Research Objective/ Aim
Women’s nutrition exams often report inappropriate or inadequate diets compared to men courses or nutritional intake (10). It was uniform in different races and countries. Many studies have often compared food intake with recommendations for energy, gender, and nutritional consumption for the general population (3, 13). The study included an indicative average food frequency questionnaire (FFQ) and average 3DD consumption (3-day Dietary intake) indicating the consumption needed for the needs of almost all healthy people. The use of FFQ can overestimate 3DD, and the Estimated Average Requirement (EAR) overestimates the nutritional needs of individuals. An accurate assessment of the physical activity of young people was essential to quantify physical behavior and to assess the impact of interventions on physical activity (14). The evaluation focused on 18-30 years of participants in the methodology for evaluating youth activities.
The scientific report of the Physical Activity (PA) Advisory Committee in 2018 shows a large amount of exercise was best for the health. The report provides a detailed overview of the benefits of disease prevention and health promotion for the most active in the United States from the latest scientific evidence. It builds and promotes the scientific documentation presented in the first report in 2008 exercise guidelines. This result refers to physically active individuals sleeping better, feeling better and working better (8, 16). There was substantial evidence that moderate to severe physical activity can improve the quality of sleep. Over time, individual sporting events have improved leadership performance (12).
Several studies have examined the link between the Body Mass Index (BMI), waist circumference (WC) and skinfold measurement and energy intake, and studies have shown that the critical nutrient components in the diet were an essential factor in the diet role of proteins, carbohydrates, and fats in adult obesity (9, 17). A 2000 World Health Organization report classified that more than 1.5 billion adults worldwide were obese, and about 4 million of whom were clinically obese. According to the 2014-15 BMI measurement, nearly two-thirds (63%) of 18-year-old Australians were overweight or obese (11).
Nutritional well-being was an essential part of the health, independence, and quality of life of the elderly. In previous studies, malnutrition was linked only to protein-energy malnutrition and could lead to adverse health risks, including loss of independence, more extended hospital stays, reduced functioning and low quality of life, increasing the risk of fractures and deaths, and delaying wound healing and slow recovery surgery (2, 18). While young adults live independently, reduced mobility, changes in appetite and economic constraints are often linked to the manifestation of chronic diseases and their malnutrition status (12).
Participants and Methods
The objective of this review was to evaluate the method used (3DD) to assess the nutrients intake and physical activity measures for modern and middle-aged youth with the PA guidelines of Australia. 3DD and FQQ measured nutrients intakes. Calcium micronutrient intakes estimated by the two scales were hypothesized to be significantly different. The study included average energy measured by AAS and 3DD, which were theorized to be statistically independent. BMI and waist circumferences were compared with the WHO BMI classification, and WC cut off values. Body fat percentages measured by skinfolds and BIA were assumed to be related and possess significant positive relationship.
Throughout the study, healthy female students of the third year enrolled in HSN305 course took place. The scholar selected 173 students of 18 years and over. The average age of participants was 23.7 years (SD 5.3), aged between 19 and 56 years. To compare data, standards, and recommendations “18 years or older “or “18-30 ” year categories were used in the query class.
Nutritional Consumption Measures
The female partakers completed the FFQ, developed by CSIRO (Commonwealth Scientific and Industrial Research Organization, South Australia (Adelaide)). Various study samples of the population of Australia established the validity of the questionnaire. A set of parallel messages were collected using 3-day food records. FFQ was developed to evaluate the usual dietary habits as well as nutrients that promote bone health and antioxidants. It has been tested using a 3-day meal record (3-DFR) in all female participants. Repeatability was evaluated by comparing FFQ with 3DFR data. Nutritional information about sports nutrition has been combined using information about the manufacturer’s product, and added to the FFQ database. The present study contains food supplements and other pathogenic substances in edible form.
3-Day Physical Activity Diary was used to measure Physical activity of the participants (4). This questionnaire was the most commonly used method for evaluating PA and was based on the possibility that the participants can remember and recall (5). The survey varies depending on the content of the measurement, the way the data was collected and the quality of the data, such as the measurement intensity, differentiate the usual activities, and merely recent events, as well as access to data. Each operation used the metabolic equivalent (ME) of the task (6, 11). The metabolic equivalent was defined as the amount of oxygen consumed at rest, corresponding to 3.5 ml of O2 x minutes x per kilogram of body weight. The term with was a simple, convenient and easy-to-understand process that reflects the cost of the energy of physical activity as a multiple of metabolism. The value of energy conductivity can be determined by dividing the relative cost of oxygen (ml O2/kg/min) x 3.5. The AAS (Active Australia Survey) was utilized to assess physical activity (5). The Australian Activity study aims to measure participation in recreational sports activities and to evaluate the notion of local health information, i.e., health benefits, of physical activity. It offers a short and reliable range of questions that can be achieved by using an automated telephone interview (DE) or face to face.
Measurement Instruments
From the Schofield equation, BMR (Basic Metabolic Rate) was calculated (15). The Schofield equation was a method of estimating the Basic Metabolic Rate (MNR) of adult males and females. Weight was measured in kg, and standard evaluation error (SEE) was calculated. Displaying the value means that the computed BMR can extract this much calorie. For example, if a person was very muscular and weigh more than people who were equal in average height and weight, he/she can add SEE to the calculated BMR. In addition to calculating the BMR, the level of physical activity of the person or the coefficient of physical activity (PAF) was used.
A continuous variable was represented as a standard deviation tool (Normal distribution) or as an intermediate value with an Interquartile interval (Skewed distribution). Statistical analysis was performed using software STATA 14 (StataCorp LP, USA). Using p < 0.05 statistical significance has been represented (T-Test or Pearson correlation). The difference between the valuation methods obtained by different ways was evaluated using the paired t-test. The association was established by using the Pearson correlation coefficient. Micronutrient deficiency was calculated using the EAR cut-point method (calcium, folic acid, vitamin C, zinc), and the process of total probability (for iron).
Dietary intake
Table 1: Descriptive Statistics for Macronutrient Intake
Table 2: Percentage of recommended limits for daily energy intake from carbohydrate, fat, and protein
The females’ macronutrient intake (carbohydrate, fat, protein) has been reported in Table 1. Daily energy intake distribution for the group was almost normal, as the mean intake was practically equal to median intake measure with micronutrient distribution in Table 3. Compared with recommended guidelines intake in Table 2 of macronutrient intake, it was observed that energy intake from fat was comparatively higher than the other two nutrients. 95% of participants were found in absorbing nutrients from fat oriented food. 91% of participants were exposed to abide by the recommended percentage intake of nutrients from protein oriented food. On the contrary, daily energy from carbohydrate consumption was considerably below the percentage guidelines recommended of daily energy intake.
Table 3: Descriptive Statistics for Micronutrient Intake by 3DFR and FQQ
Table 3 presents the micronutrients consumption summary of the participating females from a 3-day food record. The results were also compared with the guidelines of EAR in Table 4. The comparative analysis yielded that calcium and iron intakes by the participants were well below the EAR cut points. This result reflected that females were consuming less iron and calcium. The scenario of calcium intake was also recorded by the Food Frequency Questionnaire (FFQ) for further statistical analysis. From Figure 1, consumption of iron and calcium were found to be normally distributed. This trend eventually indicated that overall calcium intake for females was considerably less than prescribed guidelines.
Physical Activity Measures
The distribution of Folate and Vitamin-C intake of the participants were found to be almost normally distributed. Table 4 indicated that scenario for Folate intake was also pitiful since Folate intake of more than 35% participants was below the cut point. Vitamin-C consumption pattern was comparatively better, though; Table 4 reflected that Vitamin-C intake for some of the females in the sample was below par. Figure 2 and Figure 3 explained that the distribution of Zinc and Vitamin-C were highly positively skewed. This result indicated the presence of a few outlier intake data, which implied that some of the females in the sample were high on consumption for these two micronutrients compared to other participants.
Table 4: Percentage of class population at risk of deficiency of various nutrients
Statistical inference was drawn to assess the difference between average calcium intake, measured by FFQ and 3-day food record (3DFR). Estimated calcium intake for the entire female population can be noted from the 95% CI in Table 5. The comparative analysis was done with the help of paired t-test, and the result in Table 5 reflects a significant dissimilarity between the calcium intake measured by FFQ and 3DFR methods. The p-value of 0.001 in t-test indicated a statistically significant high calcium intake measured by 3-day food record compared to FFQ scale.
Table 5: Paired t-test between mean calcium intakes assessed via FFQ vs. 3DFR
A further inferential parametric test was conducted to quantify the correlation of calcium intake measured by 3DFR and FFQ. Figure 4 represents a symmetrical distributed and positive relationship between the two methods. No possible outlier observations were noted from the scatter plot in Figure 4. Pearson’s correlation was used to establish and enumerate the relationship. A mid-positive, but statistically insignificant (r = 0.41, p = 0.35) correlation was observed between calcium intake measured by FFQ and 3DFR. Hence, the p-value of 0.35 indicated that the presumed Correlation was not significant, and was supported by the analysis result in Table 5.
Table 6: Descriptive statistics for energy expenditure (kJ/d) by AAS survey
Table 6 presents the detailed energy consumption of the participants in the study. The comparison of mean and median energy intake by AAS survey indicated a highly negatively skewed distribution. The results were compared with the energy intake of the 3-day diary in Table 7. Descriptive summary in Table 7 reflected almost regular energy intake for the participants, which was a markedly different result compared to energy intake summary by AAS. Table 7 also indicated that energy expenditure by the participants was much higher than energy consumed, measured in kilojoules per day. The energy balance row reported a slightly negatively skewed distribution as the median was less than mean energy difference.
The difference in average energy expenditure or physical activity measured by AAS and average 3-day diary (3DD) energy expenditure was done using a paired t-test. Table 8 presents the results of the paired t-test, which reflected that energy expenditure measured in AAS scale was significantly greater than the measure by 3DD. The correlation by Pearson for 156 female participants reflected an almost zero correlation between average energy expenditure (physical activity) measured by AAS and 3DD techniques. The correlation was also statistically insignificant (t = 0.04, p = 0.64). The p-value of 0.64 indicated that there was no noteworthy correlation whatsoever between the average physical activity measured by AAS and 3DD. Hence, the null hypothesis, assuming that there was no correlation between these measures failed to get rejected.
Anthropometric Measures
Table 9: Descriptive statistics for Anthropometric Measurements
Table 9 describes the Anthropometric measurements of the participants. Some missing values were observed from the number of observations in column 2. Distribution of height, weight, BMI, waist circumference was found to be almost normal. Comparative study for waist circumference (WC) and BMI was conducted with cut off levels in Table 10 and Table 11. Majority of the females in the sample was found to be maintaining a healthy BMI and physical health. Almost 18% of women were found to either overweight or obese. A similar analysis concerning WC cut-points reflected a resembling outcome. Waist sizes of nearly 75% of females were found to be below 80 centimeters. Rest of the 25% of women were above the 80 centimeters cut off point and were the same participants whose BMI was above 25 kg/m2.
A paired t-test analysis was done to assess the difference in fat, measured by Bioelectrical Impedance Analysis (BIA) and skinfold measurement, for 164 participants. Table 12 reflects the results, where the p-value of 0.003 was less than 0.05 (5% level of significance). The p-value indicated that there was a statistically significant difference in fat measured by the two methods, and percentage fat measured by skinfolds was significantly higher than that of the measures of the BIA method.
Table 12: Paired t-test for Difference between percent body fat assessed by skinfolds vs. BIA
Pearson’s correlation was used for finding any possible Correlation between BIA and skinfold measures of percentage fat. The analysis yielded a mid-positive Correlation between the two methods, but the significance or p-value of the Correlation was 0.64 (> 0.05). Hence, at 5% level, the null hypothesis assuming no correlation between the two methods failed to get rejected. Figure 6 scatterplot presents the pairwise plotting of percentage fat measured by BIA and skinfold method. The trend reflected a linear relation between the factors, but the apparent relationship was found to be statistically insignificant.
Dietary intake
A statistically significant greater intake of calcium measured by 3-days of food records compared to the FFQ scale was noted (10). A positive correlation between the intake of calcium measured by FFQ and 3DFR was present, but the relationship was not statistically significant. This study compared strategies and dietary consumption to assess the nutritional fitness of women. Compared to 3-days of dietary intake, the FFQ experience in this population was negative (2). The document also emphasizes that the consumption of calcium in women was less than the proportion of the cut points, such as the level of adequacy of the assessment of the EAR and its probability (3). Average Calcium (M = 726.9 mg/d) intake was less than recent recommendations of Nutrient values for 19-30 years of women (Calcium = 840 mg/d). The scenario for Folate (M = 391.2 mg/d), Iron (M = 11.7 mg/d), Vitamin-C (M = 105.1 mg/d), and Zinc (M = 10.9g/d) consumptions were above the EAR estimated cut off points (Vitamin C 30mg/d, Folate 320mg/d, Zinc 6.5mg/d, Iron 8mg/d).
Statistical Methods
Pearson correlation reflected almost zero correlation between the average energy consumption (physical activity) measured by AAS and 3DD methodology (16). Average energy expenditure measured by 3-day dairy (3DD) (M = 8290 KJ/d) and AAS (M = 1098 KJ/d) for the females were considerably greater than the AASQ recommendations for physical activity (M = 480 KJ/d) (8). Noticeably, average energy spent by 3DD (M = 8290 KJ/d) was noted to be greater than energy intake (M = 7525 KJ/d) by 3DD, which indicated a negative energy balance (1).
The difference in body fat measured by the two methods was statistically significant, and the percentage of fat measured by skin folds was significantly higher than that of the BIA method (9). The energy consumption in the AAS scale was substantially higher than the measured value of 3DD, and most of the women in the sample proved to have maintained a healthy BMI and good health. Almost 18% of women appear to be overweight or obese. Women with a waist size of nearly 75% were found to be less than 80 cm cut off point (17).
An appropriate approach would be to provide information on the number of people with an increased risk of under-utilization. Limited information in the nutritional data on the suitability of individual costs was there as the data can be falsified, particularly among the female population, which was very expensive or limited, and to achieve or maintain low weight and fat. The study did not discuss details of the impact of total daily energy intake on the obese and overweight groups. Since this was a cross-sectional study focusing on the absorption of food intake and human measurements, there was no effective presumption of the causes and effects of obesity. The consumption of nutrition can be a more urgent need for a longitudinal study.
Conclusion
Compared to the FFQ scale, calcium intake has significantly increased statistically, 3-days of food records. FFQ correlated positively with the consumption of calcium measured with 3DFR but was not statistically significant. Calcium intake in women was below a fraction of a reduction point, such as the adequacy of hearing ratings. The correlation coefficient reflects a nearly zero correlation between the average energy consumption (physical activity) measured by the AAS and the 3DD method. The difference in adipose tissue, measured by two ways, was statistically significant and the proportion of fat measured by wrinkles on the skin was significantly higher than that of the BIA method. The energy consumption on the AAS scale was substantially higher than the measured value of 3DD, and most of the women in the sample were found healthy and healthy.
The validation of FFQs of the female population also includes the relevant areas for determining the volume of food used and the proper presentation of the food types. In this study, there was no accurate measurement of physical activity to detect dietary data. Also, nutritional testing and dietary limitation can also help to assess the effectiveness of nutrition records. The final FFQ restrictions used in this study also analyze finite nutrients in inadequate nutrient data, such as iodine and fatty acids, as well as mineral substances that need iron and zinc. In the current research, dietary dependence and eating habits were categorized as 72 hours for food and beverage registrations, a common research-based approach among large populations. The diet record of 72 hours is not always representative of regular consumption. These shortcomings should also be addressed in the future.
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