Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241114EnglishN2019February21HealthcareUltrasonographic Biometry: Biparietal Diameter Measurement for Gestational Age Estimation in Singleton Pregnant Women of Karnataka
English0108Pranita R. VivekiEnglish V. S. ShirolEnglishBackground: Appropriate intrauterine fetal growth and development are fundamental for newborn health and lifelong welfare. Bi-parietal diameter provides the closest correlation with gestational age in second trimester.
Objectives: 1. To establish the reference tables for bi-parietal diameter in normal singleton pregnant women from 20 to 38 weeks of gestation from Belagavi District, Karnataka. 2. To find out the predictive accuracy of gestational age determined by bi-parietal diameter measurements with gestational age by menstrual history.
Materials and Methods: The data was collected by using predesigned pretested questionnaire from September 2016 to January 2018. Total 768 singleton pregnant women with minimum 30 cases for each gestational week from 20 to 38 weeks of gestation were studied.
Statistical Analysis: Data was analysed by Percentages, mean, standard deviation, range, standard error, percentiles and regression equation etc.
Results: The regression equation derived for bi-parietal diameter (BPD) measurements was GA = (1.275 + (3.905 X BPD in cm) where “R2”- the proportion of variation in dependent variable (GA) was 0.944 and “r”- the correlation coefficient was 0.9. By using the common regression equations for GA from 20 to 38 weeks, there was a difference of 1.44 to 2.22 weeks in actual and predicted GA at 36 to 38 weeks of pregnancy. However, the same difference was less between 20 to 35 weeks of gestation.
Conclusion: The present study findings confirmed that the fetal BPD measurements significantly vary among different population groups. So generation of population specific reference tables by a large scale study is required for more precise reporting by ultrasonography.
EnglishBiparietal Diameter, Gestational age, Ultrasonographic measurementsIntroduction:
The correct clinical diagnosis of fetal growth disturbances has important implications for proper prenatal care and for determination of the delivery time. Many curves and reference tables for fetal biometry have been published in the literature, using mean values of the bi-parietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length FL, which allow estimation of the fetal weight. Fetal biometry by ultrasonography is the most widespread method used to establish gestational age, estimate fetal size and monitor its growth1,2. Researchers have been focusing in recent years on population specific fetal biometric parameter charts for various ethnic groups and the inter population variability in foetal growth patterns. Campbell S. et al3 and Waldenstrom U et al4 observed that bi-parietal diameter was more accurate predictive of expected date of delivery (EDD) than that calculated from the first day of last menstrual period (LMP).
Role of ethnicity on fetal biometry is a well known fact.5,6 Fetal nomograms need to be revised regularly.7 Acharya P. et al8 and various other studies9-11 have observed the smaller fetal measurements than the Caucasian fetal measurements and thereby they concluded that if western parameters are applied to all, the risk of over-diagnosis of intrauterine growth retardation (IUGR), and over or under estimation of gestational age (GA) and EDD in Indian population would be more.
Therefore, the present study was planned to measure the bi-parietal diameter by ultrasonography for different groups of GA from 20 weeks to 38 weeks in the normal singleton pregnant women from local population of Belagavi District, Karnataka and to find out the predictive accuracy of GA determined by BPD measurements with GA by menstrual history.
Objectives:
1. To establish the reference tables for bi-parietal diameter in normal singleton pregnant women from 20 to 38 weeks of gestation from Belagavi District, Karnataka.
2. To find out the predictive accuracy of gestational age determined by bi-parietal diameter measurements with gestational age by menstrual history.
Materials and Methods:
A random case series study was done from September 2016 to January 2018. 768 pregnant women with minimum 30 cases for each gestational week from 20 to 38 weeks of pregnancy referred to the Department of Radiology, Belagavi Institute of Medical Sciences (BIMS), Belagavi by antenatal clinic of Department of Obstetrics and Gynaecology (OBG) for routine antenatal scanning were studied after clearance from Institutional Ethics Committee. Antenatal cases with knowledge about exact date of LMP with regular menstrual cycles of 26-33 days12 for at least 3 cycles before conception, with delivery of a live baby with birth weight more than or equal to 2500 grams, fundal height corresponding to duration of pregnancy as per obstetricians finding, who delivered within one week of the expected date of delivery (EDD) and who delivered a newborn baby without any congenital abnormality were included in the study for analysis. Exclusion criteria were - pregnant women with age below 18 and above 35 years, with height below 140 cm, history of drug abuse, tobacco / gutkha use before and during pregnancy, oral contraceptive pills for 3 months prior to conception, and previous baby with low birth weight. Pregnant women with diabetes and hypertension detected during examination or developing later during pregnancy, women with multiple gestations, oligohydromnios, polyhydromnios, intrauterine growth retardation, or intrauterine death, women with uterine abnormalities like fibroids, bi-cornuate uterus, etc.
Method of collection of data:
A predesigned, pretested, structured proforma was used for each subject separately. The ultrasonographic examination of each pregnant woman fulfilling inclusion criteria, was done after submission of completely filled ‘Form F’ in compliance to PCPNDT (Pre-conceptional and Pre-Natal Dignostic Techniques) Act, duly signed by the women undergoing ultrasonography and the radiologist conducting ultrasonography. Using standard methodology, fetal BPD was measured from the leading edge of the echo from proximal skull surface to the leading edge of the echo from distal skull surface – outer to inner diameter. The reading of only first examination of each patient was included for the study purposes, although some patients underwent multiple ultrasonographic examinations during their pregnancy period.
The patients or close relatives were contacted for information about delivery like date of delivery, onset of labor (spontaneous or induced), mode of delivery (vaginal or caesarean section or assisted one), place of delivery, birth weight of the baby, any congenital anomaly detected in newborn baby, etc. The ultrasound examination was done by a single radiologist on one ultrasound machine - iU22 Philips make real-time machine with 3.5 MHz electronic curvilinear transducer.
Statistical Analysis:
The data was analyzed using MS Excel and Statistical Package for Social Sciences (SPSS) version 20. The basic categorical variables were reported as frequencies and percentages. The correlation of abdominal circumference with gestational age was plotted using scatter plots. The descriptive statistics (mean, standard deviation and range, standard error, percentiles and regression equation) were performed for abdominal circumference for each gestational week.
Results:
The present study included total 768 cases between 20 to 38 weeks of gestation for analysis ranging from 34 to 51 cases per gestational week. The average age of the study subjects was 23.59 + 3.28 years ranging from 18 to 35 years. The mean height observed was 151.13 + 3.43 cm. Majority of the subjects (53.65%) were educated up to secondary school, followed by higher secondary school (20.05%) with average education status of 9.14 + 3.14 standard. 42 subjects (5.47%) were illiterates. Almost all (99.61%) were housewives/home makers and around 2/3rd cases were from rural area. 42.97% cases were primigravidae and 427 (55.60%) were from below poverty line family. Majority (79.30%) of the cases delivered in a government health institutes and 89.19% cases delivered normally. 47.79% newborns were females and 62.89% newborns were weighing between 2500 to 2700 gms with average birth weight of 2712.22 + 181.66 gms.
As seen in Table 1, the average GA observed in the present study with reference to BPD measurements from 4.4 to 9.4 cm along with number of cases for BPD measurements. The BPD measurements went on increasing with advancing GA. The average GA was found to be 21.70 + 0.75 weeks for the BPD measurements of 5.0 cm in 16 cases, while it was 32.65 + 1.19 weeks (13 cases) and 37.06 + 1.3 weeks ( 14 cases) for BPD measurements of 8 and 9 cm respectively
Table 2 shows the mean fetal weight in each GA, descriptive statistics like mean + SD, Minimum and maximum values, standard error of mean, 95% confidence interval for BPD measurements for each GA from 20 to 38 weeks. The mean fetal weight observed was 342 grams, 1507 grams and 2853 grams at 20 weeks, 30 weeks and 38 weeks of GA respectively. For 20 weeks of GA, the mean BPD was found to be 4.68 + 0.19 cm ranging from 4.4 to 5.1 cm with standard error of 0.03 cm and 95% confidence interval (4.6 cm – 4.74cm), while it was 7.54 + 0.26 cm ranging from 7.0 to 8.1 cm with standard error 0.04 with 95% confidence interval (7.47 cm - 7.62 cm) and 8.95 + 0.27 cm, ranging from 8.3 to 9.4 cm with standard error 0.04 with 95% confidence interval (8.86 cm - 9.0 cm) for GA of 30 weeks and 38 weeks respectively.
Diagram 1 shows the box plot describing BPD measurements about its median, first quartile, third quartile, minimum and maximum observations. Table 3 shows the growth chart for fetal BPD measurements for each GA. For 20 weeks of GA, the 5th, 50th and 95th percentile values of BPD measurements were 4.40 cm, 4.70 cm and 5.00 cm respectively, while it was 8.57 cm, 8.90 cm and 9.40 cm for 5th, 50th and 95th percentiles at 38 weeks of GA. At 20 weeks of GA, 50% of the subjects were having BPD value below 4.7 cm, while 50% of them were having below 8.9 cm at 38 weeks of GA.
The common regression equation considering all gestational weeks derived for GA estimation by BPD measurements was 1.275 + (3.905 X BPD in cm, where “R2”- the proportion of variation in dependent variable (GA) was 0.944 and “r”- the correlation coefficient was 0.9
Table 4 shows predicted GA (weeks) derived by common regression equation and separate equations for each GA for BPD measurements. By using the common regression equations for GA from 20 to 38 weeks, there was a difference of 1.44 to 2.22 weeks in average actual and predicted GA at 36 to 38 weeks of pregnancy. However, the same difference was less between 20 to 35 weeks of gestation. The GA estimated by separate regression equations for each GA was more accurate as compared to that with common regression equation.
Discussion:
The literature is replete with articles that focus on predicting menstrual age using ultrasound measurements of the fetus. A common theme among these articles is that the variability in predicting menstrual age increases as pregnancy advances for all fetal parameters and the increase in variability is undoubtedly due to actual differences in fetal size, because it has been demonstrated in populations with optimal menstrual histories, with known dates of conception and in whom age was established early in pregnancy by use of crown-rump length measurements.13
Considering the above facts, present study was conducted to measure BPD by ultrasonography for different GA groups from 20 to 38 weeks of gestation in a normal singleton pregnant women from Belagavi District, Karnataka and to find out the predictive accuracy of GA determined by USG parameters with actual GA by LMP.
The BPD has received the greatest attention in literature as the means of establishing GA.14-17 Virtually many studies have demonstrated a progressive increase in variability in from 20 weeks to a term, but the extent to which the variability increases in the late third trimester of gestation has been a subject of some disagreement in the available literature.15-17 In various studies on antenatal cases with known LMP or in whom GA was confirmed by in early pregnancy by CRL, the variability of the late third trimester BPD age predictions has been repeatedly demonstrated to be approximately + 3.5 weeks.16
Benson CB and Doubilet PM18 confirmed the large variability associated with BPD measurements in third trimester among the cases whose menstrual histories had been established early in pregnancy by CRL. However, in this study no attempt was made to eliminate multiple gestations or cases with potential growth disturbances. This study concluded that the variability in predicting menstrual age using BPD reached a peak of approximately 4.1 weeks (2 SD) in the late trimester of pregnancy.
Many studies have demonstrated a progressive increase in variability in BPD measurements from 20 weeks to term, but the degree to which the variability increases in the late third trimester of pregnancy has been a subject of some disagreement in the literature. In the studies of patients with optimal menstrual histories, the variability of late third trimester BPD age predictions has been consistently demonstrated to be approximately + 3.5 weeks.13
Fetal BPD measurement is the most reliable in the second trimester and using both A and B scans. Fetal maturity can be predicted when used between 20-30 weeks and the values become more scattered around the mean in later weeks. In high risk pregnancies like diabetes and toxaemia, statistically significant difference in the fetal BPD is seen.19 Studies on BPD in certain populations showed linear correlation between BPD and GA and fetal weight in normal fetuses.20,21
Table 5 shows mean BPD values obtained in the present study against the published standard values of the other studies by Acharya P et al8, Zaisi S et al22, Hadlock FP et al23, Campbell S et al3, Jeanty P et al24, Chitty LS25and Campbell SW26. The average GA was found to be 21.70 + 0.75 weeks for the BPD measurements of 5.0 cm in 16 cases, while it was 32.65 + 1.19 weeks (13 cases) and 37.06 + 1.3 weeks ( 14 cases) for BPD measurements of 8 and 9 cm respectively. The study by Hadlock FP et al13 reported almost same values of average GA for BPD measurements of 5 cm (21.2 weeks), 8 cm (32.5 weeks) and 9 cm (37 weeks). The mean values of BPD almost comparable till 30 weeks of GA, but thereafter the mean values in the present study were found to be lower than the other study values.
Many studies have demonstrated a progressive increase in variability in BPD measurements from 20 weeks to term, but the degree to which the variability increases in the late third trimester of pregnancy has been a subject of some disagreement in the literature. In the studies of patients with optimal menstrual histories, the variability of late third trimester BPD age predictions has been consistently demonstrated to be approximately + 3.5 weeks.
The earliest measurement of GA taken in pregnancy should usually be accepted as the definitive assessment, subsequent examinations reflecting only fetal growth in the intervening period. The ultrasound assessment of GA confirms the menstrual dates, if the measurements taken after the first trimester are within one week of GA taken from menstrual dating. Reduced accuracy of prediction of GA after 20 weeks must be appreciated.26
The observed GA was found to be almost same in a study by Hadlock FP et al 13 like that in the present study for BPD values from 20 to 38 weeks of GA. The use of reference tables from other populations may lead to an inappropriate and incorrect assessment of pregnancy.This study has provided the reference charts of BPD for normal singleton pregnant women from 20 to 38 weeks of gestation which will have relevant clinical impact. It will help to improve the accuracy of diagnosis of GA and fetal growth disturbances like IUGR, macrosomy, etc. However, the measurements of other parameters like head and abdominal circumferences, femur length would have increased the accuracy. The influence of fetal sex, maternal height, age, parity, weight, etc on fetal growth27, were not taken into account in selection of study subjects. A larger multicentered study is required to be undertaken for more accurate and valid assessment of fetal growth and GA in local population.
Conclusion:
There was a discrepancy in measurements of bi-parietal diameter in present study and in western and other different population groups, which was more especially in later part of third trimester. Use of charts derived from a different population especially in late third trimester may lead to errors in estimation of GA, fetal growth and development and deciding expected date of delivery. For this, use of reference tables prepared from local population will enhance the clinical accuracy. The normalcy of fetal parameters should be judged against local population standards.
Ethical considerations:
Ethical clearance was obtained from institutional ethics committee of KLE Academy of Higher Education and Research Belagavi. A permission letter was obtained from Belagavi Institute of Medical Sciences, Belagavi. The voluntary nature of participation and the right to withdraw at any time were emphasized. Written informed consent was obtained from every participant. Confidentiality was maintained throughout the study.
Source of Funding : Nil
Conflict of Interest : Nil
Authors’ Contribution:
All the authors participated in the designing, data collection, analysis, and writing of the study. The authors have read and approved the final paper.
Acknowledgments:
Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Englishhttp://ijcrr.com/abstract.php?article_id=2582http://ijcrr.com/article_html.php?did=2582
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241114EnglishN2019February21HealthcareDevelopment of Low Calorie Jam by Replacement of Sugar with Natural Sweetener Stevia
English0916Renu SutwalEnglish Jyotika DhankharEnglish Preeti KinduEnglish Rita MehlaEnglishRecently, much attention has been focused on potential health benefits of low calorie foods. This research was conducted for the development of low calorie apple jam by using stevia as a sweetener. The jam samples were stored in pre-sterilized glass jars and were analyzed physico-chemically (moisture, ash, pH, acidity, TSS, total sugars and reducing sugars and ascorbic acid) and organoleptically (colour, taste, appearance, flavour, texture and overall acceptability) during 28 days of storage. Apple jam prepared with sucrose served as control (T1). During storage, a decrease was observed in moisture content (76.99 to 75.33%), ash content (2.01 to 1.36%), pH (3.52 to 3.28) and ascorbic acid content (6.96 to 6.85%) while an increase was recorded in TSS (22.00 to 25.00 °B), titratable acidity (0.49 to 0.66%), total sugars (15.06 to 18.28%) and reducing sugars (5.63 to 8.40%). Statistical analysis of jam samples revealed that storage intervals had a significant (pEnglishJam, Stevia, SugarsINTRODUCTION
The pervasiveness of type 2 diabetes mellitus is growing throughout the world at an alarming rate, resulting in increased premature mortality and healthcare costs (1). It has been predicted that 1 in 10 people worldwide is expected to suffer from this disease by 2030 (2). The primary focus of suitable prevention strategies have been on lifestyle interventions. Modern consumers are now more concerned about the nutritional and caloric value of food they consume and more interested in preferring healthier food products in their diet. The importance of lifestyle prevention makes it necessary to investigate the protective role of healthy nutrients and foods (3). Fruits and vegetables are considered as essential source of nutrients and possess a low content of fats, proteins and calories. The consumption of fruits and vegetables has been related to potential health benefits as they are rich in minerals, vitamins, carbohydrates and fibers (4).
Apple has got the third highest anti-proliferative activity among fruits (5). Apple has high nutritional value and is a good source of vitamin C, Potassium and fiber. It contains 11% sugar, 0.3% proteins, 14% carbohydrates, 4% vitamins and minerals and remaining part of apple contains water (6). Apple peel also contains a large number of nutrients (5). Apple which has good healing power is affective for maintaining health and helps to relief body from many diseases such as diabetes, cardiovascular diseases, arthritis, constipation, cancer, rheumatism, dysentery, Alzheimer and also reduces chances for gallstones formation (7, 8, 9, 10).
Jam is defined as an intermediate moisture food obtained upon boiling fruit pulp with sufficient quantity of sugar (sucrose), pectin, acid, and other ingredients such as preservatives, colouring agents and flavouring materials to a gel like consistency which is firm enough to hold the fruits tissues in position (11). As per FSSAI Standards (12), Jam should contain more than 68.5% total soluble solid (TSS) content and fruit pulp content should be at least 45%. Usually, jams have been prepared with a high amount of sugars, mainly sucrose (13). However, consumption of sucrose in large quantity has been associated with adverse effects on health, such as obesity, diabetes, cardiovascular diseases and hypertension (14). Therefore the use of low calorie sweeteners for replacement of sucrosehas been evaluated.
Now-a-days, natural sweeteners are trapping more attention as the replacer of sugar. Stevia has recently gained importance as natural non caloric sweetener and is considered as the main natural substitute of sucrose due to its various modes of action. Stevioside is the main component of stevia that imparts sweetness and is 300 times sweeter than sucrose (15). Stevia exerts several beneficial effects on human health including hypoglycemic, hypotensive due to its blood pressure lowering properties and noncariogenic activities (16, 17, 18). It acts as cardiotonic as it tones, balances and strengthens the heart and also exhibits antimicrobial activities (19). Stevia is also thought to affect glucose metabolism and renel function (20, 21). Keeping in view the above points, the present study was aimed at developing a low calorie apple jam using stevia in place of sucrose.
MATERIALS AND METHODS
Fresh mature apples and sugar were procured from a local market in Rohtak and stevia powder was purchased from Growmore Biotech Ltd, Tamil Nadu, India. All chemical reagents required for analysis of sugar free jam were of analytical grade.
Preparation of apple pulp
Fully ripe, spoilage free apples were washed with clean water. After peeling, apples were cut into pieces and seeds were removed. The fruit pieces were then placed on flame and meshed with a mesher.
Processing of liquid stevia extract
The raw stevia powder was added into boiling water and the mixture was boiled for 2 minutes. After this, the mixture was filtered through a muslin cloth and stored in a jar.
Preparation of jam
Various ingredients for the preparation of low calorie apple jam were weighed according to specifications (1 kg apple pulp, 12 ml stevia extract, 2 g pectin and 500 mg potassium metabisulfite). After pulping, pectin was added and the mixture was cooked until it gave sheet flake test. At this point, TSS of the mixture was noted by hand refractrometer. Then the cooking was stopped and potassium metabisulfite (preservative) was added. The mixture was left for cooling. After cooling, liquid stevia extract was added to it. The finished product was poured into clean, dry sterilized glass jars. Then the product was cooled and capped. The final product was stored in a cool dry place. For the preparation of control jam, sugar was added in place of stevia.
Optimization of levels of stevia in jam
Levels of stevia were evaluated for sensory parameters like colour, appearance, texture, flavour, taste and overall acceptability using 9 point hedonic scale by a panel of 10 judges. 0.6% stevia extract was evaluated as most acceptable for the production of low calorie apple jam because it was as sweet as the control jam. However texture and appearance were lacking in optimized jam because of absence of sugar as sugar gives set and glossiness to jam which was observed in control jam.
Chemical analysis
Moisture content, ash content and titratable acidity were determined by AOAC (Association of Official Analytical Chemists) methods(22). Determination of TSS (Total soluble solids) and ascorbic acid were carried out using Ranganna method (23) while total sugars and reducing sugars were estimated by Lane and Eyon’s method given by Ranganna (23). pH was determined as per method given by Ranganna (24).
Microbiological analysis
Total viable count of jam samples was done using the pour plate method given by Harrigan (25). Yeasts and moulds were enumerated by pour plate method described by Harrigan (25).
Sensory analysis
The apple jams were evaluated for sensory parameters like colour, appearance, texture, flavour, taste and overall acceptability using 9 point hedonic scale by a panel of 10 judges.
Statistical analysis
Data were analyzed using one-way analysis of variance (ANOVA) procedures in a randomized complete block design with three replications. Statistical analysis was performed using the OPSTAT software version opstat1.exe (Hisar, India). The values are represented as mean ± SD.
RESULT AND DISCUSSION
Optimization of levels of stevia in jam
Levels of stevia in jam were optimized by sensory analysis using 9 point hedonic scale by a panel of 10 judges (Table 1). Stevia was added in different concentrations (0.15%, 0.3%, 0.45%, 0.6%).Among the samples, jam prepared with 0.6% stevia extract showed highest sensory score for all the parameters. It was as sweet as the control jam. Therefore, 0.6% stevia extract was selected for production of low calorie apple jam.
Effect of storage on physico-chemical characteristics of low calorie apple jam and control jam
Moisture content
Moisture is an important factor which affects the shelf life and freshness of products. Food Products having high moisture content display short shelf life. It was observed that moisture content decreased in all samples during 28 days of storage period (Table 2). For the treatments, maximum mean value was recorded in sample T2 (76.16%) while minimum value was recorded in sample T1 (33.17%). The statistical analysis revealed that storage effect on the moisture content of T1sample was significantly different from T2 (p?0.05). The difference in moisture was expected because preservative added in optimized jam (KMS) acted as a firming agent strengthening the quality of jam and also acted as glazing agent thus providing a waxy coating to prevent water loss. The mean moisture content value significantly (p?0.05) decreased from 55.49% to 53.89% during storage. This decrease in moisture content might be due to reopening of the same pack during storage for analysis. The results were in agreement with findings of Anjum et al. (26) who studied diet apricot jam and reported decrease in moisture during 60 days of storage. Similar observations were found by Ashaye and Adeleke (27) in roselle jam.
Ash content
The results obtained regarding ash content of jam samples are presented in Table 2. Ash content gives an indication of minerals composition of food products (27). Ash content represents inorganic matter remaining after destruction of organic matter (24). Among both the treatments, maximum mean value was recorded for sample T1 (1.72%) while minimum value was recorded for sample T2 (1.66%). The mean ash content value significantly (p?0.05) decreased from 2.03% to 1.38% during storage. This decrease in ash content might be due to increased activities of microorganisms by utilizing the minerals for growth which resulted in reduction of mineral content. Similar observations was found by Ashaye et al. (27) who observed a decrease in ash content of rosella jam during storage.
TSS content
The values of TSS ranged from 22ºB to 68ºB and a significant difference (pEnglishhttp://ijcrr.com/abstract.php?article_id=2583http://ijcrr.com/article_html.php?did=2583
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Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241114EnglishN2019February21Life SciencesNegativity Bias, Time Spent on Mental Processing and Subjective Well Being
English1720Sonal PaliwalEnglishBackground: Human beings, either because of evolutionary consequences, predispositions, upbringing or learning are more influenced by negative happenings or events as compared to positive ones. Though negativity bias is important for survival and while self-analysing we need to focus on our weaknesses, it is not clear whether this focus is serving a positive function. The study aimed to find out the relationship between the mental processing of strengths and weakness and the Subjective Well Being (SWB).
Material and Methods: A cross-sectional study was undertaken to understand the concept of negativity bias in undergraduate students aging 19-25 years. The Satisfaction with Life Scale (SWLS) was used to measure the SWB of the participants.
Results: The results indicated that students take less time to tell their strengths and more time to talk about their weaknesses. The SWB of students who elaborate their weaknesses is low as compared to those who give one word or one sentence description.
Conclusion: The elaboration of weaknesses is hampering our subjective well being.
EnglishStrengths, Satisfaction with Life Scale, WeaknessesIntroduction
Why do we pay more attention to negative information and why do we prefer negative to positive? Is this individual, institutional, cultural or universal? The principle of negativity bias states that “bad is stronger than good”. According to Corns (2018), “the negative consumes our attention, informs our opinions, and generally affects us disproportionally to the positive” [1]. People weigh negative information more heavily than positive information [2] and negative traits are given greater weight than positive traits in evaluations [3].
Evidence on negativity biases, across cultures and contexts, lend support to the fact that negativity bias is the product of evolution. Grossman, Ellsworth, and Hong [4] and Oishi [5] have tried to find cross-cultural differences in negativity biases and have got positive results. Since different cultures and societies have different ways to deal with anxieties and uncertainties, this may affect the way negative information is weighed and processed. We pay more attention to the information or events that are unexpected and inconsistent as compared to those that are expected and consistent. We work hard to understand such information [6] and pay more attention to it, making it easier to enter long-term memory and influence our social judgements [7]. Despite the independence of thinking, memory and perceptual processes, the underlying cognitive processes are intrinsically interrelated. Negative stimuli are perceived as more complex than positive stimuli [8] and require greater attention and cognitive processing [9]. Addis, Wong and Schacter [10] found some neural differences in the construction phase but apparent neural overlap during the elaboration phase.
With the increasing and constant focus on competition because of societal and parental pressure, the adolescents concentrate more on what they do not have rather than on what they have i.e., on negative rather than on positive. This study was planned to understand whether this shift is healthy for the younger generation and for the community at large.
Aims and Objectives
To study the negativity bias in students.
To study the method used by students in giving responses.
To study the amount of time taken to process information related to strengths and weaknesses.
To study the effect of elaborative processing of negative information on Subjective Well being (SWB).
Material and Methods
A cross-sectional study was conducted on undergraduate students studying in private colleges of Nagpur city using convenient sampling method. The data was collected between January 2018 and July 2018. No sample size was calculated for the present study.
Inclusion criteria: All the students between the age group of 19-25 years who gave informed consent were included in the study.
Exclusion criteria: Those with a history of or existing psychological disorder and those who had received any self-enhancement or soft-skills training in the past one year were excluded from the study.
Procedure
Session 1
Participants were seated in a comfortable chair and were instructed to answer the questions asked by the researcher. Participants were told that there was no time limit and they should inform the researcher when the response was finished. Only two questions, “What are your strengths?” and “What are your weaknesses?” were asked to each participant. Participants were unaware that the researcher was keeping a record of time. Responses were recorded in the predefined format shown below:
Session 2:
The SWB of the participants was measured with the Satisfaction with Life Scale (SWLS) by Diener, Emmons, Larsen and Griffin (1985) [11]. SWLS is a commonly used global satisfaction scale to measure life satisfaction as a cognitive-judgemental process. It has test-retest stability of 0.82 and construct validity of 0.68 [11]. The SWLS consists of five statements, each of which is rated on a 7-point scale from Strongly Disagree (1) to Strongly Agree (7).
Each participant was thanked and debriefed about the research. But the participants remained unaware of the purpose of the study.
Statistical Analysis
Data was analyzed using SPSS 21.0. Graphical representation of the number of responses and time taken to give responses was made. To find out the effect of elaborative processing of strengths and weakness on SWB, the students who gave elaborative responses (elaboration group) in both the categories were sorted out and compared with those who gave only one word or one sentence response (no elaboration group). Independent samples t-test was used to compare the means of elaboration and no elaboration groups. A P value of 0.05 was considered to be statistically significant.
Results: One hundred seventy-three students participated in the present study. Data from 23 participants were removed because of overlapping responses. Out of the final 150 participants, 68 were male and 82 were female.
First examined were the number of one word, one phrase/ sentence and elaborative responses in strengths and weaknesses categories. Figure 1 shows that the total number of responses in the strengths category is more as compared to the number of weaknesses but the number of elaborative responses in the weaknesses category is more as compared to strengths. This indicates that students exhibit negativity bias.
Also examined was the amount of time taken to give weakness and strength responses. Participants took more time to give weakness responses as compared to strengths (see figure 2).
Independent samples t-test results reveal that in strengths category no significant difference was found in the scores of elaboration and no elaboration group (P= 0.071). There was a significant difference in the scores of elaboration group and no elaboration group (P=0.001) in weakness category (see table 1). Results suggest that the SWB was low in participants who gave an elaborative description of their weaknesses as compared to those who did not elaborate their weaknesses.
Discussion
The results of the present study show that the amount of time spent in expressing or talking about the weakness is more. Snyder and Lopez observed that people struggle for words when they have to describe their strengths, whereas they have no shortage of words when they have to describe their weaknesses [12]. The negative information i.e., weakness not only received more weight as can be seen by participants overall elaboration but also received a greater share of attention, as reflected in the amount of time spent. The findings are consistent with Fiske’s work which suggests that the bias towards weighing negative information more heavily is reflected in the amount of perceptual attention given to that information [3]. Response to the question like, “What are your strengths/ weaknesses?” require retrieval of information from memory, more specifically autobiographical memory. Gracia-Bajos and Migueles recorded the greater number of negative than positive experiences in adolescents as compared to other age groups. According to them, “the negative narratives included more emotional details, the reference to cognitive processes, mental rehearsal and justifications than the positive narratives” [13].
Individuals take more time to express their weaknesses because weaknesses are negative traits and are considered non-normative. Since people mostly have positive characteristics, they are often assigned less responsibility for their positive traits than for their negative traits [14]. Negative information exerts influence on our judgement, so, people want to justify their weaknesses and hence take more time in describing them.
Findings also suggest that individuals, who elaborate on their weaknesses and spend more time doing so, score low on SWB. Conversano et al., [15] in their study found that positivity bias is associated with increased mental and physical well being. Negative information is more likely to have an enduring effect [16] and that negative attributes can often interfere with the enjoyment of positive attributes [2]. Bias towards negative memory retrieval (in severe depression) may inhibit competing positive memories and worsen existing negativity biases [17]. High rumination predicts the onset of depressive disorder in healthy adolescents [18]. Failing, performing badly, inability to meet the societal and parental expectations and comparison makes students feel defeated and lost. Negativity bias, once a gift from evolution is being used in a different manner. Continuous preoccupation with weaknesses is affecting our well being and this is not a good sign for the generations to come.
Conclusion
The study and the results are preliminary and need further work. Findings reveal that people take more time and are more elaborative while processing negative information as compared to the positive one. The fact that elaborative processing of negative information and subjective well being do not vary together is not a sufficient condition to conclude that there is a cause-and-effect relationship. This tendency may have adverse effects on the well being of an individual. Although self-analysis is important for improvement, the present results carry a word of caution for parents, teachers and mentors who focus more on weaknesses and make their children and students do the same. This high focus on weaknesses is becoming an obstacle in our well being. Further explorations might help in understanding the ill effects of negativity bias. A study with larger sample and from all types of Institutions may help in a better generalisation of results.
Acknowledgement: Author would like to thank all the students who participated in the study and acknowledge those who helped in recording the observations. Author acknowledges the immense help received from the scholars whose articles are cited and included in references of this manuscript. The author is also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Source of Funding: None
Conflict of Interest: None
Englishhttp://ijcrr.com/abstract.php?article_id=2584http://ijcrr.com/article_html.php?did=2584Corns J. Rethinking the Negativity Bias. Review of Philosophy and Psychology 2018; 9:607-25.
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