Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04356365 |
| Other study ID # |
REK125510 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 31, 2020 |
| Est. completion date |
April 7, 2020 |
Study information
| Verified date |
April 2020 |
| Source |
University of Oslo |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
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.
Description:
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.
