Dietary patterns, body mass index, physical activity level and energy intake among the female elderly population in Mauritius

Yashwinee Bye Ishnoo; Abdulwahed Fahad Alrefaei; Rishi Ram Doobaree; Nadeem Nazurally; Rajesh Jeewon

doi:10.12688/f1000research.131683.1

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Research Article

Dietary patterns, body mass index, physical activity level and energy intake among the female elderly population in Mauritius

[version 1; peer review: 1 approved with reservations]

Yashwinee Bye Ishnoo¹, Abdulwahed Fahad Alrefaei², Rishi Ram Doobaree³, Nadeem Nazurally⁴, Rajesh Jeewon ^1,2

Yashwinee Bye Ishnoo¹, Abdulwahed Fahad Alrefaei², [...] Rishi Ram Doobaree³, Nadeem Nazurally⁴, Rajesh Jeewon ^1,2

PUBLISHED 30 May 2023

Author details Author details

¹ Department of Health Sciences, Faculty of Medicine and Health Sciences, University of Mauritius, Réduit, Moka, Mauritius
² Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
³ Department of Medicine, Faculty of Medicine and Health Sciences, University of Mauritius, Réduit, Moka, Mauritius
⁴ Department of Agricultural and Food Science, Faculty of Agriculture, University of Mauritius, Réduit, Moka, Mauritius

Yashwinee Bye Ishnoo
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Abdulwahed Fahad Alrefaei
Roles: Funding Acquisition, Investigation, Project Administration, Resources, Validation, Writing – Review & Editing

Rishi Ram Doobaree
Roles: Investigation, Resources, Writing – Review & Editing

Nadeem Nazurally
Roles: Conceptualization, Methodology, Resources, Writing – Review & Editing

Rajesh Jeewon
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Global Public Health gateway.

Abstract

Background: The aging population is becoming a global issue and Mauritius has experienced a 20.4 % growth in the last four years. There is a lack of information regarding nutrition among the Mauritian elderly. This study aimed to investigate dietary patterns and energy intake and their associated factors, to determine the relationship between body mass index (BMI), physical activity level (PAL), nutrition knowledge (NK) and energy intake among the female older adults.
Methods: The study was carried out among 167 female participants aged 60 and above from both urban and rural areas in Mauritius. A questionnaire was used to determine their eating habits and food frequency, PAL, NK, anthropometric measurements and demographic data. Three 24-hour dietary recalls over three weeks were used to calculate the energy intake using the Tanzania food composition table and Nutritionist Pro software. SPSS was used to analyse the data. The main statistical tests used were ANOVA, Independent-samples t-test (ISTT), Pearson correlation and Chi-squared.
Results: A significant relationship was found between age group and energy intake (p = 0.027), with individuals aged 70–89 having higher energy intake. Both BMI and PAL were significantly associated with energy intake (p ˂0.001). There was a moderate significant negative relationship between NK scores and energy intake (r = -0.474, p ˂0.001). Vegetarianism, eating difficulties and residence area did not have a statistically significant relationship with energy intake. There was a negative and significant correlation between consumption frequency of whole-grain carbohydrates and BMI and a significant positive relationship between BMI and consumption frequency of refined carbohydrates.
Conclusions: The mean energy intake of the participants was much higher than the recommended daily allowance of caloric intake in India and other countries. Furthermore, the dietary pattern of the subjects was diversified including both healthy and unhealthy eating habits.

Keywords

body mass index, dietary patterns, energy intake, female elders, nutrition knowledge, physical activity level

Corresponding author: Rajesh Jeewon

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Ishnoo YB et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Ishnoo YB, Alrefaei AF, Doobaree RR et al. Dietary patterns, body mass index, physical activity level and energy intake among the female elderly population in Mauritius [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:568 (https://doi.org/10.12688/f1000research.131683.1) First published: 30 May 2023, 12:568 (https://doi.org/10.12688/f1000research.131683.1) Latest published: 30 May 2023, 12:568 (https://doi.org/10.12688/f1000research.131683.1)

Introduction

Aging refers to a series of unstoppable changes that occur from birth to death.¹ As population economics, healthcare facilities, and social well-being are rising, aging is gradually conquering the world. This, however, poses challenges as well as opportunities.² Mauritius, an island nation, located in the southwest Indian Ocean, has a mixed economic system and is a multi-cultural and multi-ethnic country.³ According to the Worldometers' elaboration of the most recent UN data, Mauritius' population is 1,275,555, as at 20^th April 2022 (https://www.worldometers.info/)⁴ including 95,635 (https://countrymeters.info/) people above 64 years old.⁵ According to a national assessment of non-communicable diseases (NCDs) conducted in Mauritius in 2015, 19.1% of people were obese.³ Healthy aging is defined by the World Health Organization as “the process of developing and maintaining functional ability that enables wellbeing in old age”.⁶ Healthy aging focuses on health-span rather than lifespan, while some definitions refer to aging in the absence of illness. In some regions of the world, individuals aged 85 and above are the fastest growing demographic. However, only a small percentage of people reach this age without getting chronic health conditions. Therefore, it is essential to comprehend the impact of modifiable lifestyle factors like diet in achieving remarkable longevity, as well as the role of these factors in health-span, if any.⁷

Food and energy intake decrease as people get older. Malabsorption, immobility, a loss in the sensations of taste and smell, having trouble chewing, and financial and social challenges are some of the causes behind this.⁸ Furthermore, factors such as gender, household composition, marital status, physical activity, nutrition knowledge (NK), body mass index (BMI), smoking and health issues influence the eating patterns of the elderly. Retirement also degrades general health and raises the risk of disease development.⁹ A study reported that diet-related health remains an issue for the senior citizen including those at malnutrition risk.¹⁰ In general, elderly individuals are less energetic, so the need to reduce energy intake is important due to a drop-in basal metabolic rate. Some people, nevertheless, assume that they do not need to modify their amount of food consumption and eating patterns because they are already eating adequately. Obesity may arise as a result which may further contribute to the emergence of non-communicable diseases.¹¹ According to the WHO, approximately 2.3 billion elder people worldwide are overweight, with over 700 million obese. Obesity is prevalent among the elder people in the United States, with 42.5% of women and 38.1% of men in the 60–79-year age group being obese. Obesity was found in 20% of women and 18% of men aged above 60, according to research from the Netherlands. The prevalence of overweight and obesity among older African Americans are even higher, especially among women.¹²

The relationship between energy intake and expenditure largely determines body fat mass. So, obesity develops when energy intake exceeds calorie expenditure.¹³ It appears that energy intake has increased, as has the consumption of meat, dairy products, fats, and oils. This is justified by a 2004 survey of people aged 60 and above.² Socioeconomic status, availability of food, physical activity level, housing arrangements and nutrition knowledge are all factors that influence energy intake. Social and economic issues have commonly been cited as the cause of unbalanced nutrient consumption. Higher socioeconomic groups tend to consume more nutritious foods, for example, low-fat and whole-grain products than those with a lower socioeconomic status. Research has shown that individuals with higher socioeconomic status have lifestyles that can help with energy balance. However, this alone is insufficient to account for the socioeconomic inequalities in obesity.¹⁴ There is an interrelationship between physical exercise and energy intake because physical activity has the capacity to alter energy expenditure, and when done repeatedly, leads to either weight loss or the requirement to increase energy intake.¹¹ A study showed that there is insufficient data to support the recommendation that physical activity may help to mitigate the decline in appetite and energy intake that happens as people age¹⁵. Nutrition knowledge is a less well-studied factor that can have an equal impact on the dietary habits of elderly people. A study found that participants who consumed less fish and more unhealthy fats, potatoes, fried foods, pasta and beer had a lower education level.¹⁴

Therefore, understanding an older person’s dietary patterns and energy intake may aid in the development of programmes to enhance nutrition and weight management.¹² Nutrition is crucial in preventing NCDs, thus, must be closely monitored.² It is important to calculate energy intake since it determines nutrient requirements and diet nutrient composition. Twenty-four-hour dietary recalls and a food composition table have been utilised by numerous energy intake studies to determine the mean energy intake of individuals of various demographics. In Mauritius, there is a scarcity of data on the daily energy intake of elderly people.¹⁴ Thus, the aim of this study is to conduct a survey to assess the factors influencing the energy intake of the Mauritian female elderly population aged 60 and above.

The main objectives of this study are as follows:

• To investigate the dietary patterns and energy intake of the female elderly population.
• To determine the correlation between BMI, physical activity level (PAL) and energy intake among the female elderly population.
• To determine whether nutrition knowledge affects the amount of energy consumed.
• To investigate the factors affecting the energy dietary patterns of older females.

Methods

Consent and participants of the survey

The study included a total of 167 older females recruited by simple random sampling from different regions of Mauritius. Four age groups were used to categorize the participants: 60 to 69, 70 to 79, 80 to 89 and 90 to 100. Samples from various ethnic groups present in Mauritius were taken into consideration, including Indo-Mauritians and African Mauritians. Prior to the start of the project, ethical approval was granted by the Departmental Research Ethics Committee (Ref number: DHSDREC/01/2022) dated 28^th January 2022 at the University of Mauritius. All participants were fully informed of the project and a written consent was obtained from participants for the use and publication of collected data. Participants were assured that all of their responses would be handled with absolute confidentiality and that the survey was completely anonymous, with no mention of participant names or addresses.

Questionnaire design

The data needed to achieve the stated objectives was collected using a self-designed questionnaire. The survey was conducted in-person since it was expected that respondents could clearly articulate themselves and that many elderly people may have low levels of knowledge. Moreover, simple Creole, the most common language in Mauritius, was used to interview the participants to make sure that the questions were properly understood. Enough time was allowed for each participant to accurately answer each question. The questionnaire, which was composed primarily of closed-ended questions, consisted of five sections: I, II, III, IV and V. The questionnaire has been deposited in Figshare, a public repository.¹⁶

Section I: Eating habits and food frequency

The food frequency section was designed by using questions from the Short Form Food Frequency Questionnaire¹⁷ that was created by the Leeds University Nutritional Epidemiology department. It included approximately 59 items divided into the following categories: carbohydrates (grains and tubers), beans/pulses, meat and eggs, seafoods, soybeans and products, dairy products, vegetables, high-fat and high-sugar snacks, and beverages.¹⁸ Participants could choose from seven frequency categories for each food item and scores as shown in Table 1.

Table 1. Food frequency scores.

Frequency	Score
Never or less than once per month	1
Once per month	2
Two per month	3
Once per week	4
Two to three per week	5
Once per day	6
Two to three per day	7

Section II: Physical activity

Questions from a short version of International Physical Activity Questionnaire (IPAQ) were used to assess physical activity.¹⁹ PAL was calculated in metabolic equivalents (MET), minute per week, and classified as low, moderate, or high intensity. The scores were as follows: low (<600 MET), moderate (600–1500 MET), and high (>1500) PAL. Table 2 shows the criteria for classification of PAL.¹⁹

Table 2. Classification of PAL according to IPAQ.

Low PAL	Moderate PAL	High PAL
<600 MET score	3 days or more of intense activity lasting at least 20 minutes each day.	At least 3 days of vigorous-intensity activity, with a total physical activity of at least 1500 MET-minutes per week.
	5 days or more of moderate-intensity activity and/or at least 30 minutes of walking per day.	7 days or more of any combination of walking, moderate-intensity, or vigorous-intensity activities, totalling at least 3000 MET-minutes per week.
	5 days or more of any combination of walking, moderate-intensity, or vigorous-intensity activities, totalling at least 600 MET-minutes per week.

Section III: Nutrition knowledge assessment

Ten nutrition-related questions from a general nutrition knowledge questionnaire for adults were utilised and modified.²⁰ The original questionnaire's food items were modified to reflect the Mauritian environment. The questions selected were mostly about calorie, sugar, fat and protein content. An overall score of 25 was achievable with higher scores implying participants were highly knowledgeable. A score of 1 was assigned for a right answer and a score of 0 for a wrong answer and "don't know".¹⁴

Section IV: Anthropometric measurements

Physical measurements, including weight and height were obtained using the same tools to reduce the possibility of errors. A measuring tape and ruler was used to measure the height and the participant stood bare foot with the head horizontal against the wall. A portable bathroom scale was used to measure weight. The BMI of the participants was then determined using the formula, weight (in kilograms) divided by height (squared in meters); and were categorised as normal, underweight, overweight or obese according to WHO classification.²¹

Section V: Personal and sociodemographic data

Age, residential area, education level, marital status, ethnic group and type of pension were all included in the sociodemographic data. Vegetarianism and eating difficulties were also assessed.

Section VI: A three-day 24-hour dietary recall

Nutritional guidelines from EPIC-Norfolk were used to create a 24-hour dietary recall form.¹⁴ Food consumption was recorded for two weekdays and one weekend over three weeks. Typically, the 24-hour recall was carried out in the sequence of consumption starting from morning to night. This form requires a list of all meals and drinks consumed the day prior, including snacks. The recall was not conducted if a person was following a special fasting regimen or had previously attended any event where people have a tendency to overeat. On the first day of the recall, the participants were shown a variety of readily accessible household utensils in order to help them accurately report the portion sizes of their meals. The utensils used were teaspoons, tablespoons, cups and glasses. Moreover, the participants were asked to recall eating and drinking incidents by time period in order to avoid concerns with inaccurate or incomplete reporting. Three dietary recalls were used to calculate the subjects' energy intake using a food composition table mostly based on the Tanzania food composition table²² and also with the help of the Nutritionist Pro Version 6.1, a licensed software which is accessible at the University of Mauritius laboratory (An open-access equivalent that performs a similar function is https://www.wikihow.com/). The mean energy intake was then calculated for each participant to be used for statistical analyses.

Statistical analyses

The data of this study was analysed using the software SPSS (Statistical Package for the Social Sciences, version 16.0). Percentages were used to express descriptive tests. ANOVA test, independent sample t-test, Pearson correlation and the Chi-squared test were used to show the relationship between the variables. Statistical significance was defined as P ˂0.05.

Results

Demographic characteristics of the female older adults

Table 3 displays the socio-demographic details of the 167 respondents by age group, area of residence, education level, marital status and ethnic group. Of the 167 respondents, 47.3% were between the ages 60 and 69, 39.5% were between the ages 70 and 79, 12.6% were between the ages 80 and 89 and only 0.6% were between the ages 90 and 100. The majority of the participants were from rural areas, which accounted for 52.7%.

Table 3. Socio-demographics of the female elderly participants.

Characteristics		n	%
Age group	60–69	79	47.3
	70–79	66	39.5
	80–89	21	12.6
	90–100	1	0.6
Area of residence	Urban	79	47.3
	Rural	88	52.7
Education level	None	50	29.9
	Primary	91	54.5
	Secondary	22	13.2
	Tertiary	4	2.4
Marital status	Single	14	8.4
	Married	66	39.5
	Divorced	12	7.2
	Widower	75	44.9
Ethnic group	Indo-Mauritian	103	61.7
	African-Mauritian	54	32.3
	Others	10	6.0

Factors influencing energy intake of female elderly adults

The mean energy intake of the 167 respondents was 2362.87 kcal. The mean BMI was 25.86 kg/m². According to the results obtained, BMI had a significant impact on energy intake demonstrating that the mean energy intake for overweight and obese participants is higher than that of normal and underweight participants (p ˂0.001). The energy intake of participants aged 70–79 and 80–89 was found to be higher, and this finding was statistically significant (p = 0.027). The mean energy intake was found to be lower in vegetarians, according to an independent sample t-test; however, this relationship was not statistically significant (p = 0.407). Moreover, eating difficulties had no significant impact on energy intake (p = 0.377). The study also indicated that participants who engage in low physical activity had a higher energy intake than those who engage in moderate or high physical activity. And, there was a statistically significant relationship between PAL and energy intake (p ˂0.001). Furthermore, results showed a moderate negative and significant relationship between NK scores and energy intake (p ˂0.001). It was found that the mean energy intake of participants residing in rural areas was higher; however, this relationship was not statistically significant (p = 0.074).

Dietary patterns of the female older adults

In general, vegetables were consumed most (M = 6.87 ± 0.50) followed by tea/coffee (M = 6.53 ± 077), butter/margarine (M = 5.50 ± 1.28), fruits (M = 5.37 ± 1.07), white rice (M = 5.25 ± 1.62), beans/pulses (M = 5.08 ± 0.71), oatmeal (M = 4.87 ± 1.65) and semi-skimmed milk (M = 1.48 ± 1.48), low fat cheese (M = 1.48 ± 1.23), brown bread (M = 1.55 ± 1.22) being the food items less frequently consumed.

Factors affecting dietary patterns of the female elderly participants

Relationship between BMI and frequency of different food items consumed

The results in Table 4 show a moderate significant negative correlation, a small significant positive correlation and a moderate positive correlation between BMI and the food items listed in the table.

Table 4. Relationship between BMI and eating patterns.

Food items	Results of Pearson Correlation BMI
Food items	R	sig. value (2 tailed)
Brown bread	-0.345^**	0.000
White bread	0.267^**	0.000
Brown rice	-0.210^**	0.006
White rice	0.254^**	0.001
Brown farata/chapatti	-0.160^*	0.039
White farata/chapatti	0.347^**	0.000
Oatmeal	-0.067	0.390
Weetabix	-0.064	0.412
Pasta	0.104	0.183
Potato (boiled/fried)	0.026	0.741
Beans/pulses	0.092	0.235
Chicken	0.239^**	0.002
Fish	-0.080	0.303
Full cream yoghurt	0.247^**	0.001
Low fat yoghurt	-0.211^**	0.006
Full cream cheese	0.173^*	0.025
Skimmed milk	-0.346^**	0.000
Full cream milk	0.308^**	0.000
Vegetables	-0.078	0.319
Fruits	-0.282^**	0.000
Sweet pastries/cakes	0.201^**	0.009
Sweet biscuits/sweets	0.167^*	0.032
Chocolates	0.401^**	0.000
Soft drinks	0.383^**	0.000
Tea/coffee	0.153^*	0.048

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

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

Relationship between NK and consumption frequency of different food items

There is a statistically significant relationship (p ˂0.001) between nutrition knowledge and education level. The results of the Pearson correlation showed that there was a small significant negative correlation at 0.01 level between NK and consumption frequency of sweet pastries/cakes, deep fried snacks and full cream yoghurt (p ˂0.001). At the 0.05 level of significance, there was a small significant negative correlation between NK and frequency of sweet biscuits/sweets (p = 0.016), chocolates (p = 0.033), butter (p = 0.048), soft drinks (p = 0.011), pasta (p = 0.040) and chicken (p = 0.035) consumption. Furthermore, a moderate significant positive correlation at the 0.01 level was obtained between NK and frequency of brown bread, brown farata, skimmed milk and fruit consumption (p ˂0.001). Another significant relationship at the 0.01 level was found between BMI and frequency of brown rice (p ˂0.001) and low-fat yoghurt (p = 0.007) consumption. This correlation was small and positive.

Effect of PAL on frequency of different food items consumed

There was a statistically significant difference between PAL and brown bread [F (2, 164) = 4.61, p = 0.011], brown rice [F (2, 164) = 3.86, p = 0.023], white rice [F (2, 164) = 3.87, p = 0.023], white bread [F (2, 164) = 4.83, p = 0.009], fish [F (2, 164) = 3.21, p = 0.043], full cream yoghurt [F (2, 164) = 5.30, p = 0.006] and soft drinks [F (2, 164) = 4.75, p = 0.010] consumption. Table 5 also demonstrates that participants who had low PAL ate a higher amount of white rice and white bread. On the other hand, those with high PAL consumed fish and white rice in greater quantities.

Table 5. Influence of PAL on consumption frequency of brown bread, brown rice, white rice, white bread, fish, full cream yoghurt, soft drinks.

Food items	PAL	Mean ± SD	Results of ANOVA
Food items	PAL	Mean ± SD	F	Sig. value
Brown bread	Low	2.62 ± 2.01
	Moderate	3.65 ± 2.09	4.61	0.011
	High	3.21 ± 2.52
Brown rice	Low	1.31 ± 0.89
	Moderate	1.77 ± 1.42	3.86	0.023
	High	2.00 ± 1.66
White rice	Low	5.57 ± 1.40
	Moderate	4.88 ± 1.68	3.87	0.023
	High	4.93 ±2.24
White bread	Low	5.07 ± 1.67
	Moderate	4.11 ±2.25	4.83	0.009
	High	4.14 ± 2.48
Fish	Low	3.68 ± 1.22
	Moderate	4.12 ± 1.28	3.21	0.043
	High	4.29 ± 0.914
Full cream yoghurt	Low	3.33 ± 1.57
	Moderate	2.48 ± 1.67	5.30	0.006
	High	2.71 ± 1.73
Soft drinks	Low	2.11 ± 1.20
	Moderate	1.61 ± 0.91	4.75	0.010
	High	1.64 ± 0.63

Relationship between residence area and consumption frequency of food items

The results of the ISTT revealed a statistically significant difference between participants living in urban and rural areas and consumption frequency of brown bread, brown rice, white rice, oatmeal, Weetabix, pasta, fish, low fat yoghurt and fruit. Table 6 also demonstrates that brown bread, oatmeal, beans/pulses, vegetables and fruit were highly consumed food items by elderly female participants living in urban areas.

Table 6. Relationship between residence area and consumption frequency of food items.

Food items	Residence area	Mean ± SD	sig.(2-tailed)	Results of ISTT mean difference	95% confidence interval
Food items	Residence area	Mean ± SD	sig.(2-tailed)	Results of ISTT mean difference	Lower	Upper
Brown bread	Urban	4.00 ± 2.08	0.000	1.75	1.15	2.35
	Rural	2.25 ± 1.83
Brown rice	Urban	1.84 ± 1.41	0.005	0.540	0.17	0.91
	Rural	1.30 ± 0.96
White rice	Urban	4.51 ± 1.76	0.000	0.232	-1.86	-0.94
	Rural	5.91 ± 1.14
Oatmeal	Urban	5.47 ± 0.96	0.000	0.233	0.68	1.60
	Rural	4.33 ± 1.94
Weetabix	Urban	3.95 ± 1.38	0.005	0.711	0.212	1.209
	Rural	3.24 ± 1.87
Pasta	Urban	3.75 ± 0.74	0.020	0.360	0.57	0.664
	Rural	3.39 ±1.21
Fish	Urban	4.23 ± 1.062	0.001	0.614	0.250	0.978
	Rural	3.61 ± 1.317
Low fat yoghurt	Urban	4.13 ± 1.324	0.000	1.331	0.851	1.811
	Rural	2.80 ± 1.801
Beans/pulses	Urban	5.16 ± 0.465	0.136	0.165	-0.052	0.381
	Rural	5.00 ± 0.871
Vegetables	Urban	6.92 ± 0.385	0.162	0.108	-0.043	0.255
	Rural	6.82 ± 0.578
Fruits	Urban	5.71 ± 1.064	0.000	0.652	0.339	0.968
	Rural	5.06 ± 0.975

Relationship between energy intake and consumption frequency of different food items

The results obtained show a moderate positive and statistically significant correlation between energy intake and white rice, chicken, full cream milk, sweet pastries, chocolates and soft drinks (p ˂0.001, significant at the 0.01 level).

A small significant positive correlation was found between energy intake and

• White bread (p ˂0.001, significant at 0.01 level)
• Pasta (p = 0.002, significant at 0.01 level)
• Full cream yoghurt (p = 0.001, significant at 0.01 level)
• Sweet biscuits/sweets (p = 0.008, significant at 0.01 level)
• Butter/margarine (p = 0.018, significant at 0.05 level)

Moreover, a moderate significant negative correlation was obtained between energy intake and brown bread, skimmed milk and fruit (p ˂0.001, significant at 0.01 level). And a small negative and significant relationship was found between energy intake and brown rice (p = 0.001, significant at 0.01 level), soybeans and products (p = 0.011, significant at 0.05 level) and low-fat yoghurt (p = 0.023, significant at 0.05 level).

A negative relationship was found between oatmeal, Weetabix, fish and energy intake (EI) while a positive relationship was observed between potato, beans/pulses, tea/coffee and EI. But these relationships were not statistically significant.

Discussion

Energy intake of female elderly people

The mean EI of the 167 female elderly participants in this study was 2363 kcal/day; and this was not consistent with any recommended dietary allowances of other countries as it was much higher than that of other countries like Indonesia, Malaysia, Philippines and Thailand, which recommend 1850, 1780, 1620–1410 and 1550 kcal respectively for its female senior citizens.²³ In the southwestern United States, elderly females reported a mean EI of 1555 ± 63.2 kcal/day, which did not support the results here.²⁴ The increase in EI of the female senior citizens of this study may be due to significant changes in diet composition such as an increased use of cooking oils, increased consumption of carbohydrates and high-energy processed foods including cakes, instant noodles and cookies.²⁵ However, the results of this study do coincide to a little extent with one study conducted in Mauritius in 2007 whereby the energy intake was higher (1978 kcal) when compared to dietary reference intakes for older adults. This increase in EI would offer a reasonable explanation for why being overweight was more common in the elderly female participants.²⁶

Link between age group and energy intake

A statistically significant relationship was obtained between age and EI in this study. The participants aged between 70 to79 and 80 to 89 had the highest mean EI (2492.83 ± 703.54, 2561.24 ± 740.26 kcal respectively) while those aged 60–69 had a mean EI of 2210.56 ± 631.68 kcal. In contrast to the results found here, which showed that the age group 60–89 had higher EI, a study in the southern United States revealed that females in the 51–70 age range had a lower mean EI value of 1571 ± 29.3 kcal.²⁴

Link between BMI and energy intake

The mean BMI of the 167 participants was 25.86 kg/m², which suggests that they were mostly overweight. Additionally, results indicate that as EI increases, BMI increases; the underweight individuals had the lowest EI (1299.57 ± 62.06 kcal) and obese participants had the highest EI (3326.55 ± 281.39 kcal). Therefore, this study shows that EI had a significant impact on BMI. This result supports a Chinese women's study, which found that overweight and obese women consumed a significantly higher proportion of their dietary energy from carbohydrates than lean women, indicating a link between EI and BMI.²⁷ Similar results were observed in Perth, Western Australia, where EI in elderly women was linked to a higher BMI; but they were under-reporting their EI.²⁸ The results of the present study has been further supported by Trichopoulou et al., (2000)²⁹ who revealed that energy intake and BMI have a positive relationship and the BMI of females was found to be much higher than males.

Influence of PAL on energy intake

This study demonstrates that the PAL of elderly females in Mauritius was low for the majority of the participants (52.10%) and there was a statistically significant relationship between PAL and energy intake. The study also showed that participants who engaged in low physical activity had a higher energy intake than those who engaged in moderate or high physical activity (Table 5). Another study, on the other hand, reported conflicting results, arguing that there is no sufficient evidence to corroborate that physical activity has an effect on EI.³⁰ Moreover, a systematic review showed that there was no consistent evidence to support the recommendation that physical activity may reduce the decrease in appetite and energy intake that occurs with age.¹⁵

Relationship between nutrition knowledge, education level and energy intake

The results showed that there was a statistically significant relationship between NK and education level. This study demonstrates that the female elderly participants having a lower education level had least nutrition knowledge. This finding was supported by a study which found that the majority of Taiwanese female elderly participants who had no formal education and attended only primary school had poor NK.³¹ Furthermore, a study in England discovered that those with degrees had a much higher NK than participants without formal education.³²

The results show a moderate negative and significant correlation between NK and energy intake, indicating that EI decreases with increasing NK. A study showed that older women having a higher education level had the biggest relationship with a higher label reading score³³ and this led to a decline in EI among them.¹⁴ Another study demonstrated that women with a higher level of knowledge were more likely to be concerned about calories and consumed less sweets, soft drinks and salty snacks,³⁴ implying that cutting back on these unhealthy meals will not result in an increase in energy intake.

Dietary patterns/food frequency of the female older adults

In the current study, some food items were found to be consumed more often among female older people aged between 60 to 100. For instance, the results indicated that vegetables, tea/coffee, butter/margarine, fruit, white rice, beans/pulses, oatmeal, white bread, potato and chicken were more frequently consumed. On the other hand, food items like semi-skimmed milk, low-fat cheese, brown rice, chocolates, soft drinks, skimmed milk and oily fish were less frequently consumed. According to a previous study done in Mauritius, it was found that the older individuals consumed a lot of rice, vegetables, and little pasta because rice is one of the country's staple foods.²⁶ The result can be further supported by a study who reported that elderly people consumed a lot of carbohydrates.³⁵

Factors affecting dietary patterns of the female elderly participants

Relationship between BMI and frequency of different food items consumed

The findings of the current study revealed that the majority of participants were classified as normal or overweight. There were also a considerable number of obese respondents (12%). This could be explained by the correlation between aging and a redistribution of both body fat and fat-free mass. With aging, loss of skeletal muscle occurs and this could be a reason why there is a bigger relative rise in intra-abdominal fat than subcutaneous or total body fat, and a relatively greater decline in peripheral fat than central fat-free mass.³⁶A study revealed that BMI may be influenced by the type of carbohydrates consumed.³⁷ In the present study, there was a negative and significant correlation between consumption frequency of whole-grain carbohydrates such as brown bread, brown rice, and brown farata/chapatti and BMI. This indicates that as consumption of wholegrain carbohydrates increases, BMI decreases. Results from meta-regression on cross-sectional data support these findings, which show a negative relationship between wholegrain consumption and BMI.³⁸ In this study, there was also a negative correlation between BMI and oatmeal and Weetabix intake but it was not statistically significant. Moreover, a positive and significant relationship was found between BMI and consumption frequency of refined carbohydrates such as white bread, white rice and white farata/chapatti in this study. This indicates that as consumption of refined carbohydrates increases, BMI increases. These results corroborate those of a Mediterranean cohort which found that white bread consumption (≥ two servings per day) had a strong relationship with the risk of becoming overweight or obese.³⁹ A positive relationship was observed between chicken, full cream yoghurt, full cream milk, sweet pastries/cakes, sweet biscuit/sweets and chocolates intake and BMI, indicating that high consumption of these food items leads to an increase in BMI; between BMI and consumption frequency of fish, low fat yoghurt, skimmed milk and fruit, a significant negative relationship was noted. To support these findings, one study reported that low-fat dairy products were inversely connected to changes in body weight, whereas whole fat dairy products were linked to a little but substantial rise in body weight; the highest category of dairy consumption was also linked to a lower chance of being overweight and having abdominal obesity.⁴⁰ Regarding beverage consumption (soft drinks and tea/coffee), the positive correlation between BMI reported in this study corresponds with a study on Mauritian women whereby higher intake of soft drinks resulted in high BMI.³ Another study demonstrated that soft drink consumption was linked to an increased risk of abdominal obesity over a 10-year period.⁴¹ However, in one study, consuming one or more portions of tea per day was linked to obesity, but the link was not statistically significant.⁴²

Relationship between nutrition knowledge and consumption frequency of different food items

In the current study, a significant negative relationship was found between NK and consumption frequency of sweet pastries/cakes, deep fried snacks, full cream yoghurt, sweet biscuits/sweets, chocolates, butter, soft drinks, pasta and chicken. This shows that some subjects with low NK consumed high amount of these food items and vice-versa. Moreover, it was also observed that NK and frequency of brown bread, brown farata, skimmed milk, fruit, brown rice and low-fat yoghurt consumption had a positive and significant correlation. This indicates that subjects with high NK consumed more of these food items, which were healthier. A study reported that an older population with less education consistently had a higher likelihood of eating a diet high in sweets and low in protein-rich vegetables.⁴³ However, no significant relationship was discovered between the other foods and NK. Although older persons have high nutrition knowledge, their eating habits may not be consistent with NK due to their inability to put their technical nutrition knowledge to use.⁴⁴

Relationship between residence area and consumption frequency of food items

The results of this study revealed a statistically significant difference between participants living in urban and rural areas and consumption frequency of brown bread, brown rice, white rice, oatmeal, Weetabix, pasta, fish, low fat yoghurt and fruit. Table 6 demonstrates that brown bread, oatmeal, beans/pulses, vegetables and fruits were the highly consumed food items by the elderly female participants living in urban areas while in rural areas white rice, oatmeal, beans/pulses, vegetables and fruit were highly consumed. It can be seen that in both urban and rural areas consumption of oatmeal, beans/pulses, vegetables and fruit were high. However, the other food items showed no significant difference. Few studies have been conducted to study the relationship between residence area and frequency of food items consumed.

Relationship between energy intake and consumption frequency of different food items

Intake of energy was positively correlated with consumption of white bread, white rice, pasta, chicken, full cream yoghurt, full cream milk, sweet pastries, sweet biscuits, chocolates and soft drinks, as shown in Table 4. This indicates that consumption of these food items increases energy intake. The main function of carbohydrates is to supply the body's cells with energy and dietary fibre.⁴⁵ However, data from the USDA's food consumption surveys from 1989 to 1991 and 1994 to 1996 showed that a higher intake of carbohydrates was the main factor contributing to increased energy intake.⁴⁶ This corroborates the result of the current study showing that high intake of white bread, white rice and pasta increased energy intake. Chicken was found to increase energy intake among the participants. Certain parts of the chicken, such as the breasts, are frequently sold with or without the skin. Typically, thighs, wings, and drumsticks are sold with the skin still attached. Skin increases the amount of fat and calories in the chicken piece. The cooking methods for chicken including deep frying, cooking with extra fat, sugar, or salt in marinades and pan frying in oils or butter can all contribute to an increase in calories and fat.⁴⁷ Therefore, this explains why consumption of chicken led to an increase in energy intake. This study also discovered that frequent consumption of sweet pastries, sweet biscuits and chocolates increased energy intake and this was consistent with a study conducted among Norwegian adults which classified these food items as snacks and they were among the top five in an energy-contributing food group.⁴⁸ A study concluded that energy-dense snacks may increase energy intake and weight when consumed in higher amounts.⁴⁹ Soft drinks frequently include sweeteners like high-fructose corn syrup, that can greatly increase EI and cause obesity⁵⁰ and this is consistent with the current study. Moreover, a meta-analysis discovered definite correlations between soft drink use and higher energy intake and body weight. There was persistent evidence that consumers did not reduce their intake of other foods to compensate for the extra energy they get from soft drinks, leading to higher total energy intake.⁵¹

Conclusions

The present study discovered that the mean energy intake of the female elderly participants was much higher than the recommended daily allowance of caloric intake in India and other countries. It was also found that age was significantly associated with energy intake whereby respondents aged 80–89 and 70–79 had a higher energy intake. Moreover, BMI had a significant impact on energy intake and the overweight and obese subjects had a higher energy intake. The majority of the female older adults were overweight. There were a few factors that had influenced the energy intake of the respondents, namely PAL and NK. For instance, the majority of the participants having low PAL were found to have a higher energy intake. Furthermore, a negative correlation was found between NK and energy intake, indicating that participants with good NK had lower energy intake. It should be noted that having a good NK was connected with having a high degree of education. However, some factors including vegetarianism, eating difficulties and residence area did not significantly affect energy intake. In general, the dietary pattern of the subjects was diversified. Consumption frequency of carbohydrates, dairy products, vegetables, fruit, beans/pulses, oatmeal and Weetabix were high. It was observed that refined carbohydrates (white bread, white rice, white farata/chapatti) were consumed more frequently than the whole-grain carbohydrates (brown bread, brown rice, brown farata/chapatti). In addition, low-fat dairy products were not consumed regularly. The study also reported that the participants consumed snacks high in fat and sugars such as sweet biscuits, sweet pastries, deep fried snacks, chocolates and soft drinks to some extent. Various factors were found to have an impact on the dietary pattern of the participants. Firstly, a positive correlation was obtained between BMI and consumption frequency of refined carbohydrates. In other words, a high consumption of refined carbohydrates was linked to a higher BMI. Also, consumption of sweet biscuits, sweet pastries, deep fried snacks, chocolates and soft drinks may have led to an increase in the BMI. Additionally, the dietary pattern was influenced by NK. It was also observed that participants living in urban areas had a healthier dietary pattern, which included more whole-grain carbohydrates and low-fat dairy products. The present study’s findings are both positive and disappointing in terms of how NK affects food choices because higher NK levels were linked to higher consumption of both nutritious and unhealthy meals, while no links were observed between NK and the other food items.

Data availability

Underlying data

Figshare: Underlying data for ‘Dietary patterns, BMI, PAL and energy intake among the female elderly population in Mauritius’, https://doi.org/10.6084/m9.figshare.22201381.¹⁶

This project contains the following underlying data:

• Data file: Raw data.xlsx
• Data file: Codebook for SPSS docx

Extended data

Figshare: Extended data for ‘Dietary patterns, BMI, PAL and energy intake among the female elderly population in Mauritius’, https://doi.org/10.6084/m9.figshare.22201381.¹⁶

This project contains the following extended data:

• Data file: Final Questionnaire Survey.pdf

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0)

Acknowledgements

We would like to express our gratitude to the University of Mauritius for their support with this research project. This work was supported by the Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia.

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Author details Author details

¹ Department of Health Sciences, Faculty of Medicine and Health Sciences, University of Mauritius, Réduit, Moka, Mauritius
² Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
³ Department of Medicine, Faculty of Medicine and Health Sciences, University of Mauritius, Réduit, Moka, Mauritius
⁴ Department of Agricultural and Food Science, Faculty of Agriculture, University of Mauritius, Réduit, Moka, Mauritius

Yashwinee Bye Ishnoo
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Abdulwahed Fahad Alrefaei
Roles: Funding Acquisition, Investigation, Project Administration, Resources, Validation, Writing – Review & Editing

Rishi Ram Doobaree
Roles: Investigation, Resources, Writing – Review & Editing

Nadeem Nazurally
Roles: Conceptualization, Methodology, Resources, Writing – Review & Editing

Rajesh Jeewon
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Ishnoo YB, Alrefaei AF, Doobaree RR et al. Dietary patterns, body mass index, physical activity level and energy intake among the female elderly population in Mauritius [version 1; peer review: 1 approved with reservations]. F1000Research 2023, 12:568 (https://doi.org/10.12688/f1000research.131683.1)

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Reviewer Report 06 Oct 2023

Ogechi Chinyere Nzeagwu, Department of Human Nutrition and Dietetics, Michael Okpara University of Agriculture, Umudike, Abia, Nigeria

Approved with Reservations

https://doi.org/10.5256/f1000research.144547.r194249

Comments on the different sections

1) Title – Remove ‘s’ in patterns and ‘the’ before female

2) Abstract – Still remove ‘the’ before female. In methods, questionnaire cannot be used to determine anthropometric measurements, ... Continue reading

Comments on the different sections

1) Title – Remove ‘s’ in patterns and ‘the’ before female

2) Abstract – Still remove ‘the’ before female. In methods, questionnaire cannot be used to determine anthropometric measurements, rather state that ‘the values for anthropometric measurements were recorded in the questionnaire’. Also indicate at which level you judged the relationships as significant.

3) Introduction – In the second paragraph, line 9 into 10 where you have ‘According to WHO ……. ending with 700 million obese’ should have a reference as well as the next sentence starting with ‘Obesity is prevalent …… down to research from the Netherlands should also have reference.

In the third paragraph line seven ‘Research has shown ……..help with energy balance should also have a reference

Last paragraph, In Mauritus, there is scarcity of data instead of a scarcity of data

Objectives – On the main objectives, there should have been one like ‘to assess the nutrition knowledge of the study population’ before the third one ‘to determine whether nutrition knowledge affects the amount of energy consumed’

4) Methods – Section IV – Anthropometric measurements – It might be necessary for the authors to indicate if there were exclusion criteria here because of older persons that have kyphosis or are bent and will have difficulty standing erect for height measurement or did they use proxy for height for such people.

5) Results – The authors should have presented result on energy intake before bringing up factors influencing energy intake

Under factors affecting dietary patterns – Relationship between BMI and frequency of different food items consumed, it might be necessary for the authors to explain what they meant by small, moderate significant positive correlation etc

Again in the relationship between NK and consumption frequency of different food items – the result presented did not represent what the caption is saying.

5) Discussion was well handled

6) References – Some of the references were more than 10 years back. The Authors should try to replace some of the old references with more current and recent references

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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06 Oct 2023 | for Version 1

Ogechi Chinyere Nzeagwu, Department of Human Nutrition and Dietetics, Michael Okpara University of Agriculture, Umudike, Abia, Nigeria

11 Views Cite this report Responses(0)

Approved With Reservations

Comments on the different sections

1) Title – Remove ‘s’ in patterns and ‘the’ before female

2) Abstract – Still remove ‘the’ before female. In methods, questionnaire cannot be used to determine anthropometric measurements, rather state that ‘the values for anthropometric measurements were recorded in the questionnaire’. Also indicate at which level you judged the relationships as significant.

3) Introduction – In the second paragraph, line 9 into 10 where you have ‘According to WHO ……. ending with 700 million obese’ should have a reference as well as the next sentence starting with ‘Obesity is prevalent …… down to research from the Netherlands should also have reference.

In the third paragraph line seven ‘Research has shown ……..help with energy balance should also have a reference

Last paragraph, In Mauritus, there is scarcity of data instead of a scarcity of data

Objectives – On the main objectives, there should have been one like ‘to assess the nutrition knowledge of the study population’ before the third one ‘to determine whether nutrition knowledge affects the amount of energy consumed’

4) Methods – Section IV – Anthropometric measurements – It might be necessary for the authors to indicate if there were exclusion criteria here because of older persons that have kyphosis or are bent and will have difficulty standing erect for height measurement or did they use proxy for height for such people.

5) Results – The authors should have presented result on energy intake before bringing up factors influencing energy intake

Under factors affecting dietary patterns – Relationship between BMI and frequency of different food items consumed, it might be necessary for the authors to explain what they meant by small, moderate significant positive correlation etc

Again in the relationship between NK and consumption frequency of different food items – the result presented did not represent what the caption is saying.

5) Discussion was well handled

6) References – Some of the references were more than 10 years back. The Authors should try to replace some of the old references with more current and recent references

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

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[1] 1. Abdollahi S, Zeinali F, Azam K, et al.: Identifying Major Dietary Patterns Among the Elderly in Tehran Health Homes. Jundishapur J. Health Sci. 2015; 7(4): 26–34. Publisher Full Text Reference Source

[2] 2. Mandary ABS: An Investigation on the Nutritional and Health Status of the Mauritian Elderly Population. Degree. University of Mauritius; 2019.

[3] 3. Padaruth OD, Gomdola D, Bhoyroo V, et al.: Is Soft Drink Consumption Linked to Higher Body Mass Index and Energy Intake Among Adults in Mauritius? Curr. Res. Nutr. Food Sci. 2019; 7(3): 725–737. Publisher Full Text

[4] 4. Harley J: Walker: Overpopulation in Mauritius A survey. Geogr. Rev. 1964; 54(2): 243–248. Publisher Full Text

[5] 5. Suntoo R: Population Ageing and the Theory of Demographic Transition: The Case of Mauritius. University of Mauritius research. 2012; 12. Reference Source

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Dietary patterns, body mass index, physical activity level and energy intake among the female elderly population in Mauritius

Abstract

Keywords

Introduction

Methods

Consent and participants of the survey

Questionnaire design

Table 1. Food frequency scores.

Table 2. Classification of PAL according to IPAQ.

Statistical analyses

Results

Demographic characteristics of the female older adults

Table 3. Socio-demographics of the female elderly participants.

Factors influencing energy intake of female elderly adults

Dietary patterns of the female older adults

Factors affecting dietary patterns of the female elderly participants

Table 4. Relationship between BMI and eating patterns.

Table 5. Influence of PAL on consumption frequency of brown bread, brown rice, white rice, white bread, fish, full cream yoghurt, soft drinks.

Table 6. Relationship between residence area and consumption frequency of food items.

Discussion

Energy intake of female elderly people

Link between age group and energy intake

Link between BMI and energy intake

Influence of PAL on energy intake

Relationship between nutrition knowledge, education level and energy intake

Dietary patterns/food frequency of the female older adults

Factors affecting dietary patterns of the female elderly participants

Conclusions

Data availability

Underlying data

Extended data

Acknowledgements

References

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