Smartphone use and social media addiction in undergraduate students

Introduction

The Internet is tremendously useful in a variety of applications, including productive electronic commerce, instant knowledge sharing, cultural exchange, and enjoyment.^1–3 Smartphones are devices that combine Internet and phone functionality. They provide qualitatively distinguishing features in addition to the benefits of the Internet. Children use smartphones to watch videos, express themselves, communicate with friends, and search for information. The portability and convenience of a smartphone allow it to be utilized anywhere and at any time. However, although smartphones provide several benefits in our lives, we must be aware of their negative implications, the most concerning of which is smartphone addiction, which relates to the unrestrained use of smartphones. Individuals with smartphone addiction endure emotional, mental, and physical challenges.^2,3 Prolonged use of smartphones exposes individuals to several detrimental physical and psychological effects. For example, dry eyes, carpal tunnel syndrome, repetitive strain injury, wrist, neck, back, and shoulder pain, migraine headaches, thumb, index, and middle finger pain, and phantom pocket vibration syndrome are physical symptoms associated with smartphone addiction.^4,5

Even though smartphone addiction does not remain listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition⁶ or the upcoming International Classification of Diseases, Eleventh Revision, evidence suggests that there is an increasing perception of the issue.⁷ Smartphone addiction is a new type of addictive behavior that has developed from the rapid proliferation of smartphones across the internet, resulting in a severe behavioral addiction.⁸ According to the National Information Society Agency in Korea, smartphone addiction surpasses internet addiction.⁹ Lin et al.¹⁰ identified four characteristics of smartphone addiction, including compulsion, functional impairment, tolerance, and withdrawal. It has been found to correlate with a variety of negative effects on physical health, including brain tumors, cancer, a weakened immune system, neck and wrist pain, and sleep disorders.^11,12 Prolonged smartphone use at nighttime might cause insomnia, stress, and sadness.¹³ Recent studies have further illuminated the complex relationship between smartphone use, social media addiction, and mental health. Ergün et al.¹⁴ examined how social media addiction is associated with poor mental health, finding that this relationship is mediated by internet addiction and phubbing. Similarly, Kalınkara et al.¹⁵ explored the dynamic relationships among social media addiction, depression, anxiety, academic self-efficacy, general belongingness, and life satisfaction, highlighting the multifaceted nature of technology’s impact on psychological well-being. The phenomenon of phubbing - the act of snubbing someone in a social setting by focusing on one’s phone instead of the person - has also gained attention. Talan et al.¹⁶ investigated the effects of smartphone addiction, social media addiction, and fear of missing out (FoMO) on university students’ phubbing behavior, revealing significant influences of smartphone and social media addiction on this phenomenon. Furthermore, Świątek et al.¹⁷ explored the relationship between problematic smartphone use and social media fatigue, identifying self-control as a crucial mediating factor. This finding underscores the importance of considering psychological factors in understanding and addressing technology-related issues.

Screen time and Internet use have been shown to have an impact on sleep,^18,19 and smartphone addicts have been shown to have poorer sleep quality than non-smartphone addicts.²⁰ It has also been found to have detrimental psychological impacts.²¹ Excessive use of smartphones negatively affects the quality of students’ sleep^22–25 and result in insomnia.²⁶ Many studies have established a correlation between smartphone usage and mental disorders, such as stress and anxiety.²⁷ Other potential adverse consequences include loss of control²⁸/and maladaptive behavioral problems.²⁹ Smartphone addiction has a poor correlation with both life satisfaction and performance in school.³⁰

Meanwhile some of the individuals who are predominant smartphone users are adolescents. Adolescence, as delineated by developmental psychology and esteemed organizations such as the World Health Organization (WHO), covers from early adolescence to late adolescence. In many definitions, it may also comprise young adults up to the age of 19 or even stretch into the early 20s. Undergraduates, in contrast, refer to those who are actively pursuing a bachelor’s degree at a college or university. The terms ‘undergraduate’ and ‘adolescent’ refer to separate facets of an individual’s existence. Although there are variations, there is a significant convergence in the normal age range linked to each. Adolescence, as per developmental psychology and organizations such the World Health Organization (WHO), refers to the period from early to late teens and may even include young adults up to the age of 19 or even early 20s in certain definitions. Most undergraduates commence their tertiary studies promptly after finishing secondary education, usually at approximately 18 or 19 years old. As a result, a significant proportion of college students are in the late adolescent stage. A substantial amount of study has been devoted to the examination of smartphone addiction, with university students and adolescents serving as the primary cohorts for investigation.^22,25 Although the emphasis has resulted in significant findings regarding the trends and repercussions of using smartphones among the youth population, it also presents the possibility of selection bias. The focus on younger age groups and life stages in this analysis of smartphone addiction might not encompass its intricacies.^23,24 In the context of this study’s invalidity, this study seeks to contribute by examining the broader consequences associated with smartphone addiction. Through this approach, we aim to contribute to a more holistic comprehension of its ramifications, recognizing that the phenomenon of smartphone addiction transcends the conventional focus on adolescent and university students. They are inextricably linked to their smartphones, which they consider to be a second personality.³¹ In addition, they spent the most time of their daily routine with smartphone applications, such as, mobile messengers, web browsing, gaming, and social media.³² Several smartphone owners insist that they could not really operate without their devices.³³ They go through a variety of physical and psychological changes during their growth. Despite the fact that teenagers depend on their parents for survival and identity, they are also working to separate themselves from them in order to grow as individuals and carve out a place for themselves. They become more dependent on smartphones during these transitional periods. Compared to adults, they are significantly more sensitive to and embrace new technologies. They express themselves online as “digital natives,” aiming to stay current with fashion trends, using a variety of apps, and seeking emotional connections and support.³⁴ They specialize in multitasking and require fast feedback and input.³⁵ They are a critical period characterized by various biological, psychological, and social changes that significantly impact a young individual’s growth and prospects. These transformations enable teenagers to acquire the necessary abilities to attain increased independence, establish connections with others, cultivate a favorable perception of their physical appearance, and discover their own sense of identity. Nevertheless, they are also accompanied by a heightened inclination to engage in risky behavior at a period when the cognitive faculties of the brain, such as emotional regulation, are not yet fully matured. Within this particular framework, some studies substantiate the correlation between reliance on smartphones and the manifestation of aggressive behavior in middle school adolescents.³⁶ Therefore, ego-resilience (individual), parental behavior (family), and peer attachment (society) were employed as variables to examine their interactions with both the internal and exterior environments from biological, psychological, and social standpoints. Ego-resilience exerted control over the influence of smartphone dependency on aggression, with smartphone dependency acting as a mediator in this association with aggression. The results indicate that cultivating ‘ego-resilience’ is crucial in mitigating the adverse consequences of excessive smartphone usage in both teenagers and young adults.

Dual-tasking refers to the act of executing two activities simultaneously, whereas multitasking refers to the act of performing more than two tasks simultaneously.³⁷ Engaging in activities such as walking or working while simultaneously utilizing various features of a smartphone can be referred to as dual- or multitasking. Nevertheless, engaging in multiple tasks simultaneously can potentially result in a loss of cognitive capacity, hence increasing the risk of experiencing a fall or injury in an unforeseen and unintentional circumstance. The capacity to sustain equilibrium in both stationary and moving scenarios serves as the foundation for executing functional tasks during the course of diverse routine activities.³⁸ The functional activities requiring dynamic balance are achieved by the coordinated interplay of the ankle joints, knee joints, hip joints, and their respective accompanying muscles, as well as the shoulder joints and their surrounding muscles. Cognitive ability is closely linked to dynamic balance. Engaging in dual-tasking with a smartphone while walking, such as listening to music, sending a message, online surfing, or playing a game, is known to impair the dynamic balance required for functional activities by diminishing cognitive capacity. The act of using a mobile phone while walking is a common scenario known as a dual-task paradigm.³⁹ This means that individuals are required to concurrently engage in mobile phone operations and walking duties. The notion of finite capacity scheduling suggests that the outcome of different activities determines the extent to which cognitive or motor performance is affected.^40,41 The mobile phone task involves integrating numerous single activities, such as mathematics, language, memory, and motor skills, while walking, as opposed to doing a single task individually. The demand for cognitive resources in this activity is notably higher compared to other simpler activities.⁴² Prior research has demonstrated that engaging in the dual-task paradigm of using a mobile phone while walking is more prone to inducing diminished awareness of the environment, impaired motor function in the lower extremities, and distraction compared to other uncomplicated tasks performed while walking among pedestrians.^42,43

The increasing significance of smartphones in everyday life, particularly among undergraduate student aged 18 to 22, emphasizes the delicate link between technology use and mental well-being. The objective of this study is to explore the impact of smartphone addiction on cognitive abilities, particularly reaction times, and to analyze the psychosocial repercussions of excessive smartphone usage. This initial investigation primarily examines the direct influence of smartphone addiction on reaction times while also recognizing the potential involvement of several risk factors.

Methods

Participants

The participants were recruited via poster advertising in the local area. The primary outcome of the study was sample size that was calculated as follow (eq (1))

(1)

$$\mathit{n}=\frac{\mathbf{2}{\mathit{s}}^{\mathbf{2}}{\left({\mathit{Z}}_{\propto }+{\mathit{Z}}_{\mathit{\beta }}\right)}^{\mathbf{2}}}{{\mathit{d}}^{\mathbf{2}}}$$

when n is number of sample sizes, s is standard deviation, Z∝ is z-score at 95% confidence level, Zβ is 99% confidence level, d is mean difference of virtual reaction time. In this study, we used “d = 0.45” and “s = 0.58” as follow,⁴⁴ while Z _β and Z _α were 0.842 and 1.96, respectively.

Flowchart of inclusion participants was shown in Figure 1. An initial sample size was 29 in each group which allowing for a dropout rate of 10% (n=3). Finally, at least 64 participants (32 per group) were recruited in this study. The participant recruited for this study was undergraduate students aged between 18 and 22 years, and had used smartphones for social media every day for at least a year before participation. Also, the question survey was developed in this study to exclude participants who had myopia, poor vision, impaired vision, or color blindness, as well as auditory or any perception deficiencies, upper body muscle weakness, sensory loss associated with any type of neurological illness, major surgery, or limb injuries. Participants with myopia and impaired vision were excluded from this study, particularly those involved in cognitive activities, response time assessments, or visual processing investigations, due to various reasons. Firstly, this study largely depends on visual stimuli and seeks to ensure that all participants have a consistent baseline for visual acuity. This is done to minimize variations in response times or accuracy that could be mistakenly attributed to variances in vision rather than the specific cognitive or behavioral variable being investigated. Bach⁴⁵ examined this matter and emphasized the need for precise measurement of visual acuity in research environments. Furthermore, Bach highlighted how fluctuations in acuity can impact the dependability of visual evaluations. Simultaneously, we have apprehensions regarding the influence on response durations. Given that this study assesses response times to visual stimuli, any discrepancies in visual acuity could potentially introduce substantial confounding factors, since individuals with uncorrected visual impairments may inherently exhibit slower or less accurate responses. Strang et al.⁴⁶ reported this problem, demonstrating that even little visual impairments can greatly affect the speed and accuracy of visual processing. This supports the need to regulate visual acuity in investigations where reaction time is an important parameter.

Figure 1. Flowchart of inclusion participants.

Measurement

The reaction time (RT) of an organism is an assessment of how rapidly it responds to a stimulus. The RT is the amount of time that passes between when the stimulus is sent and when the subject shows the relevant voluntary reaction. Three forms of RT were characterized by DukeElder S.⁴⁷; Luce⁴⁸; Welford.⁴⁹ (1) Simple RT: In this scenario, there is only one stimulus and one reaction. (2) Recognition RT: Any stimulus should be responded to, whereas others should not. (3) Choice RT: There are several stimuli and reactions in this situation. The nervous system recognizes the stimulus in human RT. The message is subsequently relayed to the brain by the neurons. The message is sent from the brain to the spinal cord, and eventually to the hands and fingers. Average RTs have been reported to be approximately about 190 and 160 m/s for light and sound stimuli, respectively.⁴⁵ Fast RTs can be beneficial in some activities, such as athletics and sports, but slow RTs might have catastrophic consequences when driving.

The Multichoice Reaction Timer (Grand Sport brand, 383059/No.BHX 0012), produced in Bangkok (Thailand), was used to measure the reaction time to visual stimuli as well as the response time (Figure 2). It consists of a simple interface for the researcher with controls for generating stimuli, a monitor displaying the light of communicated stimuli in three colors (red, blue, and yellow), a set of three hand-operated buttons, and a screen of the obtained score with a second’s precision. The two stations of the apparatus were placed along a line 2 meters apart, such that the controller could perceive the optical pulses but the controller’s interface was out of the subject’s range of vision. A hand-operated toggle and a visual stimulus were used to measure response time to a single stimulus. Each subject was told to sit in front of the light box with their hands on the table. The participants were instructed to press the button as soon as they saw a light on the box (red, blue, or yellow), which measured the response time in seconds while three light stimuli were used randomly in the ten trials and repeated three times for each condition recorded in the exam. The average response time from the test were used for the analysis of the risk for smartphone addiction. In this study, Choice RT involves multiple stimuli and multiple responses, which directly corresponds to the setup of the study where participants respond to different colored lights with different buttons. The need to recognize the color of the light and choose the correct button to press makes it a choice reaction time task, as there are several stimuli (different colors) and several possible responses (different buttons).

Figure 2. Multichoice Reaction Timer (Grand Sport brand, 383059/No.BHX 0012).

This study focuses on the evaluation of reaction times (RT) as a method for measuring the rate at which an individual reacts to a stimuli. It is crucial to specify that the specific sort of reaction time we are emphasizing is referred to as ‘Choice RT.’ The relevance of this particular type of reaction time lies in the fact that participants are presented with various stimuli and are required to make a deliberate choice on the appropriate response to carry out.

In further detail, Choice RT scenarios are distinguished by the existence of multiple potential stimuli, each necessitating a separate and specific reaction. Our experimental setup involves presenting participants with lights of varying hues, specifically red, blue, and yellow. Participants are required to hit a designated button for each color. This choice element distinguishes it from ‘Simple RT,’ which involves a single stimulus and automatic reaction, and from ‘Recognition RT,’ where the aim is to respond to certain stimuli while ignoring others.

Results

Figure 1 depicts a flow diagram of the recruitment process. The eligibility of 102 subjects was determined. Overall, 38 participants were excluded because they did not match the inclusion criteria (n = 18) or declined to participate (n = 20). The demographic information of all participants is listed in Table 2. A total of 64 undergraduate students (13 males, mean age 20.61±1.16 years) were selected from the University of Phayao, Phayao Province in Thailand.⁵³ They were separated into two groups according to the SAS-SV score. There weren’t any statistically significant distinctions in gender, age, weight, or height between the two groups. The SAS-SV-TH score difference was only statistically significant at a p-value of 0.000.

By comparing among the three conditions of smartphone use (Figure 3), a within-group analysis, the RT of the conversation and texting conditions in both groups was significantly improved compared with their control condition. Analyzing the reaction times under different smartphone usage situations reveals clear trends for persons with and without addiction in Table 3. The non-addiction group exhibited the shortest average reaction time of 0.99 seconds after avoiding from smartphone usage. The results also showed a relatively narrow range of values, as evidenced by the standard deviation of 0.28 seconds. Participating in phone conversations increased response time to an average of 1.22 seconds, while the variation remained similar to the condition when no phone was used. Nevertheless, the texting activity showed the most notable increase, with an average response time of 1.73 seconds and a significantly larger standard deviation, indicating more variation in how texting impacted the reaction times of persons in this particular group. In comparison, the addiction group exhibited a slightly higher initial response time of 1.04 seconds when not using smartphones, while the range of values was narrower. The effect of phone calls was particularly noticeable among those with addiction, as their average response time increased to 1.309 seconds and was accompanied by a higher standard deviation, indicating a larger variation in response times among individuals. The “Addiction” group also saw a comparable impact from texting, with an average reaction time of 1.74 seconds and the largest level of variability across all conditions, as demonstrated by the standard deviation. Reaction times consistently increased in both groups as they transitioned from avoiding smartphones to texting. Although the “Addiction” group had somewhat greater average reaction times in every situation, their reaction times were more consistent under the no-use condition compared to the “No addiction” group. The 95% confidence intervals for the “Addiction” group exhibited higher values, indicating a potential association between addiction and longer reaction times. However, the overlapping intervals of the two groups suggest that the difference in reaction times between the two groups is not significant. In summary, the descriptive statistics reveal a noticeable pattern of longer reaction times linked to the usage of smartphones, particularly for tasks that demand more cognitive effort, such as texting (Table 4).

Figure 3. Reaction time in the difference conditions of the smartphone use.

The ANOVA analysis in Table 5 showed that those without addiction had an F-statistic of 29.41 and a p-value of roughly <0.001, which indicates high significance. This result demonstrates a statistically significant difference in response times when comparing the absence of smartphone usage, making phone calls, and sending text messages among individuals in this group. The inference is that the nature of smartphone engagement significantly impacts the response times of non-addicted persons, with certain activities resulting in slower reactions compared to others. Similarly, the analysis conducted for the “Addiction” group yielded an F-statistic of 23.18, along with a significant p-value of roughly <0.001. This validates that, similar to the “No addiction” group, there are statistically significant disparities in reaction time among those with addiction across the three smartphone-using circumstances. The experimental conditions in which the reaction time was assessed (absence of smartphone usage, during a phone call, or while texting) significantly influenced the recorded reaction times. The notable F-statistics in both groups indicate that the change in reaction times is not consistent across conditions; rather, it fluctuates based on whether individuals were not utilizing their smartphones, were involved in a phone call, or were texting. The p-values, which are significantly lower than the typical alpha level of 0.05, provide strong evidence that the observed changes in reaction times are not a result of random chance but rather indicate a genuine impact of the smartphone use condition. These findings emphasize the significance of the circumstances in which smartphones are used when evaluating their influence on cognitive performance, specifically in terms of reaction time.

In summary, the descriptive statistics and ANOVA analyses provide a thorough understanding of how reaction times are affected by smartphone usage in different situations for persons with and without addiction. The descriptive statistics indicate that reaction times exhibit an upward trend as individuals transition from not using smartphones to making phone calls, and then to sending text messages, for both groups. The heightened fluctuation in reaction times seen while texting, as indicated by the greater standard deviations, implies that this task may necessitate additional cognitive resources, thereby exerting a more pronounced impact on reaction times. This pattern is consistent for both the non-addiction and addictiongroups, although individuals with addiction exhibit somewhat faster reaction times overall. The one-way ANOVA results provide additional support for these findings, demonstrating statistically significant variations in reaction times among the three smartphone use circumstances within each group. The “No addiction” group’s F-statistic and p-value suggest that various smartphone activities have a differential impact on reaction times. The addiction group likewise has a significant impact of smartphone use circumstances on reaction times, as evidenced by the ANOVA results.

Discussion

This study aims to investigate the influence of smartphone addiction on visual reaction time (VRT) in several settings, such as control, texting, and chatting situations, as hypothesized. The results of our study did not support our initial hypothesis, which suggested that those with higher degrees of smartphone addiction would have slower VRT due to cognitive overload. Specifically, we found no significant differences in VRT between the group of individuals with smartphone addiction and the control group, regardless of the settings. This result implies a more intricate relationship between smartphone addiction and cognitive performance than originally anticipated.

Although we hypothesized that regular smartphone use, which requires higher cognitive capacities, could hinder the ability to perform two tasks simultaneously, resulting in longer reaction times, the findings did not confirm this expectation. One potential reason for this surprising discovery could be attributed to the adaptive capacities of individuals who use smartphones frequently. The study found that social networking was the most popular smartphone application among those with addiction, and they used it for much longer periods of time compared to the control group. The significant involvement with smartphones may have resulted in a type of task-specific cognitive adjustment, allowing these individuals to retain similar VRTs as those who are less involved with smartphones. In any of the three conditions, there wasn’t any substantial difference in visual reaction time (VRT) here between the smartphone addict group and the control group. The results were not inconsistent with our hypothesis that smartphone-addicted people may show a lower VRT. However, there are possible mechanisms that provide for the different hypotheses. Perhaps the most likely mechanism is the idea that those individuals who are smartphone addicts have a higher rate of smartphone use. Social networking was the most popular smartphone application among the smartphone addicts. The average usage time of texting or talking was almost 30 minutes per session, with several sessions per day. On the other hand, individuals in the control group took only 5–15 minutes per time for entertainment applications. In the test conditions, the smartphone addicts group had similar smartphone usage. Eye-hand coordination when texting in social networking apps and when performing repetitive tasks such as performance practice and brain training has been found.³⁴ As a result, the participants were unable to use the extra time to extend their reach duration. Rather, they finished the assignment in the same amount of time, allowing them more opportunity to switch focus and optimize dual-task attention. This impacts the brain and results in improved cognitive functioning.³⁵ The lack of significant disparities in VRT among the groups contradicts our initial assumptions and prompts a reassessment of the ways in which smartphone addiction impacts cognitive function. According to capacity theory and bottleneck accounts, we hypothesized that the dual-task interference would be more pronounced in those with smartphone addiction. Nevertheless, the comparable performance observed in both groups indicates that factors such as task prioritization and cognitive resource allocation likely have a substantial influence on moderating the impact of smartphone usage on attention and reaction times.

Furthermore, the participants in both groups demonstrated the same results for VRT in 3 conditions of smartphone use. When texting and talking on their smartphones, all participants exhibit slower VRT. Dual-task interference, according to capacity theory, results from the concurrent allocation of a restricted group of general-purpose resources, or efficient clustering.^50,51 When mixed tasks exceed (consolidated or specific) resource availability, one or both activities perform poorly. Bottleneck accounts, on the other hand, stress the serial structure of the dual-task process as a result of single-channel screening or information timetabling during the stimuli decoding, identification, and judgment phases.⁵² Since such instances of disturbance exist, it is argued that the nervous system temporarily delays operations solely on a single task in favor of processes on the prioritized task, resulting in poor efficiency on the non-priority activity. Participants may have coordinated task prioritizing by altering the timing or scheduling of tasks to improve the processing of information and prevent a processing bottleneck.^52–55 This result was consistent with the study by Yu and Huang⁵⁶ which reported that dual tasking significantly increases the RT. Increased reaction times due to cognitive distraction have been reported earlier.⁵⁷ This shows that the stimuli can be seen or heard while doing another task but are not processed normally as the brain is overloaded. Our study shows that the RT during the talking condition of smartphone use is faster than the texting condition. The expenditure on decision making, and planning was much higher in the texting condition. Texting caused participants to physically move their focus between the smartphone and light stimulation, in addition to turning cognitive resources in a similar direction that conversation does.⁵⁸ There are limitations to this study that must be considered. Our study only investigated the reaction time for light stimuli. Future research should attempt to evaluate auditory reaction time, as well as studies in a different age group.

Although this study utilized a careful methodology, which involved conducting a power analysis to determine the required sample size for detecting variations in reaction times under control, texting, and calling conditions, it is crucial to acknowledge a potential limitation in our examination of the impacts of smartphone addiction. Firstly, the initial power analysis was primarily intended to ensure an adequate sample size for detecting the experimental effects of dual-tasking conditions. However, it may not have been properly adjusted to accurately identify the more subtle but important influence of different levels of smartphone addiction on these cognitive tasks. After careful consideration, we acknowledge that the study may not have enough statistical power to definitively investigate the subtle impacts that the intensity of smartphone addiction may have on individuals’ performance in these activities. This constraint implies that the conclusions on smartphone addiction should be approached with care since the study could not have possessed sufficient statistical strength to identify all significant effects linked to the intensity of smartphone addiction. Subsequent investigations in this field would be enhanced by performing a power analysis particularly designed to evaluate the impact of smartphone addiction. Secondly, the study’s focus on a specific age group limits the generalizability of our findings to other age groups or populations. The cognitive effects of smartphone addiction may manifest differently across various life stages and demographic groups. Thirdly, our study primarily examined visual reaction times. While this provides valuable insights, it does not capture the full spectrum of cognitive functions that may be affected by smartphone addiction. Auditory reaction times and other cognitive domains, such as attention, memory, and executive functions, warrant further investigation. Lastly, the cross-sectional nature of our study limits our ability to establish causal relationships between smartphone addiction and cognitive performance. Longitudinal studies would be better suited to tracking the development and progression of smartphone addiction and its cognitive impacts over time. To address these limitations, subsequent investigations in this field would be enhanced by several key improvements. Firstly, performing a power analysis specifically designed to evaluate the impact of smartphone addiction would be crucial. This may involve increasing the sample size or employing a more precise measure of addiction intensity. Additionally, expanding the study to include various age groups and populations would yield a more comprehensive understanding of the cognitive consequences associated with smartphone addiction. Future research should also explore auditory reaction times and other cognitive domains to provide a more holistic picture of the cognitive effects of smartphone addiction. Conducting longitudinal studies would be valuable in tracking changes in smartphone usage patterns and their effects on cognitive function over time. Lastly, investigating the possible task-specific cognitive adjustments in those who use smartphones frequently could help explain any unexpected findings or reduced detrimental impacts on dual-task performance. By implementing these enhancements, future studies can provide more robust and generalizable insights into the complex relationship between smartphone addiction and cognitive functioning. It is essential to overcome these constraints in order to enhance our comprehension of the cognitive consequences of smartphone addiction and to create specific therapies to mitigate its negative impacts. Regarding the results and the original hypotheses of this investigation, further research should explore in greater detail the subtle mechanisms by which extended smartphone usage impacts cognitive functions across various domains and populations.

Conclusions

This study was conducted on 64 teenagers to investigate the effect of smartphone use for social media on Visual Reaction Time (VRT). There was no significant difference statistically in the reaction time between adolescents with and without smartphone addiction in all test conditions (no smartphone use, texting, and talking using a smartphone). However, the adolescents show prolonged reaction times when they must perform the dual-tasking. Therefore, the adolescent should avoid other activities when using a smartphone.

Data availability