interacting biological, family, school and social influences. The World Health Organization estimates that approximately one in seven adolescents aged 10–19 years experiences a mental health condition, with depression, anxiety and behavioral disorders contributing substantially to illness and disability.[1] At the same time, smartphones have become a central component of adolescent social and educational life, creating interest in whether particular patterns of use are associated with emotional and behavioral difficulties.

Evidence is stronger for problematic smartphone use than for screen time considered simply as hours per day. Sohn et al. reported that problematic smartphone use among children and young people was associated with increased odds of depression, anxiety and stress.[2] Yang et al. also found significant associations with poor sleep quality, depression and anxiety.[3] However, cross-sectional evidence cannot establish whether smartphone behavior precedes psychological difficulties or whether adolescents with pre-existing emotional, attention or peer problems use smartphones more intensively as a coping strategy.

Sleep is a plausible pathway linking smartphone behavior with adolescent functioning. Night-time use can displace sleep, increase cognitive and emotional arousal and expose users to notifications and ongoing social interaction. Prospective evidence reviewed by Dibben et al. supports a relationship among interactive electronic-device use, sleep and mental health,[4] while meta-analytic evidence links problematic smartphone use with poor sleep.[3] Because insufficient or disrupted sleep can affect attention, mood and self-regulation, it may partly explain associations between smartphone use and behavioral outcomes.

Family context is also important. Parental monitoring, household rules and parenting style may influence both device-use patterns and adolescent well-being. Yogesh et al. reported associations among smartphone addiction, parenting styles and mental well-being in Indian adolescents,[6] while Gangadharan et al. identified family and use-related correlates of mobile-phone addiction.[8] A socio-ecological perspective therefore suggests that smartphone behavior should not be considered in isolation from sleep, family supervision, academic functioning and physical activity.[13]

The Strengths and Difficulties Questionnaire (SDQ) provides a standardized multidomain measure of emotional symptoms, conduct problems, hyperactivity/inattention, peer problems and prosocial behavior.[12] This is useful because smartphone-use patterns may relate differently to specific behavioral domains. The present study aimed to examine the association between PSU and SDQ-defined behavioral difficulties, compare different smartphone-use measures, evaluate sleep as a potential explanatory pathway, and identify independent behavioral correlates after adjustment for demographic, family and lifestyle factors.

Design and sample: This analytical cross-sectional study included 400 school-going adolescents aged 10–19 years selected using multistage stratified random sampling. The exact geographical setting, participating institutions and study dates were not specified in the uploaded thesis draft and must be completed from the approved research records before submission. Eligibility required enrolment in selected institutions, availability during data collection, ability to understand the questionnaire language and appropriate consent/assent. Substantially incomplete questionnaires affecting primary exposure or outcome measures were excluded.

Measures: Smartphone exposure was characterized using daily duration, night-time use, checking frequency, age at first use and purpose. PSU was measured using the 10-item SAS-SV with sex-specific cut-offs of ≥31 for boys and ≥33 for girls.[14] Behavioral difficulties were assessed with the self-report SDQ, which measures emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and prosocial behavior and yields a total difficulties score.[12] The analysis used three-band categories (normal, borderline and abnormal). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), with a global score >5 classified as poor sleep.[15] Family, academic and lifestyle variables included parental monitoring, household rules on phone use, academic performance and physical activity.

Statistical analysis: Frequencies, percentages, means and standard deviations summarized the sample. Chi-square tests compared categorical outcomes. Unadjusted odds ratios (ORs) with 95% confidence intervals quantified associations between PSU and abnormal SDQ domains and between smartphone-use patterns and poor sleep. Spearman’s rho assessed correlations between smartphone-use measures and SDQ scores. A bootstrap mediation analysis with 5,000 resamples evaluated the indirect association of SAS-SV scores with SDQ total difficulties through sleep quality; because the data were cross-sectional, this was interpreted as statistical mediation rather than proof of a causal pathway. Binary logistic regression identified independent predictors of abnormal SDQ total difficulties. Adjusted odds ratios (AORs) with 95% CIs were reported. Model fit was assessed using the Hosmer–Lemeshow test and Nagelkerke R².

Ethics and bias control: Institutional Ethics Committee approval, administrative permissions, consent/assent, privacy and confidentiality were required by the protocol. Exact approval details should be inserted before submission. Standardized administration and validated tools were used to reduce measurement bias, but self-report, residual confounding and reverse causality remain limitations.

Among 400 adolescents, the mean SDQ total difficulties score was 14.2 ± 5.8. Abnormal total difficulties were present in 20.0%, and another 17.0% were borderline.

 Table 1. Distribution of behavioral problems by SDQ domain (n=400)

Emotional symptoms (18.0%) and hyperactivity/inattention (16.0%) were the most frequent abnormal domains. Overall, 37.0% of adolescents were either borderline or abnormal on total difficulties.

 Table 2. Association between problematic smartphone use and abnormal SDQ scores

PSU was associated with approximately 3.5-fold higher odds of abnormal total difficulties. The strongest domain-specific association was with hyperactivity/inattention, followed by emotional symptoms. Peer problems and low prosocial behavior did not reach statistical significance.

 Table 3. Spearman correlations between smartphone-use measures and SDQ scores

*p<0.05; **p<0.01. The SAS-SV score showed the strongest correlation with total SDQ difficulties (ρ=0.42), stronger than daily duration (ρ=0.29). Earlier age at first use was weakly associated with greater difficulties.

 Table 4. Poor sleep quality according to smartphone-use pattern

Poor sleep quality (PSQI >5) was present in 180 adolescents (45.0%). Night-time use showed the strongest association with poor sleep. Bootstrap mediation analysis showed a total standardized effect of SAS-SV on SDQ total difficulties of β=0.26, a direct effect of β=0.18 and an indirect effect through sleep of β=0.08 (95% CI 0.05–0.12), corresponding to approximately 31% statistical mediation.

 Table 5. Family, academic and lifestyle factors in relation to PSU and abnormal SDQ

A graded relationship was seen with parental monitoring: PSU rose from 20.7% with high monitoring to 48.3% with low monitoring, while abnormal SDQ scores rose from 12.1% to 31.7%. Absence of household phone rules, below-average academic performance and inadequate physical activity were also associated with both outcomes.

 Table 6. Multivariable logistic regression predicting abnormal SDQ total difficulties

After adjustment, PSU remained the strongest independent correlate of abnormal total difficulties. Poor sleep, low parental monitoring and night-time use also remained significant. Daily duration >4 hours was no longer significant. Model statistics were Nagelkerke R²=0.28 and Hosmer–Lemeshow p=0.62; all VIF values were <2.

One in five adolescents in this study had an abnormal SDQ total difficulties score, with emotional symptoms and hyperactivity/inattention the most frequent abnormal domains. A further 17.0% were borderline, meaning that more than one-third showed at least some behavioral difficulty on screening. These figures should not be interpreted as psychiatric diagnoses because the SDQ is a screening instrument.[12] Nevertheless, the findings indicate a substantial burden of emotional and behavioral symptoms in the study population.

The central finding was the strong association between PSU and behavioral difficulties. Adolescents meeting SAS-SV criteria had 3.46 times the odds of an abnormal total SDQ score compared with those without PSU. The association was particularly marked for hyperactivity/inattention (OR 3.03) and emotional symptoms (OR 2.62). This is broadly consistent with Sohn et al., who reported pooled associations of problematic smartphone use with depression and anxiety,[2] and with Yang et al., who found elevated risks of depression, anxiety and poor sleep.[3] Indian studies have likewise reported significant relationships between mobile-phone dependence or smartphone obsession and behavioral or mental-health outcomes.[6,9,10]

The correlation analysis suggests that the pattern and quality of use may be more relevant than quantity alone. SAS-SV scores correlated more strongly with total SDQ difficulties (ρ=0.42) than daily duration (ρ=0.29). Night-time use and checking frequency also showed meaningful correlations, particularly with emotional and hyperactivity/inattention scores. In multivariable analysis, daily duration >4 hours lost statistical significance, while PSU and night-time use remained independently associated with abnormal SDQ scores. This finding aligns with reviews showing that associations based solely on total screen time are often small or inconsistent.[5]

Sleep emerged as an important correlate. Poor sleep affected 45.0% of participants and was markedly more common among adolescents with PSU, night-time use and >4 hours/day of smartphone use. Night-time use carried the highest unadjusted odds of poor sleep (OR 4.08). This pattern is biologically and behaviorally plausible because late device use can delay bedtime, maintain cognitive arousal and expose adolescents to continuing notifications and social interaction. Systematic reviews have linked problematic smartphone use and interactive device use with adverse sleep outcomes.[3,4] The statistical mediation analysis suggested that sleep accounted for about 31% of the SAS-SV–SDQ association. Because the study was cross-sectional, this finding should be described as compatible with partial mediation rather than proof of a causal mechanism.

Family context was independently relevant. Low parental monitoring nearly doubled the adjusted odds of abnormal behavioral difficulties, and both PSU and abnormal SDQ scores increased across decreasing levels of monitoring. Homes without phone-use rules also had higher prevalence of both outcomes. These results are consistent with evidence linking parenting style and family context to adolescent smartphone behavior.[6] They also support a socio-ecological approach in which adolescent digital habits are influenced by individual, family and school environments.[13]

The study has several limitations. Its cross-sectional design prevents determination of temporal direction and reverse causality is plausible. All measures were self-reported, objective device logs were unavailable, and the SAS-SV and SDQ are screening rather than diagnostic tools. A school-based sample may not generalize to out-of-school adolescents. Residual confounding by factors such as cyberbullying or pre-existing mental-health conditions may remain. Future longitudinal studies should combine objective smartphone-use data with repeated behavioral and sleep assessments.

Problematic smartphone use was significantly associated with adolescent behavioral difficulties, particularly hyperactivity/inattention and emotional symptoms. Poor sleep quality, night-time smartphone use and low parental monitoring were independent correlates, whereas total daily duration was not significant after adjustment. These findings suggest that prevention and early guidance should focus on impaired control, night-time use, healthy sleep and supportive family monitoring rather than treating all smartphone use as harmful.

1. World Health Organization. Mental health of adolescents. Fact sheet. Updated 1 September 2025.
2. Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. 2019;19:356.
3. Yang J, Fu X, Liao X, Li Y. Association of problematic smartphone use with poor sleep quality, depression, and anxiety: a systematic review and meta-analysis. Psychiatry Res. 2020;284:112686.
4. Dibben GO, et al. Adolescents' interactive electronic device use, sleep and mental health: a systematic review of prospective studies. J Sleep Res. 2023;32(5):e13899.
5. Stiglic N, Viner RM. Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open. 2019;9:e023191.
6. Yogesh M, Ladani H, Parmar D. Associations between smartphone addiction, parenting styles, and mental well-being among adolescents aged 15–19 years in Gujarat, India. BMC Public Health. 2024;24:2462.
7. Davey S, Davey A. Assessment of smartphone addiction in Indian adolescents: a mixed method study by systematic-review and meta-analysis approach. Int J Prev Med. 2014;5(12):1500–1511.
8. Gangadharan N, Borle AL, Basu S. Mobile phone addiction as an emerging behavioral form of addiction among adolescents in India. Cureus. 2022;14(4):e23798.
9. Yadav MS, Malar Kodi S, Deol R. Impact of mobile phone dependence on behavior and academic performance of adolescents in selected schools of Uttarakhand, India. J Educ Health Promot. 2021;10:327.
10. Ambiha R, et al. Smartphone obsession linked behavioural changes among Indian adolescents. Bioinformation. 2023;19(10):1025–1028.
11. Vaghasiya S, Rajpopat N, Parmar B, Tailor KA, Patel HV, Varma J. Pattern of smartphone use, prevalence and correlates of problematic use of smartphone and social media among school going adolescents. Ind Psychiatry J. 2023;32(2):410–416.
12. Goodman R. The Strengths and Difficulties Questionnaire: a research note. J Child Psychol Psychiatry. 1997;38:581–586.
13. Amudhan S, Prakasha H, Mahapatra P, Burma AD, Mishra V, Sharma MK, Rao GN. Technology addiction among school-going adolescents in India: epidemiological analysis from a cluster survey for strengthening adolescent health programs at district level. J Public Health (Oxf). 2022;44(2):286–295.
14. Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One. 2013;8(12):e83558.
15. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.