Over the last few decades, there have been significant dietary and lifestyle changes worldwide. In Morocco, these changes have led to serious nutritional disorders and increased risk of morbidity and mortality particularly among vulnerable groups such as women of childbearing age. We aimed to assess the average daily energy and macronutrient intakes and to investigate their association with socioeconomic factors and weight status among women aged 19–49 years in urban areas. A total of 542 women attending public health centers were recruited. Socioeconomic and demographic data were collected using a questionnaire. Anthropometric measurements were taken using standardized equipment. Food consumption data were obtained through the 24-hour dietary recall method, and the macronutrient composition of foods was estimated based on the Moroccan food composition table and the Nutrilog software. The average daily energy intake among the study population was 1591 kcal, composed of 56% from carbohydrates, 28% from fats, and 16% from protein. Reported energy intake by the majority of women (81.5%) was lower than recommended daily allowances for energy. There was a significant positive correlation between educational level and energy (
Malnutrition is defined as deficiencies, excesses, or imbalances in individuals’ intake of energy and/or nutrients [
Paradoxically, many parts of the world are currently exposed to overweight and obesity epidemic, with over 39% of adults being overweight globally especially due to an energy imbalance that results from increased caloric intake and reduced energy expenditure [
Over the last few decades, the Moroccan population has been facing rapid dietary and lifestyle changes. Changes have led to serious nutritional disorders and increased prevalence of noncommunicable diseases (NCD) in all age groups. For instance, the survey conducted in 2018 reported a high prevalence of hypertension, diabetes, and hypercholesterolemia among Moroccan women aged 18 years and over (29.8%, 12.6%, and 14.0%, respectively) [
As all Moroccan regions, Rabat-Salé-Kenitra region has been experiencing a dramatic increase in obesity prevalence rates among women during the past two decades [
This study is a descriptive cross-sectional survey. It was carried out on healthy women recruited from public health centers located in Rabat-Salé-Kenitra region. It included women aged 19–49 years who were not under any permanent medical treatment. Pregnant and lactating women, women suffering from mental illnesses, and those who participated in the pilot study were excluded from the current study.
The study took place in urban areas of Rabat, Salé, and Skhirat-Témara prefectures known for their high rates of obesity (ENSF, 2003-2004) [
The women were recruited from 21 selected urban health centers based on the following criteria: accessibility of our field team and high participation of women to cover the number required for the age group under study.
The study included 542 Moroccan childbearing age women aged between 19 and 49 years who visited the health centers during the period of data collection. Only women who met the inclusion criteria and provided their consent to participate in the survey were recruited.
To calculate the sample size, we used the formula developed by Cochran (1977) [
The estimated prevalence of obesity in the targeted region was 14.4% with an error margin of 5% [
The anthropometric parameters of each participant were measured according to the WHO standard procedures [ Women with underweight: BMI < 18.5 kg/m2 Women in a normal weight range: 18.5 kg/m2 ≤ BMI ≤ 24.99 kg/m2 Overweight women: 25 kg/m2 ≤ BMI ≤ 29.99 kg/m2 Obese women: BMI ≥ 30 Kg/m2
Waist circumference was measured midway between the lower edge of the last palpable rib and the top of the iliac crest [
The data on socioeconomic and demographic factors were collected at the beginning of the study through direct interviews. We used an adequate questionnaire that was adapted from other ones used nationally for a similar purpose [
The collected information included women’s education level, marital status, occupation, occupation of the household head, number of children, household size, household monthly global expenses, and food expenses.
This questionnaire includes information related to quantity and nature of food eaten (bread, tomato, tea, water, etc.) for breakfast, lunch, and dinner as well as midmorning and midafternoon snacks.
In this study, the multipass approach was used to validate the 24-hour recall questionnaire. The principle of this method consists of 5 steps [ Quick list: to collect a list of foods consumed the previous day. Forgotten foods list: to collect foods that may have been forgotten during the quick list. Questions probe for foods by categories: nonalcoholic beverages, alcoholic beverages, sweets, savory snacks, fruits, vegetables, cheese, bread and rolls, and other foods. Time and occasion: to collect time and name of eating occasion for each food, used to sort foods chronologically and group into eating occasions. Detail and review: to collect a detailed description of each food consumed, including the amount eaten and additions to the food, and also to review eating occasions and times between occasions to elicit forgotten foods. Final review: to collect additional foods not remembered earlier.
Women were asked to provide all information about foods and drinks consumed for two separated days using visual aids to approximate the serving sizes of various foods: a photo manual (SU.VI.MAX) for estimating consumed portions of food and drink [
The quantities consumed were converted using the Food Quantification Table, available in the corresponding book, then entered into the Nutrilog Food Information Software (SAS, version: 2.31, with Moroccan Food Composition Table), which was used to determine nutrients and vitamins content of each food. At the same time, all details on vitamin and mineral supplements consumed during the investigation period were noted and the average macronutrient intake over two days was reported to estimate the exact quantities permitting comparison with recommended dietary intakes.
The data matrix compiled by the Nutrilog software, combined with socioeconomic and demographic data, was analyzed with statistical software for the social sciences (SPSS version 13.0). The normality of the distribution of anthropometric and nutritional variables was assessed using the Kolmogorov-Smirnov (KS) test [
The study protocol was approved by the Ethics Board of the Faculty of Medicine and Pharmacy, Mohammed University in Rabat, Morocco (Ethical Approval number 69 delivered on 31 January 2017). Before data collection, invited participants were informed about the study objectives and methods, and both oral and written consent were obtained from all who were recruited.
The demographic and socioeconomic characteristics of the study population are presented in Table
Demographic and socioeconomic characteristics of the study population.
Variables | % | 95% CI | |
---|---|---|---|
19 to 29 | 240 | 44.2 | 40.4–48.5 |
30 to 40 | 203 | 37.5 | 33.4–41.3 |
41 and more | 99 | 18.3 | 15.1–21.6 |
Arab | 418 | 77.1 | 73.8–80.6 |
Amazigh | 96 | 17.7 | 14.6–20.7 |
Sahraoui | 12 | 2.2 | 1.1–3.5 |
Rifian | 9 | 1.7 | 0.7–2.8 |
Jebli | 7 | 1.3 | 0.2–2.4 |
Illiterate | 93 | 17.2 | 14.0–20.3 |
Primary | 113 | 20.8 | 17.2–24.5 |
Secondary | 208 | 38.4 | 34.3–42.8 |
Higher education | 128 | 23.6 | 19.9–27.3 |
Single | 111 | 20.5 | 17.3–24.2 |
Married | 405 | 74.7 | 70.8–78.2 |
Divorced | 22 | 4.1 | 2.6–5.5 |
Widowed | 4 | 0.7 | 0.2–1.5 |
Without job | 383 | 70.7 | 66.6–74.5 |
Student | 52 | 9.6 | 7.4–12.2 |
With job | 107 | 19.7 | 16.6–23.1 |
Without job | 16 | 3.0 | 1.7–4.4 |
With job | 516 | 95.2 | 93.4–96.9 |
Retired | 10 | 1.8 | 0.7–3.1 |
No child | 133 | 24.5 | 20.8–28.6 |
1 to 2 children | 271 | 50.0 | 45.6–53.7 |
3 or more | 138 | 25.5 | 22.1–29.3 |
Lower than 122 | 4 | 0.7 | 0.2–1.7 |
122 to 195 | 39 | 7.2 | 5.0–9.4 |
196 to 244 | 144 | 26.6 | 22.9–30.4 |
244 to 366 | 119 | 22.0 | 18.5–25.3 |
Higher than 366 | 124 | 22.9 | 19.2–26.4 |
Not known | 112 | 20.7 | 17.3–24 |
Lower than 65.36 | 21 | 3.9 | 2.4–5.5 |
65.47 to 98.03 | 55 | 10.1 | 7.7–12.7 |
98.14 to 130.71 | 56 | 10.3 | 7.8–12.7 |
130.82 to 174.28 | 132 | 24.4 | 21.0–28 |
174.39 to 272.32 | 97 | 17.9 | 14.8–21.4 |
Higher than 272.32 | 69 | 12.7 | 10.1–15.7 |
Not known | 112 | 20.7 | 17.3–24.0 |
<4 people | 107 | 19.7 | 16.6–23.2 |
4 to 7 people | 390 | 72.0 | 68.1–75.6 |
>7 people | 45 | 8.3 | 6.1–10.7 |
The anthropometric parameters of the participants are presented in Table
Age and anthropometric characteristics of the study population.
Mean | Error standard | 95% confidence interval | Minimum-maximum | |
---|---|---|---|---|
Age (years) | 31.96 ± 8.42 | 0.36 | — | 19–49 |
Weight (kg) | 67.84 ± 13.64 | 0.59 | — | 39–128 |
Height (cm) | 1.60 ± 0.06 | 0.01 | — | 1.42–1.78 |
BMI (kg/m2) | 26.35 ± 5.21 | 0.22 | — | 15.73–42.06 |
Underweight group | 29 (5.4%) | 1.0 | 3.7–7.4 | |
Normal group | 204 (37.6%) | 2.1 | 33.4–41.7 | |
Overweight group | 186 (34.3%) | 2.1 | 30.3–38.4 | |
Obesity group | 123 (22.7%) | 1.8 | 19.2–26.4 | |
WC (cm) | 87.60 ± 12.98 | 0.56 | — | 57–128 |
HC (cm) | 101.68 ± 12.98 | 0.47 | — | 43–135 |
WHR | 0.86 ± 0.08 | 0.01 | — | 0.64–1.52 |
Prevalence of overall obesity, overweight (a), and abdominal obesity (b) among the study population. (a) BMI groups. (b) WHR groups.
Table
Average daily energy and macronutrients intakes by age groups.
Variables | 19 to 29 years ( | 30 to 40 years ( | 41 years and more ( | All ( |
---|---|---|---|---|
Energy (kcal/d) | 1648.4 ± 740.7 | 1543.9 ± 587.3 | 1549.8 ± 611.3 | 1591.2 ± 664.4 |
Underconsumption compared to EERß | 184 (76.7) | 175 (86.2) | 83 (83.8) | 442 (81.5) |
Overconsumption relative to EERß | 56 (23.3) | 28 (13.8) | 16 (16.2) | 100 (18.5) |
Protein (g) | 63.8 ± 34.2 | 59.6 ± 31.0 | 59.6 ± 28.7 | 61.5 ± 32.1 |
Animal protein (g)ɣ | 13.0 [3.7–26.0] | 11.1 [2.7–26.2] | 9.3 [2.0–25.9] | 11.7 [3.2–25.9] |
Vegetable protein (g)ɣ | 40.4 [27.9–58.9] | 36.4 [25.3–52.4] | 40.4 [26.4–49.7] | 38.9 [26.5–55.1] |
% of protein in energy intake | 15.7 ± 4.4 | 15.4 ± 4.8 | 15.6 ± 4.3 | 15.6 ± 4.5 |
Carbohydrates (g) | 225.0 ± 99.7 | 211.0 ± 77.0 | 220.0 ± 84.1 | 218.8 ± 89.0 |
% of carbohydrates in energy intake | 55.8 ± 10.6 | 55.7 ± 10.7 | 57.6 ± 10.5 | 56.1 ± 10.6 |
Fat (g) | 54.7 ± 36.9 | 51.3 ± 33.9 | 47.8 ± 31.2 | 52.1 ± 34.8 |
% of fat in energy intake | 28.3 ± 10.0 | 28.9 ± 10.9 | 26.7 ± 10.6 | 28.2 ± 10.4 |
Saturated fatty acids (g)ß | 26.5 [20.1–33.6] | 25.0 [19.60–34.90] | 25.0 [19.4–34.2] | 7.4 ± 6.8 |
% of fatty acids in energy intakeɣ | 6.9 [5.1–9.4] | 6.81 [5.2–9.10] | 6.6 [4.9–8.2] | 7.4 ± 3.8 |
Polyunsaturated fatty acids (g)ɣ | 15.6 [11.3–23.5] | 14.0 [10.8–20.9] | 15.0 [11.6–21.7] | 4.7 ± 4 |
% of polyunsaturated fat in energy intakeɣ | 4.1 [2.9–5.8] | 3.8 [2.9–5.5] | 3.9 [2.6–5.3] | 4.7 ± 2.9 |
Table
Association of energy and macronutrients intakes with weight status and demographic and socioeconomic factors among the study population.
Energy (kcal/d) | Carbohydrates (g) | Protein (g) | Fat (g) | |||||
---|---|---|---|---|---|---|---|---|
Age | 0.069 | 0.106 | −0.055 | 0.200 | −0.058 | 0.177 | −0.065 | 0.133 |
Ethnic background | −0.013 | 0.757 | −0.035 | 0.414 | −0.016 | 0.714 | 0.007 | 0.879 |
Education level | 0.138∗∗ | 0.001 | 0.148∗∗ | 0.001 | 0.125∗ | 0.004 | 0.092∗ | 0.032 |
Marital status | −0.021 | 0.623 | −0.038 | 0.374 | 0.019 | 0.651 | −0.018 | 0.682 |
Women’s occupation | 0.009 | 0.828 | 0.006 | 0.897 | 0.043 | 0.315 | 0.006 | 0.883 |
Size of household | −0.075 | 0.080 | −0.046 | 0.286 | −0.091∗ | 0.034 | −0.079 | 0.066 |
Monthly expenditure | −0.014 | 0.741 | 0.040 | 0.352 | 0.038 | 0.378 | −0.036 | 0.402 |
Monthly expenditure for food | −0.005 | 0.915 | 0.055 | 0.200 | −0.017 | 0.698 | −0.042 | 0.335 |
Both underweight and obese women had a slightly higher average of daily energy and macronutrient intake compared to normal weight and overweight groups, but the difference was not statistically significant (Table
Association of energy and macronutrients intakes with nutritional status.
Variables | Underweight ( | Normal weight ( | Overweight ( | Obese ( | |
---|---|---|---|---|---|
Energy (kcal/d) | 1744.0 [1308.5–2183] | 1497.0 [1155.5–1957.5] | 1501.5 [1174–1924] | 1546.0 [1194–1924] | 0.350 |
Carbohydrates (g) | 54.0 [33.6–70.75] | 46.3 [28.87–65.27] | 44.6 [27.8–66.12] | 48.6 [24.5–69.70] | 0.707 |
The present study aimed to provide information on the nutritional status of childbearing age women living in urban areas of the Rabat-Salé-Kenitra region. To our knowledge, it was the first time to estimate macronutrient and energy intakes and investigate their association with weight status and demographic and socioeconomic factors among this age group at both regional and national level, using three dietary assessment tools (food survey (24 h recall), Nutrilog software, and Moroccan food composition table).
Our results showed that 34.3% and 22.7% of women were overweight and obese, respectively. These prevalence remains high compared to those reported in a previous national survey published in 2005 [
Moreover, as excess body weight is recognized as an important risk factor for many noncommunicable diseases [
Despite all the efforts made at the national, regional, and international levels, malnutrition remains a major health problem in the EMRO region and its health consequences are too important to be neglected [
Similarly, the daily protein intake of our study population (61.5 ± 32.1 g) was lower than that found in similar age groups from South Africa and Kuwait (125.3 g and 67.4 ± 2.3 g, respectively) [
Protein intake was dominated by vegetable proteins that accounted for more than two-thirds of the daily protein intake in our study population. Although it is difficult to compare our results with those found by other studies because of their great variability. Our findings are in agreement with those reported by Steyn et al. who found a low contribution of proteins to energy intake in Kenyan women, particularly for animal protein intake [
Regarding carbohydrates, the estimated daily intake was 218.89 ± 89.05 g and it accounted for an average of 56 % of the mean daily energy intake. This amount exceeded both the recommended daily carbohydrates intake (130 g/d) and its contribution to energy requirements (50–55% from carbohydrates) [
The average fat intake was 52.2 ± 34.8 g and its contribution to the average daily energy intake was 28%. This contribution corresponds to the internationally recommended rate (15–30%) [
In the current study, a significant positive association of education level with energy, carbohydrate, protein, and fat intakes was observed. Our results are in agreement with those reported by Fryar et al. who observed a similar correlation between energy intake and education level among American women of Mexican origin [
Our results revealed a negative correlation between household size and protein intake, which confirm results reported by other authors [
Regarding the association of weight status with energy and macronutrient intakes, we found no statistically significant difference between various BMI groups. In agreement with our results, a previous study has shown an inconsistent association between energy and macronutrient intakes and increased BMI among Thai urban women [
The present study has certain limitations. First, our findings are based on the 24-hour dietary recall method and the associated underreporting of energy and macronutrient intakes is a well-documented problem of such dietary assessment tools. Second, the cross-sectional design of the study makes it impossible to draw causal relationships between different variables. However, this study provides data that can help advance knowledge about the association of dietary variables with weight status and socioeconomic factors and show to researchers and nutrition policymakers the importance of addressing macronutrient and energy intake in nutritional status research, which is very relevant in light of the current global obesity epidemic.
In conclusion, most women did not meet the recommended daily energy intake. The daily macronutrient and energy intakes were positively associated with educational level. In contrast, there was a negative association of household size and age with carbohydrate, protein, and fat intakes. Despite the low average energy intake, more than half (57%) of the study population was either overweight or obese. These results emphasize the need for interventions to improve the dietary intake in nutritionally vulnerable women. Considering the role of overweight and obesity as major risk factors for many chronic diseases [
Noncommunicable diseases
Body mass index
Waist circumference
Hip circumference
Waist-to-hip ratio
High commission for planning
Statistical Package for the Social Sciences
Eastern Mediterranean regional office.
Access to data is restricted in order to respect the rights of third parties and the confidentiality of participants.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this paper.
The authors thank all the women who participated in this study and also thank all the staff of the urban health centers of prefectures.