A Review of the Use of the Health Belief Model for Weight Management

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Peer-reviewed

Research Article

Factors influencing weight direction behavior among college students: An awarding of the Health Belief Model

• Maryam Saghafi-Asl,
• Soghra Aliasgharzadeh,
• Mohammad Asghari-Jafarabadi

x

• Published: Feb 7, 2020
• https://doi.org/ten.1371/journal.pone.0228058

Correction

20 May 2021: Saghafi-Asl M, Aliasgharzadeh S, Asghari-Jafarabadi M (2021) Correction: Factors influencing weight management behavior amongst college students: An awarding of the Health Belief Model. PLOS ONE sixteen(5): e0252258. https://doi.org/10.1371/journal.pone.0252258 View correction

Figures

Abstruse

Background

Overweight and obesity have go a significant public health business organisation in both developing and developed countries. Due to the health implications of weight-reduction behaviors, it is important to explore the factors that predict their occurrence. Therefore, the present study was performed to examine factors affecting the behavioral intention of weight management as well as assess the predictive power of the Health Belief Model (HBM) for trunk mass index (BMI).

Methods

This cantankerous-exclusive study was conducted amidst 336 female person students recruited from dormitories of Tabriz Academy of Medical Sciences, using quota sampling technique. Information were collected past a structured questionnaire in seven parts (including perceived severity, perceived susceptibility, perceived benefit, perceived barrier, cue to activity, self-efficacy in dieting and physical action, and behavioral intention of weight management), based on the HBM. Structural equation modeling (SEM) was conducted to identify the human relationship between HBM constructs and behavioral intention of weight management. Linear regression model was performed to test the power of the HBM to predict students' BMIs.

Results

Higher level of perceived threats (sum of perceived susceptibility and severity) (β = 0.41, P<0.001), perceived benefits (β = 0.nineteen, P = 0.009), self-efficacy in do (β = 0.17, P = 0.001), and self-efficacy in dieting (β = 0.16, P = 0.025) scales was significantly related to greater behavioral intention of weight management. Moreover, perceived threat mediated the relationships between perceived cue to activeness, perceived benefits, self-efficacy in do, and weight direction practices. The fit indices of the SEM model seemed adequate. The terminal regression model explained approximately 40% of variance in BMI (P<0.001). Additionally, perceived severity, barrier, and self-efficacy in dietary life were the significant variables to predict students' BMIs.

Conclusions

These findings advise that health instruction programs based on the HBM needs to be integrated in preventive health programs and health interventions strategies to ensure adherence and well-being of the participants.

Citation: Saghafi-Asl Thousand, Aliasgharzadeh South, Asghari-Jafarabadi Chiliad (2020) Factors influencing weight management behavior among college students: An application of the Health Belief Model. PLoS ONE 15(2): e0228058. https://doi.org/10.1371/journal.pone.0228058

Editor: Berta Schnettler, Universidad de La Frontera, Chile

Received: Baronial 5, 2019; Accepted: January 6, 2020; Published: February 7, 2020

Copyright: © 2020 Saghafi-Asl et al. This is an open up access article distributed nether the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are inside the manuscript and its Supporting Data files.

Funding: This work was supported by the Tabriz University of Medical Sciences to MS-A. The funder had no role in study design, data drove and analysis, conclusion to publish, or grooming of the manuscript.

Competing interests: The authors have alleged that no competing interests be.

Introduction

Overweight and obesity accept become epidemic rise trends in both developed and developing countries [i–four]. According to estimates past World Health System (WHO) in 2016, there were approximately ane.9 billion overweight adults aged xviii years and above from which at to the lowest degree 650 million were obese [five]. The growing trend in the transition from overweight status to obesity often occurs at ages 18–29 years. Obesity is an of import concerns of health care professionals, as it is accompanied by numerous physical and psychological problems including coronary heart illness, diabetes, and several cancers [half-dozen–viii]. Obesity likewise imposes enormous financial burdens on both governments and individuals [9]. Several factors contribute to obesity including genetics and behavioral and environmental parameters such equally physical action and dietary behavior [10].

The collegiate period is a critical time for altering physical activity and dietary patterns which lead to weight gain of students [11, 12]. Thus, weight management remains an important health claiming for this population. Several preventive and treatment programs are applied for weight control [13]. Nevertheless, compliance with weight-loss treatments varies among women for a range of reasons [13, fourteen]. Previous studies have shown that psychosocial factors such as perceptions about health and obesity, and self-efficacy play important roles in the success of weight loss and maintenance programs [15–17].

To develop effective weight direction interventions for higher students, it is important to understand the factors that predict the occurrence of appropriate weight reduction behavior. The Health Belief Model (HBM) is a health-specific social cognitive model that attempts to predict and explicate why individuals change or maintain specific health behaviors [18]. This model assumes that private involvement in health-related behaviors is determined past understanding vi following constructs: Perceived severity (an individual's perception of the seriousness and potential consequences of the status), Perceived susceptibility (an individual's cess of their risk of getting a disease or condition), Perceived do good (an individual's beliefs virtually whether the recommended behavior will reduce the risk or severity of impact), Perceived barrier (an individual's assessment of the difficulties and cost of adopting behaviors), Cue to activity (the internal or external motivations promoting the desired behavior), and Self-efficacy (an individual'south belief about their capabilities to successfully perform a new wellness behavior). These half-dozen constructs provide a conceptual framework for designing both long and short-term health behavior interventions (Fig 1) [xviii, 19].

Several studies examined the factors affecting weight control intention through HBM [20–23]. Park et al. examined factors affecting behavior intention of weight reduction amongst female middle-school students, using HBM [20]. They found that perceived threat (a sum of severity and susceptibility), cues to action, and perceived self-efficacy were significantly associated with behavioral intention of weight reduction for all respondents [xx]. McArthur et al. tested the predictive ability of HBM (which consisted of perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to activity) for trunk mass index (BMI) among a college student sample [21]. They constitute significant positive associations between ratings on the perceived susceptibility, perceived barriers, and perceived benefits scales and BMI. Findings also revealed significant inverse associations betwixt ratings on the perceived severity, and external cues to activity scales and BMI [21].

To the all-time of our knowledge, no research has been conducted on the whole HBM constructs for the prediction of weight direction among higher students. Therefore, the present report aimed to (1) develop and assess the validity and reliability of an HBM-based questionnaire for weight management behavior, (2) explore the furnishings of all HBM constructs on weight direction behaviors among college students. Based on the 2nd objective, nosotros proposed the following hypotheses:

H1: Behavioral intention of weight management will be positively influenced past perceived threat, perceived benefits, and cocky-efficacy in dieting and exercise. H2: Perceived barriers will negatively influence behavioral intention of weight management. H3: Perceived threat will mediate relationship between cues to action and behavioral intention of weight direction, and (3) make up one's mind the predictive power of HBM constructs for the BMI of students.

Methods

Inquiry design and sampling

This cross-exclusive study was conducted among Iranian students from dormitories of Tabriz University of Medical Sciences from June to September 2018. It is suggested that the ratio between the sample size and the number of model parameters in the range of ten:one or even xx:i seem appropriate [24]. The hypothesised model in this study incorporated 22 parameters. Considering a 16:1 ratio, the sample size was determined to be 352 for the study. In order to allow for potential missing information, the initial sample size was set at 380. In the process of sampling, a sample of 380 subjects who agreed to participate was evaluated, 14 of whom given imperfect data in questionnaire were excluded from the study. Therefore, the final sample size in assay was 366. The subjects were selected through quota sampling method; all dormitories were chosen then in proportion of number of students' resident in each dormitory and in accordance with the estimated sample size, a quota was assigned to each one and the convenience sampling from these dormitories was carried out. Information were collected through personal interviews, using a structured questionnaire. Informed consent was obtained from all participants, earlier the onset of the written report.

Measurement tool

The first version of the questionnaire used in measuring HBM variables was derived from Park (2011) and McArthur et al. (2017) [xx, 21]. Eighty-nine statements were included and represented 8 perceptional and behavioral categories, as follow: 13 questions on perceived severity consisting of iii subscales (emotional/mental, health, physical wellness/ fettle, and social professional); 7 questions on perceived susceptibility consisting of ii subscales (lifestyle and environmental); fourteen questions on perceived barriers consisting of 3 subscales (practical concerns, emotional/ mental health, and awareness); 13 questions on perceived benefits including 3 subscales (emotional/ mental health, physical health/ fitness, and social/ professional); 12 questions on cues to activity consisting of 2 subscales (internal and external cues to action); 18 questions on self-efficacy in dieting including 2 subscales (Habits and preferences and Emotional/mental health); 7 questions on self-efficacy in exercise, and 5 questions on behavioral intention of weight management consisting of two subscales (dieting and exercising). All statements were rated using a five-point Likert scale ranging from ane (strongly disagree) to v (strongly agree). In order to determine the content validity, ten specialists and professionals (exterior the team) in the field of Health and Diet were consulted. And then, based on the Lawshe's Table, items with college values of Content Validity Ratio (CVR) (i.e. higher than 0.62 for ten people) and Content Validity Index (CVI) (i.e. higher than 0.75) were considered adequate [25]. CVI and CVR showed satisfactory results for each detail (CVI range: 0.78–i.00 and CVR range: 0.fourscore–1.00). Reliability was calculated using internal consistency (Cronbach's Alpha). Alpha coefficients equal to or higher than 0.70 were considered satisfactory [26]. The overall reliability of the instrument based on the Cronbach's alpha, was 0.92. To assess the examination-retest reliability of the questionnaire, a subgroup of xxx randomly selected students were asked to repeat the survey after a ii-week interval. Intraclass correlation coefficient (ICC) was computed to evaluate the stability over fourth dimension. ICC indicated splendid agreement (ICC = 0.86).

Statistical analysis

Data analyses were conducted using STATA version 12. The characteristics and beliefs of the participants were described, using means (SD) and frequencies (percentages), wherever appropriate. Weight groups were divided into three categories: underweight (BMI<18.5 kg/m²), normal weight (18.5≤BMI<25 kg/m^two), and overweight (BMI≥25 kg/m2). There were few obese students, who were put into the overweight group. Chi-foursquare tests were applied to analyze categorized variables. The mean differences were determined by Kruskal Wallis examination. In the case of pregnant results, Isle of mann-Whitney U test with Bonferroni correction was used to assess the pair-wise comparisons.

Multiple imputation in expectation–maximization (EM) algorithm method was run to manage missing information [27]. Path analysis was used as a tool for structural equation modeling (SEM) to determine the relationship betwixt HBM constructs and behavioral intention of weight management and recognize directly and indirect influence of independent variables toward dependent variables. The magnitude of the relationship was measured past path coefficients and correlations, equally standardized estimates. Goodness of fit indices selected for model evaluation were: normed chi-square (χ2/df, values lower than 5 were accepted); comparative fit index (CFI, values greater than 0.90 were accepted); Tacker Lewis alphabetize (TLI, values greater than 0.90 were accepted); standardized root mean squared residual (SRMR, values lower than 0.05 were accustomed); and root mean foursquare error of approximation (RMSEA, values lower than 0.08 were accepted) [28, 29].

A hierarchical linear regression analysis was performed to estimate the relationships betwixt HBM scales, demographic characteristic, and BMI. P-Values less than 0.05 were considered equally statistically significant.

Results

Baseline characteristics

A total of 336 students completed the questionnaires. The mean age of the students was 22.02 (±3.02; range, 18–43) years. Based on self-reported weight and height information, the mean BMI was 22.62 (±three.17; range, fifteen.63–32.72) kg/thou². The baseline characteristics of the participants based on three weight groups are presented in Table 1. The marital status of the students was significantly different amidst weight groups (P = 0.002). The majority (89.9%) of the students were single.

In that location was a significant human relationship between family history of obesity and weight status of the student (P = 0.004). Approximately, 68 percentage of the participants had at least 1 obese family unit member. Nearly one-half of the students had experience trying to lose weight. This experience differed significantly amid weight groups (P<0.001). Most of the students controlled their diet and exercised to lose their weight. More than half of the students responded that they attempted to manage their weight to amend their appearance, while almost one-thirds did and so for health. There were significant differences in "the reasons for weight reduction" among under- and normal-weight and overweight groups (P<0.001). The socioeconomic status of the students was not significantly dissimilar amidst weight groups.

Weight-related beliefs of the participants past weight status

Weight-related beliefs of the students comprising the mean scales and related subscales ratings (SD), and the Cronbach's alpha are presented in Table 2. The hateful scores of the 13-item perceived severity of the overweight consequences were three.26±0.76 for all respondents that showed significant differences amongst the 3 groups (P≤0.001). Students in the underweight grouping showed the highest mean score for perceived severity (three.84±0.57). The beliefs for the physical wellness/fitness subscale received higher ratings than the other severity subscales (3.44±0.85). Underweight and normal weight students rated the emotional/mental health subscale college than overweight students (P≤0.001). The mean score of physical health/fitness and social/professional subscales showed significant differences among the three groups (P≤0.001).

The mean score of the total perceived susceptibility of obesity hazard was three.46±0.76 for all respondents. Students in the underweight group had the highest score (3.64±0.66); even so, there were no significant differences amidst the 3 groups.

The mean score of the xiv-item perceived barriers to adopting good for you eating and physical activity habits were 2.94±0.75 for all respondents that showed significant differences amongst the three groups (P≤0.001). In addition, students in overweight group showed the strongest perceived barrier (iii.60±0.73); followed by students in the normal weight (2.81±0.64), and underweight (two.39±0.59) group. Beliefs from the emotional/mental wellness subscale received higher rating than other ones.

The mean score of the 13-item perceived benefits to adopting healthy eating and physical activity habits were 3.73±0.67 for all respondents. At that place were no significant differences in mean rating on full scale among the three groups. The Emotional/mental health subscale construct received college rating than other ones.

The hateful score of the perceived cues to activeness for weight management was iii.49±0.70 for all respondents. Normal-weight students had the highest score (3.54±0.65), but there were no significant differences amidst the three groups. The mean rating of external and internal cues to action were not dissimilar amid the study groups.

The mean rating on the self-efficacy in dieting was three.22±0.64 for all respondents that showed significant differences amidst three groups (P≤0.001). As, students in the underweight group showed the strongest belief about their self-efficacy in dieting (3.81±0.42); followed by students in the normal-weight (three.27±0.58) and overweight grouping (2.82±0.67).

The mean rating on the self-efficacy in exercise was 3.27±0.79 for all respondents. Students in the normal-weight group had the highest score (3.39±0.71) and indicated significant differences in comparison to those in the overweight group (P≤0.001). But these ii groups showed no significant difference, compared to the underweight grouping.

The mean rating on behavioral intention of weight management was three.07±0.78 for all respondents. The result showed that students intended to manage their weight by exercising rather than dieting. The hateful score of the total behavioral intention of weight management and the two subscales did not demonstrate significant differences among the three groups.

Path models

Effects of the final model of HBM constructs on weight direction behaviors are displayed in Fig two. This model was identified given the good fit indices (χ2/df = 2.68, CFI = 0.99, TLI = 0.95, RMSEA = 0.07, SRMR = 0.02) for the all students sample. The model indicated that perceived threats, perceived barriers, perceived benefits, self-efficacy in dieting and cocky-efficacy in practice straight afflicted behavioral intention of weight management. Higher level of aforementioned scales was significantly related to greater behavioral intention of weight management. Moreover, cues to action, perceived benefits and self-efficacy in practise indirectly affected behavioral intention of weight direction through the impact of perceived threats. Tables 3 shows full, directly, and indirect effects of HBM constructs on weight management behavior. Perceived threats and perceived benefits were the greatest predictor of weight loss behaviors with a total correlation coefficient of 0.forty and 0.35, respectively. All of these associations were significant, except for the association of perceived barriers and behavioral intention of weight management.

Fig 2. Effects of Health Conventionalities Model constructs on behavioral intention of weight management.

Path coefficients were shown above. *Pregnant at 0.05 level. χ2/df = 2.68, CFI = 0.99, TLI = 0.95, RMSEA = 0.07, SRMR = 0.02.

https://doi.org/10.1371/journal.pone.0228058.g002

HBM every bit a predictor for BMI

Table 4 presents findings from the ii-step hierarchical regression analysis constructed to exam the ability of HBM and some of the full general characteristics to predict the BMIs of higher students. The models were constructed from data provided by all students who responded to the whole HBM calibration. When perceived severity, perceived susceptibility, perceived benefits, perceived barriers, cues to action, and self-efficacy in dieting and self-efficacy in exercise were regressed confronting BMI, the model was highly meaning (P<0.001). The offset model explained approximately 34% of the variance of the students' BMIs. Self-efficacy in dieting and perceived severity had an inverse pregnant association with BMI. Self-efficacy in dieting (β = -1.63, P<0.001), perceived barriers (β = i.18, P<0.001), and perceived severity (β = -1.17, P<0.001) seemed to be the most important amongst these seven variables. Findings besides revealed significant positive associations between ratings on the perceived barriers and BMI. In model 2, those demographic variables that had a meaning correlation with BMI were added to model 1. The inclusion of age and marital status increased the R², and explained 40% of the variance in BMI (P<0.001).

Give-and-take

The present study was conducted to investigate the factors influencing behavioral intention by applying HBM and estimate the relationships between several conventionalities scales and the BMIs of students. Weight loss is usually less successful, despite applying various weight-loss programs, bachelor to the public; once succeeded, the maintenance as well every bit long-term weight-loss plan compliance rates are unremarkably low [30]. Therefore, the identification of psychological predictors of weight management could contribute to improv treatment efficacy [15–17].

The present results showed that students with unlike weight statuses had different perceptions about obesity and weight reduction beliefs. The synthetic SEM in this study supported the theoretical framework, indicating that health beliefs can directly and indirectly predict educatee'southward behavior intention for weight management. In improver, the HBM scales partially predicted the students' BMIs.

The electric current finding showed that the near common weight direction methods among students were exercise and dieting. This issue is consistent with those of other studies that examined weight-loss practices among university students [31, 32]. Well-nigh, 55% of the students responded that they attempted to control their weight for a better appearance. The electric current findings are in line with those of other studies which have indicated that keeping up appearance was the main reason for managing body weight among academy students [31]. The socioeconomic atmospheric condition of the participants were not related to their weight status. Previous studies accept reported contradictory results regarding the clan between socioeconomic status and BMI [xx, 33–35]. The lack of standard methods for categorizing SES might be the principal reason for this contradiction [36].

Overweight students in comparison with other groups showed lower ratings on perceived severity and self-efficacy in dieting and self-efficacy in exercise, but higher ratings on all subscales of perceived barriers to adopting good for you eating and physical activity habits. The higher ratings on the severity belief scale given by underweight and normal-weight students may have motivated them to manage their weight, since individuals make changes if they perceive that their electric current status could accept serious health complications. Some previous studies have shown that obese people accept less perceived self-efficacy in relation to eating and exercise than non-obese groups [37–39]. Participants' perceived self-efficacy reflects the conviction in their chapters to perform a new wellness behavior. A person with a college level of confidence will more likely engaged in a specific good for you eating behavior to meliorate wellness. In this regard, information technology has been reported that obese Americans are more than likely to name several barriers to weight-loss behaviors, compared with not-obese individuals [twoscore]. The results demonstrated that emotional/mental factors, unawareness of healthy food choices, and applied obstacles hamper students to refrain from unhealthy eating behaviors or calorie-dense foods. Moreover, underweight and normal-weight students gave higher, but not significant ratings to perceived susceptibility beliefs than overweight students. Unlike previous studies, the electric current results suggested that these groups of students may not consider themselves susceptible enough to being overweight to take further action. Moore et al. reported that African American normal-weight women reported the same perceived threat of obesity-related diseases equally overweight women [41]. In fact, an inappropriate perception of one'southward own weight and inadequate information about the consequences of obesity could lessen the perceived threat of being obese. Students in underweight and normal-weight groups showed the strongest beliefs about the emotional/mental benefits of adopting salubrious eating and physical activity habits. Differences did non reach the significance level in other subscales of perceived benefit. These results are inconsistent with prior research [42, 43]. Such findings suggest that apprehension of the favorable outcomes of adopting healthy eating habits and engaging in regular physical activity can encourage participants to manage their weight.

In the present study, the constructed SEM provides a ameliorate agreement of the mechanism through which psychosocial factors affect weight direction behavior. The results of path analysis indicated that perceived variables including threat and self-efficacy in dieting, have a meaning direct outcome and perceived benefits and cocky-efficacy in practise have significant direct and indirect effects on predicting weight management behavior. College levels of the mentioned perceptions further predicted a college take a chance of executing behavioral intention of weight management. Perceived threat exerted the greatest influence on behavioral intention of weight management in all respondents, followed by perceived benefit. These results are in agreement with those that suggest perceived benefits, threat, and self-efficacy as stiff predictors of some health behaviors [42–44]. Bishop et al. reported that perception of threat and self-efficacy account for a considerable amount of the variance in the functioning of patient condom practices [44]. When the rate of cocky-efficacy or person'due south confidence in their ability to perform a specific behavior was high, the probability of incorporating wellness beliefs changes was besides increased. O'Connell et al. constitute that dieting benefits were the almost powerful predictor of dieting behavior, especially for obese adolescents [43]. In a study by Kang et al., perceived benefits was the nigh important predictor of intention to command obesity among female person students [42]. This result suggests that if patients are aware of the benefits of managing weight by dieting and exercise, they might become involved in the programs.

The results showed that perceived barriers to eating salubrious foods and to undertaking regular physical activeness could not significantly affect behavior intention of weight management. This issue was consistent with the results of some [twenty, 45], but non other [46, 47] studies which have reported that a college perception of the difficulties and price of performing behaviors are negatively related to a lower likelihood of performing the positive health behaviors. In the present study, the perceived barriers were increased among students living in dormitories due to problems such as lack of time, bereft noesis, and insufficient skills in preparing good for you food [48, 49]; thus this component failed to justify the behavioral intention of weight management.

In the present research, perceived threat mediated the relationship between cues to action and behavioral intention of weight direction. This suggests that external and internal cues would arouse a person's perceived threat of the risk of obesity by influencing perceived seriousness, susceptibility, or both which led the students to weight direction behavior. For example, the person believes that others judge her unfairly, owing to her weight or an obese family fellow member or a friend, and a serious health problem developed from beingness obese.

In both regression assay models, perceived severity, perceived barriers, and self-efficacy in dieting were the significant variables in predicting the BMIs of all respondents. Self-efficacy in dieting seemed to be the about significant parameter among the iii variables. The final model, in which the demographic variables were added, explained approximately twoscore% of the variance of students' BMIs. The results of the current study showed that students who causeless themselves to exist confident in their ability to perform the behavior had lower BMIs. This is uniform with previous results showing that obese women scored significantly less than the non-obese on self-efficacy in relation to food [37]. The significant inverse association between perceived severity and students' BMIs in both regression models proposed that students who noticed the possible negative physiological, psychological, and social consequences of beingness obese (eastward. g., chronic disease, mental health problems, difficulties in social human relationship) had lower BMIs. The significant positive associations betwixt the ratings of the perceived barriers scales and students' BMIs suggested that participants who regarded difficulties (east. g., lack of time, knowledge, and motivation) and cost of performing behaviors had higher BMIs.

There were several worth noting limitations in the design and performance of this study. The main limitation was the cross-sectional, non-experimental design, which provides only a glimpse of the population at a specific point of time. In addition, merely dormitory students of medical sciences were included herein, which confines the generalizability of the findings to all college students. Moreover, all the subjects were females, that are more prone to control eating habits and weight. Too, the anthropometric data was collected through self-reporting and data was collected through personal interviews that could lead to bias in the results. Future studies are needed to utilize HBM to place the associations betwixt weight-related behavior of diverse samples and their weight management behaviors. In addition, it would be worthwhile to expand interventional studies to investigate the effect of HBM-based educational programs on weight management in college students or other populations.

Conclusions

The significant variables in predicting behavioral intention of weight direction were perceived threat, perceived benefits, self-efficacy in dieting and cocky-efficacy in practise, and cues to action. In addition, it was reported that students have different perceptions about obesity and weight reduction beliefs by weight status. These results suggest that to ensure the adherence and success of the participants in wellness intervention, it is essential to design and implement wellness education programs forth with dietary approaches. Such programs should emphasize the negative outcomes of obesity, benefits of adopting a salubrious lifestyle, increase of self-efficacy in dieting and concrete activity, and internal and external stimuli for higher students.

Supporting data

Acknowledgments

The authors would like to thank Dr. Hossein Karimzadeh, Assistant Professor, Department of Urban Planning of Tabriz Academy for his aid with information analysis, and also the participants who involved in this study. Besides, the authors would similar to acknowledge the support of this work past Student Research Committee of Tabriz Academy of Medical Sciences.

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