|Year : 2018 | Volume
| Issue : 3 | Page : 86-91
Development and validation of a food frequency questionnaire for pregnant women of Tamil Nadu, India
Rajarajeswari Kuppuswamy1, Vidhya Venugopal1, Aruna Subramaniam2
1 Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra Medical College and Research Institute (Deemed to be University), Chennai, Tamil Nadu, India
2 Faculty of Nursing, Sri Ramachandra Medical Centre and Research Institute (Deemed to be University), Chennai, Tamil Nadu, India
|Date of Web Publication||20-Jun-2018|
Sri Ramachandra Medical College and Research Institute (Deemed to be University), Chennai, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Food and diet pattern vary across different regions and different communities and so a food frequency questionnaire (FFQ) has to be developed for a specific population. Objective: The objective of the study was to develop and to validate a FFQ for pregnant women of Tamil Nadu. Methods: Twenty four hour recalls (HRs) were collected from 200 pregnant women of select districts of Tamil Nadu. A detailed food list was prepared with the data from 24 h diet recalls and after a supplemental focus group discussion. A comprehensive FFQ was developed and it was piloted on 20 women to check its feasibility. The FFQ had 114 items which were divided into seven groups. FFQ validation was conducted on 109 pregnant women using two 24 HRs as the standard measure. Results: FFQ that was developed had the most commonly consumed food items of the region. The intake of nutrients calculated from FFQ when compared with 24 HRs was higher. A significant correlation was found for intake of nutrients. Pearson correlation coefficient between the two methods ranged from 0.3 to 0.9. Conclusion: The FFQ thus developed and validated is a useful tool in estimating the nutrient intake of pregnant women and its results are broadly comparable with other studies conducted in South India which indicates that it can be used for the future studies of nutritional assessments of pregnant women.
Keywords: 24-h dietary recalls, food frequency questionnaire, pregnant women, validation
|How to cite this article:|
Kuppuswamy R, Venugopal V, Subramaniam A. Development and validation of a food frequency questionnaire for pregnant women of Tamil Nadu, India. Int J Nutr Pharmacol Neurol Dis 2018;8:86-91
|How to cite this URL:|
Kuppuswamy R, Venugopal V, Subramaniam A. Development and validation of a food frequency questionnaire for pregnant women of Tamil Nadu, India. Int J Nutr Pharmacol Neurol Dis [serial online] 2018 [cited 2019 Sep 21];8:86-91. Available from: http://www.ijnpnd.com/text.asp?2018/8/3/86/234814
| Introduction|| |
The importance of nutrition during pregnancy with respect to the pregnancy outcomes such as intrauterine growth retardation, Low birth weight, preterm delivery, is well-known and extensively documented. This has been further emphasized by the recent changes in quality and availability of food, lifestyle, and food habits among the rural community as well. The World Health Organization collaborative study concluded that prepregnancy weight predicted the risk of low birth weight (LBW) with an odds ratio of >2 and about one half of all LBW in industrialized countries are born preterm., There is considerable uncertainty about maternal nutrition during pregnancy in industrialized countries, where profound malnutrition is uncommon, and it is found that placental and birth weights were unrelated to the intake of any macro nutrients. Maternal nutritional factors, both before and during pregnancy, account for >50% of cases of LBW in many developing countries. Young  have suggested that nutritional factors may account for 60% of the observed variation in birth weight.
The consequences of the poor nutritional status of women during pregnancy not only affect maternal health but may also have a negative impact on birth weight and early development of the child. Although several maternal nutritional variables such as maternal height, Pre- pregnancy weight, Pre- Pregnancy BMI, Mid upper arm circumference serve as an indicator of maternal nutritional status, assessment of actual nutrient intake of the women along with anthropometric assessment during pregnancy would be the ideal way to determine the nutrient status.
Various methods of dietary intake assessment in population are available such as single or multiple 24 h recalls (HRs), weighed diet records, self-reports of diet history, and food frequency questionnaires (FFQ) FFQs are the commonly used nutritional assessment tool in many epidemiological studies ,,,,,,,,,, and are generally used to ascertain an individuals' standard eating patterns and the total intake of a nutrient can be calculated from it.
As the foods and dietary patterns vary across different communities and geographical locations, neither the FFQs nor the inferences made out of such studies can be extrapolated for any study population and so an FFQ was developed and validated. In Tamil Nadu, there are studies which have assessed the nutritional status of pregnant women using FFQ; however, published FFQ is unavailable. Even if FFQ is available for the same population, it has to be developed and validated because even subtle changes in the design of FFQs can affect their performance and so each instrument should ideally be evaluated separately (Willet 1987) and it has to be validated in the relevant population, especially when it is to be used in a large longitudinal epidemiological study.
Validation of an FFQ is rare in field research, and there is very little opportunity to develop and validate a FFQ for pregnant women which were attempted in a large epidemiological study, Tamil Nadu Air pollution and Health Effects study (TAPHE) that provided the platform for the same. The present study was conducted during the recruitment of pregnant women for the TAPHE study, and a subset of the participants was randomly selected for the present study to develop and validate an FFQ for pregnant women. The aim of the study was to develop an FFQ for the pregnant women and validate it against a series of two 24-h diet recalls.
| Methods|| |
TAPHE study participants (pregnant women) were recruited from Primary Health Centers of Thiruvallur and Kancheepuram districts and from Urban Health Posts (UHP) of Chennai Corporation. The participants who were recruited for this study was a subset of 1200 TAPHE study participants who were from 110 villages and 10 urban zones of Chennai district (during the study period, there were 10 zones of Chennai corporation) of Tamil Nadu. To prepare the food list, 100 participants each from rural and urban area were randomly selected, and 24 HR were collected. For the focus group discussion which was used to supplement the food list, 20 pregnant women from both rural and urban were selected. Pilot testing of the developed FFQ was done on 10 pregnant women each from rural and urban areas, and feasibility of the instrument was checked. When TAPHE study participant visited the health facility for their antenatal visit, after obtaining informed consent, they were interviewed for the present study. If the pregnant women had been diagnosed to have diabetes or have conceived artificially, they were excluded from the study.
Process of development of food frequency questionnaire
A detailed food list was prepared using the 24 HR collected from 200 pregnant women. It had more than 150 items, and during the focus group discussion, more analysis was done on the commonly consumed foods in both rural and urban areas. The dietary consumption thus reported was the basis for preparation of the food list. Once the list was formed, an FFQ was developed including all the commonly consumed foods. Portion sizes were reported by the participants using the standard household measures which include glass, ladle, bowl, teaspoon, and tablespoon.
Focus group discussion
Focus group discussion was conducted for the development of FFQ. A group of 10 rural and urban pregnant women was formed separately and made to discuss the common foods they consume during pregnancy and the foods they commonly avoid. The discussion was observed, and the salient points noted to complete the list of food item in the FFQ by deleting the rarely consumed items and adding most commonly consumed foods.
Food frequency questionnaire
From the food list, the food items were compiled under the following groups: (1) Cereals, (2) Lentils/Dhal, (3) Nonvegetarian, (4) Fats/oils/sugars, (5) Milk, milk products, and beverages, (6) Fruits, and (7) Vegetables. Foods can also be broadly classified into vegetarian and nonvegetarian items; however, classification of food items into seven groups was easier for sorting of data and analysis as vegetarian food items were predominant. The nonvegetarian food includes fish, meat (red and white), and eggs. There was not much difference between the rural and urban diet as both the study groups were selected from the government-run health facilities and their socioeconomic status was almost the same. Hence, a common FFQ was developed for both rural and urban pregnant women. The developed FFQ had 114 items and was designed to be an interviewer-administered questionnaire. The average portion size, per day, per week, or per month frequency of intake of each food item was documented.
24-h dietary recalls
The reference method used for FFQ was two 24 HR which were collected one in a weekday and one in weekend to capture the variability in food intake throughout the week for a pregnant woman. The food intake from the previous day morning for the succeeding period of 24 h was collected by prompting them to recollect what they ate during early morning and then for breakfast, in mid-morning, for lunch, evening snacks, dinner, and late night including a toffee or a small snack. The first 24 HR was collected at the time of first interview along with FFQ administration. The second 24 HR was collected from the primary health centers when they come for follow-up visit or through the telephone. The contact numbers were collected from the participants during the first interview, and so it was easy to get the recalls on the specified day. Mean daily intake was estimated from the two 24 HR.
Analysis of the food intake data
The daily intake of foods obtained from the FFQ was analyzed using customized software which was developed specifically for the project, and when the food item is entered, it calculates the nutritive value of the foods and generates the report participant wise and also an overall report. Nutritive values in the software are based on the Indian food composition tables developed by National Institute of Nutrition, Hyderabad, and the food Atlas More Details developed by Madras diabetes Research Foundation. The software has a nutrient composition of 174 south Indian food items.
The data were analyzed using SPSS version 19. Mean and standard deviation of energy, carbohydrate, protein, and fat were calculated from both FFQ and 24 HR. The contribution of major food groups was also calculated. Pearson correlation coefficient and the Bland–Altman plot were used to assess the agreement between the two methods of dietary assessment.
| Results|| |
Of the 120 women who were approached for participating in the validation study, 109 (90.8%) completed the FFQ and two 24 HR. A total of 11 women completed the FFQ and a 24 HR but could not be contacted for the second diet recall. The general characteristics of the participants who participated in the study are given in [Table 1]. The mean age of the women was 25.61 ± 4 years. The mean hemoglobin concentration of the pregnant women was 10.75 ± 0.94 which shows they are in mild anemic state. [Table 2] gives the social characteristics of the pregnant women. Nearly half of the participants (49.5%) had higher secondary education. Majority of the women (85.3%) were not gainfully employed.
The mean daily macronutrient intake of the participants as observed from FFQ and 24 HR are given in [Table 3]. The FFQ reported higher intakes as compared to the 24 HR. Pearson's correlation coefficients for the macronutrients calculated by the FFQ and 24 HR are also shown in [Table 3]. Correlations between the FFQ and 24 HR, which is the reference method, were significant for all nutrients. For all the macronutrients, the correlations were positive.
|Table 3: Mean daily macronutrient intakes based on food frequency questionnaire and 24-h recall (n=109)|
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The contribution of each food group in the developed FFQ to the total energy intake is represented in [Table 4]. Fruits and cereals contribute to maximum energy through Carbohydrate. Nonvegetarian foods such as fish, meat, and eggs contribute higher energy through protein and fat and fat-rich snack foods contribute more to energy through fat apart from nonvegetarian foods.
|Table 4: Contribution of each food group to total energy intake as assessed by food frequency questionnaire|
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[Figure 1]a,[Figure 1]b,[Figure 1]c,[Figure 1]d displays the findings of the Bland–Altman analysis for all the macronutrients. In these Bland–Altman plots, mean intake from both the dietary method was plotted in X-axis, and the difference in intakes of the participants was plotted in Y-axis. The plots for energy, protein, and fat were similar. The correlation between carbohydrate and fat were significant at the 0.05 level.
|Figure 1: (a-d) The Bland–Altman plots showing limit of agreement between the methods|
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| Discussion|| |
This FFQ was developed and validated for use in a study which aims to examine the relationship between maternal nutrition and environmental risk factors and birth weight of the newborn. The results of this development and validation study suggest that the newly developed FFQ is valid for its intended purpose. This FFQ that was developed had almost all the food items that are usually consumed by the pregnant women of lower economic class and lower middle economic class women. Pregnant women who attended the antenatal clinic of the government health facility were approached to participate in this study irrespective of their gestational age and had almost equal representation from each trimester. Mouratidou et al. chose pregnant women between 14 and 18 weeks of gestation for their validation study as they stated that the women were less likely to be affected by nausea and vomiting, which is known to occur in the first trimester.
We used two 24 h dietary recalls as reference to FFQ. Average of the two 24 HR were taken to correlate with the FFQ derived nutrient values for validation. The three-consecutive-day weighing method is the most popular dietary assessment method among Japanese dietitians because this dietary survey method has been adopted in the national nutrition survey for half a century.
As in most other studies,,,, the present FFQ overestimated dietary intake. The mean value obtained in this study is comparable with that of Mouratidou et al. study done on Caucasian pregnant women in the UK. In the same study, they had used the Bland–Altman analysis to assess agreement between the FFQ and the 24 HR and to obtain further information that the correlation coefficient itself cannot provide. Five-day estimated food records were used to validate the FFQ in a study on pregnant women by Erkkola et al. 2001 and in this study too, it was found that the intake was higher as determined by FFQ than that assessed by food records.
In a study to validate an FFQ for pregnant women by Baer et al. 2005, two sets of 24 HRs were used to validate the FFQ which was administered three times to pregnant women, and the correlation ranged from 0.09–0.67 for Phase I to 0.27–0.63 for Phase II of the study. In this present study, the correlation ranged from 0.35 to 0.93. The relatively good correlation between the two methods may be due to reporting of higher intake in the weekend 24 HRs. When the average of two 24 HRs (1 weekday and one-weekend recall) were calculated, it was found that the daily intake was more or less similar to the daily intake as computed from an FFQ.
Since the food list which was prepared from the rural and urban recalls were almost similar, a common FFQ was developed and used for both rural and urban pregnant women, and it almost had similar outputs. In a study, they had different FFQs for rural and urban population with varying food items as opposed to the present study.
Validation studies vary in the social characteristics, sample sizes, questionnaire design, and the study methods.,,,,,,,,, Hence, a comparison of results of the various validation studies is difficult and requires only broader comparisons.
Limitations of the study
In this study, FFQ was chosen as a tool of measuring the nutrient intake of pregnant women as it would do it retrospectively even though it is not the gold standard method of assessment. Pregnant women were told to recall the diet intake of the preceding 3 months as opposed to 24 HR which requires the recall of the diet intake during the past 24 h. Dietary intake recall affects the quality of the FFQ data as pregnant women tend to report their habitual intake when asked to report their frequency of intake during the past 3 months of pregnancy. To reflect the changes in the food availability and consumption, FFQ needs to be modified periodically. In this study, only macronutrient intake was assessed whereas in many other studies which were done to validate an FFQ used almost all or important micronutrient as well.
| Conclusion|| |
The present study supplements much other research conducted on the dietary assessment of pregnant women using an FFQ and its validation in Tamil Nadu. On the basis of our results, the FFQ that had been developed can be used for the assessment of nutritional status of pregnant women of any trimester. Further research is needed to evaluate the validity of FFQ against other methods of dietary assessment such as weighed food records.
The investigators would like to thank the Indian Council of Medical Research (ICMR) for the fellowship rendered to the author (RK), Dr. Kalpana Balakrishnan, Director, SRU- ICMR Centre for advanced research on Air quality, climate and health who lead the study, Dr. S. Sankar, faculty and research staff of the Department of Environmental Health Engineering of Sri Ramachandra Medical college and Research Institute for their support. Authors are thankful to Mrs. G. Kaleeswari for the assistance provided for data collection. Authors also thank the pregnant women who participated in the study.
Financial support and sponsorship
RK was supported through funds provided by the Indian Council of Medical Research (ICMR) for the SRU-ICMR Center for Advanced Research on Air Quality, Climate and Health (SRU-CAR).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Duggan C, Watkins JB, Koletzko B, Allan Walker W. Nutrition in Pediatrics: Maternal Nutrition and Pregnancy Outcome. USA: PMPH USA; 2016.
Kelly A, Kevany J, Onis MD, Shah PM. A WHO collaborative study of maternal anthropometry and pregnancy outcomes. Int J Gynaecol Obstet 1996;53:219-33.
Ramakrishnan U. Nutrition and low birth weight: From research to practice. Am J Clin Nutr 2004;79:17-21.
Mathews F, Yudkin P, Neil A. Influence of maternal nutrition on outcome of pregnancy: Prospective cohort study. BMJ 1999;319:339-43.
Kramer M. Maternal nutrition and adverse pregnancy outcomes: Lessons from epidemiology. In: The Impact of Maternal Nutrition on the Offspring. Vol. 55. Karger Publishers; 2004. p. 1-15.
Young M. The accumulation of protein by the fetus. Fetal Physiol Med 1976:59-79.
Godfrey K, Robinson S, Barker DJ, Osmond C, Cox V. Maternal nutrition in early and late pregnancy in relation to placental and fetal growth. BMJ 1996;312:410-4.
Muthayya S. Maternal nutrition & low birth weight – What is really important? Indian J Med Res 2009;130:600-8.
Bharathi AV, Kurpad AV, Thomas T, Yusuf S, Saraswathi G, Vaz M, et al
. Development of food frequency questionnaires and a nutrient database for the prospective urban and rural epidemiological (PURE) pilot study in South India: Methodological issues. Asia Pac J Clin Nutr 2008;17:178-85.
Erkkola M, Karppinen M, Javanainen J, Räsänen L, Knip M, Virtanen SM, et al
. Validity and reproducibility of a food frequency questionnaire for pregnant Finnish women. Am J Epidemiol 2001;154:466-76.
Vian I, Zielinsky P, Zilio AM, Mello A, Lazzeri B, Oliveira A, et al
. Development and validation of a food frequency questionnaire for consumption of polyphenol rich foods in pregnant women. Matern Child Nutr 2015;11:511-24.
Mouratidou T, Ford F, Fraser RB. Validation of a food frequency questionnaire for use in pregnancy. Public Health Nutr 2006;9:515-22.
Pinto E, Severo M, Correia S, dos Santos Silva I, Lopes C, Barros H, et al
. Validity and reproducibility of a semi quantitative food frequency questionnaire for use among portuguese pregnant women. Matern Child Nutr 2010;6:105-19.
Vioque J, Navarrete Muñoz EM, Gimenez Monzó D, García de la Hera M, Granado F, Young IS, et al
. Reproducibility and validity of a food frequency questionnaire among pregnant women in a Mediterranean area. Nutr J 2013;12:26.
Baer HJ, Blum RE, Rockett HR, Leppert J, Gardner JD, Suitor CW, et al
. Use of a food frequency questionnaire in American Indian and Caucasian pregnant women: A validation study. BMC Public Health 2005;5:135.
Cheng Y, Yan H, Dibley MJ, Shen Y, Li Q, Zeng L, et al
. Validity and reproducibility of a semi quantitative food frequency questionnaire for use among pregnant women in rural China. Asia Pac J Clin Nutr 2008;17:166-77.
Loy SL, Marhazlina M, Azwany Y, Jan JM. Development, validity and reproducibility of a food frequency questionnaire in pregnancy for the Universiti Sains Malaysia birth cohort study. Malays J Nutr 2011;17:1-18.
Wei EK, Gardner J, Field AE, Rosner BA, Colditz GA, Suitor CW, et al
. Validity of a food frequency questionnaire in assessing nutrient intakes of low income pregnant women. Matern Child Health J 1999;3:241-6.
Rao S, Yajnik CS, Kanade A, Fall CH, Margetts BM, Jackson AA, et al.
Intake of micronutrient-rich foods in rural Indian mothers is associated with the size of their babies at birth: Pune Maternal Nutrition Study. J Nutr 2001;131:1217-24.
Cade J, Thompson R, Burley V, Warm D. Development, validation and utilisation of food frequency questionnaires – A review. Public Health Nutr 2002;5:567-87.
Sudha V, Radhika G, Sathya RM, Ganesan A, Mohan V. Reproducibility and validity of an interviewer administered semi quantitative food frequency questionnaire to assess dietary intake of urban adults in Southern India. Int J Food Sci Nutr 2006;57:481-93.
Willett W. Nutritional epidemiology: Issues and challenges. Int J Epidemiol 1987;16:312-87.
Balakrishnan K, Sambandam S, Ramaswamy P, Ghosh S, Venkatesan V, Thangavel G, et al
. Establishing integrated rural urban cohorts to assess air pollution related health effects in pregnant women, children and adults in Southern India: An overview of objectives, design and methods in the Tamil Nadu Air Pollution and Health Effects (TAPHE) study. BMJ Open 2015;5:e008090.
Gopalan C, Rama Sastri BV, Balasubramanian SC. Nutrition Value of Indian Foods. National Institute of Nutrition 1980.
Sudha V, Mohan V, Anjana RM, Kamala K, editors. Dr Mohan's Atlas of Indian Foods. 1st ed. Chennai: Dr Mohan's Health Care Products Pvt Ltd.; 2013.
Sundara Rao PSS, Richard J, Introduction to biostatistics and research methods. PHI Learning Pvt Ltd, 5th edn, 2012.
Date C, Yamaguchi M, Tanaka H. Development of a food frequency questionnaire in Japan. J Epidemiol 1996;6:S131-6.
Rodríguez MM, Méndez H, Torún B, Schroeder D, Stein AD. Validation of a semi quantitative food frequency questionnaire for use among adults in Guatemala. Public Health Nutr 2002;5:691-9.
Gupta PC, Hebert JR. Quantitative food frequency questionnaires – Applicability in India. Natl Med J India 1999;12:138-9.
Sevak L, Mangtani P, McCormack V, Bhakta D, Kassam Khamis T, dos Santos Silva I, et al
. Validation of a food frequency questionnaire to assess macro and micro nutrient intake among South Asians in the United Kingdom. Eur J Nutr 2004;43:160-8.
Brantsaeter AL, Haugen M, Alexander J, Meltzer HM. Validity of a new food frequency questionnaire for pregnant women in the Norwegian mother and child cohort study (MoBa). Matern Child Nutr 2008;4:28-43.
Hebert JR, Gupta PC, Bhonsle RB, Sinor PN, Mehta H, Mehta FS, et al
. Development and testing of a quantitative food frequency questionnaire for use in Gujarat, India. Public Health Nutr 1999;2:39-50.
Hebert JR, Gupta PC, Bhonsle RB, Murti PR, Mehta H, Verghese F, et al
. Development and testing of a quantitative food frequency questionnaire for use in Kerala, India. Public Health Nutr 1998;1:123-30.
Schaefer EJ, Augustin JL, Schaefer MM, Rasmussen H, Ordovas JM, Dallal GE, et al
. Lack of efficacy of a food frequency questionnaire in assessing dietary macronutrient intakes in subjects consuming diets of known composition. Am J Clin Nutr 2000;71:746-51.
Chen Y, Ahsan H, Parvez F, Howe GR. Validity of a food frequency questionnaire for a large prospective cohort study in Bangladesh. Br J Nutr 2004;92:851-9.
Hu FB, Rimm E, Smith Warner SA, Feskanich D, Stampfer MJ, Ascherio A, et al
. Reproducibility and validity of dietary patterns assessed with a food frequency questionnaire. Am J Clin Nutr 1999;69:243-9.
Nanri A, Shimazu T, Ishihara J, Takachi R, Mizoue T, Inoue M, et al
. Reproducibility and validity of dietary patterns assessed by a food frequency questionnaire used in the 5 year follow up survey of the Japan Public Health Center Based Prospective Study. J Epidemiol 2012;22:205-15.
Iqbal R, Ajayan K, Bharathi AV, Zhang X, Islam S, Soman CR, et al
. Refinement and validation of an FFQ developed to estimate macro and micronutrient intakes in a South Indian population. Public Health Nutr 2009;12:12-8.
Tefft ME, Boniface DR. Estimating food and nutrient intake from food frequency questionnaire data by reference to a standard weighed diet survey. J Hum Nutr Diet 2000;13:219-24.
Sachan A, Gupta R, Das V, Agarwal A, Awasthi PK, Bhatia V, et al
. High prevalence of Vitamin D deficiency among pregnant women and their newborns in Northern India. Am J Clin Nutr 2005;81:1060-4.
Leader A, Wong KH, Deitel M. Maternal nutrition in pregnancy. Part I: A review. Can Med Assoc J 1981;125:545-9.
[Table 1], [Table 2], [Table 3], [Table 4]