The effect of smombie and phubbing levels on psychological well-being in adolescents

Abstract

Objective

This study was conducted to examine the effect of smombie (attention distraction and alienation from the environment due to smartphone use) and phubbing (ignoring others by being preoccupied with one’s phone during face-to-face interactions) levels on adolescents’ psychological well-being.

Method

This descriptive cross-sectional study was conducted with 626 adolescents living in Turkey between June–September 2025. Data was collected via an online survey administered through Google Forms. The data collection tools used were the Smombie Scale for Adolescents, the General Phubbing Scale, and the Psychological Well-Being Scale. R programming language 4.1.3, G*Power 3.1, and SPSS-22 programs were used in the analysis of the study.

Results

According to hierarchical regression analysis results, smombie level explained 15.7% of the variance in psychological well-being (R² = 0.157; F(1,624) = 117.64, p < 0.001) and was found to be negatively associated with psychological well-being (β = − 0.398; 95% CI [− 0.475, − 0.329]). With the addition of phubbing level, the total variance explained by the model increased to 22.3% (R² = 0.223; F(2,623) = 90.70, p < 0.001). In this model, both smombie (β = − 0.171; 95% CI [− 0.266, − 0.079]) and phubbing (β = − 0.344; 95% CI [− 0.224, − 0.130]) levels showed statistically significant and negative relationships with psychological well-being; with phubbing being a stronger predictor.

Conclusion

This study demonstrates that smombie and phubbing behaviors in adolescents are negatively associated with psychological well-being. The cross-sectional design of this study limits causal inferences. Therefore, future research should employ a longitudinal design to determine the direction of these relationships and their effects over time.

Introduction

Adolescence is defined as a period of transition from childhood to adulthood, marked by rapid biological and physical development, along with sexual and psychosocial maturation [69, 76]. Adolescence is a critical developmental stage during which individuals form their identity through interaction with their social environment and are neurobiologically most sensitive to social stimuli. During this period, young people develop various representations of self appropriate to different social contexts while also striving to form a consistent and authentic identity [81]. Increased sensitivity to feedback such as peer approval and social acceptance, due to reward systems in the brain, plays an important role in adolescents’ social relationships. Adolescents exhibit heightened activation in their social and emotional processing regions, including the vmPFC (ventromedial prefrontal cortex), TPJ (temporoparietal junction), and amygdala, in response to negative feedback, thereby ensuring conformity to social expectations [15]. Furthermore, from a neurodevelopmental perspective, the early maturation of the limbic system and the relatively late development of the prefrontal cortex create cognitive-emotional imbalances in adolescents. The result leads adolescents to be more reactive to environmental stimuli and more emotionally volatile [46, 60]. This developmental imbalance manifests as increased risk-taking, weakened impulse control, and impaired emotional regulation [46, 49, 60]. When these biopsychosocial characteristics intersect with the digital age’s culture of instant feedback, constant online presence, and social comparison-based online interactions, adolescents’ vulnerability profile significantly expands [14, 61]. This situation reveals that digital technologies have become environmental determinants because they shape the neurological and psychosocial foundations of adolescents’ self-perception, social evaluation, and emotional regulation [53].

Interactions with digital environments become particularly important during adolescence, a critical stage in the acquisition of habits and behavioral patterns [58]. Adolescents, who face social and psychological difficulties due to developmental processes, experience additional challenges in coping with technological developments [16]. With the increase in smartphone use, new risk areas affecting adolescents’ psychological well-being have emerged. This situation has paved the way for the emergence of the “smombie” phenomenon, which is used to describe individuals who focus their attention largely on their mobile devices while walking. This behavior pattern raises concerns about its potential effects on adolescents’ mental health, as developmental stages are characterized by increased vulnerability to mental health disorders [22]. Smombie may increase feelings of loneliness by limiting adolescents’ socialisation, which can lead to psychological problems [36]. Adolescents tend to frequently compare themselves with others on social media platforms. This behaviour can negatively affect their self-esteem and reduce their life satisfaction. Furthermore, social comparisons made through social media can undermine adolescents’ supportive social perceptions. This situation can threaten adolescents’ psychological well-being [35]. The effects of smombie on psychological well-being can have significant consequences not only at the individual level but also at the societal level [79]. Furthermore, smombie can negatively affect adolescents’ social characteristics, causing them to experience difficulties in face-to-face communication [70]. Some recent studies have shown that smombie behavior can have neutral or functional effects in certain situations. It has been suggested that smombie behavior may serve a self-regulatory function by providing short-term escape and stress reduction [3]. A large-scale field study conducted in Spain found that individuals exhibiting smombie behavior did not experience serious effects such as accidents or attention disorders and that this behavior was routine and short-lived [25].

In interpersonal communication, diverting attention away from the other person and toward a smartphone is a behavior that weakens the quality of social interaction and is defined in the literature as “phubbing” [42]. In other words, it is described as the act of belittling the person one is interacting with by focusing on one’s phone instead of the person in front of them in a social setting [32]. This behaviour manifests itself in people’s daily activities, meals, meetings, lessons or time spent with friends, where they choose their smartphones over the person opposite them [26, 51]. Phubbing is a form of social exclusion and is used to describe the disruption caused by mobile phone use in social interaction [82]. Research has shown that excessive smartphone use can increase loneliness and dissatisfaction with social life among adolescents. Furthermore, excessive smartphone use can negatively affect general psychological well-being, as well as psychological problems such as anxiety, depression and social isolation [41, 54]. However, there are also differing findings on this subject in the literature. Phubbing, which fulfills social needs and strengthens the sense of belonging, can increase life satisfaction. Phubbing, which reduces the stress and anxiety created by social interaction, can serve as a temporary emotional escape and self-regulation strategy [43]. Understanding and empathy in interpersonal relationships can reduce the negative effects caused by phubbing [27].

This study is structured based on the General Addiction Framework to explain the effects of smombie and phubbing behaviors on psychological well-being in adolescents. According to this theoretical framework, addiction begins as a behavior serving an emotional regulation function, which is then reinforced by reward cycles and loss of control, eventually becoming central to the individual’s life [37]. The excessive use of tools such as technology, the internet, and smartphones is also part of behavioral addictions. In this regard, smombie and phubbing can be considered a result of individuals turning to digital tools to satisfy their emotional needs [8, 72]. Within the General Addiction Framework, such digital behaviors, while providing short-term relief, are associated with reward seeking, escape, and loss of control mechanisms, which in the long term reinforce the addiction cycle and negatively impact the individual’s psychological well-being [37].

Recent studies have investigated the correlation between smartphone usage and psychological well-being among adolescents in a multidimensional context; however, smombie and phubbing behaviors have typically been analyzed separately [45, 57, 77]. Research in Turkey has predominantly concentrated on smartphone addiction and phubbing [33, 78]. Nonetheless, the notion of smombie has been predominantly neglected, discussed solely in relation to distraction or traffic safety [25]. No research in the Turkish literature has investigated smombie and phubbing behaviors concurrently, specifically targeting an adolescent sample. Consequently, this study seeks to offer a distinctive contribution to the literature as one of the initial investigations to thoroughly examine smombie and phubbing behaviors within the Turkish context. The research integrates digital addiction behaviors through the lens of the General Addiction Framework, thoroughly elucidating their impact on adolescents’ psychological well-being.

Research hypotheses

• H₁: As the level of smombi increases in adolescents, psychological well-being decreases.

• H₂: As the level of phubbing increases among young people, their psychological well-being decreases.

Method

Study design

This descriptive and cross-sectional study took place from June to September 2025. The study aimed to investigate the correlation between the prevalence of smombie and phubbing behaviors and the psychological well-being of adolescents in Turkey. The study was reported based on the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [80].

Sample and population

The study’s population comprised adolescents aged 10–19 living across Turkey. The study’s participants were adolescents aged 10–19 living in various regions of Turkey. Because the exact size of the population was not known, Cochran’s formula for unknown populations was used to figure out the sample size.

The formula uses a 95% confidence level (Z = 1.96), a 5% margin of error (d = 0.05), and p = 0.5 assumptions. Accordingly, the minimum sample size was found to be 384.

In the study, the convenience sampling technique, one of the non-probability sampling methods, was used for sample selection. This method is based on including adolescents who voluntarily filled out the online form and were accessible. In this context, the research link was shared through social media groups (e.g., school, youth, and student communities) and email lists verified by the research team, and participation was entirely voluntary.

At the end of the data collection process, a total of 626 valid participant data were obtained, and there was no missing data due to Google Forms’ “one response per session” feature. As a result of the post hoc power analysis performed after data collection, the power of the study was calculated as a medium effect size (99% power, 95% confidence level) [13].

The study included adolescents aged 10–19 who voluntarily agreed to participate in the study and completed the online informed consent form. Conversely, the study did not include individuals who were not aged 10–19 or who did not voluntarily agree to participate.

Ethical approval and participant rights

The research has been approved by the Ethics Committee of the Faculty of Social and Human Sciences at Sakarya University (Ethics Committee Decision No: XX/XX, Date: DD/MM/YYYY).

Parental/guardian consent was obtained online prior to participation in the research. Parents were provided with an information text in digital format containing the purpose of the research, confidentiality principles, and conditions of participation; parents who checked the option “I give permission for my child to voluntarily participate in this research” at the end of the text provided electronic consent. Following parental consent, adolescent participants were directed to the survey via the same online form.

An information form tailored to the age of adolescent participants was displayed at the beginning of the online survey; individuals who checked the “I agree to participate” option were included in the study. No identifying information, such as identity or IP address, was collected from participants; all data were analyzed anonymously.

Data collection

The data collection process was conducted entirely online. The study link was only made accessible via verified links shared by the research team.

To maintain data integrity and prevent duplicate responses, Google Forms’ “one response per session” feature was enabled. Additionally, data automatically generated by the system, such as IP addresses and timestamps, was checked to filter out duplicate entries from the same device or network. No IP addresses or identifying data were stored; they were only temporarily monitored to detect potential duplicates. No personal data such as identity, school, or contact information was collected from participants.

The data collection process took an average of 10–12 min. The form could not be submitted until the survey was completed, and all questions had to be answered. As a result, there is no missing data.

Data collection tools

As data collection tools, the “Personal Information Form”, the “Smombie Scale for Adolescents”, the “General Phubbing Scale” and the “Psychological Well-Being Scale”, developed by researchers following a literature review, were utilised.

Personal ınformation form

It consists of questions created by researchers that contain individuals’ identifying characteristics.

Smombie scale for adolescents

The Smombie Scale for Adolescents is an adaptation of the Smombie Scale developed by Park and Kim [56] for adults, modified for the adolescent group by Park and Oh [57]. The scale consists of 15 items and 4 subscales. These subscales are (1) Perceived risk (4 items), (2) Mobile phone use while waiting (4 items), (3) Pending notifications (3 items), and (4) Mobile phone addiction (4 items). All items are rated on a 5-point Likert scale (1 = Never, 5 = Always). The total score that can be obtained from the scale, which has no reverse items, ranges from 15 to 75 points. Higher scores indicate a higher tendency toward smombie behavior.

The Turkish adaptation was conducted by Sönmez Sarı et al. [68]. In this adaptation, confirmatory factor analysis (CFA) results showed that the four-factor structure fit well (χ²/df = 2.21, CFI = 0.94, TLI = 0.92, RMSEA = 0.059). The internal consistency coefficient (Cronbach’s α) was reported as 0.85 for the scale and 0.78–0.88 for the subscales [68]. In our study, Cronbach’s Alpha value was found to be 0.90.

General phubbing scale

The scale developed by Chotpitayasunondh and Douglas [12] and adapted into Turkish by Gavcar and colleagues [33] consists of 4 subscales and 15 items [11, 33]. The subscales of the scale are (1 Nomophobia (4 items, (2 Interpersonal Conflict (4 items, (3 Self-Isolation (4 items, and (4 Problem Acceptance (3 items. All items are rated on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree. There are no reverse-scored items in the scale,a low score indicates a low level of sociotelism, while a high score indicates a high level of sociotelism.

In Gavcar and colleagues’ adaptation, construct validity was tested using confirmatory factor analysis (CFA), and the suitability of the four-factor model was confirmed (χ²/df = 2.23, RMSEA = 0.08, CFI = 0.95, TLI = 0.94, SRMR = 0.05). The internal consistency coefficient (Cronbach’s α) was reported as 0.94 for the scale and 0.77–0.92 for the subscales [33]. In our study, Cronbach’s Alpha value was found to be 0.95.

Psychological well-being scale

This scale, developed by Diener and colleagues [18] to measure psychological well-being, was adapted into Turkish by Telef (2013) [18, 75]. The 7-point Likert-type, single-dimensional scale consists of 8 items and does not contain reverse items. The scale is rated on a 7-point Likert scale (1 = strongly disagree–7 = strongly agree). A high score on the scale indicates a high level of psychological well-being [75].

In Telef’s adaptation, confirmatory factor analysis (CFA) results indicated that the scale’s unidimensional structure was well-fitted (χ²/df = 4.64, RMSEA = 0.08, SRMR = 0.04, GFI = 0.96, NFI = 0.94, CFI = 0.95, IFI = 0.95). The internal consistency coefficient (Cronbach’s α) was found to be 0.86 for the scale [75]. In our study, Cronbach’s Alpha value was found to be 0.91.

Data analysis

The analysis of the study data was performed by using SPSS 22.0,, and G*Power 3.1 Statistical package software. Percentage, arithmetic mean, standard deviation, minimum and maximum values were calculated using SPSS 22.0. Necessary normality tests were performed in the process of analyzing the data and it was understood that the data showed normal distribution (kurtosis and skewness −1.5 to + 1.5) [73]. P value of < 0.05 was considered statistically significant. Predictive analyses were performed with R programming language version 4.1.3. Various machine learning and deep learning algorithms (KNN, SVM (svmRadialSigma), artificial neural networks (avNNet, monmlp, pcaNNet), RF (rf), XGBoost (xgbLinear), and alternative regression methods (Ridge, Lasso, Elastic Net)) were implemented using R programming language version 4.3.1 through the caret package.

Results

It was determined that 50.2% of the individuals participating in the study were male, 69.6% were in secondary education, 52.9% had an income equal to their expenses, 79.6% lived in a nuclear family, and their average age was 16.13 ± 2.57 (Table 1).

Table 1.

Descriptive characteristics of individuals (n = 626)

Demographic characteristics		n	%
Gender	Female	312	49,8
	Male	314	50,2
Educational Status	Primary education	63	10,1
	Secondary education	436	69,6
	Higher education	127	20,3
Income Level	Income Less Expenses	236	37,7
	Income Equivalent to Expenses	331	52,9
	Income Exceeds Expenses	59	9,4
Family Type	Nuclear Family	498	79,6
	Extended Family	128	20,4
	± SD (Min–Max)
Age (Years)	16.13 ± 2.57 (10–19)

When examining the analysis results of hierarchical regression models conducted to reveal the effects of smombie level and phubing level on psychological well-being level, the 95.0% Confidence Interval for B;

Statistical estimates for Model 1 indicate that the model is meaningful and usable (F(1,624) = 117.638, p = 0.001). In the regression model, when examining the t-test results regarding the significance of the regression coefficient, it was determined that an increase in participants’ smombie level (t = −10.846, p < 0.001) caused a statistically significant decrease in their ‘psychological well-being’ level (Table 2).

Table 2.

Results of hierarchical regression analysis conducted to determine the effect of smombie level and phubbing level on psychological well-being level

Predictive variables	Psychological well-being (Dependent variable)
	B	SD	β	t	p*	95,0% confidence interval for B
Model 1						Lower Bound	Upper Bound
(Constant)	51,524	1,290		39,945	,001	48,991	54,057
Smombie	-,402	,037	-,398	−10,846	,001	-,475	-,329
Model 2
(Constant)	51,657	1,239		41,703	,001	49,224	54,089
Smombie	-,173	,047	-,171	−3,642	,001	-,266	-,079
Phubbing	-,177	,024	-,344	−7,335	,001	-,224	-,130
R	Model 1: 0.398 Model 2: 0.475
R2/Adjusted R2	Model 1: 0.159/0.157 Model 2: 0.226/0.223
R2 Change	Model 1: 0.159 Model 2: 0.067
F	Model 1: 117.638 Model 2: 90.698

Hierarchical regression analysis*

Statistical estimates for Model 2 indicate that the model is meaningful and usable (F(2,623) = 90.698, p = 0.001). In the regression model, when examining the t-test results regarding the significance of the regression coefficient, it was found that the increase in participants’ smombie level (t = −3.642, p < 0.001) and the increase in their phubing level (t = −7.335, p < 0.001) caused a statistically significant decrease in the level of psychological well-being (Table 2).

Analysis using a machine learning approach

Hyperparameter tuning was performed on the training dataset to ensure optimal performance of the algorithms (Fig. 1). The most successful hyperparameter values obtained from Fig. 1 are determined, and predictions are made on the test data.

Fig. 1.

Models established for predicting Psychological Well-Being using K-Nearest Neighbors regression (KNN), Support Vector Machine regression (SVM), Artificial Neural Network regression (NN), Model Averaged Neural Network regression (avNNet), Random Forest regression (RF), XGBoost regression (XGBoost), Regression (REG (Elastic-net, Lasso, Ridge)), Monotone Multi-Layer Perceptron Neural Network regression (MONMLP), Quantile Regression Neural Network regression (QRNN), Decision Tree regression (DT), Principal Component Regression (PCR) and Gradient Boosting Machine regression (GBM) algorithms, and the determination of the best hyperparameters based on the logarithm-transformed training data

When examining the RMSE and MAE values in Fig. 2, the test prediction results of these optimal hyperparameters are obtained. The explanatory power of the GBM method on the training data, measured by the R2 value, is calculated as 0.2362043. This indicates that our independent variables explain only about 24% of the variance in the Psychological Well-Being variable.

Fig. 2.

The metric values corresponding to the predictions of the test data, obtained through the inverse transformation of the models that provided the most accurate results according to the hyperparameter values

The prediction performance of the model on the test data is shown in Fig. 3.

Fig. 3.

Psychological well-being test data prediction using the GBM method

SHAP values show the extent to which each variable influences the model’s predictions and the magnitude of this influence. According to the graph, Phubbing is identified as the most important variable in predicting the Psychological Well-Being variable. The performance comparison of all variables in the prediction model was conducted using machine learning algorithms. Shapley Values (Shapley Additive Explanations, SHAP) were used to understand the importance and contribution attributed to each variable by the model. To avoid any bias during the performance comparison, the SHAP values of the variables in the best-performing model were examined. SHAP values show the extent to which each variable influences the model’s predictions and the magnitude of this influence.

According to the graph, Phubbing is identified as the most important variable in predicting the Psychological Well-Being variable. The values shown on the x-axis of the SHAP graph reflect the magnitude of change in log-odds, from which the probability of success can be derived. If a variable’s SHAP value is greater than zero, it indicates a positive effect for most observations. If this value is less than zero, it indicates a negative effect for most observations.

In the SHAP graph, the names of the variables are listed on the y-axis in order of importance, alongside their average SHAP values. The x-axis shows the magnitude of change in log-odds. The original values of the variables are expressed in colors, which can take on two different colors for Boolean variables or cover a wide color spectrum for numerical variables.

It was found that the Phubbing variable has an effect that is 5.186 units higher compared to other variables. The observations with the highest impact of this variable are shown by the purple dots on the graph. These purple dots create both positive and negative effects, but they generally have a greater negative effect. This means that as Phubbing increases, the predicted Psychological Well-Being tends to decrease (Fig. 4).

Fig. 4.

Determining the contributions of variables to the model for predicting psychological well-being using shapley values

Figure 5 is examined together, the relationships among Smombie, Phubbing, and Psychological Well-Being become more evident. The relationships among Smombie, Phubbing, and Psychological Well-Being become more evident. The most prominent association is the strong positive correlation between Smombie and Phubbing. Individuals who engage more in smombie behavior also tend to exhibit higher levels of phubbing. Both variables show moderate and statistically significant negative correlations with Psychological Well-Being. As smombie or phubbing behaviors increase, psychological well-being declines.

Fig. 5.

Distribution, ınteraction, and density graph of phubbing, smombie and psychological well-being variables

The regression lines in Fig. 6 support these findings. The scatter plot between Smombie and Phubbing shows a clear upward trend. The plots for Smombie–Psychological Well-Being and Phubbing–Psychological Well-Being show downward slopes, consistent with the negative correlations reported in Fig. 5.

Fig. 6.

3D surface plot of psychological well-being by smombie and phubbing

The histograms on the diagonal, along with the fitted density curves, show that Smombie and Phubbing have right-skewed distributions. Most participants report lower levels on these scales, while fewer report high levels. In contrast, Psychological Well-Being has a left-skewed distribution. Higher well-being scores are more frequent in the sample.

The 2D density plots visually emphasize the interaction patterns. The highest density in the Smombie–Phubbing plot appears at moderate-to-high values for both variables. In the plots that include Psychological Well-Being, dense areas are located where psychological well-being scores are lower and smombie or phubbing levels are higher. The Smombie–Psychological Well-Being plot appears more dispersed, which reflects the weaker relationship between those two variables (Fig. 5).

3D Surface Plot of Psychological Well-Being by Smombie and Phubbing give in Fig. 6. Figure 6 offers a continuous surface view. The surface generally rises along the Phubbing dimension, suggesting a positive association with Psychological Well-Being. There is no consistent upward or downward trend across Smombie values. The surface fluctuates mildly in that dimension, indicating a weaker relationship. These figüre a consistent and stronger link between Phubbing and Psychological Well-Being. The role of Smombie appears more variable and less clearly defined in comparison.

Discussion

Our study aimed to examine the relationship between smombie and phubbing behaviors and adolescents’ psychological well-being. The findings indicate that these behaviors are significantly and negatively associated with psychological well-being. This section discusses the current findings within the literature and cultural context.

The findings of our study indicate that an increase in smombie behavior among adolescents is associated with a decrease in psychological well-being. This result is consistent with similar studies in the literature. The integration of technology into the center of daily life has negatively affected psychological well-being by increasing adolescents’ problems such as internet addiction, social anxiety, and attention deficit [44, 64]. Smartphone addiction is one of the most common types of technology addiction. Psychological distress is observed when access to smartphones is denied, and it is more prevalent among adolescents than adults [2, 16]. Indeed, studies conducted in different cultures, such as China and Spain, have revealed that smombie behavior in adolescents is associated with inattention, social isolation, loneliness, and depressive symptoms and is quite common [25, 40, 48]. These results align with research indicating that smartphone addiction during late adolescence forecasts future loneliness and depressive symptoms [4, 47]. Moreover, excessive smartphone usage has been documented to diminish physical activity among adolescents and is linked to psychological issues including sleep disorders, anxiety, diminished self-esteem, and impaired cognitive control [1, 59, 63]. Smartphones, when used in a fragmented and continuous manner, increase inattentiveness to the environment and weak self-control levels. This situation has a detrimental effect on adolescents’ psychological well-being [67]. Conversely, reducing smartphone usage time has positive effects on both physical and psychological health [7, 9, 62]. However, individualistic cultures like China and Spain have conducted the majority of studies on smombies [25, 40]. In more collectivist cultures such as Turkey, where family ties and social support are strong, the effects of digital isolation may be reduced [34]. However, increasing individualism and digital intensity in recent years may weaken these protective effects [10]. Therefore, the effects of smombie behavior on Turkish adolescents should be evaluated within a cultural context.

Another significant finding of the study is that the rise in phubbing behavior is adversely correlated with psychological well-being, a relationship also corroborated by previous research in the literature. Elhai et al. [20], Ergün et al. [21], and Garrido et al. [31] similarly indicated that phubbing behavior diminishes psychological well-being while elevating levels of depression, anxiety, and stress. Nunez and Radtke [52] discovered that adolescents subjected to phubbing displayed elevated depressive symptoms relative to adults,Guazzini et al. [38] observed a positive correlation between phubbing and social anxiety, diminished self-esteem, and loneliness. The consequences of phubbing on interpersonal relationships may also be indirectly associated with psychological well-being. The literature demonstrates that elevated levels of phubbing undermine social connections, diminish relational satisfaction, and adversely affect individuals’ subjective well-being [71, 74]. Yue et al. [84] conducted a study indicating that peer phubbing diminishes relationship quality among adolescents and heightens their vulnerability to psychological issues. Xu et al. [83] discovered that loneliness mediates the relationship between phubbing and social media addiction,Gao et al. [30] and Fernández-Andújar et al. [24] identified that heightened levels of parental psychological aggression incite phubbing behavior in adolescents and correlate with depressive symptoms. These studies demonstrate that phubbing can indirectly impact individuals’ psychological well-being by undermining social connections. While our study did not directly assess social relationship variables, the finding that heightened phubbing correlates with diminished psychological well-being can be regarded as an indirect indication of this mechanism.

Smombie and phubbing are behaviors that feed off and reinforce each other, stemming from common psychological and social processes triggered by excessive smartphone use. Heavy phone use makes people feel more depressed, anxious, and lonely, which makes them want to use their phones to avoid these feelings. This phenomenon is why people who are smombies or phubbing tend to want to control their phone use [17, 29]. Phubbing diminishes social connections as a means of evading discomfort in direct interactions, whereas the distraction and online preoccupation characteristic of smombie behavior facilitate a similar process at a cognitive level [19, 25]. The traits of the digitalized social environment, like divided attention and the relegation of face-to-face communication to the background, make these two behaviors work together [39]. Moreover, it has been indicated that parental phone usage patterns exacerbate social anxiety and facilitate maladaptive technology use among adolescents, thereby creating an environment conducive to the acquisition of both behaviors [50]. These results indicate that smombie and phubbing are mutually reinforcing behavioral patterns, representing distinct manifestations of the same digital distraction and social avoidance mechanisms. However, the prevalence and consequences of these behaviors also differ within the socioeconomic context. The widespread availability of digital access and the prevalence of online culture among adolescents of high socioeconomic status may exacerbate distraction and the compulsion for constant connectivity, thereby perpetuating smombie and phubbing behaviors. Conversely, for adolescents from lower socioeconomic backgrounds, digital tools frequently serve as a substitute for inadequate social engagement, potentially leading to problematic phone usage (Luo et al., 2025). Consequently, socioeconomic factors significantly influence the development of these behaviors and their impact on psychological well-being. The level of education alone does not determine digital addiction. Highly educated people can also be addicted to social media [55, 66]. Nonetheless, competencies such as self-regulation and attention control cultivated during the educational process can assist individuals in managing their social media usage more consciously, thereby mitigating the risk of addiction [23]. Consequently, it is essential to formulate interventions that consider socio-economic disparities to mitigate both smombie and phubbing behaviors.

The study employed a multi-analysis approach, enabling a thorough investigation of the impacts of phubbing and smombie behaviors on psychological well-being through both conventional regression techniques and machine learning models. In regression analyses, phubbing was identified as a significant predictor of psychological well-being (β = –0.344), corroborating SHAP analysis that designated phubbing as the most critical variable in the model. Likewise, the SHAP results show that the diminishing impact of the smombie variable in the regression model indicates a consistent pattern across two distinct analytical frameworks. In this context, phubbing is the most important variable in the model for predicting psychological well-being. The SHAP results indicate that phubbing is a multifaceted phenomenon associated with psychological well-being at both individual and interpersonal levels. Franchina et al. [28] and Sefa and Thseen [65] reveal the digital interactions weaken adolescents’ sense of social belonging, negatively affect face-to-face relationships, and can lead to psychological problems. In this context, our study is significant in that it demonstrates that phubbing is not merely an individual behavior pattern among adolescents but also a form of interaction that weakens the quality of social bonds. However, due to the cross-sectional design, the direction of this relationship cannot be definitively determined. The possibility that adolescents with low psychological well-being may turn to digital environments and increase their phubbing and smombie behaviors should not be overlooked. Loneliness makes face-to-face interaction threatening and exhausting; individuals use their phones as an escape. This phenomenon increases phubbing by both directly leading to social avoidance and increasing problematic phone use [71]. Another study discovered a negative relationship between phubbing behavior and self-esteem levels. Individuals with low self-esteem often experience feelings of inadequacy and discomfort in social interactions, leading them to turn to their phones as a means of escape [5, 6]. In this context, it should be considered that phubbing and smombie behaviors may emerge not only as a result of specific psychosocial characteristics but also as part of a reciprocal interaction cycle that reinforces these characteristics over time.

These findings underscore the necessity of adapting concrete, evidence-based interventions to the digital environment to enhance adolescents’ psychological well-being. School-based awareness programs have been shown to strengthen social awareness, reduce digital distraction, and improve self-regulation, and because of these characteristics, they are thought to be effective in reducing the negative effects of phubbing behavior [84]. In this context, it is important that digital awareness training includes direct instruction on skills such as screen time management, notification control, attention division awareness, and digital hygiene, and that this training is systematically implemented in school settings. In addition, social-emotional learning programs and structured group activities that strengthen peer interaction can reduce adolescents’ excessive reliance on digital tools by increasing their face-to-face communication. Strategies such as self-regulation and awareness-based attention training aimed at regulating attention and preventing impulsive phone use should be implemented before adolescence to strengthen the protective effect. Furthermore, raising awareness among teachers and parents about digital tool usage habits is critical due to the role model effect,to this end, it is recommended that educational programs be conducted within primary healthcare institutions and school health nursing.

Conclusion

This study has shown that smombie and phubbing behaviors in teenagers are significant digital habits that have a negative impact on mental health. The findings indicate that digital behavior patterns are associated with individual psychological processes as well as social interaction patterns and relationship dynamics within the school environment. Consequently, initiatives aimed at enhancing adolescents’ psychological well-being must transcend the promotion of individual awareness and self-regulation skills, focusing on holistic intervention models that consider the profound structural influences of digital culture on youth. Subsequent research should focus on longitudinal methodologies to clarify the mechanisms through which phubbing and smombie behaviors affect psychological well-being. Models that examine the temporal variations in mediating factors such as loneliness, self-control, social anxiety, relationship satisfaction, and digital attention division will enhance our comprehension of the impact of digital behaviors on adolescent mental health. In this context, the study lays the groundwork for further research on the effects of digital behaviors on mental health and aids in the development of evidence-based preventive programs for use in schools.

Study limitations

The cross-sectional nature of the study represents the relevant time period of the study.

Authors’ contributions

RI, MS: Conception and design. RI: Acquisition of data. RI, MY, MS: Analysis and Interpretation of data. RI, SK, TO, NG, MY, MS: Drafting of the manuscript. RI, SK, TO, NG, MY, MS: Critical revision of the manuscript for important intellectual content. RI, SK, TO, NG, MY, MS: Statistical analysis. RI, MY, MS: Administrative technical or material support. RI, SK, TO, NG, MY, MS: Supervision. All authors: Final approval of manuscript.

Funding

This study is funded by Sakarya University.

Data availability

The data is available upon request from the corresponding author (metinyildiz@sakarya.edu.tr).

Declarations

Ethics approval and consent to participate

Approval has been obtained from the Scientific Research and Publication Ethics Committee of Sakarya University Faculty of Health Sciences in order to carry out the study. Parents were informed through the relevant schools, and adolescents voluntarily participated by ticking the consent box on the online form. Informed consent has been obtained from all participants in the study. Participants under the age of 18 have obtained “informed consent” from their parents or legal guardians for participation. The researchers adhered to the rules outlined in the Helsinki Declaration throughout the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.