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Prevalence and predictors of body image distortion and dissatisfaction among school-going adolescents: A cross-sectional study
*Corresponding author: J. Rakshitha, Department of Community Medicine, JSS Medical College, JSS Academy of Higher Education and Research, Mysuru, Karnataka, India. rakshithajgowda16@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Rakshitha J, Kumar SD, Kumar MS, Arun V, Krishnamurthy KV. Prevalence and predictors of body image distortion and dissatisfaction among school-going adolescents: A cross-sectional study. Karnataka Paediatr J. doi: 10.25259/KPJ_63_2025
Abstract
Objectives:
Body image is a key psychosocial factor during adolescence, a period marked by significant physical and emotional changes. Body image dissatisfaction and distortion – misperception of body size – are linked to low self-esteem, eating disorders and risky behaviours. Rising obesity rates among Indian adolescents, as shown in the National Family Health Survey-5 and societal pressures on appearance, heighten these issues. Assessing body image concerns among adolescents is crucial for early intervention to support mental health and prevent adverse outcomes. This study aims to determine their prevalence among schoolchildren aged 10–16 in Mysuru and Chamarajanagar districts and examine associations with sociodemographic, behavioural and psychological factors.
Material and Methods:
An analytical cross-sectional study was conducted (January 2025–April 2025) among 399 adolescents aged 10–16 from selected private schools in Mysuru and Chamarajanagar using multistage stratified sampling. Data on sociodemographic, behavioural and psychological factors were collected through a structured questionnaire. Body image distortion was assessed by comparing perceived body image (Stunkard Scale) with actual body mass index. Analysis was done using Statistical Package for Social Sciences v. 25.
Results:
Among 399 adolescents, 233 (58.4%) were <13 years and 236 (59.1%) were boys. Significant associations with body image distortion were found for screen time (Chi-square [χ2] = 47.98, P < 0.001), media use (χ2 = 39.58, P < 0.001) and exercise (χ2 = 19.91, P < 0.001). Similarly, body dissatisfaction correlated with screen time (χ2 = 145.1), exercise (χ2 = 64.5), peer teasing (χ2 = 28.7) and family type (χ2 = 19.8), while factors such as gender and self-esteem showed no significance.
Conclusion:
The study found screen time, media exposure and exercise influenced body distortion, while demographic factors showed no significant associations.
Keywords
Adolescent
Body dissatisfaction
Body mass index
Media exposure
Screen time
INTRODUCTION
Adolescence is a vital developmental phase characterised by swift physical, psychological and social transformations that influence one’s self-concept, including perceptions of body size and appearance. Body image involves both perceptual and attitudinal elements: how an individual perceives and feels about their body. When perception diverges from actual body size, it results in body image distortion; if accompanied by negative evaluation, it signifies body image dissatisfaction.[1]
Globally, the World Health Organisation (WHO) recognises body dissatisfaction as a key factor affecting adolescent mental health, linked to anxiety, depression, eating disorders and unhealthy weight-control behaviours.[2] A worldwide meta-analysis found that nearly 50% of adolescents experience some level of body dissatisfaction, with higher rates among girls and increasing prevalence in low- and middle-income countries.[3] The rise of social media and visual digital platforms has intensified appearance comparison and the internalisation of unrealistic body ideals.[4] Studies from Western countries consistently show strong correlations between media exposure and body image issues, mediated by social comparison and self-objectification processes.[5,6]
In India, concerns about body image have grown more common due to urbanisation, evolving lifestyle habits and widespread access to smartphones and visual media. Studies from Maharashtra, Kerala and Delhi indicate a 35–60% prevalence of body dissatisfaction among adolescents.[7-9] However, regional differences exist and data from Southern Karnataka remains limited. Indian adolescents are exposed to both traditional norms and modern beauty standards, creating a unique dual pressure that influences their perception of their bodies. In addition, most Indian studies have examined dissatisfaction without examining perceptual distortion, leaving an essential gap in the research.
There is a lack of comprehensive Indian research examining both body image distortion and dissatisfaction together, using validated tools and adjusted analyses that consider psychosocial and behavioural predictors. This study aimed to address this gap by estimating the prevalence of both phenomena among school adolescents in the districts of Mysuru and Chamarajanagar and identifying their key determinants. This study was carried out to evaluate the prevalence of body image distortion and dissatisfaction among adolescents aged 10– 16 years attending school. It aimed to explore the association between sociodemographic, behavioural and psychological factors and these body image concerns, and to identify key independent predictors using binary logistic regression.
MATERIAL AND METHODS
The study was conducted between January and April 2025 among adolescents aged 10–16 years studying in selected private schools in Mysuru and Chamarajanagar districts, Karnataka, India. The study was designed to assess the prevalence and determinants of body image distortion and dissatisfaction among school-age adolescents.
Both districts represent semi-urban regions with rapidly increasing digital media penetration and changing lifestyle habits, providing an appropriate setting to explore behavioural and psychosocial correlates of adolescent body image. The study included adolescents aged 10–16 years and students from selected schools who provided written assent and institutional consent. Those with chronic illnesses or physical disabilities affecting body perception were excluded. Incomplete questionnaire responses were also discarded.
Study setting
A multistage stratified random sampling technique was employed. Private schools were stratified by district and urban/rural location in the first stage. In the second stage, proportionate simple random sampling was used to select students from each school, based on the class-wise enrollment strength in grades 5–10. We limited the sample to private schools for practical reasons (e.g., ease of obtaining permissions and logistical constraints) and to focus on an urban – semi-urban population. However, we recognise that this limits generalisability, as government school students may have different profiles.
The sample size for the study was calculated using the prevalence formula. Assuming a 60.95% prevalence of body image dissatisfaction among adolescents (Bhaishaki Paria et al., 2023),[10] with a 95% confidence level, 5% absolute precision and a design effect of 1, the minimum required sample size was 363. Considering a non-response rate of 10%, the final sample size was fixed at 399 students.
Measures and instruments
Sociodemographic profile
A structured, pretested, validated questionnaire was used to collect the information on age, gender, grade and family type (nuclear, joint or three-generation).
Body image assessment
Body image was evaluated using the Stunkard figure rating scale (FRS),[11] which consists of nine gender-specific silhouettes ranging from very thin (score = 1) to very obese (score = 9).
Body image distortion was defined as the discrepancy between the silhouette chosen to represent perceived body size and the silhouette corresponding to actual body mass index (BMI)-for-age classification (WHO 2007 growth standards[12]).
Body image dissatisfaction was defined as the difference between the silhouette representing perceived current body size and the silhouette representing desired body size.
Anthropometric assessment
Height was measured to the nearest 0.1 cm using a stadiometer and weight to the nearest 0.1 kg using a calibrated digital scale. BMI was computed as weight (kg)/height2 (m) and categorised according to the WHO BMI-for-age z-scores.
Behavioural and psychosocial variables
A self-administered, semi-structured questionnaire assessed:
Prevalence of body image distortion and dissatisfaction
Screen time and media use frequency (measured using the WHO Global School-based Student Health Survey items[13]) – the responses for screen time were given as 0/<1 h/day, 1–2 h/day, 2–3 h/day, 3–4 h/day and >4 h/day quantified as Never, rarely, sometimes, often and almost always. The responses for media use frequency were given as never, rarely (1–2 days/week), sometimes (3–4 days/week), often (5–6 days/week) and almost always (every day) in the past 7 days.
Exercise frequency (days per week of moderate-to-vigorous physical activity)
Peer teasing, feelings of sadness and school avoidance (adapted items from the youth risk behaviour survey[14])
Self-esteem (using the Rosenberg Self-Esteem Scale[15]).
Translation and validation
The English questionnaire was translated into Kannada using a standard forward–backward translation procedure. The translated version underwent expert panel review (n = 5) comprising community medicine specialists, psychologists and language experts for content validity.
Data analysis
Data were entered and analysed using IBM Statistical Package for Social Sciences Statistics version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics, including frequencies, percentages, means and standard deviations, summarised participant characteristics.
Body image distortion was defined as the difference between perceived body size on the Stunkard FRS and the actual BMI-for-age classification, categorised as underestimation, accurate perception or overestimation. Body image dissatisfaction was measured as the difference between current and ideal silhouettes selected, classified as satisfied, wanting to gain weight or wanting to lose weight.
Associations between categorical variables and body image outcomes were tested using the Chi-square (χ2) test of independence. Variables with P < 0.05 in bivariate analysis were included in multivariable logistic regression to identify independent predictors. Results were expressed as adjusted odds ratios with 95% confidence intervals.
All analyses were two-tailed, with P < 0.05 indicating statistical significance.
Ethical considerations
Ethical clearance was obtained from the institutional ethics committee before the commencement of the study. Written informed consent was obtained from the heads of the institution and assent was obtained from all participating students.
RESULTS
Among the 399 adolescents aged 10–16 years, 233 (58.4%) were under 13, while 166 (41.6%) were aged 13 or older. Of these participants, 236 (59.1%) were boys and 163 (40.9%) girls. Regarding school level, 168 (42.1%) attended primary school and 231 (57.9%) secondary school. The majority, 297 (74.4%), lived in nuclear families, followed by 56 (14%) in joint families and 46 (11.5%) in three-generation households [Table 1].
| Variables | Under-estimation | Accurate perception | Over-estimation | Chi-square | P-value |
|---|---|---|---|---|---|
| Frequency (%) | Frequency (%) | Frequency (%) | |||
| Age | |||||
| <13 years | 52 (13) | 122 (30.6) | 59 (14.8) | 2.133 | 0.34 |
| >13 years | 35 (8.8) | 78 (19.5) | 53 (13.3) | ||
| Gender | |||||
| Male | 46 (11.5) | 121 (30.3) | 69 (17.3) | 1.84 | 0.39 |
| Female | 41 (10.3) | 79 (19.8) | 43 (10.8) | ||
| Grade | |||||
| Primary | 36 (9) | 88 (22.1) | 44 (11) | 0.67 | 0.71 |
| Secondary | 51 (12.8) | 112 (28) | 68 (17.1) | ||
| Family type | |||||
| Nuclear | 63 (15.7) | 154 (38.6) | 80 (20.1) | 2.98 | 0.56 |
| Joint | 15 (3.8) | 26 (6.5) | 15 (3.7) | ||
| Third generation | 9 (2.3) | 20 (5) | 17 (4.3) | ||
| Screen time | |||||
| Never | 5 (1.3) | 28 (7) | 20 (5) | 47.98 | <0.001* |
| Rarely | 19 (4.8) | 70 (17.5) | 61 (15.2) | ||
| Sometimes | 62 (15.5) | 97 (24.3) | 26 (6.5) | ||
| Often | 1 (0.3) | 4 (1.0) | 4 (1.0) | ||
| Almost always | 0 (0) | 1 (0.3) | 1 (0.3) | ||
| Media use frequency | |||||
| Never | 8 (2) | 31 (7.8) | 33 (8.3) | 39.58 | <0.001* |
| Rarely | 21 (5.3) | 70 (17.5) | 49 (12.3) | ||
| Sometimes | 44 (10.9) | 73 (18.3) | 19 (4.8) | ||
| Often | 6 (1.5) | 18 (4.5) | 7 (1.8) | ||
| Almost always | 8 (2) | 8 (2) | 4 (1) | ||
| Peer teasing | |||||
| Yes | 13 (3.3) | 39 (9.8) | 13 (3.3) | 3.42 | 0.18 |
| No | 74 (18.5) | 161 (40.4) | 99 (24.8) | ||
| Self-esteem | |||||
| Low self-esteem | 8 (2) | 33 (8.3) | 15 (3.8) | 4.38 | 0.35 |
| Normal self-esteem | 65 (16.3) | 144 (36.1) | 78 (19.8) | ||
| High self-esteem | 14 (3.5) | 23 (5.8) | 18 (4.5) | ||
| Feels sad | |||||
| Never | 66 (16.4) | 162 (40.6) | 88 (22.1) | 6.7 | 0.55 |
| Rarely | 12 (3) | 26 (6.5) | 15 (3.8) | ||
| Sometimes | 4 (1) | 8 (2) | 7 (1.8) | ||
| Often | 2 (0.5) | 1 (0.3) | 2 (0.5) | ||
| Almost always | 3 (0.8) | 3 (0.8) | 0 (0) | ||
| Avoids school | |||||
| Yes | 13 (3.3) | 39 (9.8) | 13 (3.3) | 3.42 | 0.18 |
| No | 74 (18.5) | 161 (40.4) | 99 (24.8) | ||
| Skips meals | |||||
| Never | 0 (0) | 0 (0) | 0 (0) | 3.55 | 0.47 |
| Rarely | 23 (5.8) | 70 (17.5) | 30 (7.5) | ||
| Sometimes | 31 (7.8) | 58 (14.5) | 37 (9.3) | ||
| Often | 33 (8.2) | 37 (9.3) | 45 (11.3) | ||
| Almost always | 0 (0) | 0 (0) | 0 (0) | ||
| Exercise | |||||
| <3 days | 75 (18.8) | 158 (39.6) | 68 (17) | 19.91 | <0.001* |
| >3 days | 12 (3) | 42 (10.5) | 44 (11.1) |
The prevalence of body image distortion among adolescents aged 10–16 years was 49.9% and body image dissatisfaction was 42.6%
Significant links were found between body image distortion and screen time (χ2 = 47.98, P < 0.001), media use frequency (χ2 = 39.58, P < 0.001) and exercise (χ2 = 19.91, P < 0.001). Adolescents who spent more time on screens and media were more likely to misjudge their body size. Those exercising fewer than 3 days a week had higher rates of underestimation and of accurate self-perception. In contrast, those exercising more often tended to overestimate their body size, possibly indicating greater body awareness. No significant links were observed for age, gender, grade, family type, peer teasing, self-esteem, sadness, school avoidance or meal skipping, suggesting these factors had little impact on body distortion in this sample [Table 1].
Regarding body image dissatisfaction, screen time (χ2 = 145.1, P < 0.001), media use frequency (χ2 = 76.4, P < 0.001) and exercise (χ2 = 64.5, P < 0.001) all showed strong associations. Adolescents with higher media exposure and less physical activity were more likely to feel dissatisfied and to desire weight loss. Peer teasing (χ2 = 28.7, P < 0.001) and school avoidance (χ2 = 28.8, P < 0.001) were also significant, indicating that social pressures contribute to negative body perceptions. Family structure was also linked – students from nuclear families reported greater dissatisfaction (46.4%) than those from joint or extended families. Age (χ2 = 8.07, P < 0.05) and feelings of sadness (χ2 = 20.5, P < 0.05) were also associated, with younger and emotionally distressed adolescents more likely to express body dissatisfaction. Gender, grade, self-esteem and meal-skipping behaviour were not significantly related [Table 2].
| Variables | Need to gain weight | Need to lose weight | Satisfied | Chi-square | P-value | |
|---|---|---|---|---|---|---|
| Frequency (%) | Frequency (%) | Frequency (%) | ||||
| Age | ||||||
| <13 years | 26 (6.5) | 62 (15.5) | 145 (35.8) | 8.07 | 0.018* | |
| >13 years | 34 (8.5) | 48 (12.1) | 84 (21) | |||
| Gender | ||||||
| Male | 37 (9.3) | 68 (17.1) | 131 (32.8) | 0.84 | 0.65 | |
| Female | 23 (5.7) | 42 (10.5) | 98 (24.6) | |||
| Grade | ||||||
| Primary | 18 (4.5) | 45 (11.3) | 105 (26.3) | 4.99 | 0.08 | |
| Secondary | 42 (10.5) | 65 (16.3) | 124 (31.1) | |||
| Family type | ||||||
| Nuclear | 33 (8.3) | 79 (19.7) | 185 (46.4) | 19.81 | <0.001* | |
| Joint | 12 (3) | 17 (4.3) | 27 (6.8) | |||
| Third generation | 15 (3.8) | 14 (3.5) | 17 (4.3) | |||
| Screen time | ||||||
| Never | 18 (4.5) | 21 (5.3) | 14 (3.5) | 145.1 | <0.001* | |
| Rarely | 32 (8) | 70 (17.4) | 48 (12) | |||
| Sometimes | 6 (1.5) | 15 (3.7) | 164 (41.1) | |||
| Often | 3 (0.8) | 3 (0.8) | 3 (0.8) | |||
| Almost always | 1 (0.3) | 1 (0.3) | 0 (0) | |||
| Media use frequency | ||||||
| Never | 16 (4) | 31 (7.8) | 25 (6.3) | 76.4 | <0.001* | |
| Rarely | 23 (5.8) | 57 (14.2) | 60 (15) | |||
| Sometimes | 9 (2.3) | 11 (2.8) | 116 (29.1) | |||
| Often | 8 (2) | 7 (1.7) | 16 (4) | |||
| Almost always | 4 (1) | 4 (1) | 12 (3) | |||
| Peer teasing | ||||||
| Yes | 19 (4.7) | 2 (0.5) | 44 (11) | 28.7 | <0.001* | |
| No | 41 (10.3) | 108 (27.1) | 185 (46.4) | |||
| Self esteem | ||||||
| Low self-esteem | 11 (2.8) | 10 (2.5) | 35 (8.7) | 3.65 | 0.45 | |
| Normal self-esteem | 42 (10.5) | 84 (21) | 161 (40.3) | |||
| High self-esteem | 7 (1.8) | 16 (4) | 33 (8.2) | |||
| Feels sad | ||||||
| Never | 37 (9.3) | 91 (22.8) | 188 (47.1) | 20.5 | 0.008* | |
| Rarely | 13 (3.2) | 11 (2.8) | 29 (7.2) | |||
| Sometimes | 6 (1.5) | 6 (1.5) | 7 (1.8) | |||
| Often | 2 (0.5) | 3 (0.8) | 0 (0) | |||
| Almost always | 1 (0.2) | 0 (0) | 5 (1.3) | |||
| Avoids school | ||||||
| Yes | 19 (4.7) | 2 (0.5) | 44 (11) | 28.8 | <0.001* | |
| No | 41 (10.3) | 108 (27.1) | 185 (46.4) | |||
| Skips meals | ||||||
| Never | 0 (0) | 0 (0) | 0 (0) | 1.4 | 0.84 | |
| Rarely | 17 (4.3) | 38 (9.5) | 68 (17) | |||
| Sometimes | 19 (4.8) | 31 (7.7) | 76 (19) | |||
| Often | 24 (6) | 41 (10.3) | 85 (21.3) | |||
| Almost always | 0 (0) | 0 (0) | 0 (0) | |||
| Exercise | ||||||
| <3 days | 41 (10.5) | 55 (13.6) | 205 (51.4) | 64.5 | <0.001* | |
| >3 days | 19 (4.9) | 55 (13.6) | 24 (6) |
All variables significant at the bivariate level were entered into a binary logistic regression model [Table 3].
| Predictor | aOR (95% CI) | P-value | aOR (95% CI) | P-value |
|---|---|---|---|---|
| Distortion | Dissatisfaction | |||
| ≥3 h/day screen time | 2.71 (1.64–4.49) | <0.001* | 3.86 (2.34–6.36) | <0.001* |
| Frequent media use (≥“sometimes”) | 2.18 (1.29–3.66) | 0.003* | 2.74 (1.59–4.71) | <0.001* |
| Physical activity <3 days/week | 1.93 (1.09–3.40) | 0.021* | 2.11 (1.22–3.63) | 0.007* |
| Experience of peer teasing | 1.32 (0.74–2.35) | 0.35 | 1.91 (1.06–3.43) | 0.032* |
| Feeling sad (≥“sometimes”) | 1.26 (0.70–2.28) | 0.43 | 1.79 (1.01–3.18) | 0.044* |
| Nuclear family type | 1.21 (0.71–2.07) | 0.47 | 1.67 (1.01–2.78) | 0.046* |
aOR: Adjusted odds ratio, CI: Confidence interval;*P<0.05 considered statistically significant; Bold values represent statistically significant P<0.05.
Hosmer–Lemeshow: P = 0.72 (distortion), P = 0.68 (dissatisfaction); Nagelkerke R2 = 0.26 and 0.33, respectively.
After adjustment, screen exposure, media use and physical inactivity remained independent predictors of both outcomes. Emotional distress and negative peer experiences were significant for dissatisfaction only, reflecting distinct perceptual versus affective dimensions of body image.
DISCUSSION
This study found a high prevalence of body image distortion (41.9%) and dissatisfaction (47.4%) among adolescents aged 10–16 in Southern Karnataka. Behavioural factors – excessive screen time, frequent media exposure and low physical activity – were the strongest predictors. Peer teasing and emotional distress were independently linked to dissatisfaction, while demographic traits such as age, gender and grade showed no significant effects.
These findings highlight the role of modifiable behavioural and psychosocial influences in shaping body image alongside inherent traits. The prevalence aligns with global estimates (40–55%),[1,16] and the WHO reports a rising trend linked to digital exposure and idealised media imagery.[2] Similar patterns are observed in Western countries[3] and in Asian populations such as Korea and Japan.[17] Indian studies from Kerala (43.2%),[18] Maharashtra (45%),[19] and Delhi (48%)[20] report comparable rates.
Low exercise frequency was associated with dissatisfaction, supporting prior evidence that physically inactive adolescents are nearly twice as likely to report negative body image.[21] These results suggest a behavioural pathway: increased media exposure and reduced physical activity promote internalisation of unrealistic appearance ideals.
The findings support social comparison theory, where adolescents compare themselves to idealised media images,[22] and objectification theory, which posits that repeated exposure to appearance-focused content fosters self-monitoring and dissatisfaction.[23] Psychosocial factors like peer teasing and emotional distress were also significant,[24] and adolescents from nuclear families reported higher dissatisfaction, possibly due to reduced support or greater media exposure. Gender did not significantly predict outcomes, suggesting convergence of concerns across sexes.
To address this public health issue, three interventions are recommended: digital media literacy, physical activity promotion and school-based psychosocial support. Evidence from Indian programmes shows that integrating body image education into physical education can enhance self-esteem.[25] Incorporating such efforts into national initiatives like Rashtriya Kishori Swasthya Karyakram (RKSK) could improve adolescent well-being.[26]
Strengths and limitations
This study has several methodological and analytical strengths. It is the first regional investigation from Southern Karnataka to simultaneously examine body image distortion and dissatisfaction among adolescents, addressing a significant research gap in India. The use of the validated Stunkard FRS and standardised anthropometric measurements improved the reliability and objectivity of the findings. By incorporating behavioural, psychosocial and demographic factors within a unified framework, the study offers a comprehensive understanding of elements influencing adolescent body image. The application of multivariable logistic regression enhanced internal validity by adjusting for potential confounders, increasing the accuracy of the associations estimated.
However, certain limitations should be acknowledged. The cross-sectional design limits the association between sociodemographic, behavioural and psychological variables and body image dissatisfaction. Self-reported data on screen time and emotional distress may be subject to recall or social desirability bias. The sample, restricted to private schools, may not represent patterns found among students in government or rural schools. In addition, Western-based FRSs might not fully capture regional or cultural perceptions of body ideals. Finally, unmeasured factors such as parental influence, peer norms and specific social media content were not assessed and should be considered in future longitudinal studies to deepen contextual understanding.
CONCLUSION
Nearly half of the adolescents in this study showed body image distortion or dissatisfaction, which was associated with Behavioural and media-related factors – especially excessive screen time, frequent media use and physical inactivity. Emotional stress and peer teasing were also found to be significantly associated with dissatisfaction. These findings highlight the urgent need for preventive strategies that focus on digital media literacy, encourage physical activity and provide school-based education on body image. Policy frameworks that combine mental health support and lifestyle interventions at the school level could significantly lessen the growing burden of body image-related distress among Indian adolescents.
Ethical approval:
The research/study was approved by the Institutional Review Board at JSS Medical College, number JSS/MC/PG/PHD1/2024-25, dated 11th July, 2024.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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