Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04184219 |
| Other study ID # |
DS |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 26, 2019 |
| Est. completion date |
March 11, 2020 |
Study information
| Verified date |
March 2020 |
| Source |
Medical Universtity of Lodz |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
This study aims to evaluate (1) the level of knowledge about dietary supplements (KaDS) among
people potentially interested in health issues in Poland and (2) the fraction of these people
using dietary supplements (UoDS). The study seeks determinants of KaDS and UoDS in this
population as well. The study requires a participant to fill an online survey.
RESEARCH QUESTIONS:
1. Knowledge about dietary supplements:
1. What is the level of knowledge about dietary supplements among people potentially
interested in health issues?
2. What are the characteristics of the population members, who are unknowledgeable
about dietary supplements?
3. How to model the level of knowledge about dietary supplements in this population?
2. Use of dietary supplements:
1. What is the fraction of people potentially interested in health issues who use
dietary supplements?
2. What are the characteristics of the population members, who use dietary
supplements?
3. How to model whether a population member uses dietary supplements or not?
Description:
I. MEASURES ASSESSED IN THE STUDY:
The selection of measures to be evaluated in the study was based on the scientific literature
review and personal interest of the researchers:
1. Measures related to dietary supplements:
1. knowledge about dietary supplements (KaDS) - assessed with a 17-item KaDS
questionnaire, which was developed as a Polish version by Karbownik et al. (2019).
A respondent will be asked to rate each of the 17 statements concerning dietary
supplements as "true" or "false". KaDS will be primarily coded as a sum of both
General and Specific questionnaire subscales. KaDS will be operationalized as an
18-level ordinal variable.
2. self-rated sources of KaDS - assessed separately for 5 categories: "medical
doctors", "pharmacists", "dietitians", "friends (with no medical education)",
"media (magazines, TV, radio, Internet)". A single-item question will be asked for
each category: "to what extent do you get knowledge about dietary supplements
from...?". Each category will be operationalized as a 4-level ordinal variable
(from "not at all" to "to a large extent"). TV - television.
3. use of dietary supplements (UoDS) - assessed with a single-item question: "have you
used any dietary supplements within the past 30 days?". UoDS will be
operationalized as a 2-level categorical variable ("no" and "yes").
4. positive personal experience with dietary supplements - assessed with a single-item
question: "if you take dietary supplement, do you feel it helps you?". The measure
will be operationalized as a 3-level categorical variable ("no", "yes" and "not
applicable, I don't use dietary supplements"). Cases presenting "not applicable..."
response, will be coded in the analyses as "no".
5. negative personal experience with dietary supplements - assessed with a single-item
question: "if you take dietary supplement, do you feel it hurts you?". The measure
will be operationalized as a 3-level categorical variable ("no", "yes" and "not
applicable, I don't use dietary supplements"). Cases presenting "not applicable..."
response, will be coded in the analyses as "no".
6. interest in dietary supplements - assessed with a single-item 5-point semantic
differential: from "No. This is completely indifferent to me." to "Yes! Every day I
look for information on this topic.". The measure will be operationalized as a
5-level ordinal variable.
7. trust in advertising dietary supplements (TiADS) - assessed with an 8-item TiADS
questionnaire, which was developed as a Polish version by Karbownik et al. (2019).
A respondent will be asked to express her/his opinion about the information
conveyed by the advertisements of dietary supplements using a 5-point semantic
differential scale. TiADS will be primarily considered as a sum of Reliability,
Intelligibility and Affect questionnaire subscales. TiADS will be operationalized
as a 33-level ordinal variable.
8. having contact with dietary supplement advertisements - assessed with a single-item
question: "have you had any contact with dietary supplement advertisements within
the past week?". The measure will be operationalized as a 2-level ordinal variable
("no" and "yes").
2. Measures related to other health issues:
1. general beliefs about medicines (BMQ-General) - assessed with an 8-item General
part of the Beliefs about Medicines Questionnaire, which was developed by Horne et
al. (1999), and adapted to Polish language and validated by Karbownik et al. (2019)
(paper currently under peer-review). A respondent will be asked to express her/his
agreement with 8 statements concerning medicines using a 5-point Likert scale: from
"completely disagree" to "completely agree". BMQ-General will be coded as 2
separate variables: BMQ-General-Overuse subscale and BMQ-General-Harm subscale.
Both the subscales will be operationalized as 17-level ordinal variables.
2. self-rated health - assessed with a single-item question: "how do you assess your
health?". The measure will be operationalized as a 4-level ordinal variable
("poor", "fair", "good", "excellent").
3. self-rated diet - assessed with a single-item 5-point semantic differential: from
"I eat a lot of fast food, chips, sweets, etc." to "I only eat healthy, balanced
meals". The measure will be operationalized as a 5-level ordinal variable.
4. self-rated physical activity - assessed with a single-item 5-point semantic
differential: from "I have no physical activity at all" to "I play sport
intensively 5 times a week". The measure will be operationalized as a 5-level
ordinal variable.
5. conventional cigarette smoking - assessed with a single-item question: "do you
smoke conventional cigarettes?". The measure will be operationalized as a 3-level
categorical variable ("never", "no, but I smoked in the past", "yes").
6. electronic cigarette use - assessed with a single-item question: "do you use
electronic cigarettes?". The measure will be operationalized as a 3-level
categorical variable ("never", "no, but I used in the past", "yes").
3. Measures of sociodemographic data:
1. age - operationalized as a continuous variable rounded to whole years. The allowed
values to be reported will be: "below 18", "18", "19", "20", ..., "113". The oldest
Pole currently (October 23, 2019) living is 113-year-old.
2. sex - operationalized as a 2-level categorical variable ("male" and "female").
3. education level - operationalized as a 5-level ordinal variable ("primary",
"secondary or vocational", "higher-bachelor", "higher-master", "higher-doctorate").
4. medical education - operationalized as a 2-level categorical variable ("no medical
education" and "medical education"). This measure will be used as auxiliary to
check whether a respondent meets exclusion criteria. Missing values in this
variable will be coded as "medical education".
5. number of inhabitants in a place of residence - operationalized as a 4-level
ordinal variable ("below 5,000", "5,000-50,000", "50,000-500,000", "over 500,000").
6. monthly net household earnings per family member - operationalized as a 4-level
ordinal variable ("below 1,000 PLN", "1,000-2,000 PLN", "2,000-3,000 PLN", "over
3,000 PLN"). PLN - Polish currency (zloty).
II. DATA ANALYSIS PLAN
1. A study participant will be asked to fill the online survey (https://www.survio.com/pl/)
assessing all the above measures.
2. Any cases of respondents, who declared having less than 18 years of age or being
medically educated will be deleted from further analyses (as stated in the inclusion and
exclusion criteria).
3. All the variables measured with semantic differential scale will have no missing values,
because the online survey software attributes a central value ("3") to a response as a
default. Missing values are possible in the other variables within the survey, as there
will be no forced answering option and no default responses. The missing values will be
managed in the following way:
1. The cases with no data, apart from the default values in semantic differential
variables, will be deleted.
2. The fraction of missing values for each variable will be calculated.
3. The fraction of cases with at least one missing value will be calculated.
4. Variables with more than 50% of missing values and cases with more than 50% of
missing values will be deleted from the database.
5. The pattern of data missingness will be assessed with Little's test for data
missing completely at random (MCAR). In case of significant violation of MCAR
assumption, for each variable, the association of missingness with all the other
variables will be examined.
6. If the fraction of cases with at least one missing value (see point "c") is less
than 5% and the result of Little's MCAR test (see point "e") is non-significant,
the cases with at least one missing value will be deleted and the analysis will be
performed with complete cases only.
7. Otherwise (to point "f"), the missing values will be imputed with multivariate
imputation by chained equations (MICE) before any further data analysis is
performed. The summation of the items constructing KaDS, TiADS and BMQ will be done
after imputation of missing values in each single item.
4. KaDS will be treated as a continuous variable and modeled with multivariate linear
regression, whereas UoDS, being a 2-level categorical variable, will be modeled with
logistic regression. Ordinal independent variables will be considered as continuous,
while included into regression models. Categorical independent variables with more than
2 levels will be converted to dummy variables before being included into regression
models. The dependent variables (DVs), KaDS and UoDS, will be modeled in the following
steps:
1. Univariate (unadjusted) associations of DVs with all the remaining measures will be
assessed (KaDS will be additionally split into General and Specific subscales for
the presentation of the results). In addition, the two-way interaction
"TiADS"×"having contact with dietary supplement advertisements" will be included.
The associations will be reported as both unadjusted and adjusted for all the
tested sociodemographic measures (see point "I. 3"). If the data is imputed (see
point "3. g"), sensitivity analysis of univariate (unadjusted) associations between
DVs and the remaining measures will be performed in the dataset of complete cases
only to test the accuracy of missing data imputation. The further multivariate
models will be built with the measures statistically significantly (according to
the raw p-values in unadjusted analyses) associated with a DV in these analyses.
2. Predictors to be retained in the final model will be selected according to the
following criteria:
- Multicollinearity. Multicollinearity of the variables will be assessed with
the Pearson's correlation matrix and exploratory factor analysis. In case of
detection of multicollinearity, two solutions will be considered: (1)
substantially collinear variables may be linearly combined if they may
represent the same construct (e.g. "self-rated diet" plus "self-rated physical
activity" plus "no cigarette smoking" may represent the construct of "health
self-care" or "conventional cigarette smoking" plus "electronic cigarette use"
may represent the construct of "nicotine dependence", etc.) or (2) only one
variable from a set of substantially collinear variables may be retained and
the other may be deleted.
- "Objectivity" of the retained measure. Highly "objective" measures will be
favored. They include the one with more convincing proof of validity: KaDS,
TiADS and BMQ, followed by the "objective" measures assessed with a single
item question: age and sex, followed by more "subjective", not easily
verifiable and possibly biased measures: the rest of the measures.
- Information criteria. The models with lower values of Akaike or Bayesian
information criterion will be preferred (best subset selection algorithm).
3. Residual analysis of the obtained models will be performed to check for assumptions
of general linear modeling. In case of substantially violated assumptions, the
processes of model construction may be repeated.
5. Internal validation will be performed with a k-fold cross validation to test for
accuracy and stability of the obtained models.
6. Subgroup analyses may be performed in the samples of respondents critical to public
health:
1. elderly people (60 or more years of age),
2. rural areas residents (below 5,000 inhabitants),
3. low income people (below 1,000 PLN of monthly net household earnings per family
member), etc.
7. P-values throughout the analyses below 0.05 will considered statistically significant.To
account for multiple hypothesis testing, if applicable, significance level correction
with Benjamini and Hochberg procedure will be applied (false discovery rate 0.05).
8. The analyses will be performed using STATISTICA Software (Statsoft) or R Software (R
Core Team).
