Psychosocial characteristics of smartphone use behaviors – comparison between urban and rural school students in India

INTRODUCTION

Smartphone use among adolescents is significantly altering social interactions and has profound psychological implications.^[1] Factors such as lack of parental supervision, insufficient social support during this critical developmental stage, and peer pressure significantly contribute to the risk of developing problematic smartphone use (PSU).^[2]

In developing countries like India, where smartphone technology has a significant market, access to smartphones among adolescents is largely influenced by gender and residential background (urban or rural).^[3,4] Urban adolescents have better access to mobile phones and the internet, enhancing communication, education, and social networking. Their increased mobile usage leads to both positive outcomes, like educational opportunities, and negative effects, such as exposure to harmful content. Rural adolescents face unique challenges in regulating their smartphone usage due to perceived social isolation, lower digital literacy among parents, lack of guidance, and erratic internet connectivity.^[5] Despite the widespread use of smartphones in India, including rural areas where 68.8% of the population resides, there is a lack of research on PSU among urban and rural adolescents.^[6] Most studies focus on internet addiction, although smartphones are the primary means of accessing the internet.^[7]

With India having the largest adolescent population in the world, it is crucial to examine smartphone usage patterns and assess the risks of developing PSU among urban and rural adolescents. This demographic represents nearly 21% of the nation’s population and is at a critical stage where their habits can significantly impact their future health and well-being. Understanding the influence of smartphones on this age group is vital for safeguarding their mental health and promoting responsible digital behavior.

The present study aims to fill this research gap by examining the demographic profile of high school smartphone users, patterns of usage, and independent predictors of PSU in urban and rural schools in North India. The findings will help identify the extent of the digital divide and specific risks in urban-rural settings and inform the development of targeted interventions to address PSU among adolescents in India. Such research is essential to promote healthy smartphone usage and mitigate the potential negative impacts on adolescent well-being.

MATERIALS AND METHODS

This cross-sectional study was conducted among schools located within the urban and rural field practice areas of the medical institute after obtaining approval from the Institutional Ethics Committee (IEC No. INT/IEC/2015/683) and permission from the District Education Officer and the respective school principals.

We studied school students aged 13–18 years who were recruited from grades 9 to 12. A sample size of 578 students was calculated using the formula for determining sample size for a single proportion, with prevalence data from a previous study.^[8] Based on the distribution of schools at the study sites, two-thirds of the participants were enrolled from rural schools, while one-third were enrolled from urban schools.

A bilingual, semi-structured questionnaire was used. The questionnaire was translated from English into the local language (Hindi) and back-translated for consistency. It was co-designed with the target students, for which a pilot study was conducted among students of similar age in a non-study area. The questionnaire consisted of three parts: socio-demographic profile, mobile usage pattern, and assessment of problematic use. The socio-demographic variables included age, gender, type of school, parents’ education level and occupation, family size, and academic performance in the last examination. In the context of the target population, the term “service” was used to describe parents’ occupations, defined as an office job (government or private), while business was defined as any commercial activity (e.g., shop owner or assistant, painter, tailor, chef, tuition teacher, photographer, goldsmith, electrician). Seven mobile phone usage parameters were recorded: (i) mobile ownership, (ii) type of mobile phone (smartphone or non-smartphone), (iii) use of other gadgets (tablets, laptops, or desktops), (iv) use of the internet for any activity other than social networking sites (SNS), (v) use of SNS (Facebook, Twitter, WhatsApp, Instagram), (vi) hours of gadget use per day, (vii) parental and school restrictions, and (viii) self-perceived need for help regarding any mental problem or excessive phone use. A smartphone was defined as a mobile phone with computing functions, a touch-screen interface, and the capability to run downloaded software applications.

A previously validated 45-item instrument^[9] assessing smartphone use was used to evaluate the characteristics of smartphone behavior among participating students, demonstrating high reliability with a Cronbach’s alpha of 0.865 in the current study sample. Although the tool is based on the International Classification of Diseases-10 criteria for substance dependence syndrome, it has the advantage of evaluating PSU, including instances not involving the internet. This tool also has the added advantage of being bilingual, making it suitable for community-based research studies.

Data were collected from April to August 2016, during school hours from students present on the data collection day. After briefing about the study, participants were enrolled after obtaining voluntary, written informed assent. The confidentiality of their responses was maintained, as was their right to refuse or withdraw from participation. It took about 20–25 min to fill the study questionnaire. Team members were present to supervise data collection, answer queries, and ensure privacy. Forms were checked for completeness following submission, and participants were contacted once for any missing responses. There was no coercion throughout this process.

Data analysis was performed using Stata version 12.0 (StataCorp, College Station, TX). Categorical variables were summarized as percentages, and continuous variables were summarized as means (standard deviation). The Pearson Chi-square test (χ²) was applied to study the association between PSU and the socio-demographic profile and gadget usage patterns by school setting (rural/urban). Logistic regression analysis was conducted with PSU as the outcome variable. Backward stepwise multiple logistic regression was used to identify factors independently associated with PSU. All variables with a P < 0.2 (for unadjusted odds ratios [ORs]) were entered into the model, and those with a P < 0.05 were retained. The results are reported as adjusted ORs (95% Confidence Interval [CI]) and P-values. The fit between the model and the data was assessed using a goodness-of-fit test. Statistical significance was set at a P < 0.05.

RESULTS

DISCUSSION

Smartphones are becoming a regular part of our educational landscape, with many educational institutes integrating these to support student learning. Yet PSU is a significant concern, affecting 38.9% of students in the present study. We found that the prevalence of PSU between urban and rural school students was similar. However, the determinants varied between the two groups. In urban schools, students aged 15–17 are more likely to exhibit PSU compared to younger peers. In rural schools, male students, those in private institutions, and users of multiple gadgets or SNS were more prone to PSU, as compared to female students studying in government schools, using a single gadget (smartphone), and non-use of SNS. Higher maternal education among rural school students was found to reduce the risk of PSU, highlighting the influence of family background on smartphone usage patterns.

The prevalence of PSU in this study aligns with previous research, despite variations in measurement scales.^[2,10,11] However, most of the studies have been conducted in urban settings, including those from India.^[8,12] While a prior study in rural Korea reported a lower prevalence (24.1%) of PSU,^[13] the present study reveals comparable levels of problematic use among urban and rural students, indicating that rural students are increasingly engaging with smartphones and facing related issues. This underscores the necessity for further research in rural contexts.

Congruent with earlier research, urban students aged 15–17 years were more likely to engage in PSU.^[14,15] This may be due to late adolescence (15–19 years) being a transitional phase in education, moving from school to higher education, which involves adjustments amidst increased freedom and responsibilities.^[16] In the present study, male gender in rural schools was an independent predictor of PSU. Across studies, it has been observed that problematic smartphone usage is influenced by gender-specific usage patterns - gaming among males,^[17,18] and social networking among females.^[19-21] However, despite gender-specific usage patterns, both genders show equal susceptibility to problematic use, highlighting the need for targeted interventions.^[18,22] The present study’s gender imbalance, with more rural males and more urban females, reflects broader educational enrollment trends in India, where urban areas have higher female enrollment,^[23] suggesting that educational and smartphone access disparities may have influenced the findings.^[24,25]

Rural school students in this study who attended private schools and used multiple gadgets were significantly more likely to experience PSU compared to those in rural government schools with limited gadget access. This aligns with previous research indicating that socioeconomic status and gadget usage are predictors of PSU.^[26,27]

Higher maternal education was found to be protective against PSU among rural students, highlighting the positive influence of caregiver education on students’ understanding and regulation of smartphone use.^[19,28] Interestingly, despite stricter regulations in rural settings, these did not predict PSU in the present study. Contrasting findings exist regarding the relationship between smartphone regulation and problematic use, with some studies linking lower regulation to higher problematic use, while others suggest the opposite.^[18,29] This inconsistency may stem from adolescents’ tendencies to overuse smartphones in response to perceived restrictions on their personal freedom. To address smartphone distractions in learning environments, dynamic interventions are needed to maintain students’ attention and support uninterrupted learning.^[30] These strategies should acknowledge the real and perceived benefits of smartphones, such as social interaction and access to information while adapting to the rapidly evolving technology landscape.

In contrast to other studies, the present study did not find a significant association between academic performance and PSU.^[14,16] This may be influenced by the self-reported nature of the data and the limited academic scores available at the time of the study. Future research could explore the dynamics of learning environments and their relationship with PSU in both urban and rural contexts.

Behavioral patterns revealed that rural students often use smartphones to connect with others, potentially addressing feelings of social isolation.^[31] In contrast, urban students reported higher engagement in risky behaviors, such as communicating with strangers, indicating different usage motivations influenced by their environments.^[32] While the self-reported patterns of smartphone usage by rural students are features of early phases of technology adoption with more socio-economic determinants, the usage pattern among urban school students is more likely to be influenced by earlier initiation and longer periods of personal or environmental exposure to smartphones, than their rural peers. School-based programs should also focus on fostering self-control among students, as this has been shown to protect against PSU. The study also noted that the rates of social issues associated with PSU, such as loss of friends and substance use in families, were lower than in previous studies, indicating a convergence of experiences between urban and rural students.^[33]

Although not a predictor of PSU in this study, 20.9% of students reported viewing pornography, with similar rates between urban and rural students. A study from India reported that rural students downloaded pornographic content more frequently than their urban counterparts.^[34] In addition, pornography has been associated with internet addiction and sexual violence among school students.^[7,35] Acknowledging the prevalence of pornographic content and developing supportive strategies could help mitigate its potential negative impacts on students.

To the best of our knowledge, this is the first study, to compare problematic smartphone usage among urban and rural school students in North India using validated bilingual study instruments. Moreover, the authors have utilized the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist to ensure that the highest reporting standards for observational studies have been met.^[36] The bilingual study instruments addressed language barriers, and anonymity helped reduce social biases.

The results could also be generalizable to other regions of the country and many other countries, as this ubiquitous smartphone technology is widely used by school students across the globe. The findings suggest that rural students are increasingly experiencing PSU similar to their urban peers, with urban users exhibiting higher thresholds for gratification due to longer exposure to technology. It also identifies diverse psychosocial determinants, emphasizing the protective role of maternal education and gender-specific behaviors. Moreover, the authors have utilized the STROBE checklist to ensure that the highest reporting standards for observational studies have been met.^[36]

However, limitations such as the cross-sectional design and lack of comprehensive academic data must be considered when interpreting these results. Self-reported information by the students may also have influenced the findings due to social desirability bias.

CONCLUSION

The prevalence of PSU among urban (43.7%) and rural (35.8%) school students in North India was comparable. Urban students were more likely to engage with strangers online, while rural students frequently checked their phones and felt guilt over spending. Key predictors for urban problematic use included being aged 15–17 years, whereas for rural students, factors included being a male student, private school enrollment, using multiple gadgets, and social media use. Higher maternal education was protective against PSU among rural school students. The findings highlight the necessity for targeted interventions addressing the unique risk factors in urban and rural settings to promote responsible smartphone use.

There is an urgent need for educational programs focused on digital literacy for students, parents, and educators. Future research should explore integrated interventions that support both academic and personal development to mitigate problematic use during critical formative years of life. Open communication between adolescents and caregivers is essential to prevent PSU.