# Profiling Adolescent Lifestyles and Their Sociodemographic Drivers: A School-Based Study from Rawalpindi, Pakistan

**Authors:** Humaira Mahmood, Babar Tasneem Shaikh, Azka Naseem, Farrah Pervaiz, Abdul Momin Rizwan Ahmad

PMC · DOI: 10.3390/children12111507 · 2025-11-06

## TL;DR

This study identifies three lifestyle clusters among adolescents in Pakistan and highlights the need for school-based interventions to address poor nutrition and physical activity.

## Contribution

The study provides new insights into adolescent lifestyle behaviors and their sociodemographic drivers in a low-income setting.

## Key findings

- Adolescents were grouped into poor, moderate, and good lifestyle clusters based on seven domains.
- Private school students and older adolescents were more likely to have healthier lifestyles.
- Physical activity and nutrition were the weakest domains, especially among females and public school students.

## Abstract

What are the main findings?
The lifestyle profiling of school-going adolescents showed three clearly defined clusters categorized as poor, moderate, and good in Rawalpindi, Pakistan.Among the seven explored domains, the highest scores were observed in positive life perspective and interpersonal relations, whereas nutrition, physical activity, and health responsibility were the lowest-scoring domains.

The lifestyle profiling of school-going adolescents showed three clearly defined clusters categorized as poor, moderate, and good in Rawalpindi, Pakistan.

Among the seven explored domains, the highest scores were observed in positive life perspective and interpersonal relations, whereas nutrition, physical activity, and health responsibility were the lowest-scoring domains.

What are the implications of the main finding?
To mitigate lifestyle-related risk factors, school-based interventions should prioritize gender and socioeconomic disparities.The findings show that an adolescent’s quality of life is influenced by multiple factors linked with their habits.

To mitigate lifestyle-related risk factors, school-based interventions should prioritize gender and socioeconomic disparities.

The findings show that an adolescent’s quality of life is influenced by multiple factors linked with their habits.

Background/Objectives: Adolescence is the phase of life when an individual develops habits that lead to their health outcomes in later life stages; yet, comprehensive evidence from low- and middle-income countries (LMICs) like Pakistan remains limited. This study examined the lifestyle behaviors of school-going adolescents in Rawalpindi and their correlation with key sociodemographic factors. Methods: A descriptive cross-sectional study was conducted in four (public and private) schools, using multistage cluster sampling (n = 675). Lifestyle behaviors were examined within seven predefined domains. K-means cluster analysis was used for the identification of distinct behavioral profiles. Associations with age, gender, and school type were examined using Chi-square tests. Correlations within clusters were analyzed using Pearson’s correlation test. Results: The majority of adolescents demonstrated positive life perspectives (88.3%) and strong interpersonal relationships (83.8%), while nutrition (16.2%), physical activity (31.7%), and health responsibility (15.0%) were weaker domains. Cluster analysis revealed three groups: poor (n = 129), moderate (n = 334), and good (n = 212) lifestyle behaviors. Statistically significant associations were found between lifestyle profiles and both age group (p = 0.037) and school type (p = 0.007), with private school students being more likely to exhibit healthier behaviors. Gender differences were notable in physical activity, but not significant in other domains. Conclusions: Interventions targeting low-performing domains, especially physical activity and nutrition, are needed—particularly for females and public school students. These findings highlight the importance of targeted, school-based lifestyle interventions in resource-limited settings.

## Full-text entities

- **Genes:** ATHS (atherosclerosis susceptibility (lipoprotein associated)) [NCBI Gene 470] {aka ALP}
- **Diseases:** injury to (MESH:D014947), respiratory diseases (MESH:D012140), diabetes (MESH:D003920), cancers (MESH:D009369), substance abuse (MESH:D019966), behavioral deficits (MESH:D019958), obesity (MESH:D009765), overweight (MESH:D050177), NCDs (MESH:D000073296), nutritional deficit (MESH:D009748), hypertension (MESH:D006973), malnutrition (MESH:D044342), deaths (MESH:D003643), cardiovascular diseases (MESH:D002318), infectious, communicable diseases (MESH:D003141)
- **Species:** Meleagris gallopavo (common turkey, species) [taxon 9103], Homo sapiens (human, species) [taxon 9606]

## Figures

1 figure with captions in the complete paper: https://tomesphere.com/paper/PMC12651301/full.md

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Source: https://tomesphere.com/paper/PMC12651301