Reproducibility and validity of a culture specific food frequency questionnaire for assessing dietary intake in the adult population of Trinidad and Tobago

doi:10.21203/rs.3.rs-1526807/v1

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Reproducibility and validity of a culture specific food frequency questionnaire for assessing dietary intake in the adult population of Trinidad and Tobago

https://doi.org/10.21203/rs.3.rs-1526807/v1

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Background

Nutritional epidemiologists use culture-specific food frequency questionnaires (FFQs) to assess the dietary intake of groups based on country, region or ethnic groups. The aim of this study was to assess the performance of a self-constructed culture-specific semi-quantitative electronic FFQ (e-FFQ) in assessing food group intake estimates when compared with multiple 1-day food records (FRs) for the adult population of Trinidad and Tobago.

Methods

The 139-item semi-quantitative e-FFQ containing local dishes and street foods was created using Google forms. It was emailed twice, 3 months apart, to 91 participants ≥ 18 years of age. The first administration of the e-FFQ was validated against 4-four one-day FRs with digital photographs which served as the reference method. Using SPSS Version 26, the validity and reproducibility of the FFQ’s food group intake estimates were determined using paired t-tests, bivariate correlations, and cross-classifications. Cross-classification and Bland-Altman plots were used to show agreement between the e-FFQ and weighted mean of the FRs.

Results

Participants consisted of 22% males and 78% females, mean age 38 ± 9.6 years. Reproducibility correlations between the reported food group intakes in the first and repeat administration of the e-FFQ ranged from moderate (r = 0.44 p ≤ 0.0001) for sweetened beverages to high (r = 0.91 p ≤ 0.0001) for alcohol. Cross-classification agreements within ± 1 quintile ranged from 70% (street foods) to 92% (alcohol). Crude validity correlations between the e-FFQ and FR ranged from (r = 0.06) for sweets to (r = 0.65) for food from animal sources, with mean of the validity correlations being 0.34. Deattenuated correlations ranged from r = 0.11 for sweets to r = 0.81 for food from animal sources. Cross-classification showed that an average of 68% of the FFQ estimates were correctly classified within ± 1 quintile of exact agreement in the FRs. Agreements between the FFQ and the FRs ranged from 55% for street foods to 95% for water, all significant at p ≤ 0.0001.

Conclusions

The study shows that this culture-specific e-FFQ is highly reproducible and is a reasonably valid tool to assess and rank food group intake estimates of an adult population living in Trinidad and Tobago. Thus, it can be used to examine associations between dietary intakes and NCDs.

validation

electronic food frequency questionnaire

food groups

Trinidad & Tobago

dietary assessment

Nutritional epidemiologists have developed food frequency questionnaires (FFQ) to assess the dietary intake of populations and specific ethnic groups within these populations. FFQs are reliable and inexpensive tools that can be used to assess long-term dietary intakes. FFQs are also easier to administer in large scale studies (1). The FFQs can then be used to determine the association between dietary intake and diseases.

Several population-based research studies have used FFQs to establish dietary patterns for different groups or regions to highlight the health protective benefits of these dietary patterns (2–5). For example, the Mediterranean dietary pattern has been researched extensively which highlighted the population’s high intakes of fiber and monounsaturated fatty acids which were associated with a reduction in the risk of heart diseases, diabetes and cancers (6–8).

The Caribbean is another region which has unique dietary patterns specific to each country in the region. Barbados and Jamaica found it prudent to develop and validate FFQs specific to their countries and to utilize them to determine associations between dietary intake and non-communicable diseases (NCDs) (9, 10). The twin-island republic of Trinidad and Tobago with a population of approximately 1.4 million has a diverse multi-ethnic population and a unique dietary pattern with contributions from several ethnic groups (11). Therefore, a culture-specific FFQ which includes local composite foods, mixed foods, and street foods is necessary to assess the dietary intake of the adult population. The dietary intake of the population can then be used to investigate the association between diet and disease given that Trinidad and Tobago has the highest prevalence of morbidity and mortality for NCDs in the Caribbean, with chronic NCDs accounting for 80% of total deaths (12). This culture-specific FFQ can be a cost-effective method to investigate the role of dietary intake in the high prevalence of NCDs at the population level. It can also be used to assess dietary intake patterns that can lead to nutrient deficiencies. Food group intake estimates from this FFQ may be used to develop nutrition policies and programs geared towards primary prevention and intervention strategies to reduce the risk and prevent the onset of several NCDs. We designed a culture-specific semi-quantitative electronic FFQ (e-FFQ) for the adult population of Trinidad and Tobago and aimed to evaluate its performance in assessing food group intake estimates when compared with multiple 1-day food records (FR).

Study Design

A cross-sectional observational study for a period of 3 months was conducted to evaluate the reproducibility and validity of a culture-specific, semi-quantitative e-FFQ in assessing food group intake for the adult population of Trinidad and Tobago. Participants were recruited using email, social media platforms, and professional associations. Interested participants were emailed a short screening questionnaire created using Google forms to determine their eligibility for the study. Persons were excluded from the study if they were less than 18 years of age, had no access to smartphone or internet, had any medical condition which required a therapeutic diet, such as Crohn’s disease, celiac disease, or end-stage renal disease, or if they would be out of the country during the study period. The first administration of the e-FFQ was emailed to participants for completion between March and April of 2019 and then re-administered 3 months later between June and July 2019. Between the first and second administrations of the e-FFQ, participants provided 4-four one-day FRs using their own smart phones over a 12-week period as shown in Fig. 1.

Study Participants

A total of 125 individuals completed the short screening form, which was used to determine eligibility for the study. Nine individuals were ineligible because they would have been out of the country during the study period; an additional three individuals did not provide their name and contact information (preferred mailing address, email address, and cell phone number); and one had Crohn’s disease. Of the 112 remaining eligible participants enrolled in the study, 14 individuals did not complete the first e-FFQ. Though 98 participants completed the first e-FFQ, seven of the participants did not complete the second administration of the e-FFQ or the FRs. Therefore, a total of 91 participants completed the study, 10 persons from Tobago and 81 from Trinidad.

Study participants were trained on how to report their food intake by using their personal mobile devices (telephones). Each participant was sent ahead of training day the pictorial instructions and a checkered fiducial marker that was used to help estimate eating utensil and food portion sizes. During the one-on-one training with the trained dietitian, participants were asked to refer to the pictorial instructions as they learned how to gather the foods they were to eat in a meal setting and the placement of the fiducial marker relative to the meal setting. Instructions were also given on what information to include in the text messages that accompany the digital photographs that were to be sent either through WhatsApp or via email. The contact information (telephone and email address) for the study were printed on the fiducial marker to facilitate delivery of the food reports.

Food Frequency Questionnaire Design

The semi-quantitative FFQ was designed as part of an electronic survey (e-survey) that was developed using Google forms. Instructions and examples for the completion of the e-FFQ were provided as part of the e-survey. To prevent missing data, the e-survey was designed to ensure that respondents answered all questions before pressing the submit button.

The e-survey consists of two parts: a 139- item food frequency questionnaire, i.e., the e-FFQ, and a section on demographics and anthropometrics. The e-FFQ groups food items according to the Caribbean’s Six food groups (13). Five other categories are included to capture consumption patterns of street foods, water, non-alcoholic beverages, alcoholic beverages and sweets. Food items of similar nutrient composition are grouped under the following categories: (1) street foods, (2) staples, with the subcategories (a) breads, cakes, and cereals, (b) rice and pasta, and (c) ground provisions; (3) vegetables; (4) legumes; (5) fruits; (6) food from animals with the subcategories (a) eggs, dairy, and dairy substitutes; (b) meat and meat substitutes; (7) fats & oils (8) water; (9) non-alcoholic beverages; (10) alcoholic beverages; and (11) sweets as shown in Table 1. The street foods category is listed first to avoid participants from reporting the same information while responding to the rest of the FFQ. In addition, instructions are included to guide participants to not include foods (such as vegetables, staples or fruits) already reported under the street foods category.

Table 1 Food groupings used in the reproducibility analysis assessed with the FFQ.

The frequency of intake section of the e-FFQ has eight categories: never or less than once per month, 1–3 times per month, once per week, 2–4 times per week, 5–6 times per week, once per day, 2–3 times per day and 4 or more times per day. Participants are asked to report on average how often they usually ate the foods relative to the standard portion size specific for the food during the past three months. Familiar utensils such as the ‘pot spoon’ (a rounded ‘pot spoon’ is equivalent to approximately ½ cup,) which is a common serving utensil in Trinidad and Tobago, is used for the portion sizes to help participants better estimate their intake of various foods. Since water is consumed daily, frequency of water consumption is assessed using different categories: less than once per day, once per day, 2–3 times per day, 4–5 times per day, 6–7 times per day, and 8 or more times per day.

The demographics and anthropometrics part of the survey consists of questions inquiring about age, sex, marital status, religion, race, level of education, employment status, physical activity, smoking status, use of dietary supplements and chronic disease history. Body mass index (BMI) was calculated based on self-reported weight and height using the formula weight (kg) divided by the square of height (m²).

Multiple One-Day Food Records with Digital photographs

Multiple one-day FRs were used as the reference method in assessing the validity of the e-FFQ because there are less correlated errors between FRs and FFQs (1). Instead of using a traditional approach (food diary or telephone calls) to collect FR, the FRs were collected electronically via email and/or WhatsApp text messages similar to the method used in a study conducted among adolescents (14).

Participants underwent one-on-one training via a WhatsApp interview using the pictorial instructions and a checkered fiducial marker that were sent to them before the training. During the FR collection, they were asked to record their food intake for the day by using personal mobile phones to take digital photographs of meals with a fiducial marker placed by the food item or meal setting, and to describe the foods and amounts eaten in a text message. The fiducial marker was used to help visually estimate portion sizes of foods in the meal setting. The digital photographs and corresponding text description of intake were sent via WhatsApp messaging or emails to the study’s contact phone number or email address listed on the fiducial marker. A trained dietitian monitored the FR data collection and contacted participants whose reports were incomplete, unclear, have missing information, or to ask clarification about food items reported to have been eaten, before the digital photographs and text information were transferred and collated in PowerPoint files.

The FRs collated in PowerPoint files, thus, consisted of: (1) information on the time of intake, type of meal (breakfast, lunch, dinner, or snacks), and the food items eaten with their corresponding amounts; and (2) digital photographs of meal settings before intake with the fiducial marker placed below the setting, as done in a previous validation study using food records with digital photographs (14).

Data Analysis

Food Group Intake Estimates

In a semi-quantitative FFQ, the number of portions of a food eaten per day is equivalent to the frequency of intake per day because the food portion sizes are fixed. Thus, serving intake per day of individual foods in the FFQ was computed by converting the frequency of intake options to frequency of intake per day. For example, 1–3 times per month = 2/30 = 0.067, 1 per week = 1/7 = 0.14 etc. Food group intake estimates were then calculated by summing the values for all food items belonging to a food group as listed in Table 1. For the reference method, FR, the 4 one-day records were composed of 2 weekday and 2 weekend-day intakes. Thus, the servings of intake of the food groups were calculated by weighting the two weekday intakes as representing 5 days of a 7-day week and each weekend day intake as representing a day of a 7-day week. The formula below was used to calculate this weighted mean.

Weighted mean = [5/2 (WD1 + WD2) + (WE1 + WE2)]/7

where WD = weekday intake; WE = weekend day intake

Reproducibility Analysis

The e-FFQ was self-administered twice to 91 participants. Descriptive statistics, such as mean and standard deviation, and tests for normality were calculated for both administrations of the FFQ using SPSS version 26. Since the food group intake distributions were near normal, comparison of the estimates in the first (FFQ1) and repeated administration of the e-FFQ (FFQ2) was determined with paired t test. Pearson correlation coefficients were calculated to determine the agreement between the two administrations of the e-FFQ (i.e. FFQ1 and FFQ 2). Cross-classification between FFQ1 and FFQ 2 was assessed by dividing food group estimate intakes into quintiles to determine the proportion of participants that were correctly classified under the same quintiles up to ± 1 adjacent quintiles.

Validation Analysis

Participants who were classified as being extreme outliers in the consumption of food groups (i.e., with highly improbable intakes less than 500 kcals and over 4000kcals per day) were excluded from the validation analysis (n = 11). Food group intake mean estimates from the first administration of the e-FFQ were compared to the weighted mean intake of the food groups as measured using the reference method (FR). The distribution for some food groups were near-normal; some food groups were log transformed to achieve near normal distributions and paired t test was used to compare means. Pearson’s correlations was used to measure the strength of association between the e-FFQ1 estimates and that of the FRs. Correlations were also determined after energy adjustment using the residual method. Residuals were computed in SPSS using regression models with total energy as the independent variable and food group intake estimates as the dependent variable. To correct for within-person variation due to repeated measurements in the reference method FR, two synthetic weeks which consisted of one weekday and one weekend day were created. The food group intake estimates for synthetic week 1 and synthetic week 2 were then adjusted for total energy intake and compared to the e-FFQ1.

Cross-classifications between the food group intakes in the two methods (e-FFQ1 and FRs) to determine the proportion of participants that was correctly categorized into the same quintiles (exact agreement and within ± 1 adjacent quintiles) were done. Bland-Altman plots were created for variables of interest to determine if both methods had good agreement by plotting the difference in food group intake estimates between the e-FFQ and the FR on the y axis against the mean of the two methods on the x axis. These food groups included staples, vegetables, fruits, street foods and sweetened beverages.

Table 2 shows the characteristics of the study participants at baseline. Male participants are about 2 years younger than their female counterparts. Both sex groups have mean BMIs bordering on the obese category, with majority of the participants being classified as overweight or obese (~74%). Most participants (89%) had some university level degree or higher, which implies that most of the participants were highly educated.

Table 1 Food groupings used in the reproducibility analysis assessed with the FFQ.

Food categories	Foods Items	n
Street Foods	Aloo pies, bake and shark/fried fish, chow (mango/pineapple), burgers (beef/chicken/vegetarian), corn soup, doubles, dhalpourie roti (beef/chicken/goat/veggie), pholourie, saheena, souse, gyros, (beef/lamb/chicken), homemade ice cream (barbadine/coconut/soursop), pies (beef/chicken/fish/cheese) punches (barbadine/peanut/channa)	14
Staples	Whole wheat bread/rolls/ hops, white bread/ rolls/ hops, sada roti/roast bake, fried bake, coconut bake, biscuit/crix/ dixie, paratha roti, banana bread, currants roll, Christmas cake/ black cake/ fruit cake, sponge cake, coo coo, corn pie, dumplings, sweet bread/coconut bread/drops, hot cereals - oats porridge, cold cereals unsweetened/high fiber - corn flakes, bran flakes; cold cereals sweetened- frosted flakes, Pasta-macaroni/ spaghetti/ noodles, macaroni pie, macaroni salad, chow mein, parboiled rice-plain, fried/vegetable rice, rice & peas/pelau, ochro/bhagi rice, English potato - baked/boiled/creamed/mashed/ potato salad, English potato - fry aloo, French fries, curried, dasheen, eddoes, yam- boiled, sweet potato- baked, boiled, cassava, green figs/banana boiled, green fig/banana salad, ripe plantain boiled	35
Vegetables	Beet root, bhagi/spinach/ dasheen bush, bodi beans/ string beans/saim, cabbage/cole slaw, callaloo, carali- fried, carrots, corn (roasted/boiled/ kernels), melongene/bhagan/eggplant, cucumbers, ochroes, patchoi, pumpkin, sweet peppers, tomatoes, tossed salad, watercress/lettuce	17
Fruits	Apple, banana, grapes, grapefruit, mango, oranges/portugals, paw paw, pineapple, pommecythere, pommerac, pears, prunes, raisins, sapodilla, watermelon	16
Legumes	Baked beans, beans, dried peas, all kinds -stewed, pigeon peas fresh, stewed/curried, channa, curried/fried, split peas/dhal, peanuts (roasted /honey roasted), cashew nuts, other nuts	8
Food from Animals	Egg whole fried/boiled/scrambled, milk – low fat/ skimmed (liquid), milk - full cream (liquid), milk - low fat/skimmed (powdered), milk - full cream (powdered), low fat evaporated milk, evaporated milk, condensed milk, soy/almond milk, yogurt (all types), vegetarian cheese-cheddar, regular cheese cheddar, Fish steam/stewed/ curried, fish, fried, saltfish/buljol- stewed, smoked herring, tuna/sardines in water, tuna/sardines in oil, chicken, curried/stewed, chicken, bbq/baked/grilled, chicken, fried, soup (beef, chicken, cow heel), beef, curried/stewed, beef patty, beef minced, lamb, goat, stewed/curried, pork, stewed/roasted, soya sausages/frankfurters/bologna/salami, chunks, stewed/curried, vege chick/vege fish.	29
Fats & Oils	Butter (all kinds), margarine (all kinds), mayonnaise, coconut oil, soybean oil, canola oil, olive oil, avocado, salad dressing, garlic sauce	7
Water	Water	1
Non-Alcoholic Beverages	Diet soft drinks/soda, regular soft drinks/soda, fruit juices (no added sugar) orange/grapefruit/pineapple/etc	3
Alcoholic Beverages	Beer (carib, shandy, etc.), red wine, white wine, rum/whiskey/ scotch/vodka	4
Other items	Chocolate, dinner mints/cough drops, tamarind balls, sugar cake, benna ball	5

Table 2. Demographic characteristics of participants at baseline.

	Male	Female	All
n (%)	19 (21)	72 (79)	91
Age (years), mean (SD)	36.9 (9.5)	38.7(9.6)	38.4 (9.6)
Height (cm), mean (SD)	177.5(7.8)	165.3(7.1)	167.9 (8.8)
Weight (kg), mean (SD)	91.8 (18.6)	84.8 (24.5)	86.3 (24.52)
Waist circ. (cm), mean (SD) D	93.8(10.6)	93.2 (16.2)	93.3 (15.2)
BMI (kg/m²), mean (SD)	29.5(6.1)	30.5 (7.9)	30.5 (7.9)
BMI Categories, %
Normal	26.3	25	25.3
Overweight	36.8	33.3	34.1
Obese	36.9	41.7	40.7
Race, %
Afro-Trinidadian	84.2	75	76.9
Indo-Trinidadian	5.3	9.7	8.8
Mixed Race	10.5	15.3	14.3
Education level, %
Some University or above	84.3	90.3	89.0
Secondary School	15.7	9.7	11.0
Employment, %
Employed	89.5	84.7	85.7
Unemployed	10.5	15.3	14.3
Exercise frequency, %
≤1 per week	21.1	33.3	30.8
1-2 times per week	31.6	29.3	29.7
≥3 times per week	47.4	37.6	39.6
Chronic Disease (NCD) Status (%)
One or more NCDs	21.1	26.4	25.3
None	78.9	73.6	74.7

Reproducibility of Food Frequency Questionnaire

Reported mean intakes for food groups were higher in e-FFQ1 than e-FFQ2 but not significantly different for fruits, fats and oils, street foods, sweets, sweetened beverages, and alcoholic beverages as displayed in Table 3. Correlations between the reported food groups intakes in the first and repeat administration of the e-FFQ were done to determine the reproducibility of the e-FFQ. Correlations ranged from medium (r=0.44) for sweetened beverages to high (r=0.91) for alcohol and had a mean correlation of 0.64.

Cross-classification into quintiles showed that 48% of participants were correctly classified into the same quintile. On average 82% was classified into the same ± 1 adjacent quintiles while less than 2% was grossly misclassified. Classification with the same ± 1 adjacent quintile ranged from 70% for street foods to 92% for alcohol as shown in Table 3. Gross misclassification across categories between e-FFQ1 and e-FFQ2 ranged from 0%-10%.

Table 3. Comparisons^a and relationships^bbetween food group intake according to e- FFQ1^c and e-FFQ2^c estimates.

Food Groups	Food Intake Estimates, svg/d		p-value^a	r^b	Agreement^d (%) Exact±1 quintile
Food Groups	e-FFQ1^c	e-FFQ2^c
	Means (SD)	Means (SD)
Staples	4.1 (3.1)	3.6 (2.4)	0.018	0.77*	81
Legumes	1.5 (1.6)	1.1 (0.9)	0.008	0.55*	81
Vegetables	3.4 (3.0)	2.8 (2.5)	0.019	0.66*	85
Fruits	3.7 (5.9)	3.2 (4.3)	0.222	0.75*	87
Food from Animals	3.7 (3.4)	3.2 (2.5)	0.037	0.74*	84
Fats and Oils	1.8 (2.1)	1.55 (1.5)	0.150	0.51*	77
Street Foods	0.7 (0.7)	0.7 (0.8)	0.659	0.50*	70
Sweets	0.8 (1.3)	0.7 (1.1)	0.333	0.66*	79
Water	6.5 (2.9)	5.1 (3.3)	<0.0001	0.58*	90
Sweetened Beverages	0.6 (0.7)	0.5 (0.7)	0.091	0.44*	79
Alcoholic Beverages	0.1 (0.5)	0.1 (0.4)	0.100	0.91*	92

^aDetermined with paired t-tests

^b Determined with Pearson’s correlations

^c e-FFQ1 is the first administration, e-FFQ2 is the second administration of the food frequency questionnaire

^dOverall percentage categorized in the same and adjacent quintile

*Correlation is significant at the 0.01 level (2-tailed)

Validation of Food Frequency Questionnaire

The e-FFQ was validated against multiple one-day FRs, the reference method used to assess the accuracy of the e-FFQ in estimating food groups intake. Table 4 shows a comparison of the mean intakes between the e-FFQ and FR. Mean food group intake estimates were higher for the e-FFQ for all food groups except sweetened beverages.

Table 4. Comparisons^a and relationships^dbetween food group intake according to e-FFQ1^b and Food Records^c.

Food Groups	Food Intake Estimates (svg/d) Means±SD^a		Correlations^d			Agreement (%)
Food Groups	*e-FFQ1 ^b*	*FRs ^c*	*Crude*	*En-adj*	*Deatt*.	*(Same ± 1 quintile)*
Staples	4.1 (3.1)	3.9 (1.7)	0.22	0.32*	0.25	63
Vegetables	3.4 (3.0)	1.4 (1.4)	0.47	0.43**	0.61	68
Legumes	1.5 (1.6)	0.9 (1.0)	0.32	0.31*	0.49	70
Fruits	3.7 (5.9)	2.1 (2.1)	0.46	0.42**	0.53	68
Food from Animals	3.7 (3.4)	3.3 (1.3)	0.65	0.66**	0.81	64
Fats and oils	1.8 (2.1)	0.2 (0.3)	0.17	0.10	0.18	57
Street Foods	0.7 (0.7)	0.5 (0.7)	0.27	0.25	0.34	55
Sweets	0.8 (1.3)	0.2 (0.4)	0.06	0.29	0.09	70
Water	6.5 (2.9)	3.3 (3.0)	0.08	0.24*	0.08	95
Sweetened Beverages	0.6 (0.7)	1.6 (1.4)	0.23	0.35*	0.30	56
Alcoholic Beverages	0.1 (0.5)	0.1 (0.2)	0.35	0.26	0.25	85

^aDetermined with paired t-tests

^d Determined with Pearson correlations

^b e-FFQ1 is the first administration,

^c FR is the weighted average of the 4 one day food records

*Correlation is significant at <0.05 level (2-tailed).

**Correlation is significant at <0.01 level (2-tailed)

The mean of the validity correlations between the e-FFQ and FR was r=0.34. Food group intake estimates were low (r=0.06) for sweets to moderately high (r=0.65) for food from animals as displayed in Table 4. Energy adjustment showed very little improvement in correlations for some food groups such as staples, food from animals, sweetened beverages, and alcohol. Deattenuation of the correlation coefficients to correct for within-person variations in intake due to multiple 1-day food records increased correlations for all food groups except water, sweetened beverages, alcohol, and sweets. Deattenuated correlations ranged from 0.09 for sweets to 0.81 for food from animals but were not significant.

Cross-classifications showed that the mean of the correctly ranked food group intakes was 32%. Cross-classification for the same ±1 adjacent quintile between the FFQ and FR ranged from 55% for street foods to 95% for water after adjusting for total energy intake as shown in Table 4. However, an average of 5.3% was grossly misclassified.

Figure 2 shows the Bland-Altman plots for staples, vegetables, fruits, street foods and sweetened beverages. Most of the points were inside of the 95% confidence interval (95% CI) limits with few observations outside the 95% CI limits.

Trinidad and Tobago has the highest prevalence of morbidity and mortality for NCDs in the Caribbean (12). Studies conducted on this population often rely on anthropometric data to assess risk factors for morbidity and mortality in the adult population (17–20). Other research studies rely on FFQ derived food and nutrient intake estimates that have not been validated or are not culturally specific for this unique population (21–23).

In 2011, a study was conducted to determine dietary intakes among adults in Trinidad and Tobago and to develop a culture specific quantitative FFQ (24). However, that FFQ was not validated and to date there is no validated FFQ specific to Trinidad and Tobago. Therefore, the validation of this culture-specific semi-quantitative FFQ will fill this research gap and can be used to establish the dietary intake pattern of Trinidad and Tobago. The e-FFQ can then be used in future studies to find associations between diet and disease, as well as to emphasize the role of diet in lifestyle interventions and prevention and reduction of NCDs.

We evaluated the reproducibility and performance of this culture-specific e-FFQ’s ability to estimate food group intake against multiple one-day food records with digital photographs using personal mobile smart phones. The e-FFQs were self-administered three months apart to determine the reproducibility of the FFQ. A comparison of means showed that staples, vegetables, legumes, food from animals and water were significantly different between the two administrations of the e-FFQ. The e-FFQ showed a reasonably high reproducibility with a mean correlation of r = 0.71. This correlation was similar to other studies with correlations between 0.5–0.7 or higher than reported by other studies (25–28). It should be noted that these studies had varying time periods for the test- retest of the FFQ ranging from 4 months to 1 year. There was also good agreement (82%) between both FFQs when participants were ranked into quintiles having the same ± 1 adjacent quintiles.

For the validation of the FFQ, the first administration of the e-FFQ was compared to the FRs as researchers have found that when the diet recall or food record is collected first, it has a training effect on the subjects and increases the recall ability, which in turn can artificially increase validity. This can be overcome by administering the FFQ first or administering the food record at the same time the FFQ is administered. Researchers have also found that the dietary habits of the subjects tend to change during the study period, which can decrease validity (1). The 4 one-day FRs were used as the reference method as it is considered a gold standard method in validation studies because FRs have less correlated errors with FFQs (29). To prevent participants from deviating from their habitual food intake, we instructed participants not to change their eating patterns when recording their food intake as the purpose of the study was to help establish dietary habits specific to Trinidad and Tobago.

The results of this study indicate that the FFQ is a reasonably good tool to assess dietary intake in this population. For this study, foods were grouped based on the six food groups, however it should be noted that foods were grouped differently in other validation studies and as a result, correlations would vary based on how food items were grouped. A study conducted in Jamaica with similar food groups also had similar correlations for the respective food groups, as their study had a correlation of r = 0.45 for dark green leafy vegetables and other vegetables (27) and this study had a correlation of r = 0.46 for vegetables. Correlations for peas and beans were also similar r = 0.39 and 0.31 respectively. Fruits and staples/cereals also had similar correlations. Correlations from other culture specific validation studies for food group intakes had similar or higher correlations between r = 0.2 and r = 0.6 (26, 28, 30, 31).

One of main strength of this study was the inclusion of local food items and street foods that are unique to Trinidad and Tobago to assess the dietary intake of the population. One trained registered dietitian collected food records from participants, provided training as needed and answered questions about the study. This helped to ensure protocols and standards were followed in collecting food records and estimating the portion sizes and different food items on each food record.

Another strength of the study was the electronic method for distribution of the FFQ which was used because of the high literacy level, and wide access to internet and electronic devices among this population (32). Other studies have also used similar approaches for validation studies with great success as it reduces cost and improves response time. It also prevents participants from submitting incomplete data as the e-FFQ allows researchers to control the quality of responses by requiring respondents to complete all items first before being allowed to press the submit button (14, 33). Another strength of the study was the use of fiducial markers as a size reference, to verify participant identity, and to authenticate the photograph of the foods and beverages taken by the subject (34).

As mentioned before, Trinidad and Tobago has a multi-ethnic and multi-cultural population, which observe several religious and cultural practices. These religious and cultural practices can influence the food intake patterns of the population. During the study period and collection of food records, some participants indicated they were observing the Lenten season and were abstaining from meat and/or alcohol or limiting the number of meals consumed for the day. The holy month of Ramadan also occurred during the study period and participants who were Muslims abstained from food or drink from sunrise to sunset for the month, thus FR for three persons were collected after the month of Ramadan. Other religious and cultural seasons or events such as Carnival, Divali and Christmas also influence the dietary habits for the population. Seasonal variations may also be caught by asking participants to report their intake during the past year and then spreading out the administration of FRs over a longer period, which we were not able to do due to time constraints.

Seasonality of foods particularly fruits can alter food intake of this population. Traditionally during mango season in Trinidad and Tobago, persons tend to ‘turn down their pots’ and instead replace 1–2 of their meals with several mangoes. Mangoes are also prepared in a variety of ways such as preserved with sugar, spices or salt and pepper (chow) and used a snack between meals. Thus, there would be an increase in fruit consumption and a decrease in other foods consumed as they are replaced by mangoes. This practice was also observed during data collection of food records from participants.

Although 91 participants completed both administrations of the FFQ and the FR, eleven were excluded due to being extreme outliers. These outliers are an obvious improbable intake that would bias the estimates of the FFQ relative to the reference method. A priori power analysis showed that 84 participants were needed for the study to detect a difference, thus exclusion of participants for the validation analysis which would lead to stronger correlations resulted in a smaller sample size of eighty (80) participants, reducing the power to establish statistical significance.

The study shows that this culture specific e-FFQ is highly reproducible to assess food group intake estimates. The e-FFQ is also a reasonably valid tool to assess and rank food group intake estimates of adults living in Trinidad and Tobago. Thus, it can be used to examine associations between dietary intakes and NCDs.

BBQ: Bar B Que

BMI: Body mass index

FFQ: Food frequency questionnaire

e-FFQ: electronic Food frequency questionnaire

FR: Food Record

ID: Identification

SD: Standard deviation

TTADA: Trinidad and Tobago Dietary Assessment

NCD: Noncommunicable diseases

SW BEV: Sweetened beverage

VEGE: Vegetarian

WD: Weekday

WE: Weekend

WK: Week

Acknowledgements

We would like to acknowledge Ms. Dawn Foster for her critical editing of English grammar and syntax of the manuscript.

Funding

This study was unfunded

Authors' information

School of Public Health, Loma Linda University, 24591 No. Circle Drive, Loma Linda, CA 92350

Lesley Ann Foster-Nicholas

School of Public Health, Loma Linda University, 24591 No. Circle Drive, Loma Linda, CA 92350

David Shavlik

School of Public Health, Loma Linda University, 24591 No. Circle Drive, Loma Linda, CA 92350

Celine Heskey

School of Public Health, Loma Linda University, 24591 No. Circle Drive, Loma Linda, CA 92350

Gina Siapco

Department of Agricultural Economics and Extension, Faculty of Food and Agriculture, University of the West Indies, St. Augustine, Trinidad & Tobago

Patricia Dyett

Authors' contributions

LAFN: conceptualized and designed the study, collected the data, analyzed, and interpreted the data, drafted the manuscript, and gave final approval of the version to be published. GS: designed the study analyzed and interpreted the data, provided contributions, and critically revised the manuscript, and gave final approval of the version to be published. DS: analyzed and interpreted the data, provided contributions, and critically revised the manuscript, and gave final approval of the version to be published. CH: provided contributions and critically revised the manuscript and gave final approval of the version to be published. PD: provided contributions and critically revised the manuscript, and gave final approval of the version to be published

Corresponding author

Correspondence to [email protected]

Ethics Declaration

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Loma Linda University. Informed consent was obtained from all study participants before they provided any personal or dietary data. All participants who met the inclusion criteria had equal opportunity to participate in the study.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Reproducibility and validity of a culture specific food frequency questionnaire for assessing dietary intake in the adult population of Trinidad and Tobago

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Abstract

Background

Methods

Results

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Background

Method

Study Design

Study Participants

Food Frequency Questionnaire Design

Multiple One-Day Food Records with Digital photographs

Data Analysis

Food Group Intake Estimates

Reproducibility Analysis

Validation Analysis

Results

Discussion

Conclusion

Abbreviations

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References

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