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Examining the Impact of Dual-Tasking on Gait of Health Individuals

Introduction

Background & Literature Review

With recent developments in science and technology, various smartphones have become deeply integrated with people’s everyday lives. Due to everyday use of this form of technology, several people perform various tasks simultaneously in their smartphone while on the move.

The process of multitasking and walking showed a difference in the gait pattern of such individuals compared to those who did not perform any type of multitasking on their phones. Variables such as gait velocity and degree of deviation were noted to be different among multitasking and non-multitasking individuals as observed by Jeon et al. (2016).

Lamberg et al. (2012) observed that the activation of memory system decreases in normally speaking over the phone while walking compared to those who are text messaging, which increases the overall distance covered. Hatfield et al. (2007) and Lamberg et al. (2012) also discovered that gait velocity decreased, limited the number of words in a text message. When people speak on the phone, the surrounding environment’s awareness decreases automatically.

The range of vision also seems to decrease when a person has a conversation over the phone while walking which is proof of reduced concentration when multitasking and walking and increases the risk of accidents (Lin & Huang, 2017). The use of smartphones allows a person to perform a multitude of tasks simultaneously and hence cause concentration to be diverted from the surroundings and divided among the different tasks being carried out.

This division in concentration leads to walking deviations which may lead to unforeseen situations such as accidents (Abdul Rahman et al., 2018). A study conducted by the Kyunganam University (Jeon et al., 2016) showed that the changes in the gait pattern are irrespective of age, gender or any other physical factor but is solely based on the concentration of the person on the task(s) that they are carrying out on their smartphone.

Lin and Huang (2017) argue that the issue of motor-cognitive interference based on multitasking on a smartphone is not well studied. The complete absorption of an individual using their smartphone to multitask and how it affects the roadside awareness is observed in completely healthy individuals with no record of muscular issues or walking issues (Lin and Huang, 2017).

It was observed that the type of tasks being carried out while walking also has a significant effect on individuals’ gait. It was noted that any application that requires the user to carry out extensive reading reduces their awareness to the roadside surroundings (Lin and Huang, 2017).

The individuals’ response time was also noted in response to events and situations that occur in the surrounding environment. Lee and Lee (2018) analyzed the overall influence of smartphone multitasking on the gait and dynamic balance of an individual.

Research Aims & Objectives

The primary research question of the current study was developed as

Does multitasking, like texting, impact the gait of healthy individuals?

The primary aim of the current research is to examine the impact of multitasking on health individuals’ gait. To achieve the aim of the research, the following objectives have been devised:

1. Examine the using laboratory equipment the impact of texting on the gait of healthy individuals.

2. Analyze the changes in velocity when conducting multitasking.

3. We are analyzing the changed in cadence when conducting multitasking.

Methods

Participants

The research experiment was conducted using sixteen participants (N=16) with ages between 18 and 65 years. Participants were selected using convenience sampling as limited time and resources hindered a broader study. The current experimentation involved teachers’ staff and post-graduate students from the University’s Department of Physical Therapy. The research required a set of inclusion criteria that allowed for specific people to participate in the study. The inclusion criteria are:

1. Participants will not have a pre-existing musculoskeletal injury.

2. Participants will not be using any kind of medication that may affect gait design.

3. Participants’ cognitive functions must be intact.

4. Participants will not have any pre-existing visual impairments.

5. Participants will need to use their smartphones.

All participants in the study used for experimentation need to have met this criterion. All others were excluded from the study. Before the participants are included in experimentation, detailed consent was provided that detailed the study aims, the procedure, and expressed that all participants’ will remain anonymous.

Data used in the research will be stored securely and used with the utmost responsibility in part of the researcher. Once participants are informed of the research, they are asked to sign consent forms that are held by the researcher.

Equipment

To experiment gait and dual tasking in a laboratory setting, a GAITRite© was used. The GaitRite is a 5-metre-long mat with sampling capability at 80Hz. This mat was positioned in the laboratory along a 20-metre oval circuit. The circuit starts 1.5 metres before the GaitRite mat. The setup of the equipment is illustrated in the figure below.

Figure 1- Gait and Dual Tasking Laboratory Setup

In addition to the experimental setup, participants that consented to take part in the study were asked to put on flat and comfortable shoes. Furthermore, the participants of the study were required to use their mobile phones, specifically smartphones. It was required that the auto-correct features, predictive text features, and check spelling functions were turned off.

Procedures

To help ensure that participants fully understood their part in the research and minimize the presence of motivational bias from the researchers, a standardized instructions sheet was provided to participants (Montibeller & Von Winterfeldt, 2015).

Participants were required to perform the double tasks, including walking and texting; or single task of walking only. The task assigned to a participant is on a random basis to protect against accidental bias and minimize the effects of selection bias (Suresh, 2011). The process of randomizing participant tasks was conducted using lots.

Each task assigned to participants had one trial. This meant a two-minute rest break was ensured between the tasks to minimize the impact of fatigue and ensure that participants in the study were fully concentrated on their specific task. This two-minute rest break also allowed the setting up of GaitRite by the research, a stopwatch for the recording and saving the results of the 1st trial while preparing for the trail by any changes needed.

The following tasks that are included in the trial are:

1. Single task- walking ten circuit laps around the designated area.

a. A subject who had dual-tasking randomization for the first task would be required to continuously text a prearranged message to the researcher after the researcher’s number has already been included in the phone. The process is conducted for about 30 secs. The subjects are, however, not required to make any corrections of errors in their texts.

b. The prearranged message for texting the researcher was “A bluebird.” The text was used because it is familiar and easy to be written by the participants, especially international students. The text was to be sent via WhatsApp.

2. Dual-task- walking and texting simultaneously for ten circuit laps around the designated area.

a. Participants were asked to continuously send the prearranged message from their mobile phones to the researcher’s phone while walking on the ten-lap circuit until the researcher tells them to stop. The message was sent via WhatsApp application.

b. The researcher used the same phone to receive a text message.

c. The prearranged message, in this case, was “I love pets”. The text was different from the text sent while standing to prevent adaptation and the learning process that accompanies similar texts’ typing. The reason behind the selection of “I love pets” was because of the equality of characters and words with the first text, i.e., “A blue bird”.

Data Collection

Researchers involved in the project were assigned specific tasks. One of the researchers were tasked with the responsibility of operating the computer GaitRite system.

This meant ensuring that equipment and system were functioning as a bluebird. The second researcher was tasked with estimating the complete time for each trial taken by the participants when texting, counting the laps around the circuit, and directing the participants to when the test is completed.

The 2nd research used a stopwatch to estimate the time required to walk the ten circuit laps for dual and single tasks. The researcher also collected data on typing speed by the number of characters per minute to make estimates during the dual-task of participants. A mark is present at 1.5 metres before the GaitRite mat starts, and another marker is 1.5 metres after the end of the mat (see figure 1).

This was used to ensure that participants were recorded when their walking speed was at an estimated constant. The ten circuit laps were conducted on the circuit for each protocol of 20 meters for TWW and standard walking. The second researcher used a stopwatch to approximate the time required to walk the ten laps in the circuit lap for both single and dual tasks.

The speed of typing in terms of characters per min were estimated during the dual task. Data that was collected on gait factors was recorded using GaitRite and exported to an Excel spreadsheet. The dual task cost was calculated using the formula:

DTC = dual-task result – single task result x100
single task result

Results

To conduct the data analysis IBM SPSS v. 24 was used to portray the study’s descriptive and inferential statistics. To analyze the impact of gait while multitasking, the factors of velocity and stride length were used.

To understand the cognitive impacts of the physical tasks, the dimensions of typing speed (character/min), number of errors, number of characters, and overall time for walking were analyzed. For this research it was decided that a paired sample t-test be conducted.

This is because the paired-sample t-test compares two means from the same individual, object, or related units (Abdul Rahman et al., 2018). In this way, the measurement taken is under two different conditions – for the current research, it was the single task and dual tasks.

The purpose of the paired sample t-test is to determine whether there is statistical evidence that the mean difference between paired observations on a specific outcome significantly differences between zero.

Descriptive Statistics

The current study had included 16 participants based on the inclusion criteria discussion in the research section methods. Of the 16 participants in the study, 3 were males, while 13 were females. On average, the participants had a height of 1.67 meters, a weight of 69.5 kg, BMI of 24.12, and the average age of 31.3 years old. It should be noted that participants had varying ranges of shoe size; however, the average show size of the sample was 6.

Inferential Statistics

Impact on Velocity of Participants

For this portion of the research, it was essential to comprehend if the mean difference between single and dual tasks was significant. The accumulated data obtained from GaitRite is in Appendix 1. For this purpose, the paired sample t-test was used to analyze the difference in the means between cadence and velocity of gait. To conduct the test, the following rules are applicable

• t- value: if it is greater than 1.96, then there is statistical significance.
• P-value: The α value of less than 0.05 rejects the null hypothesis, which is greater than 0.05, accepts the null hypothesis.
• μ1: is the sample with single tasks
• μ2: is the sample with dual tasks

When testing velocity, the following hypotheses are formed:

• H0: μ1 = μ2 (the paired population means are equal)
• H1: μ1 ≠ μ2 (the paired population means are not equal)

The following results were produced when testing statistical significance for a velocity of participants;

Table 2- Paired Sample Correlations Results- Velocity

Based on the output obtained and illustrated in table 1, the t-value obtained was 6.955 which is greater than 1.96. It is concluded that the test conducted perceives that there is a statistical significance for differences in mean velocity of participants between single tasks and dual tasks.

The mean difference in velocity of both these tasks was 27.206 cm/sec. The p-value that was resulted from the t-paired test is found to be less than 0.05. Therefore, the null hypothesis is rejected and the alternative hypothesis is accepted, which shows that the paired population means are not equal.

This can be interpreted as meaning that on average, single tasks’ velocity was 27.206 higher than velocity of dual tasks at 95% confidence interval. According to table 2, both velocity of single tasks and velocity of dual tasks are strongly and positively correlated with r= 0.669, p=0.005. It is concluded that texting has an impact on walking of the participants around the circuit-lap due to decreased velocity means produced.

Impact on Cadence of Participants

When assessing the impact of dual tasks on cadence the same paired t-test was conducted with the following assumptions

• t- value: if it is greater than 1.96 then there is statistical significance.
• p-value: The α value of less than 0.05 rejects the null hypothesis, which is greater than 0.05 accepts the null hypothesis.
• μ1: is the sample with single tasks
• μ2: is the sample with dual tasks

When testing velocity, the following hypotheses are formed:

• H0: μ1 = μ2 (the paired population means are equal)
• H1: μ1 ≠ μ2 (the paired population means are not equal)

Table 3- Paired Sample Test Results- Cadence

Table 4- Paired Sample Correlations Results- Cadence

According to table 3, the t-value produced was 5.241, which is greater than 1.96 therefore, the difference in means between single tasks cadence and dual-task cadence in participants is statistically significant.

Under the assumptions described above, the p-value produced for this paired t-test is less than 0.05. Hence, the null hypothesis is rejected, and the alternative hypothesis is accepted.

Based on table 4, the single-task cadence and dual-task cadence are strongly and positively correlated with an r= 0.911, p= 0.00. There is a significant average difference between single-task cadence and dual-task cadence of 7.106 step/min. On average, single-task cadence was 7.106 steps/min higher than dual-task cadence at 95% confidence interval.

Impact on Typing Speed of Participants

The participants were also analyzed to see if there is a difference in typing speed when carrying out single and dual tasks. The rules for conducting the paired t-test were the same as stated previously in section 3.2.1 & 3.2.2. The hypotheses developed are as follows:

• H0: μ1 = μ2 (the paired population means are equal)
• H1: μ1 ≠ μ2 (the paired population means are not equal)

Table 5- Paired Sample Test Results- Typing Speed

Table 6- Paired Sample Correlations- Typing Speed

Based on table 5, the t-value produced is 0.230, which considered being insignificant because it is less than the value of 1.96. Also, the p-value, 0.822 produced is greater than 0.05, which allows the researcher to concluded that the null hypothesis is accepted and the alternative is rejected.

Therefore, it is concluded that the paired population means are equal; there is no significant difference in the average texting speed of character/min. That is significant to report because both baseline typing speed (single-tasks) and dual-task texting speed are strongly and positively correlated with r= 0.793, p= 0.0002.

Discussion and Conclusion

Performing multiple tasks is often done naturally without much thought and hence establishes a system of attention distribution and which in turn causes difficulty in motor skills such as walking (Jeon et al., 2016). Jeon et al. (2016) stated that if a person’s attention is concentrating on one task due to the lack of a well-established strategy, an unexpected situation may arise, such as an accident. Gait or walking is considered a form of automatic movement, yet in situations where gait becomes part of a dual-task, less attention is paid to walking, and this leads to a reduction in gait velocity and tempo (Abdul Rahman et al., 2018; Li et al., 2018; Liu et al., 2017).

The results of the current study provide evidence that is similar to that of already published research. It is found that when participants experience dual-tasking of walking and texting, there is a decrease in cadence and velocity. The statistical analysis has provided that the results are statistically significant, meaning they are not based on chance alone and that the factors of dual-tasking are having an impact on the gait of individuals. What is surprising in the current research is that in multitasking and single-task, the average typing speed of individuals was not statistically significant. This can be interpreted as meaning that the results were due to chance, or there is a third factor that may influence individuals’ typing speeds. It was found that no mean difference existed for typing speed. The researchers conclude that another factor may be influencing this cognitive ability, or it can be hypothesized that all participants are very well versed in texting and may focusing greatly on their cognitive ability while lacking in their physical ability. This can be interpreted as meaning that cognitive functions may require increased focus than physical functions like walking.

References

Abdul Rahman, R. A., Rafi, F., Hanapiah, F. A., Nikmat, A. W., Ismail, N. A., & Manaf, H. (2018). Effect of Dual-Task Conditions on Gait Performance during Timed Up and Go Test in Children with Traumatic Brain Injury [Research Article]. Rehabilitation Research and Practice; Hindawi. https://doi.org/10.1155/2018/2071726

Bilney, B., M. Morris, et al. (2003). “Concurrent related validity of the GAITRite® walkway system for quantification of the spatial and temporal parameters of gait.” Gait & Posture 17(1): 68-74.

Jeon, S., Kim, C., Song, S., & Lee, G. (2016). Changes in gait pattern during multitask using smartphones. Work, 53(2), 241–247. https://doi.org/10.3233/WOR-152115

Lamberg E.M. , Muratori L.M. , Muratori. (2012) Cell phones change the way we walk. Gait and Posture;35(4):688–690
Lee, J. H., & Lee, M. H. (2018). The effects of smartphone multitasking on gait and dynamic balance. Journal of Physical Therapy Science, 30(2), 293–296. https://doi.org/10.1589/jpts.30.293

Li, K. Z. H., Bherer, L., Mirelman, A., Maidan, I., & Hausdorff, J. M. (2018). Cognitive Involvement in Balance, Gait and Dual-Tasking in Aging: A Focused Review From a Neuroscience of Aging Perspective. Frontiers in Neurology, 9. https://doi.org/10.3389/fneur.2018.00913

Lin, M.-I. B., & Huang, Y.-P. (2017). The impact of walking while using a smartphone on pedestrians’ awareness of roadside events. Accident Analysis & Prevention, 101, 87–96. https://doi.org/10.1016/j.aap.2017.02.005

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