The study was approved by the ethics committee of the institution before starting the study (VMKVMC&H/IEC/25/041). Informed consent was obtained from all participants. The study information was provided as part of the survey form in the E-mail that was circulated to the participants. Their confidentiality was protected, by anonymizing the questionnaires and delinking the identifying information, such as E-mail and IP address from the database.

The prevalence of obesity and its contributing factors were evaluated in 200 participants. The results showed that 36% were under 20 and 64% were over 20. The age group above 20 years had a higher prevalence of obesity, however the fact that age did not significantly correlate with obesity (χ² = 0.675, p = 0.411). The distribution of genders was 44.5% male and 55.5% female. Sex and obesity were found to be significantly correlated (χ² = 8.554, p = 0.004), suggesting that obesity was more common in women.

According to the participants' mean Body Mass Index (BMI), 44.5% were overweight or obese (BMI > 25), whereas 55.5% had a BMI below 25. Among the participants, 23% were day scholars and 77% stayed in hostels. However, there was no discernible connection between obesity and place of stay (p = 0.539). A significant correlation was found between current obesity status and family history of obesity (χ² = 18.15, p < 0.001), with 74.5% of respondents having a positive family history.

29% of respondents said they had gained weight, 28.5% said they had lost weight, and 42.5% said they had not changed their weight in the previous six months. At p = 0.877, this variation was not statistically significant. The percentage of people who exercised varied: 18.5% did so every day, 19% four to six times a week, 35.5% one to three times a week, and 27% once a month. Obesity and physical activity frequency had a significant relationship (χ² = 12.324, p = 0.002), indicating that a higher risk of obesity was associated with lower levels of physical activity.

Among the people who exercised, 42% did so for 30 to 45 minutes, 32.5% for 15 to 30 minutes, and 25.5% did not exercise at all. However, there was no significant correlation between this feature and obesity (p = 0.808). Lack of motivation (45.5%), lack of time (22%), and joint pain (7%) were the most frequently mentioned barriers to physical exercise; however, these barriers did not achieve statistical significance (p = 0.163).

Participants' sleep durations ranged; 44.5% slept for five to six hours, 40% slept for seven to eight hours, and 10.5% slept for less than five hours. Sleep duration and obesity did not significantly correlate (p = 0.872). The majority of respondents (59.5%) said that they were worried about their weight, compared to 34.5% who were neutral and 6% who weren't; this was not statistically significant (p = 0.271). However, a significant correlation (p = 0.013) was found between reported weight changes and obesity, with 67.5% reporting noticeable changes.

With respects to weight management strategies, 55% engaged in physical activity and 44.5% followed dietary modifications, while only 0.5% used medications or surgery. Just 6% of people were vegetarians, while 94% of people had varied diets. 33.5% reported irregular or bad eating habits, whereas 56.5% reported healthy eating habits. Although a large percentage (85%) reported consuming processed foods, there was no significant correlation between this and obesity (p = 0.758). Similarly, there was no significant difference in the frequency of consuming refined meals (p = 0.800).

In terms of having caloric beverages, 43.5% consumed them once weekly, 34.5% thrice weekly, and 22% twice weekly, though no significant relationship was found (p = 0.191). Hunger (38%), desires (37.5%), stress (16%), and boredom (8.5%) were common eating causes. 91% of participants reported eating fruits and vegetables, compared to 9% who did not. There was no significant correlation (p = 0.335) between the frequency of eating sweets after meals, which was 57.5% occasionally, 20% very often, 8% often, and 14.5% never.

The link between screen time and obesity was a noteworthy finding. 13 percent said that they spent less than an hour a day on screens, thirty-three percent said that they spent more than two hours, and fifty-four percent said that they spent more than four hours. Screen time and obesity were significantly correlated (χ² = 15.77, p = 0.001). Despite the fact that 84% of respondents were aware of the negative health implications of excessive screen time, there was no significant correlation between this awareness and BMI status (p = 0.257).

Binary logistic regression analysis identified sex (p = 0.003, OR = 1.85), family history of obesity (p < 0.001, OR = 0.24), frequency of physical activity (p = 0.021, OR = 0.68), place of stay (p = 0.032, OR = 1.54), exercise (p = 0.015, OR = 1.72), caloric beverage consumption (p = 0.0019, OR = 1.63), and screen time (p = 0.004, OR = 1.96) as significant indicators. With adjusted odds ratios of 1.85 (95% CI: 1.23–2.77), 0.24 (95% CI: 0.13–0.42), and 1.96 (95% CI: 1.25–3.06), respectively, multivariate analysis verified that sex, family history, and screen time were independent predictors of obesity.

The present study looked at the prevalence of obesity and its associated factors among 200 participants. In accordance with a number of previous studies, the data showed that obesity was significantly more prevalent in women than in men. The study by Gopalakrishnan et al. found that female medical students in Malaysia had increased obesity rates, which they attributed to women's lower levels of physical activity and dietary habits (2). Similarly, Verma et al. discovered that female students in Central India had higher rates of obesity (9). These results support the current study's finding that sex was a independent indicator of obesity. This is, however, conflicting evidence: Shafiee et al. found that gender differences were inconsistent across nations (1), while Rai et al. reported higher overweight rates among male students (4). Cultural standards, food diversity, and variations in physical activity among cultures can all contribute to this variability.

Although a larger percentage of obesity among participants over 20, age did not significantly correlate with obesity in this study. Szemik et al. found considerable weight gain over time among medical students, which is consistent with prior study linking higher age with greater obesity risk (5). Kowsalya and Parimalavalli discovered that among teenage girls, age was a significant predictor of being overweight (12). On the other hand, studies like Naik et al. supported the current findings by finding no significant correlation between age and BMI among young adult students (10). These inconsistencies imply that populations with a limited age range, such college students, may have less noticeable age-related variances.

In this study, obesity was found to be strongly and independently predicted by a family history of obesity. Numerous studies have revealed similar relationships, such as those by Naik et al. (10) and Sharma et al. (11), who highlighted the impact of shared habits and genetic predisposition. Additionally, Szemik et al. found that kids who had fat parents were much more likely to become obese themselves (5). This significant correlation, however, is contradicted by other evidence. Nowara et al. found only a weak connection between obesity and family history, suggesting that behavioral factors like eating disorders and sedentary lifestyles may outweigh genetic contributions (6). These conflicting results imply that, even in high-risk patients, lifestyle modification is still an important preventive strategy, even when heredity plays a part.

In accordance with worldwide research highlighting inadequate physical activity as a significant behavioral risk factor, physical activity was substantially linked to obesity. Low levels of physical activity is a key factor in obesity, according to the WHO's Global Physical Activity Questionnaire (16). Joshi et al. also found a strong association between medical students' greater BMI and less physical activity (8). Gupta et al. discovered that weight gain was substantially influenced by academic stress and associated sedentary behaviors (7). Some research show that activity duration and intensity are more important than frequency, which contradicts our findings. Craig et al. showed a strong correlation between the risk of obesity and activity duration as measured by IPAQ (17). However, there was no discernible connection between exercise duration and obesity in this study. This discrepancy could be explained by participants overestimating or misreporting the length of their workouts, or by irregularities in their actual practice.

Although time constraints, joint pain, and lack of enthusiasm were common barriers to physical activity, there was no statistically significant correlation between them and obesity. In contrast, Harsavarthini et al. (20) found that medical students' perceived barriers considerably reduced their levels of physical exercise, hence raising their risk of obesity. Despite being widespread, these barriers could not have been significant enough in the current investigation to show variations in BMI.

Obesity was not found to be significantly correlated with dietary practices, such as the use of refined meals and calorie-dense drinks. A large portion of the research currently in publication contradicts this finding. While Guleri et al. reported that frequent consumption of calorie-dense foods significantly contributed to overweight (22), Nowara et al. discovered clear connections between processed food intake and elevated BMI among university students (6). However, in accordance with the present findings, Rai et al. (4) did not consistently discover associations between dietary categories, such as vegetarian versus mixed diets, and obesity. The consistency of dietary patterns—94% of individuals consumed mixed diets—and similar eating habits may be the cause of the study's lack of relationship.

The results of Gopalakrishnan et al. (2) were supported by the lack of a significant association between sleep duration and obesity. However, due to hormonal changes affecting appetite, there is widespread evidence from around the world linking shorter sleep duration with an increased risk of obesity. Rather than a complete lack of correlation, the difference could be explained by the rather consistent sleep patterns in this population.

The strong correlation between screen time and obesity was a notable discovery. The frequency of obesity was higher among those who used screens for more than four hours a day. This is consistent with the findings of Mathew et al., who observed that among medical students, excessive screen time was linked to increased sedentary behavior, unhealthy snacking, and body dissatisfaction (14). Additionally, Sharma et al. found that longer screen time was positively correlated with being overweight (11). The current study's adjusted odds ratio (1.96) is consistent with research published in BMC Public Health that found screen time to be a significant predictor of a rise in BMI (6). There is conflicting evidence; other studies suggest that screen time by itself may not be a reliable indicator of obesity unless it is paired with a poor diet and poor exercise. The influence of sedentary digital activities on student populations is still shown by the significant correlation found here.

Although 84% of people were aware of the negative effects of screen usage on their health, this awareness did not translate into better weight results. This highlights the gap between behavior and knowledge, a phenomenon also noted by Ockene et al., who observed that educational interventions by themselves did not always result in students adopting successful weight control techniques (15). This implies that in order to make significant lifestyle changes, awareness must be combined with structured behavioral therapies.

Overall, the current study shows that a mix of biological, behavioral, and environmental factors affect young people' obesity. The WHO's model describing the multidimensional nature of obesity is supported by significant factors like sex, screen time, physical inactivity, and family history (3). The complex nature of obesity factors, particularly in comparatively homogeneous student populations, is highlighted by the non-significant correlations seen with food, exercise duration, and sleep. Differences between this study and other research show that obesity predictors differ depending on institutional and cultural environments. For the purpose of creating focused solutions, localized research is still essential.

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