The Impact of Using Mobile Phones on Gait Characteristics: A Narrative Review

Abstract

The aim of this study was to summarize the research status and reveal the impact of mobile phone use on gait characteristics by reviewing the existing studies in terms of research status, participants, independent variables, dependent variables, main findings, etc. Twenty-nine studies which investigated the impact of using mobile phones on gait characteristics were identified through a literature search. The majority of these studies examined the effects of mobile phone use on gait characteristics in young people. The preliminary results showed that walking while using a mobile phone has significant impacts on gait. It can decrease gait velocity, cadence, step length and stride length, along with significantly increasing step width, step time and double support time. The results varied among different mobile phone usage, which resulted from the different motor and mental demands. Additionally, age and environment could affect the results as well. As well as the kinematic characteristics, we suggest that kinetic and EMG analysis are conducted in future studies.

1. Introduction

We are in an era of rapid development for communication technology. With the rapid development of fifth-generation (5G) technology, people are becoming increasingly dependent on mobile phones, which are the main carriers of communication networks [1]. Mobile phones have become an important instrument for socializing, entertainment and even learning [2]. People’s lives have become inextricably linked to mobile phones and the use of smartphones while performing daily activities, such as walking, is common [3].

Gait is considered to be an automated motor task [4], but walking is a complex behavior that requires continuous sensorimotor information to integrate into different higher cognitive processes. This suggests that complex interactions between motor function and cognition are being recognized [5]. When additional tasks are added to walking (such as texting, talking, reading, surfing the Internet, etc.), gait is affected and this decreased gait performance due to distraction may increase risk. Studies on the influence of using mobile phones at pedestrian crossings have shown that using mobile phones to listen to music, make calls and send messages causes pedestrians to pay less attention to the traffic environment [6]. Research has shown that phone use while walking is linked to an increased number of accidental injuries, with the percentage of smartphone-related pedestrian injuries rising from 0.58% in 2004 to 3.67% in 2010 [7]. Another study showed that 20% of the 363 participants that were surveyed had a high exposure to mobile phone use while crossing the street, especially young adults who were significantly more likely than other age groups to report frequent exposure [8], which may cause serious traffic accidents [8,9]. Thus, the use of mobile phones has important impacts on the way people walk [5,10] and affects gait characteristics [11]. Although many studies have been published regarding the impacts of mobile phone use on gait, the study designs, calculated parameters and findings remain inconsistent.

The purpose of this study was to summarize the research status and reveal the impacts of mobile phone use on gait characteristics using a narrative review, which analyzed the existing studies in terms of research status, participants, independent variables, dependent variables, main findings, etc. It was also expected to point out directions for future studies within this field.

In order to achieve these goals, a computer-based search was conducted using electronic bibliographic databases, such as PubMed, Cochrane Library, Web of Science, Medline, etc. Related studies on the effects of mobile phone use on gait characteristics were searched. Keywords included “phone”, “mobile phone”, “portable mobile phone” and “SMS”, “message”, “text”, “short message” and “mobile phone use”, “mobile phone and walking”, “pedestrian”, “gait”, etc. The independent variables of this study had to include mobile phone use conditions and the results had to include at least one biomechanical parameter of gait, such as gait speed, tempo, support time, etc. In the literature screening criteria, multiple publications and articles that reported major results without gait were excluded. The literature search was based on the databases as of August 2021. A total of 29 studies were identified for this review.

2. Participant Characteristics and Study Design

2.1. Characteristics of the Participants

This study examined the participants that were reported by the 29 included studies (

Table 1. Analysis of participant characteristics.

Study	Subjects
	Gender (Male/Female)	Age (Mean ± SD in Years)	Sample Size
Alapatt et al. (2020) [15]	173/135	20–29, 30–39, 40–49, 50–59, ≥ 60 b	50, 52, 55, 51, 100 b
Di Giulio et al. (2020) [19]	6/2	27 ± 4	8
Crowley et al. (2019) [20]	7/3	24.7 ± 4.4	10
Prupetkaew et al. (2019) [17]	6/18	22.7 ± 1.8, 73.5 ± 5.6 b	12, 12 b
Hinton et al. (2018) [21]	6/7	23 ± 3	13
Krasovsky et al. (2018) [14]	——	27.8 ± 4.4, 68.9 ± 3.9 b	30, 20 b
Lee et al. (2018) [22]	19/20	22.26 ± 0.27	39
Niederer et al. (2018) [23]	13/23	24.7 ± 1.97	26
Pau et al. (2018) [18]	13/41	40.5 ± 10.5	54, 40 c
Sirhan et al. (2018) [13]	——	18–45	30, 15 c
Tian et al. (2018) [24]	11/21	20–27	32
Chen et al. (2018) [25]	5/5	21.5 ± 2.1	10
Caramia et al. (2017) [26]	6/4	21–23	10
Magnani et al. (2017) [27]	10/10	24.5 ± 3.3	20
Oh et al. (2017) [28]	22/34	23.16 ± 4.72	56
Strubhar et al. (2017) [29]	24/12	23.4 ± 2.3	36
Timmis et al. (2017) [30]	16/5	25.4 ± 6.2	21
Jeon et al. (2016) [31]	16/10	21.73	26
Kao et al. (2015) [16]	4/12	20.4 ± 2.2, 61.1 ± 10.0 b	7, 9 b
Lim et al. (2015) [32]	10/10	20.3 ± 1.2	20
Licence et al. (2015) [33]	12/18	18–50	30
Agostini et al. (2015) [34]	8/10	20–30	18
Plummer et al. (2015) [35]	12/19	22.5 ± 2.1	31
Strubhar et al. (2015) [36]	6/26	24	32
Cha et al. (2015) [37]	6/6	23.50 ± 4.89	12
Parr et al. (2014) [12]	——	20 ± 2	30
Schabrun et al. (2014) [38]	7/19	29 ± 11	26
Lamberg et al. (2012) [39]	13/20	26 ± 4	33
Demura et al. (2009) [40]	15/15	20.3 ± 0.9, 19.4 ± 0.8 a	30

^a, the comma-separated values represent different genders; ^b, the comma-separated values represent different age groups; ^c, the comma-separated values represent the patient group and control group; “——” indicates “Not mentioned in the study”.

). Three of the articles [12,13,14] did not consider gender. The 951 subjects of the remaining 26 studies included 446 males (46.90%) and 505 females (53.10%). In terms of subject age, most of the studies investigated young adults (18–40 years). However, there was lack of research on children and adolescents (0–17 years), as well as middle-aged adults (41–65 years old). In addition, only four studies [14,15,16,17] were conducted on the elderly (≥65 years old). Two trials [13,18] also conducted studies on patients with multiple sclerosis.

2.2. Independent Variables Base on Different Mobile Phone Functions

Then, the independent variables from the 29 studies were classified and summarized (

Table 2. The variables and main results of the included studies.

Independent Variables	Dependent Variables of Sports Biomechanics
	Velocity (Speed)	Cadence	Stride Length	Step Length	Step Width	Step Time	Stride Time	Double Support Time
Walking when using a phone (texting) vs. normal walking	↓ [12,13,14,15,17,18,20,22,23,24,25,26,28,29,30,31,34,35,36,37,38,39,40]	↓ [12,13,17,18,20,22,26,30,31,34,36,37,38] ↑ [23]	↓ [13,14,18,20,21,23,24,28,31,34,37,38] ↑ [32]	↓ [12,17,23,26,31,36,37]	↑ [12,23,27,32]	↑ [17,26,33]	↑ [14,24,32,34,37,40]	↑ [12,18,20,21,28,31,32,33,34,36]
Walking when using a phone (talking) vs. normal walking	↓ [19,22,23,30,31,39]	↓ [19,22,30,31] ↑ [23]	↓ [23,31]	↓ [16,23,31]	↑ [16,23,27]			↑ [20,31]
Walking when using a phone (listening) vs. normal walking	↓ [22]	↓ [22]			↑ [27]
Walking when using a phone (reading) vs. normal walking	↓ [28,29,30,38]	↓ [30,38]	↓ [28,38]					↑ [28]
Walking when using a phone (gaming) vs. normal walking	↓ [37]	↓ [37]	↓ [37]	↓ [37]			↑ [37]

↓, decreased; ↑, increased.

). Using mobile phones to send messages in different ways (email, message apps, editing, etc.) was the most frequently tested task (87.09%), followed by talking (35.48%), reading (22.58%), surfing the Internet (9.68%) and gaming (9.68%). Sixteen studies tested the effects of a single mobile phone function on gait and the remaining thirteen studies compared the differences between the impacts of various mobile phone use tasks.

Although more than half of the studies used the task of texting to test the impacts of mobile phone usage on gait, the designed texting tasks are quite different. This review divided those tasks into three levels (low, medium and high cognitive tasks) based on the difficulty of the task (Figure 1). The low cognitive tasks just required the subjects to type. For instance, subjects were required to send “Good Morning” via mobile phone while walking [14] or to continuously hit the space button at the fastest speed to evaluate their maximum click speed [3]. Medium cognitive tasks included texting answers to simple questions. Prupetkaew and Strubhar et al. required subjects to answer daily life questions, such as questions about hobbies, by mobile phones while walking [17,29]. High cognitive tasks involved solving some complex problems. In these tasks, participants were asked to type irregular words or answer professional questions [39]. The cognitive level could be important and might significantly affect dependent variables because cognitive load can affect general satisfaction and performance of subjects when completing complex tasks. However, no study has yet been conducted to examine the effects of tasks of different cognitive levels on gait. Therefore, the effects of tasks of different cognitive levels on gait should be studied in the future.

2.3. Other Investigated Factors

As well as mobile phone functions, other factors, including age, gender, environment and walking speed, have been investigated in parallel to reveal their impacts on walking (Table 2). Krasovsky and Kao discussed the differences between the gait of adults and the elderly while using a mobile phone [14,16] and Alapatt compared the gait of four age groups [15]. In contrast, Plummer adjusted the brightness of the experimental environment to study the effects of brightness [35]. Hinton, Krasovsky, Cha and Prupetkaew compared the lab environment to the real environment [3,17,21,35] and there were a number of researchers who adjusted the conditions of the trails (set obstacles) to increase the difficulty during walking [25]. Crowley’s research controlled the walk speed of their subjects to study its effects [20]. Sirhan and Pau et al. used their patients as research subjects.

2.4. Dependent Variables and Main Findings

The main biomechanical gait indicators were summarized. The main findings that indicated an association between the independent and dependent variables are presented in Table 2. Most studies on the influence of mobile phone use on gait focused on the measurement of the kinematic data of walking. Only a few studies measured kinetic and electromyography (EMG) parameters. The influence of mobile phone use on kinematic parameters were widely discussed, such as gait velocity, step width, stride length, etc., but there was some research that focused on the correlation between different cognitive tasks (different types of texting) and gait (Figure 1).

3. The Impacts of Using a Mobile Phone on Gait Characteristics

Gait kinematics were well examined among the included studies and the results of most studies were consistent. It could be concluded that using a mobile phone can significantly decrease gait velocity, cadence and step length while significantly increasing step width, step time and double support time. It was supposed that using a mobile phone would increase cognitive load, which would in turn affect gait control while walking. It is becoming increasingly clear that cognitive resources play a crucial role in maintaining postural control when walking [4,41] and that walking is an active movement that requires the effective integration of information from the visual, vestibular and proprioceptive systems [42,43]. Using a mobile phone while walking is a dual-task paradigm. Participants were asked to perform not only a cognitive task, but also a walking task. It was found that using a mobile phone while walking increases the cognitive load of the walker in many aspects (e.g., attention, memory and executive control).

Lim et al. reported that texting on a mobile phone while walking can increase stride length. This was inconsistent with the other studies [13,14,18,20,24,28,31,34,37,38], but they did not mention it in their discussion. The reason could be that Lim et al. used a treadmill rather than overground walking [13,14,18,20,24,28,31,34,37,38], which should be taken into consideration.

Niederer et al. reported that using a mobile phone while walking can increase cadence [23], which seems to be inconsistent with previous studies [12,13,17,18,19,20,22,26,30,31,34,36,37,38]. However, cadence results in this study were actually normalized using the individualized gait velocity. The authors suggested that cadence and stride length should be normalized because a decrease in these two parameters often directly results from a reduction in gait velocity. Normalization could thus be considered more appropriate for gait speed-dependent outcomes.

Kinetics and EMG data were rarely discussed within this field. Increased lateral plantar pressure and the co-contraction of the tibialis anterior and gastrocnemius lateralis were found when walking while using a phone [34].

4. The Differences between Impacts of Mobile Phone Usage Tasks

Some studies investigated the differences between the influences of various mobile phone use tasks on gait [24,26,31]. The observed changes predominantly occurred when walking while texting or reading, as opposed to when walking while talking on a mobile phone [24,26,31]. The reason for this might be that completing the walking task predominantly relied on the visual–spatial resources of the working memory. Texting or reading required the participants to focus on the screen while being aware of their surroundings, such as their walking direction. This was not the case with walking while talking on a mobile phone. Although walking and talking occurred concurrently, the head position was typically upright with the eyes free to take in environmental information, which allowed more resources to be devoted to gait control. Another viewpoint is that the dual task of walking while talking engaged only low cognitive level elements (walking and talking) whereas walking while texting required the execution of a high cognitive level task (texting) and a low cognitive level (walking) element [39]. Although texting is common motor task, it is still not a well-practiced skill compared to walking or talking. Furthermore, the impact of texting on gait is greater than that of reading [35]. In texting tasks, motor demands were required for communication interchange, including both reading and texting, which could explain why texting had the greater effects on walking gait compared to reading. In addition, a further study found that searching online or taking a selfie have a larger impact on gait than reading or dialing [23]. Speculatively, searching likely employs motor and mental demands that are comparable to reading or texting, whereas taking a selfie seems to be more challenging because of additional motor tasks, such as moving the arms.

5. The Impacts of Other Independent Variables

It is well known that age has a great impact on gait. Compared to young adults, the influences of using a mobile during walking were more significant for older adults, particularly for people aged more than 49 years [15]. Additionally, the increased step width variability indicated that participants were distracted during the walking tasks, due to the cognitive demands and visual interference. This could contribute to the higher risk of falling among the elderly.

Environment factors also affect gait. In the real environment, subjects needed to occupy more cognition to scan their surroundings compared to the lab environment, which reduced their gait velocity. Additionally, the brightness of the test environment also affected gait [37], but further studies are still needed in this area. Gender and speed were also considered to be independent variables, but there were no significant differences between the groups [28].

6. Limitations and Future Studies

With the increasing popularity of smartphones, the functions of mobile phones are becoming more diversified. However, according to one statistic regarding software downloads in various countries, which was released by “Business of Apps” in 2020 (https://www.businessofapps.com/data/most-popular-apps, accessed on 8 August 2021) [44], short-form video and online shopping apps ranked in the top three of the download list, which indicates that watching short-form videos and shopping online have become mainstream. Therefore, future studies on the effects of using smartphones on gait should be designed to be more relevant to everyday life.

Additionally, kinematic data were well examined but kinetic and EMG data were not. However, dual-task or multi-task paradigms were recruited to assess the effects of cognitive load on gait and the coordination of the neuromuscular system. Particularly, in spite of the fact that torque has a mechanical unit (N*m), it should be considered as a neurological signal because it represents the final designed central nervous system [45]. EMG data could reflect neuromuscular activities to a certain extent as well. Thus, more studies should take kinetic and EMG data into consideration in the future.

Moreover, there were few studies on children and adolescents. In fact, with the development of smartphones, children and adolescents are using mobile phones more frequently compared to the elderly. Therefore, it is necessary to investigate the situation among these groups.

Lastly, the long-time effects and addiction levels to smartphone use would also be interesting fields to study. Using a smartphone for a prolonged time could affect neck flexor muscle endurance [46], which may in turn affect gait. More research is needed to explore the long-term effects of using smartphones on gait.

7. Summary

In summary, walking while using a mobile phone has significant impacts on gait by decreasing gait velocity, cadence, step length and stride length while significantly increasing step width, step time and double support time. These potential differences in gait could contribute to an increased danger associated with texting while walking [12]. Therefore, minimizing the physical alteration or visual distraction that is associated with mobile phone manipulation may help to reduce the risk of falls [16]. Smartphone technology should also be developed to detect dual-task situations and temporarily modify functionality to reduce the risk of injury from divided attention [17]. The model of a human using a mobile phone that impacts physiological activity may be an interesting model for further development.

The results of the included studies varied between different mobile phone use tasks, which resulted from different motor and mental demands. Additionally, age and environment were also investigated in parallel to reveal their influence on gait. Compared to young adults, the influences of using a mobile phone during walking were more significant among the elderly. Additionally, the real environment occupied more cognition than the lab environment while walking. Considering the limitations of the existing studies, we suggest that more studies should be conducted using kinetic and EMG analyses in the future.