| Title: | Multi Calculator to Compute Scores of Adherence to Mediterranean Diet |
|---|---|
| Description: | Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies. |
| Authors: | Miguel Menendez [aut, cre], David Lora [ctb], Agustin Gomez-Camara [dtc] |
| Maintainer: | Miguel Menendez <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.1 |
| Built: | 2024-07-05 06:18:07 UTC |
| Source: | CRAN |
Computes Cardioprotective Mediterranean Diet Index
computeCardio(data = NULL, Vegetables, Fruit, OliveOil, OOmeasure = "gr", Legumes, Fish, Meat, RefinedRice, RefinedBread, WholeBread, Wine, frequency = "percent", output = "percent", rm.na = FALSE)computeCardio(data = NULL, Vegetables, Fruit, OliveOil, OOmeasure = "gr", Legumes, Fish, Meat, RefinedRice, RefinedBread, WholeBread, Wine, frequency = "percent", output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
Vegetables |
Numeric variable with vegetables consumption as servings. |
Fruit |
Numeric variable with fruit consumption as servings. |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument. |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Legumes |
Numeric variable with legumes consumption as servings. |
Fish |
Numeric variable with fish consumption as servings. |
Meat |
Numeric variable with meat and meat products consumption as servings. |
RefinedRice |
Numeric variable with consumption of refined rice as servings. |
RefinedBread |
Numeric variable with consumption of refined bread as servings. |
WholeBread |
Numeric variable with consumption of whole bread as servings. |
Wine |
Numeric variable with wine consumption as glasses. |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
In the score, the item about refined and whole cereals is scored positively if consumption of both white bread and rice is low or when consumption of whole-grain bread is high. Rice and whole-grain bread are considered weekly, and white bread daily: [White bread (< 1 serving/day) AND rice (< 1 serving/week)] OR whole-grain bread (> 5 servings/week). The function takes as arguments the three foods, with whatever periodicity they have been recorded in the data, as long as it is provided with the 'frequency' argument. Internally function sets them in the suitable fashion to test this score item.
There is an aditional item in the score, computed internally, that provides one point if both vegetables and fruit consumption have received 1 point each one.
Computed Cardio score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Martinez-Gonzalez, M. A., E. Fernandez-Jarne, M. Serrano-Martinez, M. Wright, and E. Gomez-Gracia. 2004. 'Development of a Short Dietary Intake Questionnaire for the Quantitative Estimation of Adherence to a Cardioprotective Mediterranean Diet'. European Journal of Clinical Nutrition 58 (11): 1550-52. doi:10.1038/sj.ejcn.1602004.
data(nutriSample) MedDiet <- computeCardio(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Legumes = P46rac, Fish = P35rac + P36rac + P37rac + P38rac, Wine = P96rac, Meat = P29rac + P30rac + P31rac + P32rac, RefinedBread = P55rac, RefinedRice = P61rac, WholeBread = P56rac, frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeCardio(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Legumes = P46rac, Fish = P35rac + P36rac + P37rac + P38rac, Wine = P96rac, Meat = P29rac + P30rac + P31rac + P32rac, RefinedBread = P55rac, RefinedRice = P61rac, WholeBread = P56rac, frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)
Computes 10-year risk of fatal or non-fatal stroke and Coronary Heart Disease according to FRESCO score ('Función de Riesgo ESpañola de acontecimientos Coronarios y Otros', 'Spanish risk function of coronary and other cardiovascular events').
computeFRESCO(data, outcome = c("Coronary", "Stroke", "All"), simplified = FALSE, Sex, Age, Smoker, BMI, Diabetes, SBP, TotChol, HDL, HBPpill, men = "male", women = "female")computeFRESCO(data, outcome = c("Coronary", "Stroke", "All"), simplified = FALSE, Sex, Age, Smoker, BMI, Diabetes, SBP, TotChol, HDL, HBPpill, men = "male", women = "female")
data |
list or data.frame which contains the variables |
outcome |
character string indicating for which outcome risk is to be computed. Allowed values are "Coronary", "Stroke" or "All", which means the output is the risk of a coronary event, stroke, or both |
simplified |
logical. Original FRESCO score was derived in two versions: the full one, which includes all the following variables; and the other is de simplified one, which uses just sex, age, smoking status and body mass index. If TRUE, the simplified version will be computed. |
Sex |
variable containing gender of the people. It can be character, factor or numeric, as far as the 'men' and 'women' arguments specify how the formula should handle this variable (See below) |
Age |
numeric with people age in years |
Smoker |
numeric variable containg smoking status. 0 = non smoker, 1 = currently smoker |
BMI |
numeric variable with Body Mass Index (weight[kilograms] / height²[meters]) |
Diabetes |
numeric which informs wether the person is diabetic. 0 = no, 1 = yes. |
SBP |
numeric variable with Systolic Blood Pressure in mmHg |
TotChol |
numeric with total serum cholesterol in mg/dl |
HDL |
numeric with serum High Density Lipoprotein cholesterol in mg/dl |
HBPpill |
numeric which means if the person is currently under treatment because of High Blood Pressure. 0 = no, 1 = yes. |
men |
character with informs of how males have been recorded in the 'Sex' argument, default is 'male'. If 'Sex' is numeric, a quoted number should be provided (for instance, men = '1' |
women |
character. Same meaning as 'men' argument, but for females. |
In Spanish population, Framingham-REGICOR function tends to overestimate cardio and cerebrovascular risk. So, FRESCO score was developed among people from 35 to 79 years, which includes a simplified version with no laboratory results, and another one a bit harder to compute with slightly improved prediction ability.
Numeric vector of same length as rows in 'data' with estimated percentage of 10-year risk of fatal or non-fatal event (Coronary Heart Diesease, or stroke or both depending on 'outcome' argument).
Miguel Menendez
Marrugat, Jaume, Isaac Subierana, Rafael Ramos, Joan Vila, Alejandro Marin-Ibanez, Maria Jesus Guembe, Fernando Rigo, et al. 2014. "Derivation and Validation of a Set of 10-Year Cardiovascular Risk Predictive Functions in Spain: The FRESCO Study." Preventive Medicine 61 (April): 66-74. doi:10.1016/j.ypmed.2013.12.031.
myself <- list(sex = "male", age = 32, tobacco = 0, bmi = 21.5) computeFRESCO(data = myself, outcome = "All", simplified = TRUE, Sex = sex, Age = age, Smoker = tobacco, BMI = bmi)myself <- list(sex = "male", age = 32, tobacco = 0, bmi = 21.5) computeFRESCO(data = myself, outcome = "All", simplified = TRUE, Sex = sex, Age = age, Smoker = tobacco, BMI = bmi)
Computes Mediterranean Diet adherence score according to Goulet et al. in 2003.
computeGoulet(data, WholeCereals, Vegetables, Fruit, LegumesAndNuts, OliveOil, OOmeasure = "gr", Olives, Dairy, Fish, Poultry, Eggs, Sweets, Meat, output = "percent", frequency = "daily", rm.na = FALSE)computeGoulet(data, WholeCereals, Vegetables, Fruit, LegumesAndNuts, OliveOil, OOmeasure = "gr", Olives, Dairy, Fish, Poultry, Eggs, Sweets, Meat, output = "percent", frequency = "daily", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
WholeCereals |
Numeric variable with consumption of whole grain products as servings. |
Vegetables |
Numeric variable with vegetables consumption as servings. |
Fruit |
Numeric variable with fruit consumption as servings. |
LegumesAndNuts |
Numeric variable with legumes, nuts and seed consumption as servings. |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument. |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Olives |
Numeric variable with olives consumption as servings. |
Dairy |
Numeric variable with dairy consumption as servings. |
Fish |
Numeric variable with fish consumption as servings. |
Poultry |
Numeric variable with poultry (other than breaded) consumption as servings. |
Eggs |
Numeric variable with eggs consumption as servings. |
Sweets |
Numeric variable with sweets consumption as servings. |
Meat |
Numeric variable with red meat and meat products consumption as servings. |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
Computes Mediterranean Diet adherence score according to Goulet et al. in 2003. It can be found as Mediterranean Score (MS) [Mila-Villarroel et al., 2011].
Computed Mediterranean Diet Adherence score according to Goulet et al. 2003. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 44 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Goulet, Julie, Benoıt Lamarche, Genevieve Nadeau, and Simone Lemieux. 2003. 'Effect of a Nutritional Intervention Promoting the Mediterranean Food Pattern on Plasma Lipids, Lipoproteins and Body Weight in Healthy French-Canadian Women'. Atherosclerosis 170 (1): 115-24. doi:10.1016/S0021-9150(03)00243-0.
Mila-Villarroel, Raimon, Anna Bach-Faig, Josep Puig, Anna Puchal, Andreu Farran, Lluis Serra-Majem, and Josep Lluis Carrasco. 2011. 'Comparison and Evaluation of the Reliability of Indexes of Adherence to the Mediterranean Diet'. Public Health Nutrition 14 (12A): 2338-45. doi:10.1017/S1368980011002606.
data(nutriSample) MedDiet <- computeGoulet(data = nutriSample, WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Vegetables = P41rac + P42rac, Fruit = P50rac + P52rac, LegumesAndNuts = P46rac + P53rac + P75rac, OliveOil = Aceitegr, OOmeasure = "gr", Olives = P54rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Fish = P35rac + P36rac + P37rac + P38rac, Poultry = P33rac, Eggs = P28rac, Sweets = P69rac + P70rac + P71rac + P72rac + P73rac, Meat = P29rac + P30rac + P31rac + P32rac, output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeGoulet(data = nutriSample, WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Vegetables = P41rac + P42rac, Fruit = P50rac + P52rac, LegumesAndNuts = P46rac + P53rac + P75rac, OliveOil = Aceitegr, OOmeasure = "gr", Olives = P54rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Fish = P35rac + P36rac + P37rac + P38rac, Poultry = P33rac, Eggs = P28rac, Sweets = P69rac + P70rac + P71rac + P72rac + P73rac, Meat = P29rac + P30rac + P31rac + P32rac, output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)
Computes Mediterranean Adequacy Index according to Alberti-Fidanza et al. 1999.
computeMAI99(data, Bread, Cereals, Legumes, Potatoes, Vegetables, FruitAndNuts, Fish, Wine, Oil, Milk, Cheese, Meat, Eggs, AnimalFats, SoftDrinks, Pastries, Sugar, Kcal, output = NULL, rm.na = FALSE)computeMAI99(data, Bread, Cereals, Legumes, Potatoes, Vegetables, FruitAndNuts, Fish, Wine, Oil, Milk, Cheese, Meat, Eggs, AnimalFats, SoftDrinks, Pastries, Sugar, Kcal, output = NULL, rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
Bread |
Numeric with energy (as Kilocalories) attributable to bread. The argument is the energy measured as Kcal, although the function will score it as percentage of energy respect total energy intake (see Details). |
Cereals |
Numeric with energy (as Kilocalories) attributable to cereals. |
Legumes |
Numeric with energy (as Kilocalories) attributable to legumes. |
Potatoes |
Numeric with energy (as Kilocalories) attributable to potatoes. |
Vegetables |
Numeric with energy (as Kilocalories) attributable to vegetables. |
FruitAndNuts |
Numeric with energy (as Kilocalories) attributable to FruitAndNuts. |
Fish |
Numeric with energy (as Kilocalories) attributable to fish. |
Wine |
Numeric with energy (as Kilocalories) attributable to wine. |
Oil |
Numeric with energy (as Kilocalories) attributable to vegetal oils. |
Milk |
Numeric with energy (as Kilocalories) attributable to milk. |
Cheese |
Numeric with energy (as Kilocalories) attributable to cheese. |
Meat |
Numeric with energy (as Kilocalories) attributable to meat. |
Eggs |
Numeric with energy (as Kilocalories) attributable to eggs. |
AnimalFats |
Numeric with energy (as Kilocalories) attributable to fats of animal origin. |
SoftDrinks |
Numeric with energy (as Kilocalories) attributable to soft drinks. |
Pastries |
Numeric with energy (as Kilocalories) attributable to pastries. |
Sugar |
Numeric with energy (as Kilocalories) attributable to sugar. |
Kcal |
Numeric with total energy intake measured as Kcal. |
output |
A character string to set which output should the formula give, allowed values are 'data.frame' and 'index'. |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
The index components are scored as percent of calories. But to make it easier to the user, arguments should provide the energy each food item provides. Also the total energy intake should be provided, so formula can internally relate them.
Mediterranean Adequacy Index is a ratio of Kcal attributable to healthy foods over Kcal attributable to unhealthy foods, so values could range from 0 to more than 100 (Alberti et al. 2009). The reference italian-mediterranean diet is 7.5 (Alberti-Fidanza et al. 1999). So, value is not a percentage, and comparability with other scores is not direct.
Periodicity argument is not provided, as the equation is a ratio and it is not to vary if food is recorded daily, weekly or monthly.
Computed Mediterranean Adequacy Index. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'index' |
Instead of the full data.frame, just the numeric vector corresponding to the absolute points of adherence to Mediterranean Diet for each person. Range can vary widely (see Details). |
Miguel Menendez
Alberti-Fidanza, A., F. Fidanza, M. P. Chiuchiù, G. Verducci, and D. Fruttini. 1999. "Dietary Studies on Two Rural Italian Population Groups of the Seven Countries Study. 3. Trend Of Food and Nutrient Intake from 1960 to 1991." European Journal of Clinical Nutrition 53 (11): 854–60.
Alberti, Adalberta, Daniela Fruttini, and Flaminio Fidanza. 2009. "The Mediterranean Adequacy Index: Further Confirming Results of Validity.” Nutrition, Metabolism and Cardiovascular Diseases 19 (1): 61–66. doi:10.1016/j.numecd.2007.11.008.
data(nutriSample) MedDiet <- computeMAI99(data = nutriSample, Bread = P55Kcal + P56Kcal + P57Kcal, Cereals = P55Kcal + P56Kcal + P57Kcal + P59Kcal + P60Kcal + P61Kcal + P62Kcal, Legumes = P46Kcal, Potatoes = P43Kcal + P44Kcal + P45Kcal, Vegetables = P41Kcal + P42Kcal, FruitAndNuts = P50Kcal + P53Kcal, Fish = P35Kcal + P36Kcal + P37Kcal + P38Kcal, Wine = P96Kcal, Oil = AceiteKcal, Milk = P19Kcal + P20Kcal + P21Kcal, Cheese = P26Kcal + P27Kcal, Meat = P29Kcal + P30Kcal + P31Kcal + P32Kcal, Eggs = P28Kcal, AnimalFats = P29grGrasa + P30grGrasa + P31grGrasa + P32grGrasa + P33grGrasa + P34grGrasa , SoftDrinks = P89Kcal + P90Kcal, Pastries = P69Kcal + P70Kcal + P71Kcal + P72Kcal + P73Kcal, Sugar = P84Kcal, Kcal = totalKcal, output = "index", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeMAI99(data = nutriSample, Bread = P55Kcal + P56Kcal + P57Kcal, Cereals = P55Kcal + P56Kcal + P57Kcal + P59Kcal + P60Kcal + P61Kcal + P62Kcal, Legumes = P46Kcal, Potatoes = P43Kcal + P44Kcal + P45Kcal, Vegetables = P41Kcal + P42Kcal, FruitAndNuts = P50Kcal + P53Kcal, Fish = P35Kcal + P36Kcal + P37Kcal + P38Kcal, Wine = P96Kcal, Oil = AceiteKcal, Milk = P19Kcal + P20Kcal + P21Kcal, Cheese = P26Kcal + P27Kcal, Meat = P29Kcal + P30Kcal + P31Kcal + P32Kcal, Eggs = P28Kcal, AnimalFats = P29grGrasa + P30grGrasa + P31grGrasa + P32grGrasa + P33grGrasa + P34grGrasa , SoftDrinks = P89Kcal + P90Kcal, Pastries = P69Kcal + P70Kcal + P71Kcal + P72Kcal + P73Kcal, Sugar = P84Kcal, Kcal = totalKcal, output = "index", rm.na = FALSE) hist(MedDiet)
Computes Mediterranean Diet adherence score known as Mediterranean Dietary Pattern, by Martinez-Gonzalez et al. 2002.
computeMDP02(data, OliveOil, OOmeasure = "gr", Fiber, Fruit, Vegetables, Fish, Alcohol, Meat, RefinedCereals, output = "percent", rm.na = FALSE, frequency = "daily")computeMDP02(data, OliveOil, OOmeasure = "gr", Fiber, Fruit, Vegetables, Fish, Alcohol, Meat, RefinedCereals, output = "percent", rm.na = FALSE, frequency = "daily")
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Fiber |
Numeric variable with consumption of Dietary Fiber as grams. |
Fruit |
Numeric variable with consumption of Fruits as grams. |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Fish |
Numeric variable with Fish consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
Meat |
Numeric variable with Meat and Meat Products consumption as grams |
RefinedCereals |
Numeric variable with Refined Cereals consumption as grams |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
Computed MDP02 score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 5 (min.) to 40 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Martinez-Gonzalez, Miguel A., Elena Fernandez-Jarne, Manuel Serrano-Martinez, Amelia Marti, J. Alfredo Martinez, and Jose M. Martin-Moreno. 2002. 'Mediterranean Diet and Reduction in the Risk of a First Acute Myocardial Infarction: An Operational Healthy Dietary Score'. European Journal of Nutrition 41 (4): 153-60. doi:10.1007/s00394-002-0370-6.
data(nutriSample) MedDiet <- computeMDP02(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", Fiber = totalFibra, Fruit = P50grCom, Vegetables = P41grCom + P42grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Meat = P29grCom + P30grCom + P31grCom + P32grCom, RefinedCereals = P55grCom + P61grCom, output = "percent", rm.na = FALSE, frequency = "daily") hist(MedDiet)data(nutriSample) MedDiet <- computeMDP02(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", Fiber = totalFibra, Fruit = P50grCom, Vegetables = P41grCom + P42grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Meat = P29grCom + P30grCom + P31grCom + P32grCom, RefinedCereals = P55grCom + P61grCom, output = "percent", rm.na = FALSE, frequency = "daily") hist(MedDiet)
Computes Mediterranean Diet Quality Index.
computeMDQI(data, FruitAndVegetables, OliveOil, OOmeasure = "gr", Fish, Cereals, Meat, SatFats, Cholesterol, Kcal = NULL, invert = TRUE, frequency = NULL, output = "percent", rm.na = FALSE)computeMDQI(data, FruitAndVegetables, OliveOil, OOmeasure = "gr", Fish, Cereals, Meat, SatFats, Cholesterol, Kcal = NULL, invert = TRUE, frequency = NULL, output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
FruitAndVegetables |
Numeric variable with consumption of fruit and vegetables as grams |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Fish |
Numeric variable with fish consumption as grams |
Cereals |
Numeric variable with cereals consumption as grams |
Meat |
Numeric variable with Meat consumption as grams |
SatFats |
Numeric variable with energy contribution of saturated fats to diet. The formula will score it as percent of total energy intake, but it can be provided in one of two ways (see Details) |
Cholesterol |
Numeric variable with cholesterol consumption as miligrams |
Kcal |
Optional numeric variable with total energy intake as kilocalories. If provided, it makes a modification in 'SatFats' argument (see Details) |
invert |
Logical. If set to TRUE (default), the score is inverted, if set to FALSE, the score is kept as in the original (see Details) |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
This score is a mediterranean adaptation [Scali et al., 2000; Gerber 2006] from a previous Diet Quality Index (DQI) by Patterson et al. [Patterson et al., 1994], thus it was named MDQI (Mediterraean DQI).
In this score, originally, higher puntuations mean LOWER adherence. As this is not the usual in mediterranean diet scores, the argument 'invert' can make it reverse. If invert = TRUE (default), higher puntuations mean higher adherence.
Saturated fats (SFA) are scored as percent of total energy that is provided by SFA. This information can be provided in one of two ways: 1) 'SatFats' argument can be directly the percent of total energy intake provided by SFA, if so, the 'Kcal' argument must be missing or NULL. 2) 'SatFats' argument can be the amount of kilocalories provided by SFA, if so, the 'Kcal' argument must be provided, for formula to know the required percentage.
Cholesterol should be provided as miligrams. If mean consumption of cholesterol is lower than 1, a warning will be produce to ask user to check units.
Computed MDQI score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, ranging from 0 to 14. Depending on 'invert' argument higher puntuations can mean higher or lower adherence (see Details) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person. Depending on 'invert' argument higher puntuations can mean higher or lower adherence (see Details) |
Miguel Menendez
Patterson, R. E., P. S. Haines, and B. M. Popkin. 1994. 'Diet Quality Index: Capturing a Multidimensional Behavior'. Journal of the American Dietetic Association 94 (1): 57-64.
Scali, Jacqueline, Aurelia Richard, and Mariette Gerber. 2001. 'Diet Profiles in a Population Sample from Mediterranean Southern France'. Public Health Nutrition 4 (02): 173-182. doi:10.1079/PHN200065.
Gerber, Mariette. 2006. 'Qualitative Methods to Evaluate Mediterranean Diet in Adults'. Public Health Nutrition 9 (1A): 147-51.
data(nutriSample) # If Saturated Fats are provided as the energy they provide, # and Kcal arguments informs about total energy intake: MedDiet <- computeMDQI(data = nutriSample, FruitAndVegetables = P50grCom + P52grCom + P41grCom + P42grCom, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35grCom + P36grCom + P37grCom + P38grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, SatFats = totalGrasaSat, Cholesterol = totalCol, Kcal = totalKcal, invert = TRUE, frequency = "daily", output = "percent", rm.na = FALSE) # If Saturated Fats are provided as the percent of energy they provide, so Kcal is not provided: nutriSample$MySFApercent <- 100 * nutriSample$totalGrasaSat / nutriSample$totalKcal MedDiet2 <- computeMDQI(data = nutriSample, FruitAndVegetables = P50grCom + P52grCom + P41grCom + P42grCom, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35grCom + P36grCom + P37grCom + P38grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, SatFats = MySFApercent, Cholesterol = totalCol, # don't provide Kcal invert = TRUE, frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet2)data(nutriSample) # If Saturated Fats are provided as the energy they provide, # and Kcal arguments informs about total energy intake: MedDiet <- computeMDQI(data = nutriSample, FruitAndVegetables = P50grCom + P52grCom + P41grCom + P42grCom, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35grCom + P36grCom + P37grCom + P38grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, SatFats = totalGrasaSat, Cholesterol = totalCol, Kcal = totalKcal, invert = TRUE, frequency = "daily", output = "percent", rm.na = FALSE) # If Saturated Fats are provided as the percent of energy they provide, so Kcal is not provided: nutriSample$MySFApercent <- 100 * nutriSample$totalGrasaSat / nutriSample$totalKcal MedDiet2 <- computeMDQI(data = nutriSample, FruitAndVegetables = P50grCom + P52grCom + P41grCom + P42grCom, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35grCom + P36grCom + P37grCom + P38grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, SatFats = MySFApercent, Cholesterol = totalCol, # don't provide Kcal invert = TRUE, frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet2)
Mediterranean Adherence score index, as modified in 2003, whith the addition of fish item.
computeMDS03(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, SFA = NULL, Sex, men = "male", women = "female", frequency = "daily", output = "percent", rm.na = FALSE)computeMDS03(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, SFA = NULL, Sex, men = "male", women = "female", frequency = "daily", output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Legumes |
Numeric variable with Legumes consumption as grams |
FruitAndNuts |
Numeric variable with consumption of Fruits and Nuts as grams |
Cereals |
Numeric variable with Cereals consumption as grams |
Potatoes |
Numeric variable with Potatoes consumption as grams |
Fish |
Numeric variable with Fish consumption as grams |
Meat |
Numeric variable with Meat consumption as grams |
Dairy |
Numeric variable with Dairy consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
Fats |
Optional. Numeric variable with a ratio of consumption of Mono Unsaturated Fatty Acids (MUFA) over Saturated Fatty Acids (SFA). If it is not provided, then individual MUFA and SFA should be provided |
MUFA |
Optional if Fats is provided. Numeric variable with consumption of Mono Unsaturated Fatty Acids, units should be the same as used with PUFA and SFA |
SFA |
Optional if Fats is provided. Numeric variable with consumption of Saturated Fatty Acids |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. If 'Sex' argument is character or factor, and values for male are either 'man', 'male', 'MAN' or 'MALE', and for females are 'woman', 'female', 'WOMAN' or 'FEMALE', then, the arguments 'men' and 'women' can be missing |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
This score is an update of the landmark first Mediterranean Diet Score (MDS), published in 1995, but including fish consumption.
Computed MDS03 score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Trichopoulou, A., A. Kouris-Blazos, M. L. Wahlqvist, C. Gnardellis, P. Lagiou, E. Polychronopoulos, T. Vassilakou, L. Lipworth, and D. Trichopoulos. 1995. "Diet and Overall Survival in Elderly People." BMJ (Clinical Research Ed.) 311 (7018): 1457–60.
Trichopoulou, Antonia, Tina Costacou, Christina Bamia, and Dimitrios Trichopoulos. 2003. "Adherence to a Mediterranean Diet and Survival in a Greek Population." New England Journal of Medicine 348 (26): 2599–2608. doi:10.1056/NEJMoa025039.
MedDiet <- computeMDS03(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)MedDiet <- computeMDS03(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)
Computes the Mediterranean Diet adherence score developed by Trichopoulou et al. in 2005 (MDS05), which is an update of their previously developed version.
computeMDS05(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, PUFA = NULL, SFA = NULL, Sex, men = "male", women= "female", frequency = NULL, output = "percent", rm.na = FALSE)computeMDS05(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, PUFA = NULL, SFA = NULL, Sex, men = "male", women= "female", frequency = NULL, output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Legumes |
Numeric variable with Legumes consumption as grams |
FruitAndNuts |
Numeric variable with consumption of Fruits and Nuts as grams |
Cereals |
Numeric variable with Cereals consumption as grams |
Potatoes |
Numeric variable with Potatoes consumption as grams |
Fish |
Numeric variable with Fish consumption as grams |
Meat |
Numeric variable with Meat consumption as grams |
Dairy |
Numeric variable with Dairy consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
Fats |
Optional. Numeric variable with a ratio of consumption of Mono and Poli Unsaturated Fatty Acids (MUFA + PUFA) over Saturated Fatty Acids (SFA). If it is not provided, then individual MUFA, PUFA and SFA should be provided |
MUFA |
Optional if Fats is provided. Numeric variable with consumption of Mono Unsaturated Fatty Acids, units should be the same as used with PUFA and SFA |
PUFA |
Optional if Fats is provided. Numeric variable with consumption of Poli Unsaturated Fatty Acids |
SFA |
Optional if Fats is provided. Numeric variable with consumption of Saturated Fatty Acids |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
This score is an update of the landmark first Mediterranean Diet Score (MDS), published in 1995, which includes fish consumption (previously introduced) and which sums MUFA + PUFA.
Original 1995 paper of Trichopoulou et al. included potatoes with cereals, but later research has challenged this view. If you want to compute the score as originally developed, provide potato consumption as 'Potatoes' argument, and you will get a NOTE informing you that both have been used together in the score. If you don't want to compute potatoes consumption, don't provide 'Potatoes' argument, and you will receive a NOTE informing you that you are diverting from the very original score.
Some score components are a combination of foods you may have as separated variables, if so, you can just add them toghether (v.gr. miFruitVariable + miNutsVariable).
Score values (MUFA + PUFA) / SFA. Depending in how your data has been developed, you can provide the ratio as 'Fats' argument or the triada 'MUFA', 'PUFA' and 'SFA', but if you provide this information by both of the ways, just 'Fats' argument will be computed, and you will receive a warning asking you to check the arguments.
Computed MDS05 score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the fullchecking package dependencies ... NOTE No repository set, so cyclic dependency check skipped data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Trichopoulou, A., A. Kouris-Blazos, M. L. Wahlqvist, C. Gnardellis, P. Lagiou, E. Polychronopoulos, T. Vassilakou, L. Lipworth, and D. Trichopoulos. 1995. "Diet and Overall Survival in Elderly People." BMJ (Clinical Research Ed.) 311 (7018): 1457–60.
Trichopoulou, Antonia, Tina Costacou, Christina Bamia, and Dimitrios Trichopoulos. 2003. "Adherence to a Mediterranean Diet and Survival in a Greek Population." New England Journal of Medicine 348 (26): 2599–2608. doi:10.1056/NEJMoa025039.
Trichopoulou, Antonia, Philippos Orfanos, Teresa Norat, Bas Bueno-de-Mesquita, Marga C. Ocke, Petra HM Peeters, Yvonne T. van der Schouw, et al. 2005. "Modified Mediterranean Diet and Survival: EPIC-Elderly Prospective Cohort Study." BMJ 330 (7498): 991. doi:10.1136/bmj.38415.644155.8F.
data(nutriSample) MedDiet <- computeMDS05(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, PUFA = totalGrasaPoliins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeMDS05(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, PUFA = totalGrasaPoliins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)
Computes a 2012 update of the widely used Mediterranean Diet Score.
computeMDS12(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Dairy, Meat, Alcohol, OOprincipal, Sex, men = "male", women = "female", frequency = NULL, output = "percent", rm.na = FALSE)computeMDS12(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Fish, Dairy, Meat, Alcohol, OOprincipal, Sex, men = "male", women = "female", frequency = NULL, output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Legumes |
Numeric variable with Legumes consumption as grams |
FruitAndNuts |
Numeric variable with consumption of Fruits and Nuts as grams |
Cereals |
Numeric variable with Cereals consumption as grams |
Potatoes |
Numeric variable with Potatoes consumption as grams |
Fish |
Numeric variable with Fish consumption as grams |
Dairy |
Numeric variable with Dairy consumption as grams |
Meat |
Numeric variable with Meat consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
OOprincipal |
Integer. This item scores wether olive oil is the main dietary fat as a dichotomous variable (1-yes, 0-no). |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
This score is an update of the widely used MDS (Mediterranean Diet Score), with some modifications, the most relevant are the following: First, it uses fixed ('a priori') cut-offs, instead of using sample derived medians. Second, instead of scoring all variables dichotomously (0-1), it scores from 0 (minimum) to 2 (maximum), with items which can receive 1 point. As another difference, it stops evaluating Mono and Poli Unsaturated fats, but instead scores Olive Oil consumption. Olive Oil is considered dichotomously.
Computed MDS score according to 2012 version. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 18 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Sofi, Francesco, Rosanna Abbate, Gian Franco Gensini, Alessandro Casini, Antonia Trichopoulou, and Christina Bamia. 2012. ‘Identification of Change-Points in the Relationship between Food Groups in the Mediterranean Diet and Overall Mortality: An “a Posteriori” Approach’. European Journal of Nutrition 51 (2): 167–72. doi:10.1007/s00394-011-0202-7.
computeMDS95
computeMDS03
computeMDS05
data(nutriSample) MedDiet <- computeMDS12(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = NULL, OOprincipal = ifelse(nutriSample$AceiteTipo == 1, 1, 0), Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeMDS12(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = NULL, OOprincipal = ifelse(nutriSample$AceiteTipo == 1, 1, 0), Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)
Fist Mediterranean Adherence score index, developed by Trichopoulou et al. which has been extensively used and modified.
computeMDS95(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, SFA = NULL, Sex, men = "male", women= "female", frequency = NULL, output = "percent", rm.na = FALSE)computeMDS95(data, Vegetables, Legumes, FruitAndNuts, Cereals, Potatoes = NULL, Meat, Dairy, Alcohol, Fats = NULL, MUFA = NULL, SFA = NULL, Sex, men = "male", women= "female", frequency = NULL, output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Legumes |
Numeric variable with Legumes consumption as grams |
FruitAndNuts |
Numeric variable with consumption of Fruits and Nuts as grams |
Cereals |
Numeric variable with Cereals consumption as grams |
Potatoes |
Numeric variable with Potatoes consumption as grams |
Meat |
Numeric variable with Meat consumption as grams |
Dairy |
Numeric variable with Dairy consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
Fats |
Optional. Numeric variable with a ratio of consumption of Mono Unsaturated Fatty Acids (MUFA) over Saturated Fatty Acids (SFA). If it is not provided, then individual MUFA and SFA should be provided |
MUFA |
Optional if Fats is provided. Numeric variable with consumption of Mono Unsaturated Fatty Acids, units should be the same as used with PUFA and SFA |
SFA |
Optional if Fats is provided. Numeric variable with consumption of Saturated Fatty Acids |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. If 'Sex' argument is character or factor, and values for male are either 'man', 'male', 'MAN' or 'MALE', and for females are 'woman', 'female', 'WOMAN' or 'FEMALE', then, the arguments 'men' and 'women' can be missing |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
This is the fist Mediterranean Diet Score, which was developed by Antonia Trichopoulou and coleagues. At present, this score is not widely used, since it was later updated by its authors. Nevertheless, as it is the first Mediterranean Diet Score developed, and is the basis of most of them, we think it deserves a places here.
Original 1995 paper of Trichopoulou et al. included potatoes with cereals, but later research has challenged this view. If you want to compute the score as originally developed, provide potato consumption as 'Potatoes' argument, and you will get a warning informing you that both have been used together in the score. If you don't want to compute potatoes consumption, don't provide 'Potatoes' argument, and you will receive a warning informing you that you are diverting from the very original score.
Computed MDS95 score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Trichopoulou, A., A. Kouris-Blazos, M. L. Wahlqvist, C. Gnardellis, P. Lagiou, E. Polychronopoulos, T. Vassilakou, L. Lipworth, and D. Trichopoulos. 1995. “Diet and Overall Survival in Elderly People.” BMJ (Clinical Research Ed.) 311 (7018): 1457–60.
data(nutriSample) MedDiet <- computeMDS95(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computeMDS95(data = nutriSample, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, FruitAndNuts = P50grCom + P52grCom + P53grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy = P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 12 * (P94rac + P96rac + P97rac + P98rac + P99rac), Potatoes = P43grCom + P44grCom + P45grCom, MUFA = totalGrasaMonoins, SFA = totalGrasaSat, Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) hist(MedDiet)
Computes Mediterranean-Style Dietary Pattern Score (MSDPS).
computeMSDPS(data, WholeCereals, Fruit, Vegetables, Dairy, Wine, Fish, Poultry, LegumesAndMore, Potatoes, Eggs, Sweets, Meat, OOprincipal, WholeCerealsK, FruitK, VegetablesK, DairyK, WineK, FishK, PoultryK, LegumesAndMoreK, PotatoesK, EggsK, SweetsK, MeatK, OliveOilK, Kcal, Sex, men = "male", women = "female", output = "percent", frequency = "daily", rm.na = FALSE)computeMSDPS(data, WholeCereals, Fruit, Vegetables, Dairy, Wine, Fish, Poultry, LegumesAndMore, Potatoes, Eggs, Sweets, Meat, OOprincipal, WholeCerealsK, FruitK, VegetablesK, DairyK, WineK, FishK, PoultryK, LegumesAndMoreK, PotatoesK, EggsK, SweetsK, MeatK, OliveOilK, Kcal, Sex, men = "male", women = "female", output = "percent", frequency = "daily", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
WholeCereals |
Numeric variable with consumption of whole grain products as servings. |
Fruit |
Numeric variable with fruit consumption as servings. |
Vegetables |
Numeric variable with vegetables consumption as servings. |
Dairy |
Numeric variable with dairy consumption as servings. |
Wine |
Numeric variable with wine consumption as glasses. |
Fish |
Numeric variable with fish consumption as servings. |
Poultry |
Numeric variable with poultry consumption as servings. |
LegumesAndMore |
Numeric variable with legumes, nuts and olives consumption as servings. |
Potatoes |
Numeric variable with potatoes consumption as servings. |
Eggs |
Numeric variable with eggs consumption as servings. |
Sweets |
Numeric variable with sweets consumption as servings. |
Meat |
Numeric variable with red meat and meat products consumption as servings. |
OOprincipal |
Integer. This argument informs wether olive oil is the main dietary fat. 0 = olive oil is not usually consumed. 1 = olive oil and other vegetable oils are usually consumed. 2 = only olive oil is usually consumed. |
WholeCerealsK |
Numeric variable with energy (as Kcal) due to consumption of whole grain products. |
FruitK |
Numeric variable with energy (as Kcal) due to consumption of fruit. |
VegetablesK |
Numeric variable with energy (as Kcal) due to consumption of vegetables. |
DairyK |
Numeric variable with energy (as Kcal) due to consumption of dairy. |
WineK |
Numeric variable with energy (as Kcal) due to consumption of wine. |
FishK |
Numeric variable with energy (as Kcal) due to consumption of fish. |
PoultryK |
Numeric variable with energy (as Kcal) due to consumption of poultry. |
LegumesAndMoreK |
Numeric variable with energy (as Kcal) due to consumption of legumes, nuts and olives. |
PotatoesK |
Numeric variable with energy (as Kcal) due to consumption of potatoes. |
EggsK |
Numeric variable with energy (as Kcal) due to consumption of eggs. |
SweetsK |
Numeric variable with energy (as Kcal) due to consumption of sweets. |
MeatK |
Numeric variable with energy (as Kcal) due to consumption of red meat. |
OliveOilK |
Numeric variable with energy (as Kcal) due to consumption of olive oil. |
Kcal |
Numeric with total energy intake (as Kcal). |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
Computes Mediterranean-Style Dietary Pattern Score according to Rumawas et al. 2009.
Where
As this scoring schema is not similar to others, we briefly explain it:
Step 1: The Score "S" of an item "i" has full score (10 points) if its consumed amount is the same as the standard recommendation (for instance, for fruit, 3 servings a day). If the amount is different, both as a lack or as an excess, more or less points are taken from the maximun possible, depending on how big this difference is.
For instance, if a particular food consumption is 80% of the recomended, the deviation from the recommendation is 20%. This "0% takes 2 points (1 point per each ten), so, instead of the maximun 10, this item deserves Si = 10 - 2 = 8 points.
Olive oil is not measured the same way as the other items. It is considered categorically: only olive oil (10 points), olive oil and other vegetable oils (5 points), no olive oil (0 points).
Step 2: After all items have been computed, they are sumed, and considered a percentage of maximun possible (13 items * 10 points = 130). So, at this step range goes from 0 to 100%.
Step 3: The previous percentage is adjusted with a correction factor "P", ranging from 0 to 1. This correction factor is the proportion of total energy intake provided by all foods included in the mediterranean diet pyramid, i.e., each of the 13 foods included in the score, over total energy intake. This allows the use of the score in non-Mediterranean populations, where large proportion of energy intake comes from foods that wouldn't be found in a mediterranean diet pyramid (like sugar sweetened soft drinks or margarine). Al the arguments about energy intake information are used to compute this correction factor.
This way a 100% is hard to reach.
Please note that Legumes are included with Nuts and Olives.
Computed Mediterranean-Style Dietary Pattern Score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to a theoretical maximun of 100% (max. adhrence) |
Miguel Menendez
Rumawas, Marcella E., Johanna T. Dwyer, Nicola M. Mckeown, James B. Meigs, Gail Rogers, and Paul F. Jacques. 2009. 'The Development of the Mediterranean-Style Dietary Pattern Score and Its Application to the American Diet in the Framingham Offspring Cohort'. The Journal of Nutrition 139 (6): 1150-56. doi:10.3945/jn.108.103424.
data(nutriSample) # wether olive oil is principal or not is stored in the sample dataset # in a different way than asked by formula. # In the data set it is 1=olive oil, 2=seeds oil, 3=both # so a transformation is performed: Oil <- ifelse(nutriSample$AceiteTipo == 2, 0, ifelse(nutriSample$AceiteTipo == 3, 1, ifelse(nutriSample$AceiteTipo == 1, 2, 0))) MedDiet <- computeMSDPS(data = nutriSample, # group of arguments about food consumption: WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Wine = P96rac, Fish = P35rac + P36rac + P37rac + P38rac, Poultry = P33rac, LegumesAndMore = P46rac + P53rac + P54rac, Potatoes = P43grCom + P44grCom + P45grCom, Eggs = P28rac, Sweets = P69rac + P70rac + P71rac + P72rac + P73rac, Meat = P29rac + P30rac + P31rac + P32rac, OOprincipal = Oil, # group of arguments about energy intake to compute correction factor: WholeCerealsK = P56Kcal + ifelse(nutriSample$P63_2 == 2, nutriSample$P61Kcal, 0), FruitK = P50Kcal + P52Kcal, VegetablesK = P41Kcal + P42Kcal, DairyK = P19Kcal + P20Kcal + P20Kcal + P22Kcal + P23Kcal + P24Kcal + P25Kcal + P26Kcal + P27Kcal, WineK = P96Kcal, FishK = P35Kcal + P36Kcal + P37Kcal + P38Kcal, PoultryK = P33Kcal, LegumesAndMoreK = P46Kcal + P53Kcal + P54Kcal, PotatoesK = P43grCom + P44grCom + P45grCom, EggsK = P28Kcal, SweetsK = P69Kcal + P70Kcal + P71Kcal + P72Kcal + P73Kcal, MeatK = P29Kcal + P30Kcal + P31Kcal + P32Kcal, OliveOilK = AceiteKcal, Kcal = totalKcal, # final arguments: Sex = SEXO, men = "Hombre", women = "Mujer", output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)data(nutriSample) # wether olive oil is principal or not is stored in the sample dataset # in a different way than asked by formula. # In the data set it is 1=olive oil, 2=seeds oil, 3=both # so a transformation is performed: Oil <- ifelse(nutriSample$AceiteTipo == 2, 0, ifelse(nutriSample$AceiteTipo == 3, 1, ifelse(nutriSample$AceiteTipo == 1, 2, 0))) MedDiet <- computeMSDPS(data = nutriSample, # group of arguments about food consumption: WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Wine = P96rac, Fish = P35rac + P36rac + P37rac + P38rac, Poultry = P33rac, LegumesAndMore = P46rac + P53rac + P54rac, Potatoes = P43grCom + P44grCom + P45grCom, Eggs = P28rac, Sweets = P69rac + P70rac + P71rac + P72rac + P73rac, Meat = P29rac + P30rac + P31rac + P32rac, OOprincipal = Oil, # group of arguments about energy intake to compute correction factor: WholeCerealsK = P56Kcal + ifelse(nutriSample$P63_2 == 2, nutriSample$P61Kcal, 0), FruitK = P50Kcal + P52Kcal, VegetablesK = P41Kcal + P42Kcal, DairyK = P19Kcal + P20Kcal + P20Kcal + P22Kcal + P23Kcal + P24Kcal + P25Kcal + P26Kcal + P27Kcal, WineK = P96Kcal, FishK = P35Kcal + P36Kcal + P37Kcal + P38Kcal, PoultryK = P33Kcal, LegumesAndMoreK = P46Kcal + P53Kcal + P54Kcal, PotatoesK = P43grCom + P44grCom + P45grCom, EggsK = P28Kcal, SweetsK = P69Kcal + P70Kcal + P71Kcal + P72Kcal + P73Kcal, MeatK = P29Kcal + P30Kcal + P31Kcal + P32Kcal, OliveOilK = AceiteKcal, Kcal = totalKcal, # final arguments: Sex = SEXO, men = "Hombre", women = "Mujer", output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)
Computes the Mediterranean Diet adherence score developed by Pitsavos et al. in 2005, it can alo be found as Dietary Score (see Details).
computePitsavos(data, WholeCereals, Fruit, Vegetables, Potatoes, Legumes, OliveOil, OOmeasure = "gr", Fish, Meat, Poultry, WholeDairy, Wine, output = "percent", frequency = "daily", rm.na = FALSE)computePitsavos(data, WholeCereals, Fruit, Vegetables, Potatoes, Legumes, OliveOil, OOmeasure = "gr", Fish, Meat, Poultry, WholeDairy, Wine, output = "percent", frequency = "daily", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
WholeCereals |
Numeric variable with Whole Cereals consumption as servings. |
Fruit |
Numeric variable with Fruit consumption as servings. |
Vegetables |
Numeric variable with Vegetables consumption as servings. |
Potatoes |
Numeric variable with Potatoes consumption as servings. |
Legumes |
Numeric variable with Legumes consumption as servings. |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument. |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Fish |
Numeric variable with Fish consumption as servings. |
Meat |
Numeric variable with Meat consumption as servings. |
Poultry |
Numeric variable with Poultry consumption as servings. |
WholeDairy |
Numeric variable with fish consumption as servings. |
Wine |
Numeric variable with Wine consumption as glasses. |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
We have chosen to name this score by its first author name (Pitsavos), althought it can be found in the literature as Dietary Score (DS) [Milà-Villarroel, 2011; D'Alesandro-De Pergola, 2015] or as a derivate from MDS (Waijers et al. [Waijers et al., 2007] refer to it as MDS-a IV)
Computed score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
D'Alessandro, Annunziata, and Giovanni De Pergola. 2015. "Mediterranean Diet and Cardiovascular Disease: A Critical Evaluation of A Priori Dietary Indexes." Nutrients 7 (9): 7863-88. doi:10.3390/nu7095367.
Mila-Villarroel, Raimon, Anna Bach-Faig, Josep Puig, Anna Puchal, Andreu Farran, Lluis Serra-Majem, and Josep Lluis Carrasco. 2011. "Comparison and Evaluation of the Reliability of Indexes of Adherence to the Mediterranean Diet." Public Health Nutrition 14 (12A): 2338-45. doi:10.1017/S1368980011002606.
Pitsavos, Christos, Demosthenes B. Panagiotakos, Natalia Tzima, Christina Chrysohoou, Manolis Economou, Antonis Zampelas, and Christodoulos Stefanadis. 2005. "Adherence to the Mediterranean Diet Is Associated with Total Antioxidant Capacity in Healthy Adults: The ATTICA Study". The American Journal of Clinical Nutrition 82 (3): 694-99. http://ajcn.nutrition.org/content/82/3/694.
Waijers, Patricia M. C. M., Edith J. M. Feskens, and Marga C. Ocke. 2007. "A Critical Review of Predefined Diet Quality Scores." British Journal of Nutrition 97 (2): 219-231. doi:10.1017/S0007114507250421.
data(nutriSample) MedDiet <- computePitsavos(data = nutriSample, WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Potatoes = P43rac + P44rac + P45rac, Legumes = P46rac, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35rac + P36rac + P37rac + P38rac, Meat = P29rac + P30rac + P31rac + P32rac, Poultry = P33rac, WholeDairy = P19grCom + P22grCom, Wine = P96rac, output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)data(nutriSample) MedDiet <- computePitsavos(data = nutriSample, WholeCereals = P56rac + ifelse(nutriSample$P63_2 == 2, nutriSample$P61rac, 0), Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Potatoes = P43rac + P44rac + P45rac, Legumes = P46rac, OliveOil = Aceitegr, OOmeasure = "gr", Fish = P35rac + P36rac + P37rac + P38rac, Meat = P29rac + P30rac + P31rac + P32rac, Poultry = P33rac, WholeDairy = P19grCom + P22grCom, Wine = P96rac, output = "percent", frequency = "daily", rm.na = FALSE) hist(MedDiet)
Computes the Mediterranean Diet adherence score used in PreDiMed trial (Prevencion con Dieta Mediterranea, Spanish which means Prevention with Mediterranean Diet)
computePredimed(data, OliveOil, OOmeasure = "gr", OOprincipal, Vegetables, Fruit, RedMeat, Butter, SoftDrinks, Wine, Legumes, Fish, Pastries, Nuts, WhiteMeat, Sofritos, output = "percent", rm.na = FALSE, frequency = NULL)computePredimed(data, OliveOil, OOmeasure = "gr", OOprincipal, Vegetables, Fruit, RedMeat, Butter, SoftDrinks, Wine, Legumes, Fish, Pastries, Nuts, WhiteMeat, Sofritos, output = "percent", rm.na = FALSE, frequency = NULL)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns. |
OliveOil |
Numeric with olive oil consumption. Units are set with the argument 'OOmeasure'. |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (10 ml). |
OOprincipal |
Integer. This item scores wether olive oil is the main dietary fat as a dichotomous variable (1-yes, 0-no). |
Vegetables |
Numeric. Vegetables consumption measured as servings. |
Fruit |
Numeric. Fruit consumption measured as servings. |
RedMeat |
Numeric. RedMeat consumption measured as servings. |
Butter |
Numeric. Butter consumption measured as servings. |
SoftDrinks |
Numeric. SoftDrinks consumption measured as servings. |
Wine |
Numeric. Wine consumption measured as servings (glasses). |
Legumes |
Numeric. Legumes consumption measured as servings. |
Fish |
Numeric. Fish consumption measured as servings. |
Pastries |
Numeric. Pastries consumption measured as servings. |
Nuts |
Numeric. Nuts consumption measured as servings. |
WhiteMeat |
Integer. This item scores wether wite meats are preferred over red meats. So it is a dichotomous variable (1-yes, 0-no). |
Sofritos |
Numeric. Number of times 'sofrito' is consumed (see Details). |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
This score was used in the landmark PreDiMed trial (Prevencion con Dieta Mediterranea, Spanish which means Prevention with Mediterranean Diet) (Estruch et al. 2013). It can also be found under the name MEDAS (MEditerranean Diet Adherence Screener) (Schroder et al. 2011)
Please note that olive oil is in thtree items: one measuring the amount of servings, other measuring if it is the main dietary fat, and another asking about 'sofrito' consumption. Supplementary material of Estruch et al. 2013 informs that one tablespoon is 10ml.
'Sofrito' is a special way to cook, a sauce made with tomato and onion, leek, or garlic, simmered with olive oil.
Computed Predimed score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 9 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Estruch, Ramon, Emilio Ros, Jordi Salas-Salvado, Maria-Isabel Covas, Dolores Corella, Fernando Aros, Enrique Gomez-Gracia, et al. 2013. "Primary Prevention of Cardiovascular Disease with a Mediterranean Diet." New England Journal of Medicine 368 (14): 1279-90. doi:10.1056/NEJMoa1200303. (Supplementary material available at http://www.nejm.org/action/showSupplements?doi=10.1056
Martinez-Gonzalez, Miguel Angel, Dolores Corella, Jordi Salas-Salvado, Emilio Ros, Maria Isabel Covas, Miquel Fiol, Julia Warnberg, et al. 2012. "Cohort Profile: Design and Methods of the PREDIMED Study." International Journal of Epidemiology 41 (2): 377-385. http://ije.oxfordjournals.org/content/41/2/377.short.
Schroder, Helmut, Montserrat Fito, Ramon Estruch, Miguel A. Martinez-Gonzalez, Dolores Corella, Jordi Salas-Salvado, Rosa Lamuela-Raventos, et al. 2011. 'A Short Screener Is Valid for Assessing Mediterranean Diet Adherence among Older Spanish Men and Women'. The Journal of Nutrition 141 (6): 1140-45. doi:10.3945/jn.110.135566.
data(nutriSample) MedDiet <- computePredimed(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", OOprincipal = ifelse(nutriSample$AceiteTipo == 1, 1, 0), Vegetables = P41rac + P42rac, Fruit = P50rac + P52rac, RedMeat = P29rac + P31rac, Butter = P79rac, SoftDrinks = P89rac + P90rac, Wine = P96rac, Legumes = P46rac, Fish = P35rac + P36rac + P37rac + P38rac, Pastries = P69rac + P70rac + P71rac + P72rac + P73rac, Nuts = P53rac, WhiteMeat = ifelse(nutriSample$P30rac > nutriSample$P29rac, 1, 0), Sofritos = rep(0, nrow(data)), # data lacks this variable, so we go on without it output = "percent", rm.na = FALSE, frequency = "daily") hist(MedDiet)data(nutriSample) MedDiet <- computePredimed(data = nutriSample, OliveOil = Aceitegr, OOmeasure = "gr", OOprincipal = ifelse(nutriSample$AceiteTipo == 1, 1, 0), Vegetables = P41rac + P42rac, Fruit = P50rac + P52rac, RedMeat = P29rac + P31rac, Butter = P79rac, SoftDrinks = P89rac + P90rac, Wine = P96rac, Legumes = P46rac, Fish = P35rac + P36rac + P37rac + P38rac, Pastries = P69rac + P70rac + P71rac + P72rac + P73rac, Nuts = P53rac, WhiteMeat = ifelse(nutriSample$P30rac > nutriSample$P29rac, 1, 0), Sofritos = rep(0, nrow(data)), # data lacks this variable, so we go on without it output = "percent", rm.na = FALSE, frequency = "daily") hist(MedDiet)
Computes the Revised Mediterranean Diet adherence score according to Buckland et al. in 2009, also known as rMED.
computeRMED(data, FruitAndNuts, Vegetables, Legumes, Cereals, Fish, OliveOil, OOmeasure = "gr", Meat, Dairy, Alcohol, Kcal, Sex, men="male", women="female", frequency = NULL, output = "percent", rm.na = FALSE)computeRMED(data, FruitAndNuts, Vegetables, Legumes, Cereals, Fish, OliveOil, OOmeasure = "gr", Meat, Dairy, Alcohol, Kcal, Sex, men="male", women="female", frequency = NULL, output = "percent", rm.na = FALSE)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
FruitAndNuts |
Numeric variable with consumption of Fruits and Nuts as grams. |
Vegetables |
Numeric variable with Vegetables consumption as grams |
Legumes |
Numeric variable with Legumes consumption as grams |
Cereals |
Numeric variable with Legumes consumption as grams |
Fish |
Numeric variable with Fish consumption as grams |
OliveOil |
Numeric variable with olive oil consumption, measure is set with the 'OOmeasure' argument |
OOmeasure |
Character string which informs about the unit of the argument 'OliveOil'. Allowed values are 'gr', 'ml' and 'serving', which means respectively grams, mililiters and servings of 1 table spoon (15 ml). |
Meat |
Numeric variable with Meat consumption as grams |
Dairy |
Numeric variable with Dairy consumption as grams |
Alcohol |
Numeric variable with Alcohol consumption as etanol grams from any beberage origin |
Kcal |
Numeric variable with energy consumption in kilocalories. |
Sex |
Vector with gender, it can be numeric, factor or character, as long as its values are provided by 'men' and 'women' arguments. If 'Sex' argument is character or factor, and values for male are either 'man', 'male', 'MAN' or 'MALE', and for females are 'woman', 'female', 'WOMAN' or 'FEMALE', then, the arguments 'men' and 'women' can be missing |
men |
A character string with the value of male gender, default is "male" |
women |
A character string with the value of female gender, default is "female" |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
the rMED questionnaire scores food consumption as grams by 1000Kcal/day, but arguments are expected to be provided as grams eaten by day.
Computed RMed score. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 18 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Buckland, Genevieve, Carlos A. Gonzalez, Antonio Agudo, Mireia Vilardell, Antoni Berenguer, Pilar Amiano, Eva Ardanaz, et al. 2009. 'Adherence to the Mediterranean Diet and Risk of Coronary Heart Disease in the Spanish EPIC Cohort Study'. American Journal of Epidemiology, January, kwp282. doi:10.1093/aje/kwp282.
data(nutriSample) MedDiet <- computeRMED(data = nutriSample, Kcal = totalKcal, FruitAndNuts = P50grCom + P52grCom, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, OliveOil = Aceitegr, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy= P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 10 * (P94rac + P96rac + P97rac + P98rac + P99rac), Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) summary(MedDiet)data(nutriSample) MedDiet <- computeRMED(data = nutriSample, Kcal = totalKcal, FruitAndNuts = P50grCom + P52grCom, Vegetables = P41grCom + P42grCom, Legumes = P46grCom, Cereals = P55grCom + P56grCom + P57grCom + P59grCom + P60grCom + P61grCom + P62grCom, Fish = P35grCom + P36grCom + P37grCom + P38grCom, OliveOil = Aceitegr, Meat = P29grCom + P30grCom + P31grCom + P32grCom, Dairy= P19grCom + P20grCom + P20grCom + P22grCom + P23grCom + P24grCom + P25grCom + P26grCom + P27grCom, Alcohol = 10 * (P94rac + P96rac + P97rac + P98rac + P99rac), Sex = SEXO, men = "Hombre", women = "Mujer", frequency = "daily", output = "percent", rm.na = FALSE) summary(MedDiet)
Computes Mediterranean Diet adherence score accoring to the literature review by Sofi et al. in 2014.
computeSofi(data, Fruit, Vegetables, Legumes, Cereals, Fish, Meat, Dairy, Alcohol, OliveOil, output = "percent", rm.na = FALSE, frequency = NULL)computeSofi(data, Fruit, Vegetables, Legumes, Cereals, Fish, Meat, Dairy, Alcohol, OliveOil, output = "percent", rm.na = FALSE, frequency = NULL)
data |
Your data set with nutritional information about food or nutrient consumption. Each row is expected to be a person, and food or nutrient intake are in columns |
Fruit |
Numeric variable with fruit consumption as servings (1 serving: 150g) |
Vegetables |
Numeric variable with vegetables consumption as servings (1 serving: 100g) |
Legumes |
Numeric variable with legumes consumption as servings (1 serving: 70g) |
Cereals |
Numeric variable with cereal consumption as servings (1 serving: 130g) |
Fish |
Numeric variable with fish consumption as servings (1 serving: 100g) |
Meat |
Numeric variable with meat and meat products consumption as servings (1 serving: 80g) |
Dairy |
Numeric variable with dairy consumption as servings (1 serving: 180g) |
Alcohol |
Numeric variable with alcohol intake as Alcohol Units (1 Alcohol Unit: 12g) |
OliveOil |
Integer variable indicating if olive oil consumption is consumed as 0 = occasional use, 1 = frequent use or 2 = regular use |
output |
A character string to set which output should the formula give, allowed values are 'data.frame', 'score' and 'percent' (default). |
rm.na |
Logical. If set to FALSE (default), a diet score will be computed only if a person has all score components informed. If set to TRUE, NA values in score components will be drop off and a value of available components will be returned, but percent of score adherence will be computed with basis of the whole score range (see Details) |
frequency |
A character string. Allowed values are 'daily', 'weekly' and 'monthly'. It informs about the frequency which food or nutrient consumption refers to (i.e. wether the rest of arguments are 'grams per day' or 'grams per week' or 'grams per month') |
This questionnaire vas developed after a systematic literature review (Sofi et al., 2014). To set its cut-offs it considered the amounts of food in the included studies, which studied adherence to mediterranean diet and health status.
Computed score according to Sofi et al. 2014. Depending on 'output' argument, value can be a data.frame, or a vector:
if output = 'data.frame' |
A data frame with a row corresponding to each person in data. Columns are the score of each component, as well as the global score as natural sum ('absolute' column) and as percentage ('percent' column) |
if output = 'score' |
Instead of the full data.frame, just the integer vector corresponding to the absolute points of adherence to Mediterranean Diet for each person, from 0 (min.) to 18 (max.) |
if output = 'percent' |
Instead of the full data.frame, just the numeric vector corresponding to the percent of adherence to Mediterranean Diet for each person, from 0 (min. adherence) to 100 percent (max. adhrence) |
Miguel Menendez
Sofi, Francesco, Claudio Macchi, Rosanna Abbate, Gian Franco Gensini, and Alessandro Casini. 2014. 'Mediterranean Diet and Health Status: An Updated Meta-Analysis and a Proposal for a Literature-Based Adherence Score'. Public Health Nutrition 17 (12): 2769-82. doi:10.1017/S1368980013003169.
data(nutriSample) # wether olive oil is principal or not is stored in the sample dataset # in a different way than asked by formula. # In the data set it is 1=olive oil, 2=seeds oil, 3=both # so a transformation is performed: Oil <- ifelse(nutriSample$AceiteTipo == 2, 0, ifelse(nutriSample$AceiteTipo == 3, 1, ifelse(nutriSample$AceiteTipo == 1, 2, 0))) Sofi <- computeSofi(data = nutriSample, Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Legumes = P46rac, Cereals = P55rac + P56rac + P57rac + P59rac + P60rac + P61rac + P62rac, Fish = P35rac + P36rac + P37rac + P38rac, Meat = P29rac + P30rac + P31rac + P32rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Alcohol = P94rac + P96rac + P97rac + P98rac + P99rac, OliveOil = Oil, output = "data.frame", rm.na = FALSE, frequency = "daily") hist(Sofi$percent)data(nutriSample) # wether olive oil is principal or not is stored in the sample dataset # in a different way than asked by formula. # In the data set it is 1=olive oil, 2=seeds oil, 3=both # so a transformation is performed: Oil <- ifelse(nutriSample$AceiteTipo == 2, 0, ifelse(nutriSample$AceiteTipo == 3, 1, ifelse(nutriSample$AceiteTipo == 1, 2, 0))) Sofi <- computeSofi(data = nutriSample, Fruit = P50rac + P52rac, Vegetables = P41rac + P42rac, Legumes = P46rac, Cereals = P55rac + P56rac + P57rac + P59rac + P60rac + P61rac + P62rac, Fish = P35rac + P36rac + P37rac + P38rac, Meat = P29rac + P30rac + P31rac + P32rac, Dairy = P19rac + P20rac + P20rac + P22rac + P23rac + P24rac + P25rac + P26rac + P27rac, Alcohol = P94rac + P96rac + P97rac + P98rac + P99rac, OliveOil = Oil, output = "data.frame", rm.na = FALSE, frequency = "daily") hist(Sofi$percent)
A sample of 192 Spanish people from a nutriepidemiological study, from DRECE group (Dieta y Riesgo de Enfermedad Cardiovascular en España [Diet and Cardiovascular Risk in Spain]). Food consumption was assessed by a Frequency Food Questionnaire. For all foods or nutrients, food consumption is stored as daily consumption.
data("nutriSample")data("nutriSample")
A data frame with 192 observations on the following variables.
IDEinteger, identification number
PROVINCIAgeographic region of procedence of the person
SEXOgender information
EDADage as years
FUMADORsmoking habit of the person 0 = never smoker, 1 = current smoker, 2 = former smoker
HIPERTENSOif the person has prior diagnose of High Blood Pressure
MEDIC_TENSif the person is under blood lowering pressure treatment
HIPER_COLESif the person has prior diagnose of Dyslipidemia
MEDIC_COLESif the person is under lipid lowering treatment
ANT_CARDIOif the person has a history of coronary events
DIABETESif the person has prior diagnose of Diabetes Mellitus
pesoweight in kg
alturaheigth in cm
TAS1first systolic blood pressure measurement in mmHg
TAD1first diastolic blood pressure measurement in mmHg
TAS2second systolic blood pressure measurement in mmHg
TAD2second diastolic blood pressure measurement in mmHg
Colesterolplasmatic total Cholesterol
LDLplasmatic Low Density Lipoprotein
HDLplasmatic High Density Lipoprotein
TGplasmatic triglycerides
APO.Bplasmatic Apolipoprotein B
APO.Aplasmatic Apolipoprotein A
P19grComEdible portion (in grams) of whole milk
P19KcalKcal attributable to consumption of whole milk
P19racServings of whole milk
P20grComEdible portion (in grams) of skimmed or semi-skimmed mil
P20KcalKcal attributable to consumption of skimmed or semi-skimmed mil
P20racServings of skimmed or semi-skimmed mil
P21KcalKcal attributable to consumption of milk enriched with omega-3 acid
P22grComEdible portion (in grams) of whole yogurt
P22racServings of whole yogurt
P22KcalKcal attributable to consumption of whole yogurt
P23grComEdible portion (in grams) of skimmed or semi-skimmed yogurt
P23racServings of skimmed or semi-skimmed yogurt
P23KcalKcal attributable to consumption of skimmed or semi-skimmed yogurt
P24grComEdible portion (in grams) of enriched with probiotics yogurt
P24racServings of enriched with probiotics yogurt
P24KcalKcal attributable to consumption of enriched with probiotics yogurt
P25grComEdible portion (in grams) of dairy products, usually desserts, like custard, junket, flan or requeson
P25racServings of dairy products, usually desserts, like custard, junket, flan or requeson
P25KcalKcal attributable to consumption of dairy products, usually desserts, like custard, junket, flan or requeson
P26grComEdible portion (in grams) of unripened cheese
P26KcalKcal attributable to consumption of unripened cheese
P26racServings of unripened cheese
P27grComEdible portion (in grams) of cheese (hard, semi-hard, ball, blue...)
P27KcalKcal attributable to consumption of cheese (hard, semi-hard, ball, blue...)
P27racServings of cheese (hard, semi-hard, ball, blue...)
P28KcalKcal attributable to consumption of eggs
P28racServings of eggs
P29grComEdible portion (in grams) of red meat (cattle, lamb, pork)
P29grGrasaFat intake attributable to consumption of red meat (cattle, lamb, pork)
P29KcalKcal attributable to consumption of red meat (cattle, lamb, pork)
P29racServings of red meat (cattle, lamb, pork)
P30grComEdible portion (in grams) of white meat (poultry, rabbit)
P30grGrasaFat intake attributable to consumption of white meat (poultry, rabbit)
P30KcalKcal attributable to consumption of white meat (poultry, rabbit)
P30racServings of white meat (poultry, rabbit)
P31grComEdible portion (in grams) of cold cuts ("embutido")
P31grGrasaFat intake attributable to consumption of cold cuts ("embutido")
P31KcalKcal attributable to consumption of cold cuts ("embutido")
P31racServings of cold cuts ("embutido")
P32grComEdible portion (in grams) of serrano ham
P32grGrasaFat intake attributable to consumption of serrano ham
P32KcalKcal attributable to consumption of serrano ham
P32racServings of serrano ham
P33grGrasaFat intake attributable to consumption of York ham
P33racServings of York ham
P33KcalKcal attributable to consumption of York ham
P34grGrasaFat intake attributable to consumption of offal (guts, pluck or organ meats)
P35grComEdible portion (in grams) of white fish
P35KcalKcal attributable to consumption of white fish
P35racServings of white fish
P36grComEdible portion (in grams) of blue fish
P36KcalKcal attributable to consumption of blue fish
P36racServings of blue fish
P37grComEdible portion (in grams) of shellfish
P37KcalKcal attributable to consumption of shellfish
P37racServings of shellfish
P38grComEdible portion (in grams) of tinned fish
P38KcalKcal attributable to consumption of tinned fish
P38racServings of tinned fish
P41grComEdible portion (in grams) of salads
P41KcalKcal attributable to consumption of salads
P41racServings of salads
P42grComEdible portion (in grams) of boiled or grilled vegetables
P42KcalKcal attributable to consumption of boiled or grilled vegetables
P42racServings of boiled or grilled vegetables
P43grComEdible portion (in grams) of boiled or roasted potatoes
P43KcalKcal attributable to consumption of boiled or roasted potatoes
P43racServings of boiled or roasted potatoes
P44grComEdible portion (in grams) of fried home cooked potatoes (not frozen)
P44KcalKcal attributable to consumption of fried home cooked potatoes (not frozen)
P44racServings of fried home cooked potatoes (not frozen)
P45grComEdible portion (in grams) of fried frozen potatoes or eaten in restaurants or fast food
P45racServings of fried frozen potatoes or eaten in restaurants or fast food
P45KcalKcal attributable to consumption of fried frozen potatoes or eaten in restaurants or fast food
P46grComEdible portion (in grams) of legumes
P46KcalKcal attributable to consumption of legumes
P46racServings of legumes
P50grComEdible portion (in grams) of fresh fruit
P50KcalKcal attributable to consumption of fresh fruit
P50racServings of fresh fruit
P52grComEdible portion (in grams) of dried figs, dried grapes, dried plums or dates
P52racServings of dried figs, dried grapes, dried plums or dates
P52KcalKcal attributable to consumption of dried figs, dried grapes, dried plums or dates
P53grComEdible portion (in grams) of nuts (almonds, pistachios, walnuts, hazelnuts or peanuts)
P53KcalKcal attributable to consumption of nuts (almonds, pistachios, walnuts, hazelnuts or peanuts)
P53racServings of nuts (almonds, pistachios, walnuts, hazelnuts or peanuts)
P54KcalKcal attributable to consumption of olives
P54racServings of olives
P55grComEdible portion (in grams) of white bread
P55KcalKcal attributable to consumption of white bread
P55racServings of white bread
P56grComEdible portion (in grams) of whole grain bread
P56KcalKcal attributable to consumption of whole grain bread
P56racServings of whole grain bread
P57grComEdible portion (in grams) of toast bread
P57KcalKcal attributable to consumption of toast bread
P57racServings of toast bread
P59grComEdible portion (in grams) of breakfast cereals
P59KcalKcal attributable to consumption of breakfast cereals
P59racServings of breakfast cereals
P60grComEdible portion (in grams) of fiber enriched breakfast cereals
P60KcalKcal attributable to consumption of fiber enriched breakfast cereals
P60racServings of fiber enriched breakfast cereals
P61grComEdible portion (in grams) of white rice
P61KcalKcal attributable to consumption of white rice
P61racServings of white rice
P62grComEdible portion (in grams) of paella (a traditionl Spanish dish based on rice with yellow colorant)
P62KcalKcal attributable to consumption of paella (a traditionl Spanish dish based on rice with yellow colorant)
P62racServings of paella (a traditionl Spanish dish based on rice with yellow colorant)
P63_2A question about consumption of whole bread (1) or white bread (0)
P69KcalKcal attributable to consumption of pastries
P69racServings of pastries
P70KcalKcal attributable to consumption of churros and fritters
P70racServings of churros and fritters
P71KcalKcal attributable to consumption of cakes
P71racServings of cakes
P72KcalKcal attributable to consumption of chocolate or bonbons
P72racServings of chocolate or bonbons
P73KcalKcal attributable to consumption of ice cream
P73racServings of ice cream
P75racServings of sunflower seeds
P79racServings of butter
P84KcalKcal attributable to consumption of sugar
P89KcalKcal attributable to consumption of soft drinks
P89racServings of soft drinks
P90KcalKcal attributable to consumption of diet soft drinks
P90racServings of diet soft drinks
P94racServings of beer
P96KcalKcal attributable to consumption of wine
P96racServings of wine
P97racServings of vermouth, fine wine or sweet wine
P98racServings of liquor or anisette
P99racServings of spirits (whiskey, cognac, gin)
Aceitegrolive oil consumption in grams
AceiteKcalKcal attributable to olive oil consumption
AceiteTipokind of oil preferred by the surveyed person (1 = olive oil, 2 = seeds oil, 3 = both)
totalgrTotal Food consumption, included edible and not edible, in grams
totalgrComTotal Edible food consumption, in grams, including liquid foods like milk
grBebidastotal beberage intake in ml not coming directly from drinken water
grSinBebidastotal food consumption in grams, without liquid components
totalCHtotal CarboHydrates consumption (grams per day)
totalProttotal Protein consumption (grams per day)
totalGrasatotal Fat consumption (grams per day)
totalGrasaSattotal Saturated Fat consumption (grams per day)
totalGrasaMonoinstotal Monounsaturated Fat consumption (grams per day)
totalGrasaPoliinstotal Polyunsaturated Fat consumption (grams per day)
totalColtotal Cholesterol consumption (in mg per day)
totalFibratotal Fiber consumption (grams per day)
totalKcaltotal kcal eaten per day
Gutiérrez Fuentes JA, Gómez Gerique JA, Rubio Herrera MA, Gómez de la Cámara A, Grupo DRECE. Capítulo 1. DRECE: introducción. Med Clin (Barc). 2011;12(4):1–2.
Gómez Gerique JA, Herrera R, Gómez de la Cámara A, Grupo DRECE. Capítulo 2. El proyecto DRECE. Med Clin (Barc). 2011;12(4):3–5.
Gómez Gerique JA, Herrera R, Gómez de la Cámara A, Gutiérrez Fuentes JA, Grupo DRECE. Capítulo 3. DRECE I (1991). Med Clin (Barc). 2011;12(4):6–15.
Gómez de la Cámara A, Gutiérrez Fuentes JA, Gómez Gerique JA, Herrera R, Grupo DRECE. Capítulo 4. DRECE II (1996). Evolución del perfil cardiovascular y morbilidad en poblaciones de riesgo. Med Clin (Barc). 2011;12(4):16–21.
Gómez de la Cámara A, Herrera R, Gutiérrez Fuentes JA, Jurado Valenzuela C, Cancelas Navia P, Gómez Gerique JA, et al. Capítulo 5. DRECE III (2004). Mortalidad y factores de riesgo cardiovascular. Med Clin (Barc). 2011;12(4):22–30.
Gutiérrez Fuentes JA, Gómez Gerique JA, Gómez de la Cámara A, Cancelas Navia P, Jurado Valenzuela C, Herrera R, et al. Capítulo 6. DRECE IV (2008). Hábitos alimentarios actuales y evolución de la dieta en la población española. Med Clin (Barc). 2011;12(4):31–34.
Gómez-de la Cámara A, Pinilla-Domínguez P, Vázquez-Fernández Del Pozo S, García-Pérez L, Rubio-Herrera MA, Gómez-Gerique JA, et al. Costs resulting from premature mortality due to cardiovascular causes: A 20-year follow-up of the DRECE study. Rev Clin Esp. 2014 Oct;214(7):365–70.
data(nutriSample) summary(nutriSample$totalKcal)data(nutriSample) summary(nutriSample$totalKcal)
Diferent scores of Mediterranean Diet set cutoffs of daily, weekly or monthly consumption. Additionally, a dataset can be stored as diferent frequency of consumption. This function has been created to be called by others, it just multiplies or divides by the suitable numbre (for instance, from 'daily' to 'weekly' it just multiplies by 7)
periodicity(x, OriginalFreq, TargetFreq)periodicity(x, OriginalFreq, TargetFreq)
x |
numeric variable or a list of numeric variables, which want to be converted |
OriginalFreq |
character string. The frequency in which information was captured (should be provided by user). Allowed values are 'daily', 'weekly' or 'monthly' |
TargetFreq |
character string. The frequency in which information has to be transformed. Allowed values are 'daily', 'weekly' or 'monthly'. Usually it will be provided by another formula, depending in its scoring scheme |
A numeric vector, or a list of numeric vectors.
Miguel Menendez
foodA <- c(1,2,3) foodB <- c(3,2,1) L <- list(foodA = foodA, foodB= foodB) # Use with a numeric variable periodicity(foodA, "daily", "weekly") #Use with a list periodicity(L, "daily", "weekly")foodA <- c(1,2,3) foodB <- c(3,2,1) L <- list(foodA = foodA, foodB= foodB) # Use with a numeric variable periodicity(foodA, "daily", "weekly") #Use with a list periodicity(L, "daily", "weekly")