Immediate Effects of Texting and Music Listening on Balance: A Comparative Analysis of Static and Dynamic Outcomes in Young Adults

Abstract

Purpose

This study aimed to investigate the immediate effects of texting and listening to music with headphones, both separately and in combination, on the static and dynamic balance of young adults.

Method

A cross-sectional study was conducted on 35 healthy young adults. The FreeMed-Baropodometric platform was used to assess static balance outcomes, including sway length, sway velocity, and displacement of the center of pressure in the mediolateral (DeltaX) and anteroposterior (DeltaY) directions, and dynamic balance outcomes, including initial contact, loading response, midstance, and terminal stance times. The participants underwent four conditions: standing still without any additional tasks, standing while texting, standing while music listening with headphones, and standing while both texting and listening to music with headphones. Additional measurements included forward head posture analysis, and New York Posture Rating Scale. Data were analyzed using Friedman and Wilcoxon tests with Bonferroni correction for multiple comparisons.

Results

Texting compared to listening to music with headphones or standing significantly affected static balance outcomes, including sway length, velocity, and Delta X and Delta Y, particularly in monopodalic conditions (p < .001). In contrast, listening to music with headphones with or without texting did not significantly impact static or dynamic balance outcomes (p > .05).

Conclusions

Texting can adversely affect static balance, whereas listening to music with headphones does not significantly impact static or dynamic balance in young-healthy individuals. These findings suggest that individuals should consider stopping and sitting when texting to maintain balance and safety.

Keywords

smartphone music postural balance gait young adult

Introduction

In today’s world, smartphones have become an integral part of modern life, serving not only as communication tools but also as devices for information access and entertainment. Their deep integration into daily routines has increased the tendency toward multitasking, which often divides attention between simultaneous activities (Thornton et al., 2014). The demographic most affected by this is undoubtedly young people, with 1 in 4 demonstrating problematic smartphone use. They now navigate their daily activities while simultaneously using smartphones (Johannes et al., 2018; Sohn et al., 2019).

Research indicates that texting while walking is widespread among youth, and a considerable proportion report accidents or near-miss incidents related to this behavior (Kwon et al., 2013). Likewise, listening to music through headphones has become a common habit, with studies showing that adolescents spend significant amounts of time engaged in music listening during routine activities (Elmwafie, 2022; Paping et al., 2021).

Since maintaining attention and stability are closely linked to body posture, these multitasking behaviors raise important questions about their potential influence on postural control. Postural control, defined as the ability to maintain body stability and orientation, is a fundamental requirement for safe and effective performance of daily activities. It encompasses both steady-state (often referred to as static) (Zatsiorsky & Duarte, 1999) balance during quiet stance and dynamic balance during movement.

Steady-state and dynamic balance are core components of overall physical stability, essential for the execution of daily activities and the prevention of falls. Steady-state balance refers to the ability to maintain a stable position while standing still, whereas dynamic balance involves posture control during motion (Rizzato et al., 2023). Both forms of balance are crucial for maintaining physical performance, particularly in young, healthy individuals who often engage in multitasking behaviors that involve the use of smartphones (Alexopoulou et al., 2020). Understanding the relationship between these common smartphone activities and balance is essential for developing guidelines to reduce potential risks and enhance safety.

Even in young, healthy individuals, impaired postural control may cause discomfort or increase fall risk. With the widespread use of smartphones, especially multitasking activities like texting or listening to music, understanding their impact on postural stability is essential (Gonzalez & Aiello, 2019). Several studies have examined this issue in the context of smartphone use, specifically focusing on gait, posture and the success of the motor task that was given (Jeon et al., 2016; Lino et al., 2023). Previous studies have examined the impact of smartphone use on balance with different approaches. Onofrei et al. examined the influence of smartphone use on postural stability in healthy young adults and reported significant impairments in quiet standing tasks measured with posturography (Onofrei et al., 2020). Metin et al. (2023) focused on gait performance and spinal kinematics, showing that smartphone use during walking negatively affected dynamic balance and increased musculoskeletal discomfort (Metin et al., 2023). More recently, Kazi et al. (2023) analyzed dual-task performance with smartphone use and demonstrated that multitasking impaired dynamic balance and attentional resources (Kazi et al., 2023). While these studies highlight the potential risks of smartphone-related multitasking, none of them investigated the combined effects of texting and music listening on both steady-state and dynamic balance.

No previous research appears to have examined the combined immediate effects of smartphone and headphone usage on balance, including both steady-state and dynamic parameters, in young, healthy individuals. This study investigates how music listening, texting, and their combination affect balance in young individuals, with the goal of raising awareness about the influence of smartphone use on postural control. The hypotheses of this study are H0: The effects of music listening, texting, and their combination on balance are not different from each other. H1: The effects of music listening, texting, and their combination on balance are different from each other.

Methods

Study Design

This was a cross-sectional study conducted at the Faculty of Physical Therapy and Rehabilitation, Hacettepe University. Ethical approval was obtained from the Hacettepe University Health Sciences Research Ethics Committee (SBA 23/354). All participants gave written informed consent prior to participation. The clinical trial number for this study is NCT06400576.

Participants

A total of 35 healthy young adults between the ages of 18 and 30 years were recruited through advertisements and social media announcements. Eligible participants were required to have a craniocervical angle greater than 46° (Cetin et al., 2023b), to be conscious, cooperative, and willing to participate. Individuals were excluded if they did not provide voluntary consent, had congenital or acquired spinal deformities, cervical or lumbar disc herniation, neurological or vestibular disorders, or musculoskeletal system diseases. All participants provided written informed consent prior to the study.

Outcome Measures

Sociodemographic information, including age, body mass index, gender, and whether participants regularly used headphones during daily activities or work, was collected. Average smartphone usage time, listening to music with headphones while sitting or studying, and walking were also questioned.

Balance Assesments

Balance was assessed using the FreeMed baropodometric platform (Sensor Medica, Rome, Italy; 60 × 50 cm) with a sampling frequency of 400 Hz, which provides sufficient temporal resolution to capture rapid and subtle center of pressure shifts and prevents signal distortion and loss of detail (Marin et al., 2021; Muehlbauer et al., 2011).

Steady-state balance was evaluated over 20 seconds using sway length (mm), sway velocity (Avspeed, °/s), mediolateral displacement (ΔX, mm), anteroposterior displacement (ΔY, mm), and pressure distribution parameters (Matla et al., 2021). All steady-state outcomes were assessed under eyes-open (OE) and eyes-closed (CE) conditions, both in monopodalic (MP, left and right) and bipodalic (BP) stances.

Dynamic balance was assessed over at least 10 gait cycles per leg, with parameters including initial contact time (s), loading response (s), midstance time (s), and terminal stance time (s). For this assessment, participants were asked to walk at least 10 gait cycles for each leg on a 150 cm walking platform, which was extended with additional passive platforms, constituting an extension of the active panel (Matla et al., 2021). These dynamic balance parameters were measured in seconds and used to assess the “orientation in space” and “cognitive processing” component of the postural control (Horak, 2006).

All measurements were performed under four different conditions. The experimental protocol included four task conditions: (1) performing the primary task without any additional activity, (2) performing it while listening to music through headphones, (3) performing it while texting, and (4) performing it while both texting and listening to music simultaneously. In the steady-state balance assessments, these conditions were implemented during standing trials under eyes-open and eyes-closed, bipodalic and monopodalic stances; however, conditions involving texting were conducted only under the eyes-open condition. In the dynamic balance assessments, the same four conditions were applied during walking trials, ensuring consistency across testing scenarios. Participants performed each of the four conditions separately for both static (standing) and dynamic (walking) assessments. The order of conditions was randomized to reduce familiarity and fatigue effects.

For the texting task, participants received detailed instructions beforehand. The screen was divided into two sections: the upper section displayed a standardized 25-word message, and the lower section contained a blank input field. Upon the researcher’s “Start” command, participants were instructed to reproduce the displayed message in the blank field.

For the music task, participants were briefed on the procedure and informed when the music would begin. All participants listened to the same instrumental track to avoid variability in emotional response or rhythm perception. The track was mid-tempo (120 bpm), emotionally neutral, and had a consistent rhythm, while the volume was standardized at 70 dB. This design minimized familiarity effects and ensured uniform testing conditions across participants (Liang et al., 2021; Liu et al., 2018).

Posture Assesments

Forward head posture was evaluated with sagittal photographs using reflective markers placed on anatomical landmarks. Images were analyzed in MATLAB to calculate the craniocervical angle (tragus–C7 line vs. vertical line through C7). Angles ≥46° indicated forward head posture (Cetin et al., 2023a). Each measurement was repeated twice by trained physiotherapists.

Overall posture was assessed using the New York Posture Rating Scale (NYPRS), which evaluates 13 body regions with scores from 1 (severely impaired) to 5 (normal). The total score ranges from 13 to 65 and is classified as: excellent (>45), good (40–44), fair (30–39), poor (20–29), very poor (<19) (McRoberts et al., 2013).

Sample Size Calculation

A total of 35 participants completed all assessments and were included in the analyses. The primary outcome was Avspeed in the OE–Monopodalic-L condition (average sway velocity during single-leg stance). Pairwise contrasts between conditions were analyzed using Wilcoxon signed-rank tests. Effect sizes were calculated as ∣Z∣/N with N = 35 and are reported alongside p-values. For the primary endpoint, effect sizes across the four condition comparisons ranged from r = 0.34 to r = 0.72, indicating moderate to large magnitudes.

Statistical Analysis

Statistical analyses were conducted using SPSS Version 26.0 (IBM Corp., Armonk, NY, USA). The variables were investigated using visual (histogram probability plots) and analytical methods (Kolmogorov-Simirnov/Shapiro-Wilk’s test) to determine whether or not they are normally distributed. Descriptive analyses were presented using medians and the interquartile range (IQR) for the non-normally distributed and ordinal variables. Friedman tests were conducted to test whether there is a significant change in all parameters, due to violations of parametric test assumptions (non-normal distribution and low number cases, respectively). The Wilcoxon test was performed to test the significance of pairwise differences using Bonferroni correction to adjust for multiple comparisons.

Results

Demographic Characteristics

The study included a total of 35 participants. The demographic characteristics and outcome measures are summarized in Table 1. The mean age of the participants was 23.80 years (SD = 2.68). The mean body mass index of the participants was 22.05 kg/cm² (SD = 22.00). The sample consisted of 26 females (74.28%) and 9 males (25.71%). Participants reported an average daily smartphone usage time of 7.12 hours (SD = 2.04). A significant majority of participants (85.71%, n = 30) reported listening to music with headphones while walking. Additionally, 68.57% (n = 24) of participants reported listening to music with headphones while sitting or studying. The mean degree of forward head posture among participants was 56.94° (SD = 7.23).

Table 1.

Demographic Characteristics and Outcome Measures of Participants

Variables	Mean ± SD (Min-Max)
Age (years)	23.80 ± 2.68 (18–30)
Body mass index (kg/cm²)	22.05 ± 22.00 (17.41–30.30)
Gender n (%)
Female	26 (74.28)
Male	9 (25.71)
Daily smartphone usage time (hours)	7.12 ± 2.04 (3–10)
Listening to music with headphones while walking, n (%)
Yes	30 (85.71)
No	5 (%14.28)
Listening to music with headphones while sitting or studying, n (%)
Yes	24 (68.57)
No	11 (31.42)
Forward head posture degrees (°)	56.94 ± 7.23 (46–55)
Newyork posture rating scale (13–65)	53.24 ± 5.77 (41–63)

Max = maximum, Min = minimum, SD = standard deviation.

Steady-State Balance Outcomes

The comparisons of steady-state balance outcomes in four different conditions are presented in Table 2.

Table 2.

The Comparisons of Steady-State Balance Outcomes in Four Different Conditions

	Standing without any aditional tasksMedian (IQR)	Standing with headphoneMedian (IQR)	Standing with textingMedian (IQR)	Standing with headphone and textingMedian (IQR)	p	p1	p2	p3	p4	p5	p6
Sway length (mm)
OE- bipodalic	60.16–91.30	59.89–91.42	57.81–90.45	60.65–93.90	.283^a
CE- bipodalic	64.75–96.69	68.75–95.75			.870^b
OE- monopodalic-L	220.73–313.67	225.41–335.69	302.11–284.82	310.06–393.70	.000 ^a	.572	.001	.015	.014	.015	.140
OE- monopodalic-R	283.29–380.73	278.22–368.72	332.11–432.19	327.23–439.26	.000 ^a	.534	.002	.000	.000	.000	.912
CE- monopodalic-L	454.80–706.88	489.05–674.32			.961^b
CE- monopodalic-R	448.52–695.76	430.12–702.47			.238^b
AVspeed (°)
OE- bipodalic	6.30–9.77	6.07–9.24	5.89–9.32	6.97–9.81	.227^a
CE- bipodalic	6.24–9.50	7.06–9.58			.92^b
OE- monopodalic-L	13.34–22.66	14.26–21.07	18.84–32.42	17.38–28.18	.000 ^a	.731	.000	.001	.000	.000	.060
OE- monopodalic-R	13.18–20.78	12.21–20.87	18.10–27.22	17.84–26.80	.000 ^a	.706	.000	.000	.000	.000	.784
CE- monopodalic-L	31.13–53.14	32.10–50.55			.974^b
CE- monopodalic-R	26.27–54.51	28.75–51.17			.502^b
Delta X (mm)
OE- bipodalic	2.33–5.58	2.16–6.08	2.33–5.74	1.89–6.66	.928^a
CE- bipodalic	2.47–5.01	2.52–6.55			.229^b
OE- monopodalic-L	8.45–18.14	7.02–14.84	12.12–19.91	13.84–23.78	.000 ^a	.105	.000	.008	.000	.001	.191
OE- monopodalic-R	10.88–16.67	9.69–15.83	13.14–23.32	16.34–22.85	.000 ^a	.404	.000	.000	.000	.000	.489
CE- monopodalic-L	25.21–39.48	24.51–35.49			.896^b
CE- monopodalic-R	23.93–38.52	23.61–39.68			.481^b
Delta Y (mm)
OE- bipodalic	2.61–5.04	2.44–9.69	2.37–5.68	2.51–5.52	.819^a
CE- bipodalic	2.84–5.33	5.06–7.78			.199^b
OE- monopodalic-L	11.58–22.33	13.74–18.74	14.26–27.40	17.86–33.67	.000 ^a	.941	.002	.044	.000	.021	.060
OE- monopodalic-R	12.74–21.91	11.28–21.12	17.50–27.53	16.54–30.00	.000 ^a	.523	.006	.004	.000	.000	.561
CE- monopodalic-L	32.74–59.17	30.27–66.29			.471^b
CE- monopodalic-R	30.72–60.66	30.53–70.51			.589^b

CE = closed eyes, L = left, OE = open eyes, R = right.

^aFriedman Test confirmed the differences between four different conditions.

^bWilcoxon Test confirmed the differences between standing and standing with headphone groups.

p1: Post hoc analyses indicated differences in standing group compared to standing with headphone.

p2: Post hoc analyses indicated differences in standing group compared to standing with texting.

p3: Post hoc analyses indicated differences in standing group compared to standing with headphone and texting.

p4: Post hoc analyses indicated differences in standing with headphone group compared to standing with texting.

p5: Post hoc analyses indicated differences in standing with headphone group compared to standing with headphone and texting.

p6: Post hoc analyses indicated differences in standing with texting compared to standing with headphone and texting. Bold values indicate statistically significant differences (p < .05).

Sway Length

For Open Eyes-Bipodalic, there was no significant difference observed (p > .05). Similarly, no significant differences were found for Closed Eyes-Bipodalic (p > .05), for Closed Eyes-Monopodalic-Left and Right Side (p > .05).

However, for Open Eyes-Monopodalic-Left, a significant difference was observed (p = .000), with post hoc analyses indicating differences between the conditions 1 and 3 (p2 = .001), condition 1 and 4 (p3 < .05), condition 2 and 3 (p4 < .05), and condition 2 and 4 (p5 < .05).

For Open Eyes-Monopodalic-Right, a significant difference was also noted (p = .000). Post hoc analyses showed significant differences between the conditions 1 and 3 (p2 < 0.01), condition 1 and 4 (p3 = .000), condition 2 and 3 (p4 = .000), condition 2 and 4 (p5 = .000).

Average Speed

No significant difference was observed for Open Eyes-Bipodalic, (p > .05), for Closed Eyes-Bipodalic, (p > .05). for Closed Eyes-Monopodalic-Left (p = .974) or Closed Eyes-Monopodalic-Right (p > .05).

For Open Eyes-Monopodalic-Left, a significant difference was found (p = .000). Post hoc analyses indicated significant differences between conditions 1 and 3 (p2 = .000), condition 1 and 4 (p3 = .001), condition 2 and 3 (p4 = .000), condition 2 and 4 (p5 = .000).

For Open Eyes-Monopodalic-Right, a significant difference was observed (p = .000). Post hoc analyses revealed significant differences between condition 1 and 3 (p2 = .000), condition 1 and 4 (p3 = .000), condition 2 and 3 (p4 = .000), and condition 2 and 4 (p5 = .000).

Delta X and Delta Y

According to Delta X results, there was no significant difference observed for Open Eyes-Bipodalic, (p = .928) or Closed Eyes-Bipodalic (p > .05). For Open Eyes-Monopodalic-L, a significant difference was observed (p = .000). Post hoc analyses indicated significant differences between conditions 1 and 3 (p2 = .000), condition 1 and 4 (p3 < .01), condition 2 and 3 (p4 = .000), condition 2 and 4 (p5 = .001).

For Open Eyes-Monopodalic-R, a significant difference was also noted (p = .000). Post hoc analyses showed significant differences between conditions 1 and 3 (p2 = .000), condition 1 and 4 (p3 = .000), condition 2 and 3 (p4 = .000), and condition 2 and 4 (p5 = .000).

According to Delta Y results, no significant difference was observed for Open Eyes-Bipodalic, (p > .05) or Closed Eyes-Bipodalic (p > .05) or Closed Eyes-Monopodalic-Left (p > .05) or Closed Eyes-Monopodalic-Right (p > .05).

For Open Eyes-Monopodalic-Left, a significant difference was found (p = .000). Post hoc analyses indicated significant differences between conditions 1 and 3 (p2 < .01), condition 1 and 4 (p3 < .05), condition 2 and 3 (p4 = .000), condition 2 and 4 (p5 < .05). For Open Eyes-Monopodalic-Right, a significant difference was observed (p = .000). Post hoc analyses revealed significant differences between conditions 1 and 3 (p2 < .01), condition 1 and 4 (p3 < .01), condition 2 and 3 (p = .000), condition 2 and 4 (p5 = .000).

Dynamic Balance Outcomes

For dynamic balance outcomes, the comparisons revealed no statistically significant differences across all four conditions. Initial contact, loading response, midstance, and terminal stance times showed p-values greater than .05, indicating no significant effect of the different conditions on dynamic balance (Table 3).

Table 3.

The Comparisons of Dynamic Balance Outcomes in Four Different Conditions

	Standing without any additional tasksMedian (IQR)	Standing with headphoneMedian (IQR)	Standing with textingMedian (IQR)	Standing with headphone and textingMedian (IQR)	p ^a
Initial contact (msn)
Right	55.00–65.25	56.00–63.75	55.25–61.00	54.00–61.00	.228
Left	55.00–66.50	56.00–66.75	55.00–65.25	54.25–67.75	.390
Loading response (msn)
Right	68.00–78.00	68.00–77.00	65.00–76.00	66.00–83.00	.133
Left	65.50–76.75	66.00–77.00	66.00–78.00	66.00–82.75	.249
Mid stance (msn)
Right	161.00–303.25	172.00–288.25	178.00–307.25	166.25–303.00	.572
Left	164.25–302.25	161.50–247.25	171.25–294.00	169.00–261.00	.204
Terminal stance (msn)
Right	235.50–325.00	221.50–301.00	231.75–336.25	221.25–312.75	.062
Left	218.75–275.25	214.25–256.25	212.00–284.25	208.50–291.75	.648

IQR = interquartile of range.

^aFriedman Test confirmed the differences between four different conditions.

Discussion

This study investigated the effects of different conditions; standing without any additional tasks, standing with headphones, standing while texting, and standing with both headphones and texting on steady-state balance outcomes, including sway length, average speed, and displacement in the X and Y directions, and on dynamic balance outcomes, including initial contact, loading response, midstance, and terminal stance. The results of this study reveal that steady-state balance outcomes were notably affected by texting compared to only standing or music listening with headphones. In addition to that, steady-state balance outcomes were notably affected by their combinations to only music listening with headphones. The results for dynamic balance outcomes reveal that the conditions tested did not significantly impact certain aspects of balance.

Texting often involves physical changes in posture, such as leaning forward or tilting the head, which can disrupt balance (Chen & Nguyen, 2023; Tapanya et al., 2021). Increased neck flexion while texting significantly increases gravitational moment and cervical erector spinae muscle activity, leading to discomfort (Yoon et al., 2021). In addition to that, texting requires visual focus on a small screen, which often necessitates a forward lean or head tilt. Contrary to that, listening to music does not involve this visual demand and generally does not require changes in body posture, so its impact on balance is less pronounced. Furthermore, Kahraman et al. determined that the task of music listening may not affect balance. During the test protocols, participants focused not only on the music but also on following commands from the researchers, directing their attention to motor tasks. This suggests that participants prioritized balance control over listening to music. The researchers also found that different kinds of music, including classical, rock, and pop, had no significant effect on static or dynamic balance in young adults (Kahraman et al., 2019). Consistent with these findings, Forti et al. (2010) reported that listening to various types of music—including Mozart, Köhler compositions, and participants’ preferred music—did not produce significant changes in static posturographic measures in healthy young individuals. However, listening to Mozart’s Jupiter symphony led to a subtle shift in sensory strategy, reflected by a decreased reliance on visual input and a compensatory increase in vestibular and somatosensory contributions (Forti et al., 2010). Similar to the findings in these studies, this study also included young individuals as participants. Their high capacity to easily compensate for balance changes may explain why we did not observe any significant alterations in balance while listening to music. Although not in young individuals, listening to Mozart’s symphony improved postural balance in middle-aged women, particularly under challenged postural conditions (Waer et al., 2023). Based on the results of these studies, it can be concluded that listening to music does not negatively affect balance.

Texting involves a significant cognitive load because it requires multitasking while maintaining posture (Schabrun et al., 2014). This cognitive distraction is more demanding than listening to music, which is less engaging cognized. Music typically does not require active cognitive processing or multitasking, which means it is less likely to interfere with the attentional resources needed for balance. A study indicated that listening to music may be a safer and more beneficial alternative compared to the postural changes and reduced environmental awareness often associated with texting (Rauscher, 1993).

On the other hand, dynamic balance outcomes, including initial contact, loading response, midstance, and terminal stance times, did not exhibit significant differences across conditions. In a study, while using a smartphone during walking, cadence, walking speed, and step length decreased, while step duration and double support duration increased (Metin et al., 2023). A device with basic gait assessment features was used in this study, whereas more advanced equipment has been used in other research for comprehensive gait evaluation. Additionally, based on the researcher team’s clinical evaluation experiences, the participants walked at a slow pace during the dynamic balance assessments by focusing activities. The clinical assessment environment may not fully reflect the results in daily life because it has different characteristics from the natural environment in which daily activities are performed. This difference may limit the power of the assessments to represent the functional status in daily life. Since walking requires more cognitive processes and motor activity compared to standing, individuals can focus on making fewer errors by increasing their attention while following instructions given during multitasking. “In controlled clinical conditions, no significant deterioration in dynamic balance performance was observed in participants who compensated for motor tasks to maintain balance. However, the difference between the laboratory environment and real-life conditions suggests that higher risks may exist in daily life.

One study reported that walking while texting divided attention between two tasks (Crowley et al., 2019; Krasovsky et al., 2018), leading to reduced performance in both, particularly in crowded and complex environments (Soangra & Lockhart, 2017). Texting increased cognitive load, requiring more attentional resources to maintain balance, which, in turn, raised the risk of falling (Pai et al., 2010; Schabrun et al., 2014). In this context, young adults may allocate more attention to cognitive tasks when environmental conditions are safe. However, as environmental complexity increases, this priority may shift, and tasks such as texting may become less prioritized. This prioritization depends on postural reserve, threat perception, skill level, and the complexity of the task (Schabrun et al., 2014). Future research should investigate the effects of environmental complexity and unpredictability on dual-task performance and fall risk in more detail. Additionally, to better understand the risks associated with mobile device usage, research should be conducted in environments with high ecological validity. While young individuals possess high compensatory capacity, it is important to note that the fall risks encountered while texting and walking can have serious and costly consequences. Falls, especially in crowded environments such as school campuses, can lead to severe outcomes. Therefore, it is suggested that these types of risks be examined more comprehensively.

Given its relevance to balance, posture was assessed in the present study. The analysis, conducted using normative reference values, indicated that participants exhibited proper posture without forward head alignment. A study found that texting while walking created the most challenging situation for the neck extensor muscles because of heightening stress and muscle load, especially in individuals with forward head posture (Yoon et al., 2021). To eliminate this effect, only individuals with normal forward head posture angles were included in the study.

Limitations

This study has several limitations. First, the controlled laboratory environment may not fully replicate real-world conditions. Participants might exhibit different behaviors compared to their natural daily routines due to the awareness of being observed. Second, the device used for assessing balance parameters was limited in its ability to measure dynamic balance, focusing only on basic metrics such as step length, cadence, and walking speed. More advanced assessment tools could provide clearer insights in future studies. Also, since the sample was mostly female (74.28%), the results may not fully represent the general population. Future research should aim for a more balanced gender distribution to better understand possible differences between males and females in dual-task balance performance.

Conclusions

In young, healthy individuals, texting leads to adverse effect on static balance, including sway length, velocity, and center of pressure displacements. In contrast, listening to music with headphones, which is a part of daily life for young individuals, did not significantly impact static or dynamic balance. Passive activities like music listening may not disrupt postural stability. In activities that require more cognitive load, such as texting, it may be advisable for individuals to focus on one task at a time when engaging in activities that challenge balance. Although multitasking is a common aspect of modern life, the results of this study highlight the importance of task prioritization, especially in contexts requiring postural stability. Rather than entirely avoiding multitasking, individuals—particularly young adults—should develop awareness of when certain tasks, such as texting, may compromise safety and balance. For example, pausing to text in crowded or unstable environments may significantly reduce the risk of imbalance and potential injury. Public health campaigns and educational interventions could benefit from integrating this knowledge into everyday technology use strategies for youth.

Footnotes

Acknowledgments

The authors would like to thank Hacettepe University for providing institutional support and research facilities for this study.

ORCID iDs

Hatice Cetin

Berkant Anil Ozcelik

Dilan Yucedag

Baris Cetin

Ethical Consideration

This study was approved by the Hacettepe University Health Sciences Research Ethics Committee (Approval number: SBA 23/354).

Consent to Participate

All participants provided written informed consent prior to inclusion in the study.

Consent for Publication

This study does not include any individual person’s data (images, videos, or identifiable information) requiring consent for publication.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.*

Author Biographies

Hatice Cetin is an Assistant Professor at Hacettepe University, Faculty of Physical Therapy and Rehabilitation, in the Department of Neurological Physiotherapy and Rehabilitation. Her research focuses primarily on neuromuscular disorders, pain, and the use of virtual reality in rehabilitation. She is actively involved in clinical research and innovative therapeutic interventions, particularly in technology-integrated rehabilitation methods.

Berkant Anil Ozcelik is a Research Assistant and Master of Science student at Hacettepe University, Faculty of Physical Therapy and Rehabilitation, in the Department of Neurological Physiotherapy and Rehabilitation. His research interests focus on neurological rehabilitation, rehabilitation technologies, artificial intelligence, and virtual reality.

Dilan Yucedag is a Master Science student at Hacettepe University, Faculty of Physical Therapy and Rehabilitation, in the Department of Neurological Physiotherapy and Rehabilitation. Her research interests focus on neurological rehabilitation, rehabilitation technologies, artificial intelligence, and virtual reality.

Baris Cetin is a post-doc researcher at Hacettepe University, Faculty of Physical Therapy and Rehabilitation. His researches focuses primarily on neurological and neuromuscular disorders, upper extremities mobility and the use of exergames in the field of rehabilitation.

References

Alexopoulou

Batsou

Drigas

(2020). Mobiles and cognition: The associations between mobile technology and cognitive flexibility. International Journal of Interactive Mobile Technologies, 14(3), 146–156. https://doi.org/10.3991/ijim.v14i03.11233

Cetin

Kose

Gurses

Turkmen

Aksoy

Oge

H. K.

(2023a). Effects of virtual reality for postural control in chronic neck pain: A single-blind, randomized controlled study. Journal of Manipulative and Physiological Therapeutics, 46(4), 191–200. https://doi.org/10.1016/j.jmpt.2024.02.006

Cetin

Turkmen

Bal

G. A.

Tekerlek

Bilgin

Köse

(2023b). Factors affecting the performance of the deep cervical flexors in young people using smartphones. CRANIO®, 41(4), 380–388. https://doi.org/10.1080/08869634.2022.2078944

Chen

Y. L.

Nguyen

H. T.

(2023). User walking speed and standing posture influence head/neck flexion and viewing behavior while using a smartphone. Healthcare, 11(23), 3027.

Crowley

Madeleine

Vuillerme

(2019). The effects of mobile phone use on walking: A dual task study. BMC Research Notes, 12(1), 352–356. https://doi.org/10.1186/s13104-019-4391-0

Elmwafie

S. M.

(2022). Health risks of electronic display devices and safety practice awareness level among children: Effect of a health educational program. Pediatric Nursing, 48(1), 13–20. https://doi.org/10.62116/pnj.2022.48.1.13

Forti

Filipponi

Di Berardino

Barozzi

Cesarani

(2010). The influence of music on static posturography. Journal of Vestibular Research, 20(5), 351–356. https://doi.org/10.3233/VES-2010-0361

Gonzalez

M. F.

Aiello

J. R.

(2019). More than meets the ear: Investigating how music affects cognitive task performance. Journal of Experimental Psychology: Applied, 25(3), 431–444. https://doi.org/10.1037/xap0000202

Horak

F. B.

(2006). Postural orientation and equilibrium: What do we need to know about neural control of balance to prevent falls? Age and Ageing, 35(Suppl 2), ii7–ii11. https://doi.org/10.1093/ageing/afl077

10.

Jeon

Kim

Song

Lee

(2016). Changes in gait pattern during multi-task using smartphones. Work, 53(2), 241–247. https://doi.org/10.3233/WOR-152115

11.

Johannes

Veling

Verwijmeren

Buijzen

(2018). Hard to resist? Journal of Media Psychology, 31(4), 214–225. https://doi.org/10.1027/1864-1105/a000248

12.

Kahraman

Beşli

Ünver

Şengül

Y. S.

(2019). Farklı müzik türlerinin Genç Erişkinlerin Statik ve Dinamik Denge kontrolüne etkisi. Sağlık Bilimleri ve Meslekleri Dergisi, 6(2), 342–349. https://doi.org/10.17681/hsp.472302

13.

Kazi

M. N.

Limbasiya

R. D.

Ramaligam

Shah

Shakil

S. A.

(2023). Effects of smartphone multitasking on dynamic balance. International Journal of Development Research, 13(12), 64407–64410. https://doi.org/10.37118/ijdr.27587.12.2023

14.

Krasovsky

Weiss

P. L.

Kizony

(2018). Older adults pay an additional cost when texting and walking: Effects of age, environment, and use of mixed reality on dual-task performance. Physical Therapy, 98(7), 549–559. https://doi.org/10.1093/ptj/pzy047

15.

Kwon

Lee

J. Y.

Won

W. Y.

Park

J. W.

Min

J. A.

Hahn

Choi

J. H.

Kim

D. J.

(2013). Development and validation of a smartphone addiction scale (SAS). PLoS One, 8(2), Article e56936. https://doi.org/10.1371/journal.pone.0056936

16.

Liang

Guan

Wang

Chen

Song

(2021). The effect of music tempo and volume on acoustic perceptions under the noise environment. Sustainability, 13(7), 4055. https://doi.org/10.3390/su13074055

17.

Lino

T. B.

Scarmagnan

G. S.

Sobrinho-Junior

S. A.

Tessari

G. M.

Gonçalves

G. H.

Pereira

H. M.

Christofoletti

(2023). Impact of using smartphone while walking or standing: A study focused on age and cognition. Brain Sciences, 13(7), 987. https://doi.org/10.3390/brainsci13070987

18.

Liu

Wei

Yuan

Wang

Zhao

(2018). Effects of musical tempo on musicians’ and non-musicians’ emotional experience when listening to music. Frontiers in Psychology, 9, 2118. https://doi.org/10.3389/fpsyg.2018.02118

19.

Marin

Kawczyński

Carnevale Pellino

Febbi

Silvestri

Pedrotti

Lovecchio

Vandoni

(2021). Displacement of centre of pressure during rehabilitation exercise in adolescent idiopathic scoliosis patients. Journal of Clinical Medicine, 10(13), 2837. https://doi.org/10.3390/jcm10132837

20.

Matla

Filar-Mierzwa

Ścisłowska-Czarnecka

Jankowicz-Szymańska

Bac

(2021). The influence of the physiotherapeutic program on selected static and dynamic foot indicators and the balance of elderly women depending on the ground stability. International Journal of Environmental Research and Public Health, 18(9), 4660. https://doi.org/10.3390/ijerph18094660

21.

McRoberts

L. B.

Cloud

R. M.

Black

C. M.

(2013). Evaluation of the New York posture rating chart for assessing changes in postural alignment in a garment study. Clothing and Textiles Research Journal, 31(2), 81–96. https://doi.org/10.1177/0887302x13480558

22.

Metin

Topuz

Yagci

(2023). Smartphone use affects gait performance, spinal kinematics and causes spinal musculoskeletal discomfort in young adults. Musculoskeletal Science and Practice, 66, Article 102819. https://doi.org/10.1016/j.msksp.2023.102819

23.

Muehlbauer

Roth

Mueller

Granacher

(2011). Intra and intersession reliability of balance measures during one-leg standing in young adults. The Journal of Strength & Conditioning Research, 25(8), 2228–2234. https://doi.org/10.1519/JSC.0b013e3181fb393b

24.

Onofrei

R. R.

Amaricai

Suciu

David

V. L.

Rata

A. L.

Hogea

(2020). Smartphone use and postural balance in healthy young adults. International Journal of Environmental Research and Public Health, 17(9), 3307. https://doi.org/10.3390/ijerph17093307

25.

Pai

Y.-C.

Bhatt

Wang

Espy

Pavol

M. J.

(2010). Inoculation against falls: Rapid adaptation by young and older adults to slips during daily activities. Archives of Physical Medicine and Rehabilitation, 91(3), 452–459. https://doi.org/10.1016/j.apmr.2009.10.032

26.

Paping

Vroegop

Koenraads

le Clercq

C. M. P.

Goedegebure

Baatenburg de Jong

R. J.

Van Der Schroeff

(2021). A smartphone application to objectively monitor music listening habits in adolescents: Personal listening device usage and the accuracy of self-reported listening habits. Journal of Otolaryngology - Head & Neck Surgery, 50(1), 11. https://doi.org/10.1186/s40463-020-00488-5

27.

Rauscher

F. H.

Shaw

G. L.

K. N.

(1993). Music and spatial task performance. Nature, 365(6447), 611. https://doi.org/10.1038/365611a0

28.

Rizzato

Benazzato

Cognolato

Grigoletto

Paoli

Marcolin

(2023). Different neuromuscular control mechanisms regulate static and dynamic balance: A center-of-pressure analysis in young adults. Human Movement Science, 90, Article 103120. https://doi.org/10.1016/j.humov.2023.103120

29.

Schabrun

S. M.

van den Hoorn

Moorcroft

Greenland

Hodges

P. W.

(2014). Texting and walking: Strategies for postural control and implications for safety. PLoS One, 9(1), Article e84312. https://doi.org/10.1371/journal.pone.0084312

30.

Soangra

Lockhart

T. E.

(2017). Dual‐task does not increase slip and fall risk in healthy young and older adults during walking. Applied Bionics and Biomechanics, 2017(1), Article 1014784. https://doi.org/10.1155/2017/1014784

31.

Sohn

S. Y.

Rees

Wildridge

Kalk

N. J.

Carter

(2019). Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: A systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry, 19(1), 356. https://doi.org/10.1186/s12888-019-2350-x

32.

Tapanya

Puntumetakul

Swangnetr Neubert

Boucaut

(2021). Influence of neck flexion angle on gravitational moment and neck muscle activity when using a smartphone while standing. Ergonomics, 64(7), 900–911. https://doi.org/10.1080/00140139.2021.1873423

33.

Thornton

Faires

Robbins

Rollins

(2014). The mere presence of a cell phone may be distracting. Social Psychology, 45(6), 479–488. https://doi.org/10.1027/1864-9335/a000216

34.

Waer

F. B.

Sahli

Alexe

C. I.

Man

M. C.

Alexe

D. I.

Burchel

L. O.

(2023). The effects of listening to music on postural balance in middle-aged women. Sensors, 24(1), 202. https://doi.org/10.3390/s24010202

35.

Yoon

Choi

Han

Shin

(2021). Neck muscular load when using a smartphone while sitting, standing, and walking. Human Factors, 63(5), 868–879. https://doi.org/10.1177/0018720820904237

36.

Zatsiorsky

V. M.

Duarte

(1999). Instant equilibrium point and its migration in standing tasks: Rambling and trembling components of the stabilogram. Motor Control, 3(1), 28–38. https://doi.org/10.1123/mcj.3.1.28