thought of at the time of pre-registration: if the investigators do such exploratory analyses, the investigators will explicitly state them as exploratory in the published manuscript following common publishing guidelines Note that this the project outline, study plan and analysis was registered upon application to the Regional Committees for Medical and Health Research Ethics (REC) and Norwegian Centre for Research Data 10 days prior data collection, two committee which evaluate the rational for data collection and hypotheses as well as evaluate the ethical aspects of the study before allowing the data to be collected. The study is registered on clinicaltrials.gov after completed data collection, although this registration is prior to any analysis of the data. The registration was first made on osf.io as this is the place were cross-sectional studies usually are registered where it is still to be found, but was subsequently registered on clinicaltrials as the investigators found out that it was possible to register cross-sectional studies on clinicaltrials.gov (traditionally associated with clinical studies and RCTs), reading the guidelines of the target journal for the present study.
Study Type
OBSERVATIONAL
Enrollment
10,084
Cross-sectional study investigating the association of NPIs with mental health
University of Oslo
Oslo, Norway
Patient Health Questionnaire 9
The Patient Health Questionnaire 9 (PHQ-9; Kroenke, Spitzer \& Williams, 2001) is used to measure symptoms of depression in accordance with the diagnostic criteria for major depressive disorder. The questionnaire consists of nine items where each is scored on a four-point Likert scale (0-3), with the range of scores from 0 to 27. Higher scores indicate greater depression severity, and scores above 10 are considered as the cut-off that indicating that the patient is within the depressive area.
Time frame: All data was collected between March 31st 2020 and April 7th 2020, a period where the NPIs (non-pharmacological interventions) against the COVID-19 pandemic were identical and constant in Norway
Generalized Anxiety Disorder 7
The Generalized Anxiety Disorder 7 (GAD-7; Spitzer, Kroenke, Williams \& Löwe, 2006) is a questionnaire consisting of seven items measuring symptoms of anxiety and worry. The items are scored on a four-point Likert scale (0-3), with the scores ranging from 0 to 21. Higher scores indicate greater anxiety severity, and scores that are above the cut-off of 10 are considered to be in the clinical range (Spitzer et al., 2006). Specific cut-off for Norwegian samples have been found yielding a cut-off of 8 and above for high sensitivity and specificity (Johnson, Ulvenes, Øktedalen \& Hoffart, 2019).
Time frame: All data was collected between March 31st 2020 and April 7th 2020, a period where the NPIs (non-pharmacological interventions) against the COVID-19 pandemic were identical and constant in Norway
Measures of adherence
Measures of adherence to all eight non-pharmacological interventions (NPIs) employed against COVID-19 is measured, with each item being on a five-point Likert scale (0-4).
Time frame: All data was collected between March 31st 2020 and April 7th 2020, a period where the NPIs (non-pharmacological interventions) against the COVID-19 pandemic were identical and constant in Norway
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