Abstract
BACKGROUND
The mental health impact of smartphone use remains incompletely understood. Although several prior studies have suggested a correlation between smartphone usage and non-suicidal self-injury (NSSI), reliance on self-reported data limits the accuracy of exposure assessment. Objective measurements are needed to elucidate this relationship better.
AIM
To examine the associations of objectively assessed smartphone duration and unlock frequency with NSSI.
METHODS
This cross-sectional study included 17851 college students from China. NSSI was assessed using the Ottawa Self-Injury Inventory, which includes ten NSSI behaviors. Smartphone duration and unlock frequency were objectively monitored through participant-submitted screenshots. Binary logistic and restricted cubic spline models were used for analyses.
RESULTS
Of 17851 participants, 460 (2.6%) exhibited NSSI in the past month. The mean (SD) weekly smartphone duration and unlock frequency were 49.1 (29.9) hours and 390.9 (278.2) times. After adjusting for sociodemographic characteristics, health-related behaviors, and negative life events, smartphone use was significantly and positively associated with NSSI. Compared to students with 0-21 hours/week of smartphone duration, those with ≥ 63 hours/week exhibited a significantly higher risk (odds ratio = 1.60; 95% confidence interval: 1.23-2.11). Similarly, compared to students with 0-50 times/week of smartphone unlock frequency, those with ≥ 400 times/week (odds ratio = 1.48; 95% confidence interval: 1.11-2.00) had significantly higher NSSI risk. Restricted cubic spline analyses confirmed a monotonic increase in NSSI risk with increasing smartphone duration (P value for non-linear = 0.971) and unlock frequency (P value for non-linear = 0.225).
CONCLUSION
Excessive smartphone duration and unlock frequency are associated with an elevated risk of NSSI among college students, underscoring the need to consider smartphone use behaviors in NSSI prevention.
Keywords: Excessive smartphone use, Non-suicidal self-injury, Smartphone duration, Smartphone unlock frequency, Objective measures, College students
Core Tip: The mental health impact of smartphone use remains incompletely understood. The present study assessed objective smartphone duration and unlock frequency by collecting participants’ smartphone use records screenshots, finding that excessive smartphone duration and unlock frequency were positively associated with non-suicidal self-injury among college students. Restricted cubic spline analyses found a monotonic increase in non-suicidal self-injury risk with increasing smartphone duration and unlock frequency, emphasizing the need to consider digital behavior patterns in developing prevention strategies.
INTRODUCTION
Non-suicidal self-injury (NSSI), defined as the deliberate and self-inflicted harm to one’s body tissue without suicidal intent, has emerged as a pressing public health concern, particularly among adolescents and young adults[1,2]. Beyond the direct bodily harm, NSSI is also associated with increased risks of depression, anxiety, substance abuse, and suicide[3,4]. College students represent a psychologically vulnerable population, due to the complex interplay of academic pressure, emotional development challenges, and social adaptation stress during this developmental stage[5,6]. Accordingly, understanding the factors contributing to NSSI among college students is critical for its early identification and effective prevention.
An expanding body of evidence has linked excessive smartphone use with a range of psychological problems, including anxiety, depression, sleep disturbances, emotional dysregulation, and impaired interpersonal functioning[7-9], particularly among young people. These adverse effects are often attributed to behavioral patterns such as compulsive checking, disrupted sleep routines, social media-induced social comparison, and fear of missing out. Concurrently, the global penetration of smartphones and digital media has reached unprecedented levels. Worldwide, over 5.6 billion people own a smartphone, and nearly 5 billion are active users of social media platforms, with over 200 million new users added in the past year alone[10]. Daily smartphone duration has also risen sharply in recent years, with the global average now reaching 3 hours and 46 minutes per day[10]. These statistics underscore the growing centrality of smartphones in daily life and raise critical concerns about their potential implications for mental health.
Despite growing attention to the mental health implications of digital technology, there remains limited research directly linking problematic smartphone use to NSSI. Current evidence, primarily derived from self-report questionnaires, has associated prolonged smartphone duration and smartphone dependency with NSSI in adolescent populations[11-13]. However, these studies involved relatively small sample sizes, usually no more than a few hundred participants, limiting the generalizability and robustness of their findings.
Furthermore, dependence on subjective estimates of smartphone use introduces considerable risks of recall bias and misclassification[14-16]. This methodological limitation is particularly critical for individuals with psychological issues, as they may significantly misrepresent their actual smartphone use patterns. Several studies have shown a weak correlation between smartphone use assessed by self-report method and objective measurements. This has prompted a growing demand for objective measurement to provide more precise and ecologically valid assessments of actual smartphone use behavior. Notably, smartphone duration and unlock frequency represent different dimensions of smartphone usage. Whereas smartphone duration reflects the total amount of time spent engaged with the device, unlock frequency captures habitual checking behaviors. Prior studies suggested that smartphone unlock frequency can be a behavioral marker of compulsive device-checking behavior[17], which are often associated with core components of smartphone addiction, such as notification dependency or nomophobia. Notably, one study reported that smartphone unlock frequency exerts a greater influence on individual mental health than duration[18]. However, smartphone unlock frequency is difficult to capture via self-report accurately, and thus, no research has directly investigated its association with NSSI to date. To address these limitations, the present study collects screenshots of smartphone usage to investigate the relationships of objectively assessed smartphone duration and unlock frequency with NSSI among a large sample of Chinese college students.
MATERIALS AND METHODS
Recruitment and sample size
Between October and November 2022, all 24156 students from a comprehensive university in Shaanxi province, Northwest China, were enrolled to participate in this study. Prior to data collection, training sessions were conducted for all class counselors to explain the study objectives and procedures. Subsequently, these counselors facilitated the students’ anonymous completion of a structured electronic questionnaire. From the 22047 questionnaires received, submissions were excluded if they met any of the following criteria: (1) Completion time < 500 seconds; (2) Failure on ≥ 1 attention-check question; or (3) Inability to provide verifiable smartphone usage documentation. Ultimately, 17851 undergraduate students provided valid smartphone use records screenshots and completed questionnaires were involved in final analyses.
Measures
Objectively assessed smartphone usage: Smartphone duration and unlock were objectively assessed using participants’ submitted smartphone use records screenshots. Participants were instructed to enable smartphone usage tracking two weeks prior to formal data collection to ensure the availability of usage records. Step-by-step instructions tailored to major smartphone operating systems guided participants in capturing screenshots displaying duration and unlock counts accumulated during the preceding week. Based on the data distribution and supported by previous studies on smartphone duration patterns[19-21], participants were categorized into four groups of 0-21 hours/week, 21-42 hours/week, 42-63 hours/week, and ≥ 63 hours/week. Unlock frequency was similarly grouped into quartiles of 0-50 times/week, 50-150 times/week, 150-400, times/week and ≥ 401 times/week.
NSSI: NSSI was assessed using the Ottawa Self-Injury Inventory (OSI), a validated tool widely used in clinical and epidemiological research[22]. The OSI includes ten items assessing a range of NSSI behaviors, such as head banging, hitting, cutting, burning, scratching, biting, pinching, and stabbing. Participants were classified as having NSSI if they reported any of these behaviors during the past month. Prior studies have confirmed the reliability and construct validity of OSI in Chinese populations[23]. In the current study, the internal consistency was high (Cronbach’s α = 0.94).
Covariates: A structured questionnaire was designed to collect potential confounding variables that might influence the association between smartphone usage and NSSI[24,25]. Sociodemographic information included gender, grade, ethnicity, household registration, siblings, and parental educational attainment. Health-related behaviors were assessed according to criteria described in prior studies[26]. Participants were classified as current smokers or drinkers if they had smoked at least one cigarette or consumed at least one glass of alcohol in the past 30 days. Physical activity was assessed using the International Physical Activity Questionnaire Short Form[27], with participants categorized into low, moderate, and high activity tiers based on the calculated metabolic equivalents. Participants’ experiences of family disasters and failed relationships were assessed via the questions, “Have you encountered family adversities (e.g., death of a family member, parental divorce, etc.) in the past year?” and “Have you experienced a failed romantic relationship in the past year?” Family disasters or failed relationships were defined as an affirmative answer to these questions, respectively.
Statistical analysis
Categorical variables were presented as n (%), while continuous variables were described using means ± SD. We employed binary logistic regression analysis to examine the link between objectively assessed smartphone usage and NSSI, presenting the results as odds ratios (ORs) accompanied by 95% confidence intervals (CIs). Model 1 adjusted for sociodemographic characteristics. Model 2 additionally included health-related behaviors, and Model 3 further incorporated negative life events. To assess dose-response relationships, restricted cubic spline regression was applied. Interaction effects were tested between smartphone use and gender to explore possible effect modification. All statistical analyses were performed using R software (version 4.0.2), with a two-sided P value < 0.05 considered statistically significant.
RESULTS
Table 1 summarizes the demographic and behavioral characteristics of the study population. Among the 17851 participants, 6170 (34.6%) were male. A total of 17126 (95.9%) identified as Han ethnicity, with 7674 (43.0%) coming from rural areas and 6154 (34.5%) from single-child families (Table 1). Notably, 460 participants (2.6%) reported engaging in NSSI in the past month. On average, participants reported a mean (SD) smartphone duration of 49.1 (29.9) hours/week, and a mean (SD) unlock of 390.9 (278.2) times/week. Students with NSSI reported higher average weekly duration (P < 0.001) and more unlocks (P < 0.001).
Table 1.
Characteristics of participants, n (%)
|
Characteristics
|
Categories
|
Participants
|
NSSI
|
P value
|
|
|
No
|
Yes
|
||||
| Gender | Male | 6170 (34.6) | 6029 (34.7) | 141 (30.7) | 0.08 |
| Female | 11681 (65.4) | 11362 (65.3) | 319 (69.3) | ||
| Grade | 1st | 6155 (34.5) | 5979 (34.4) | 176 (38.3) | < 0.001 |
| 2nd | 4555 (25.5) | 4415 (25.4) | 140 (30.4) | ||
| 3rd | 4688 (26.3) | 4581 (26.3) | 107 (23.3) | ||
| 4th+ | 2453 (13.7) | 2416 (13.9) | 37 (8.0) | ||
| Race | Han | 17126 (95.9) | 16691 (96.0) | 435 (94.6) | 0.16 |
| Others | 725 (4.1) | 700 (4.0) | 25 (5.4) | ||
| Registered permanent residence | Rural | 7674 (43.0) | 7511 (43.2) | 163 (35.4) | 0.001 |
| Urban | 10177 (57.0) | 9880 (56.8) | 297 (64.6) | ||
| Siblings | No | 6154 (34.5) | 5983 (34.4) | 171 (37.2) | 0.24 |
| Yes | 11697 (65.5) | 11408 (65.6) | 289 (62.8) | ||
| Maternal educational attainment | Middle school or under | 9981 (55.9) | 9745 (56.0) | 236 (51.3) | 0.06 |
| High school | 4163 (23.3) | 4053 (23.3) | 110 (23.9) | ||
| College or above | 3707 (20.8) | 3593 (20.7) | 114 (24.8) | ||
| Parental educational attainment | Middle school or under | 8656 (48.5) | 8456 (48.6) | 200 (43.5) | 0.01 |
| High school | 4438 (24.9) | 4330 (24.9) | 108 (23.5) | ||
| College or above | 4757 (26.6) | 4605 (26.5) | 152 (33.0) | ||
| Family disaster | Yes | 1620 (9.1) | 1539 (8.8) | 81 (17.6) | < 0.001 |
| No | 16231 (90.9) | 15852 (91.2) | 379 (82.4) | ||
| Hospitalization experience | Yes | 1700 (9.5) | 1600 (9.2) | 100 (21.7) | < 0.001 |
| No | 16151 (90.5) | 15791 (90.8) | 360 (78.3) | ||
| Failed exams | Yes | 7360 (41.2) | 7066 (40.6) | 294 (63.9) | < 0.001 |
| No | 10491 (58.8) | 10325 (59.4) | 166 (36.1) | ||
| Failed relationships | Yes | 3401 (19.1) | 3207 (18.4) | 194 (42.2) | < 0.001 |
| No | 14450 (80.9) | 14184 (81.6) | 266 (57.8) | ||
| Smoking | Yes | 3165 (17.7) | 3019 (17.4) | 146 (31.7) | < 0.001 |
| No | 14686 (82.3) | 14372 (82.6) | 314 (68.3) | ||
| Drinking | Yes | 4401 (24.7) | 4185 (24.1) | 216 (47.0) | < 0.001 |
| No | 13450 (75.3) | 13206 (75.9) | 244 (53.0) | ||
| Rational diet | Yes | 2397 (13.4) | 2349 (13.5) | 48 (10.4) | 0.07 |
| No | 15454 (86.6) | 15042 (86.5) | 412 (89.6) | ||
| Physical exercise | Moderate or high | 3694 (20.7) | 3596 (20.7) | 98 (21.3) | 0.79 |
| Low | 14157 (79.3) | 13795 (79.3) | 362 (78.7) | ||
| Smartphone duration (hours/week), mean ± SD | 49.1 ± 29.9 | 48.9 ± 29.8 | 56.9 ± 31.5 | < 0.001 | |
| Smartphone duration (hours/week) | 0-21 | 4149 (23.2) | 4069 (23.4) | 80 (17.4) | < 0.001 |
| 21-42 | 2922 (16.4) | 2865 (16.5) | 57 (12.4) | ||
| 42-63 | 4626 (25.9) | 4508 (25.9) | 118 (25.7) | ||
| ≥ 63 | 6154 (34.5) | 5949 (34.2) | 205 (44.6) | ||
| Smartphone unlock frequency (times/week), mean ± SD | 390.9 ± 278.2 | 389.5 ± 276.4 | 437.1 ± 327.4 | < 0.001 | |
| Smartphone unlock frequency (times/week) | 0-50 | 4062 (22.8) | 3986 (22.9) | 76 (16.5) | < 0.001 |
| 50-150 | 5972 (33.5) | 5840 (33.6) | 132 (28.7) | ||
| 150-400 | 4249 (23.8) | 4128 (23.7) | 121 (26.3) | ||
| ≥ 400 | 3568 (20.0) | 3437 (19.8) | 131 (28.5) | ||
NSSI: Non-suicidal self-injury.
As presented in Table 2, both increased smartphone duration and unlocks were positively correlated with NSSI among college students. In the sociodemographic-adjusted model (model 1), students with ≥ 63 hours/week of smartphone duration had significantly increased NSSI risk (OR = 1.63; 95%CI: 1.25-2.13). Similarly, compared to students with 0-50 times/week of smartphone unlocks, those with 150-400 times/week (OR = 1.45; 95%CI: 1.09-1.96) and ≥ 400 times/week (OR = 1.77; 95%CI: 1.32-2.37) had elevated risks of NSSI. Continuous analyses supported that every 21 hours/week increase in duration was associated with a 17% increase in NSSI likelihood (OR = 1.17; 95%CI: 1.10-1.25), and each additional 50 times/week smartphone unlocks was linked to a 4% higher NSSI risk (OR = 1.04; 95%CI: 1.02-1.05). Further adjustment for health-related lifestyles in model 2 did not materially alter these associations. The relationships remained consistent in the fully adjusted model (model 3), which controlled for sociodemographic factors, health-related behaviors, and negative life events. Participants reporting ≥ 63 hours/week of smartphone duration continued to exhibit increased odds of engaging in NSSI (OR = 1.60; 95%CI: 1.23-2.11), while no significant associations were found for the 21-42 hours/week (OR = 1.09; 95%CI: 0.77-1.54) and 42-63 hours/week (OR = 1.22; 95%CI: 0.91-1.65) groups. Each 21-hours/week increase in smartphone duration remained significantly correlated with NSSI risk (OR = 1.16; 95%CI: 1.08-1.24). Concerning unlock frequency, only participants in the highest category of ≥ 400 times/week demonstrated significantly elevated NSSI risk (OR = 1.48; 95%CI: 1.11-2.00) compared to the reference group of 0-50 times/week, while mid-range categories were not significant. Nevertheless, continuous modeling showed that every 50 times/week additional unlocks increased the NSSI risk by 3% (OR = 1.03; 95%CI: 1.01-1.04). All models demonstrated statistically significant dose-response relationships (P for trend < 0.05; Table 2). Moreover, as depicted in Figure 1, both duration and unlock frequency exhibited monotonic upward trends with NSSI risk (all P value for non-linear > 0.05).
Table 2.
Association between smartphone use and non-suicidal self-injury
| Smartphone use | Categories |
OR (95%CI)
|
||
|
Model 11
|
Model 22
|
Model 33
|
||
| Smartphone duration, continuous variable | Per 21 hours/week | 1.17 (1.10, 1.25) | 1.15 (1.08, 1.23) | 1.16 (1.08, 1.24) |
| Smartphone duration, categorical variable | 0-21 hours/week | 1 (reference) | 1 (reference) | 1 (reference) |
| 21-42 hours/week | 1.08 (0.76, 1.53) | 1.17 (0.82, 1.65) | 1.09 (0.77, 1.54) | |
| 42-63 hours/week | 1.31 (0.98, 1.76) | 1.36 (1.02, 1.83) | 1.22 (0.91, 1.65) | |
| ≥ 63 hours/week | 1.63 (1.25, 2.13) | 1.60 (1.23, 2.11) | 1.60 (1.23, 2.11) | |
| P trend value | < 0.001 | < 0.001 | < 0.001 | |
| Smartphone unlock frequency, continuous variable | Per 50 times/week | 1.04 (1.02, 1.05) | 1.031 (1.02, 1.05) | 1.03 (1.01, 1.04) |
| Smartphone unlock frequency, categorical variable | 0-50 times/week | 1 (reference) | 1 (reference) | 1 (reference) |
| 50-150 times/week | 1.12 (0.85, 1.50) | 1.07 (0.80, 1.43) | 0.98 (0.73, 1.31) | |
| 150-400 times/week | 1.45 (1.09, 1.96) | 1.41 (1.05, 1.90) | 1.33 (0.99, 1.79) | |
| ≥ 400 times/week | 1.77 (1.32, 2.37) | 1.63 (1.21, 2.19) | 1.48 (1.11, 2.00) | |
| P trend value | < 0.001 | < 0.001 | 0.001 | |
Model 1, adjusted for gender, grade, race, registered permanent residence, siblings, and parental educational attainment.
Model 2, adjusted for gender, grade, race, registered permanent residence, siblings, parental educational attainment, current smoking, current drinking, physical activity, and rational diet.
Model 3, adjusted for gender, grade, race, registered permanent residence, siblings, parental educational attainment, current smoking, current drinking, physical activity, rational diet, family disasters, hospitalization experience, failed exams, and failed relationships.
OR: Odds ratio; CI: Confidence interval.
Figure 1.
Restricted cubic splines regression analysis between smartphone use and non-suicidal self-injury risk. A: Restricted cubic splines regression analysis between smartphone duration and non-suicidal self-injury risk; B: Restricted cubic splines regression analysis between smartphone unlock frequency and non-suicidal self-injury risk. Adjusted for gender, grade, race, registered permanent residence, siblings, parental educational attainment, current smoking, current drinking, physical activity, rational diet, family disasters, hospitalization experience, failed exams, and failed relationships. NSSI: Non-suicidal self-injury; OR: Odds ratio.
The findings of the gender-stratified analyses are detailed in Table 3. The current study found significant gender-specific differences in the relationships of smartphone duration (P for interaction = 0.03) with sleep duration. Among participants with moderate duration of 42-63 hours/week, females (OR = 1.72; 95%CI: 1.20-2.50) exhibited a higher risk of NSSI compared to males (OR = 0.82; 95%CI: 0.47-1.40). Conversely, in the highest duration of ≥ 63 hours/week, females (OR = 1.64; 95%CI: 1.17-2.34) displayed a lower risk of NSSI than males (OR = 1.71; 95%CI: 1.11-2.68). While smartphone unlock frequency was associated with NSSI only in females, no significant gender interaction was found (all P for interaction > 0.05).
Table 3.
Gender-specific association between smartphone use and non-suicidal self-injury
|
Smartphone use
|
Categories
|
OR (95%CI)1
|
P value for interaction
|
|
|
Male
|
Female
|
|||
| Smartphone duration, continuous variable | Per 21 hours/week | 1.19 (1.06, 1.33) | 1.14 (1.05, 1.23) | 0.67 |
| Smartphone duration, categorical variable | 0-21 hours/week | 1 (reference) | 1 (reference) | 0.03 |
| 21-42 hours/week | 0.90 (0.50, 1.59) | 1.40 (0.89, 2.17) | ||
| 42-63 hours/week | 0.82 (0.47, 1.40) | 1.72 (1.20, 2.50) | ||
| ≥ 63 hours/week | 1.71 (1.11, 2.68) | 1.64 (1.17, 2.34) | ||
| P trend value | 0.011 | 0.007 | ||
| Number of smartphones unlocks, continuous variable | Per 50 times/week | 1.02 (0.10, 1.04) | 1.04 (1.02, 1.05) | 0.20 |
| Number of smartphones unlocks, categorical variable | 0-50 times/week | 1 (reference) | 1 (reference) | 0.17 |
| 50-150 times/week | 0.71 (0.43, 1.19) | 1.29 (0.90,1.85) | ||
| 150-400 times/week | 1.08 (0.65, 1.81) | 1.60 (1.11, 2.32) | ||
| ≥ 400 times/week | 1.08 (0.65, 1.82) | 1.99 (1.39, 2.88) | ||
| P trend value | 0.232 | < 0.001 | ||
These analyses were adjusted for grade, race, registered permanent residence, siblings, parental educational attainment, current smoking, current drinking, physical activity, rational diet, family disasters, hospitalization experience, failed exams, and failed relationships.
OR: Odds ratio; CI: Confidence interval.
DISCUSSION
The relationship between smartphone usage and mental health remains a key focus within the digital era. However, existing studies have largely overlooked NSSI, which is a high-risk behavior with substantial public health relevance. Using passively monitored objective data on smartphone duration and unlocks from 17851 college students; this study identified significant positive associations and dose-response relationships of smartphone duration and unlock frequency with NSSI. Individuals having more smartphone duration and unlock frequency exhibited a higher risk of NSSI.
Although substantial research has examined the effects of smartphone usage on mental well-being, to date, no study has evaluated the relationship of smartphone duration, unlocks, with NSSI in college students. Existing evidence was confined to three studies in middle school populations[11-13]. For example, a study from China found that daily smartphone duration and pre-sleep smartphone screen time were positively associated with NSSI in middle school students, with higher usage levels associated with NSSI[12]. Another investigation reported that middle school students using smartphones ≥ 4 hours daily on weekends had a 1.74-fold higher risk of NSSI than those using < 0.5 hours[13]. Additionally, a Japanese study demonstrated that adolescents using smartphones after lights-out had a 1.56-fold increased risk of NSSI than non-users[11]. Crucially, college students, often termed digital natives, undergo pivotal life transitions when starting university, marked by diminished parental oversight and minimal institutional restrictions on device usage. These conditions make them more prone to maladaptive smartphone usage than middle school students[28]. Moreover, as college students navigate the critical developmental stage from adolescence to early adulthood amid China’s rapidly accelerating societal pace, their elevated psychological distress has garnered widespread concern. Thus, investigating the relationship between smartphone usage and NSSI among college students, where digital dependency is pervasive and mental health burdens are significant, holds unique public health relevance.
Furthermore, the previous three studies are limited to middle school populations and solely rely on participants’ self-reported smartphone usage data[11-13]. In previous studies[29,30], traditional self-report methods primarily captured data on participants’ smartphone use behaviors through subjective single-choice questions such as “How much time do you spend on your smartphone each day? (< 1 hour, 1-2 hours, 2-3 hours, 3-4 hours, > 4 hours)” and “How frequently do you use your smartphone? (rarely, sometimes, often, frequently)”. Data obtained in this manner may be subject to substantial recall bias and misclassification probability, potentially affecting results. Furthermore, self-report methods may be prone to reporting bias. Adolescents may underreport smartphone duration or unlock frequency due to social desirability[14]. Several studies have found that smartphone use behaviors measured by self-reported methods did not correlate well with smartphone use behaviors measured objectively using new technologies[14,15,30-34]. For instance, a study conducted in 2025 compared self-reported estimates of smartphone usage over the past month with continuous objective smartphone use derived from digital traces among 41 adolescents and 40 parents of adolescents[30]. Concordance between self-reported and objectively measured total daily smartphone use, assessed via intraclass correlation coefficients (ICCs), was moderate for adolescents (ICC = 0.65) but poor for adults (ICC = 0.18). Spearman’s rank correlation coefficients (rho) indicated modest associations for both adolescents (rho = 0.64) and adults (rho = 0.48). A prior study recruited 93 participants to prospectively record the number of incoming and outgoing voice calls made over the preceding day, week, or month, and further estimate the duration (in minutes)[34]. These self-reported measures were then compared against objective call detail records obtained from four mobile network operators. The results revealed modest concordance between the self-reported data and the operator logs, indicating substantial discrepancies and suggesting that self-report methodologies inadequately capture actual usage patterns. Taken together, objective measurement data obtained through screenshots of smartphone usage records in the present study provide accurate and granular data, effectively mitigating recall bias, misclassification bias, and reporting bias. Third, the self-report method is difficult to capture smartphone unlock frequency accurately, and thus, no research has directly investigated its association with NSSI to date. Smartphone duration and unlock frequency represent different dimensions of smartphone usage, whereas smartphone duration reflects the total time spent on the device, and unlock frequency captures habitual checking behaviors. A prior study suggested that smartphone unlock frequency reflects compulsive device-checking behavior[17], a fundamental aspect of smartphone addiction, and exerts a greater influence on individual mental health than does duration[18]. The objective measurement in this study effectively assesses smartphone unlock frequency, which has been neglected in previous studies. Finally, smartphone use behaviors via self-report were mostly treated as categorical variables in traditional regression models to analyze their risk coefficients with NSSI, neglecting the trend of association between continuous changes in smartphone use and the risk of NSSI. The objective measurement in this study captures accurate smartphone data, and can further characterize the association trends of continuous changes in smartphone duration and unlock frequency with NSSI risk. These findings may help transform the abstract advocacy for rational smartphone use on campus into passive monitoring interventions based on measurable behavioral indicators.
Although empirical exploration of its association with NSSI remains limited, several studies have reported a correlation between smartphone addiction and NSSI[35-39]. For instance, a multicenter study involving 38 schools further demonstrated that students with smartphone addiction exhibited a significantly higher 12-month NSSI prevalence (OR = 2.37) than non-addicted controls[36]. In a case-control study among NSSI adolescent patients, a significant positive correlation was reported between smartphone addiction and NSSI[37], where each point increase in smartphone addiction score was associated with an 11.57-point increase in NSSI severity. A separate cohort study likewise revealed that adolescents with smartphone addiction had a 4.28-fold higher risk of NSSI compared to non-addicted peers[35]. Smartphone addiction assessment tools (e.g., Smartphone Addiction Scale) rely on abstract dimensions such as withdrawal symptoms, functional impairment, and tolerance, as well as subjective items like “needing prolonged use to maintain the same satisfaction”. This makes smartphone addiction a less actionable direct target for intervention. Notably, smartphone addiction and objective usage behaviors represent distinct measurement domains, with the former emphasizing excessive use, loss of control, and social dysfunction or psychobehavioral abnormalities[40,41]. In contrast, smartphone use behaviors quantify cumulative user engagement and serve as a proxy for usage intensity, which differs from smartphone addiction. Examining the associations of smartphone duration and unlocks with NSSI carries greater practical implications for early identification and intervention of NSSI than smartphone addiction. Although evidence supported an association between smartphone addiction and NSSI, shifting the research focus to quantifiable usage metrics (e.g., duration, unlock frequency) in the current study is of greater practical significance for guiding smartphone interventions integrated into modern daily life. This approach overcomes the subjectivity limitations of Smartphone Addiction Scales by focusing on observable variables with behavioral characteristics, providing clear targets for developing screen and intervention protocols.
The link between smartphone usage and NSSI highlights technology’s transformative influence on psychosocial functioning and behavioral patterns, which can be conceptualized through established theoretical frameworks. Specifically, the four-function model of NSSI[42] identifies emotion regulation as a core function of NSSI. The Compensatory Internet Use Theory[43] posits that smartphone immersion increasingly serves as a maladaptive coping strategy for college students seeking temporary relief from real-world stressors(e.g., academic pressure, interpersonal conflicts) and associated affective discomfort. Paradoxically, smartphone use as a stress-avoidance strategy displaces adaptive emotion-regulation activities (e.g., sleep, exercise, and socializing in the real world), thereby accumulating stress burdens and exacerbating emotional dysregulation. This may ultimately accelerate NSSI as an externalized outlet for psychological pain. Then, the findings in the present study are consistent with the motivational-volitional integration model of NSSI, which suggests that the transition from NSSI ideation to behavior requires diminished executive control, behavioral modeling, and method accessibility. Prolonged smartphone duration may elevate exposure to peer NSSI behaviors and related content, enhancing imitative NSSI tendencies through social learning mechanisms. Potential neurobiological mechanisms may include the following. First, alterations in cortisol and 5-hydroxytryptamine may also be responsible for the association between smartphone use and NSSI. One study found that daily duration of online gaming was significantly correlated with elevated salivary cortisol levels[44]. Cerniglia et al[45] demonstrated that the decrease of 5-hydroxytryptamine is also related to the dysfunction of the prefrontal cortex caused by internet addiction. Smartphone use, like internet use, is a form of technology use and shares similar characteristics. Second, NSSI exhibits prominent addictive features. Excessive smartphone use, particularly smartphone unlock frequency, indicates a compulsive behavior to check the device, frequently motivated by dependency on notifications or nomophobia, which is an essential aspect of smartphone addiction[46]. This type of behavior is also strongly associated with reward-seeking cycles[47]. Third, excessive smartphone use, smartphone addiction, and smartphone dependency essentially represent an impulse control disorder[41,48,49]. Evidenced by neuroimaging studies linking the development of problematic smartphone use to dysregulation of executive functions and inhibitory control in the prefrontal cortex[50]. Given that inhibitory control constitutes one of the key intrinsic factors underlying NSSI[51], this may further explain the finding in the present study that excessive smartphone use is associated with an increased risk of NSSI.
However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference, and longitudinal studies are needed to clarify temporal relationships. Second, this research concentrated on a college student demographic in China, which may restrict the applicability of our findings to other populations. Third, although smartphone use was objectively measured, data collection relied on participants voluntarily submitting screenshots, which may introduce selection bias. Additionally, the collected smartphone use data also lacks more specific and qualitative context, which may hinder the interpretation of results. Fourth, NSSI was assessed using the self-report OSI, and this assessment may be subject to recall bias or under-reporting due to social desirability concerns. Furthermore, we were unable to capture the frequency and functions of NSSI, nor explore their specific relationships with smartphone use. Future studies should aim to investigate these dimensions to further clarify the nature of the association. Fifth, the assessment of negative life events in this study may be inadequate, as it relied on a limited set of questions from a self-designed instrument. Some unmeasured confounders may influence the observed associations between smartphone use and NSSI, including substance use, mental illnesses treated with psychotropic medication, personality traits, and broader aspects of psychopathology. Finally, we did not assess possible mediators such as sleep disturbance, emotional dysregulation, or internet addiction, which may help explain the observed associations. Future studies should adopt prospective designs to explore the causal mechanisms linking smartphone use to NSSI, incorporating psychophysiological and behavioral data.
CONCLUSION
Excessive smartphone use, including duration and unlock frequency, is significantly associated with a higher risk of NSSI among college students. The risk of NSSI increases steadily with greater smartphone use, emphasizing the need to consider digital behavior patterns in developing prevention strategies.
ACKNOWLEDGEMENTS
The authors would like to thank the University’s Mental Health Education Center for its involvement in this study and all participants.
Footnotes
Institutional review board statement: This study is approved by the Second Affiliated Hospital of Xi’an Jiaotong University, No. 2022248.
Informed consent statement: All participants provided informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade B, Grade B
Novelty: Grade B, Grade B, Grade B, Grade C
Creativity or Innovation: Grade B, Grade B, Grade B, Grade C
Scientific Significance: Grade B, Grade B, Grade B, Grade C
P-Reviewer: Chakrabarti S, MD, Professor, India; Kar SK, MD, Professor, India S-Editor: Wu S L-Editor: A P-Editor: Zhao YQ
Contributor Information
Jian Yin, Department of Laboratory, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China.
Guang-Dong Wang, Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China.
Pei-Xi Wu, Xi’an Mingde Institute of Technology, Xi’an 710124, Shaanxi Province, China.
Hui Yang, Department of External Cooperation and Exchange, Shaanxi Provincial Health Commission, Xi’an 710003, Shaanxi Province, China.
Ze-Shi Liu, Department of Laboratory, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China.
Yan-Ping Zhang, Department of Laboratory, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China. hongpijidan@mail.xjtu.edu.cn.
Data sharing statement
Dataset available from the corresponding author. Participants gave informed consent for data sharing was not obtained but the presented data are anonymized and risk of identification is low.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Dataset available from the corresponding author. Participants gave informed consent for data sharing was not obtained but the presented data are anonymized and risk of identification is low.