The authors acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada (SSHERC).

Before beginning the data collection, it is important to receive all the necessary ethical research approval for human participants. This should be done through the appropriate review boards and/or human participants review committees.
This protocol was approved and certified by the ethics board from HEC Montr&#233;al for the Tech3Lab research facility.
1. Preparation of the visual stimulus
<ol>
	<li>Create the experimental template for the visual stimulus with a ...

<ol>
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The authors have nothing to disclose.

A critical choice when using the protocol would be ensuring the quality of the neurophysiological data. There is an inherent complication to using a tool like EEG during movement, because excessive movement can create a lot of noise in the data. It is therefore important to consider, prior to the data collection, how the data will be prepared to remove as many artefacts as possible without modifying the actual signal. Nonetheless, it is still quite likely that there will be higher rates of data exclusion because particip...

An erratum was issued for:&#160;<a href="https://www.jove.com/t/60555/measuring-the-switch-cost-of-smartphone-use-while-walking">Measuring the Switch Cost of Smartphone Use While Walking</a>. An&#160;author&#39;s name&#160;was&#160;updated.
The name was corrected from:
Gabrielle-Na&#239;m&#233; Mourra
to:
Gabrielle Na&#239;m&#233; Mourra

An erratum was issued for:&#160;<a href="https://www.jove.com/t/60555/measuring-the-switch-cost-of-smartphone-use-while-walking">Measuring the Switch Cost of Smartphone Use While Walking</a>. An&#160;author&#39;s name&#160;was&#160;updated.

Erratum: Measuring the Switch Cost of Smartphone Use While Walking

Because both smartphone penetration and the tendency to multitask are increasing, it is important to understand the impact smartphone use while walking has on attention. The literature has demonstrated repeatedly that task switching comes with a cost1, including smartphone use while walking. Studies have found that using a smartphone while walking can be distracting and dangerous2,3,4. These dangers have been linked to the attentional impairments of doing such a task3,4,5,6,7. Due to the complex nature of the pedestrian environment, studying it in an experimental context that is ecologically valid can be problematic. Nonetheless, conducting such studies in actual pedestrian environments can come with complications of their own because many extraneous variables can come into play, and there is a risk of harm to the participant due to distractions. It is important to be able to study such a phenomenon in a relatively safe environment that remains as realistic as possible. In this article, we describe a research methodology that studies the task-switching cost of texting while walking, while both increasing the validity of the task and mitigating the potential risks involved.
When using a smartphone while walking, individuals are forced to switch from the smartphone tasks to walking and environment-related tasks. Hence, in order to study such a phenomenon, we found it pertinent to frame this method within the literature on multitasking, specifically focused on the task switching paradigm. In order to do this, the task switching paradigm was used1, having participants switch between a pre-stimulus task and a post-stimulus task. One of the two pre-stimulus tasks involved multitasking, while the other one did not. In the post-stimulus task, participants had to respond to a stimulus whose perception is influenced by divided attention8. Moreover, experimental laboratory studies that try to be as ecologically valid as possible have often used virtual pedestrian environments to understand the attentional impact of smartphone use while walking4,9. Nonetheless, in order to capture the underlying neurophysiological mechanisms, we chose to focus on the specific task-switching reaction to one stimulus to minimize the number of stimuli participants had to react to. In this way, we can pinpoint more precisely the task-switching cost coming purely from switching attention away from the smartphone and towards the stimulus. With our study design, we use behavioral measures (i.e., task-switching cost) and neurophysiological data to better understand the attentional impairments found during pedestrian smartphone use.
During a task-switching experiment, participants usually performed at least two simple tasks pertaining to a set of stimuli, with each task requiring a different set of cognitive resources referred to as a &#8220;task-set&#8221;1. When individuals are forced to switch between tasks, their mental resources need to adapt (i.e., inhibition of previous task-set and activation of the current task-set). This &#8220;task-set reconfiguration&#8221; process is believed to be the cause of the task-switching cost1. The task-switching cost is usually determined by observing the differences in either the response time and/or the error rate between trials where participants switch between tasks and those where they do not10. In our experiment, we had three task-sets: 1) responding to a point-light walker stimulus; 2) texting on a smartphone while walking; and 3) simply walking. We compared the switch cost between two different conditions: 1) simply walking prior to responding to the stimulus, and 2) walking while texting prior to responding. In this way, we captured the cost of multitasking on a smartphone prior to switching the task and were able to directly compare it to the non-multitasking switch cost of simply walking before the appearance of the visual stimulus. Because the smartphone used in this study was of a specific brand, all participants were screened prior to the experiment to be sure they knew how to properly use the device.
In order to simulate a realistic experience representative of the pedestrian context, we decided to use a point-light walker figure as a visual stimulus, representing a human form walking with a 3.5&#176; deviation angle towards the left or the right of the participant. This figure is made up of 15 black dots on a white background, with the dots representing the head, shoulders, hips, elbows, wrists, knees, and ankles of a human (Figure 1). This stimulus is based on biological motion, which means that it follows the pattern of movement that is typical of humans and animals11. Furthermore, this stimulus is more than ecologically valid; it requires complex visual processing and attention in order to be analyzed successfully12,13. Interestingly, Thornton et al.8 found that proper identification of the point-like walker&#8217;s direction is greatly impacted by divided attention, making it suitable as a performance measure when studying task-switching costs when multitasking. Participants were asked to verbally state the direction the figure was walking. The appearance of the walker was always preceded by an auditory cue that signaled its appearance on the screen.
Performance on the point-light walker task and neurophysiological data allowed us to determine the attentional impact of both conditions and help determine what caused them. Performance was measured by looking at the error rates and response times when determining the direction of the point-light walker figure. In order to understand the underlying cognitive and attentional mechanisms involved in the attentional impairments we found with the performance measure, we assessed the participants&#39; neurophysiological data using the EEG actiCAP with 32 electrodes. EEG is an appropriate tool in terms of temporary precision, which is important when trying to see what causes poor performance at specific moments in time (e.g., the appearance of the point-light walker figure), although artefacts may be present in the data due to movements. When analyzing the EEG data, two indexes are particularly relevant: 1) alpha oscillations; and 2) cognitive engagement. Research has found that alpha oscillations may represent working memory control as well as active inhibition of task-irrelevant brain circuits14,15,16,17. By comparing the alpha oscillations at baseline levels with those occurring with the stimulus presentation18,19, we obtained the alpha ratio. With this ratio, we determined the event-related changes that could be underlying the attentional impairment observed when texting while walking. With regards to cognitive engagement, Pope et al.20 developed an index where beta activity represents increased arousal and attention, and alpha and theta activity reflect decreases in arousal and attention21,22. This analysis was done to determine whether increased engagement prior to the appearance of the stimulus would complicate the task set reconfiguration required in order to respond to the walker figure.
With the methodology described in this paper, we seek to grasp the underlying mechanisms that impact task-switching performance in participants engaged in multitasking episodes. The walking condition represents a non-multitasking task-switch performance that is compared to a multitasking task-switch performance (i.e., texting while walking). By measuring the roles of task-set inhibition and task-set activation, we sought to better understand the switch costs that occur when texting while walking. It is relevant to note that the original study was done in an immersive virtual environment23 but was later replicated in an experimental room (see Figure 2) with a projector displaying the walker figure on a screen in front of the participant. Because this virtual environment is no longer available, the protocol was adapted to the current experimental room design.

<table border="0" cellpadding="0" cellspacing="0" style="width:1068px;" width="1068">
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		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">The Observer XT</td>
			<td style="width:292px;">Noldus</td>
			<td style="width:83px;"></td>
			<td style="width:477px;">Integration and synchronization software: The Noldus Observer XT (Noldus Information Technology) is used to synchronize all behavioral, emotional and cognitive engagement data.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MediaRecorder</td>
			<td style="width:292px;">Noldus</td>
			<td style="width:83px;"></td>
			<td>Audio and video recording software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">FaceReader</td>
			<td style="width:292px;">Noldus</td>
			<td style="width:83px;"></td>
			<td>Software for automatic analysis of the 6 basic facial expressions</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">E-Prime</td>
			<td style="width:292px;">Psychology Software Tools, Inc.</td>
			<td style="width:83px;"></td>
			<td>Software for computerized experiment design, data collection, and analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">BrainVision Recorder</td>
			<td style="width:292px;">Brain Vision</td>
			<td style="width:83px;"></td>
			<td>Software used for recording neuro-/electrophysiological signals (EEG in this case)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Analyzer</td>
			<td style="width:292px;"></td>
			<td style="width:83px;"></td>
			<td>EEG signal processing software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qualtrics</td>
			<td style="width:292px;">Qualtrics</td>
			<td style="width:83px;"></td>
			<td>Online survey environment</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Tapis Roulant</td>
			<td style="width:292px;">ThermoTread GT Office Treadmill</td>
			<td style="width:83px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Syncbox</td>
			<td style="width:292px;">Noldus</td>
			<td style="width:83px;"></td>
			<td>Syncbox start the co-registration of EEG and gaze data by sending a Transistor-Transistor Logic (TTL) signal to the EGI amplifier and a keystroke signal to the Tobii Studio v 3.2.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Move2actiCAP</td>
			<td style="width:292px;">Brain Vision</td>
			<td style="width:83px;"></td>
			<td>Add-on for a digital wireless system for EEG</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">iPhone 6s</td>
			<td style="width:292px;">Apple</td>
			<td style="width:83px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">iMessage</td>
			<td style="width:292px;">Apple</td>
			<td style="width:83px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">iPad</td>
			<td style="width:292px;">Apple</td>
			<td style="width:83px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

measuring the switch cost of smartphone use while walking

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk on a treadmill under two experimental conditions: a control condition (i.e., simply walking) and a multitasking condition (i.e., texting while walking). During these conditions, the participants must switch between the tasks related to the experimental condition and a direction determining task. This direction task is done with a point-light walker figure, seemingly walking towards the left or the right of the participant. Performance on the direction task represents the participant&#8217;s task-switching costs. There were two performance measures: 1) correct identification of the direction and 2) response time. EEG data are recorded in order to measure the alpha oscillations and cognitive engagement occurring during the task switch. This method is limited in its ecological validity: pedestrian environments have many stimuli occurring simultaneously and competing for attention. Nonetheless, this method is appropriate for pinpointing task-switching costs. The EEG data allow the study of the underlying mechanisms in the brain that are related to differing task-switching costs. This design allows the comparison between task switching when doing one task at a time, as compared to task switching when multitasking, prior to the stimulus presentation. This allows understanding and pinpointing both the behavioral and neurophysiological impact of these two different task-switching conditions. Furthermore, by correlating the task-switching costs with the brain activity, we can learn more about what causes these behavioral effects. This protocol is an appropriate base for studying the switching cost of different smartphone uses. Different tasks, questionnaires, and other measures can be added to it in order to understand the different factors involved in the task-switching cost of smartphone use while walking.

This study protocol was originally conducted with 54 participants, each responding to 88 direction trials. Half of those trials occurred when participants were simply walking prior to the stimulus presentation; the other half occurred when the participants were texting while walking prior to the stimulus presentation.
Behavioral results 
Performance on the point-light walker&#8217;s direction represents task-switching costs, with lower performance representing higher task...

Watch this Scientific Journal Video about Measuring the Switch Cost of Smartphone Use While Walking at JoVE.com

Measuring the Switch Cost of Smartphone Use While Walking

This study design measures the task-switching cost of using a smartphone while walking. Participants undergo two experimental conditions: a control condition (walking) and a multitasking condition (texting while walking). Participants switch between these tasks and a direction determining task. EEG data as well as behavioral measures are recorded.

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk ...

We all know that a number of pedestrians involved in accidents has risen worldwide, and one reason for this is this, people texting while walking, everywhere. Studying this phenomenon and the psychology involved in it is critical, but doing so in a safe manner is a challenge. We developed a method to do so that we'll present in this video.Begin by turning on the four recording computers, the EEG amplifier, the projector, the treadmill, the speakers, and the smartphone. To set up the recording equipment, open the synchronization software with the specific sub-routine created for the study with markers at 10 seconds. The synchronization software sends a pulse that appears in the form of a marker and light pulse in the EEG and video recording every 10 seconds.Open the video recording software. The camera should automatically turn on as well. If not, manually turn them on.Open and set up the EEG recording software for the participant. Open the folder containing the visual stimulus executable script file made with the visual experiment presentation software. Prepare the EEG setup and materials according to the procedures suggested by the manufacturers.After welcoming the participant and having them sign the consent form, read the prepared script describing the experimental process. To begin setting up the EEG measure the participant's head circumference for the EEG cap. For this experiment, an EEG ACTA cap with 32 pre-amplified electrodes was used.Have the participant stand on the treadmill and attach the treadmill safety key to the participant. Set the treadmill speed to 0.8 miles per hour. Have the participant walk for two minutes to become familiar with the speed.Go to the workflow sheet on the computer to choose the condition order for the current participant. Two orders are possible. In order A, trials one and three use the texting condition.While trials two and four use the control condition. In order B, trials one and three use the control conditions and trials two and four use the texting conditions. Make sure that all the recording software is ready to be started in synchrony.Then begin the EEG recording. Start the synchronization software and make sure the video recording has begun simultaneously. Have the participant get back on the treadmill and slowly increase the speed back to 0.8 miles per hour.Turn on the visual stimulus program and start running it. Read the instructions corresponding to the selected trial to the participant. Run the stimulus executable script file for the experiment trials.Enter the participant number and the code chosen for the specific conditions. Begin the trial. Instruct the participant to walk on the treadmill and respond to the visual stimulus every time it appears by answering left or right.Signal to the participant that the trial is about to begin and start the visual stimulus trial. Write down the participants'response every time they respond to the visual stimulus. When a participant fails to respond, leave the field blank.At the end of the trial, reduce the treadmill speed to 0.4 miles per hour. Then have the participant sit down and drink some water. After the break, have the participant step back onto the treadmill and gradually increase the speed back to 0.8 miles per hour as they walk.While the participant is walking on the treadmill, hand them the smartphone. Instruct the participant to text as they would naturally whether that is with one hand or two, and to respond to the visual stimulus every time it appears by answering left or right. Signal to the participant that the trial is about to begin and start the visual stimulus trial.Have the research assistant follow the conversation script and keep the conversation going throughout the trial. Write down the participant's response every time they respond to the visual stimulus. When a participant fails to respond, leave the field blank.At the end of the trial, take the smartphone from the participant, lower the speed on the treadmill, and have the participant sit down and drink some water. Remove the EEG cap and take the participant to a shower where they can wash their hair if they so choose. Give the participant their compensation and thank them for their participation.Ensure that the participant leaves with their copy of the consent form and that they retrieve all their personal items. Participants'responses were analyzed for two variables, correct identification and response time. Participants were more likely to identify the correct direction for the point light walker stimulus when they were not texting prior to the appearance of the stimulus.No significant difference in reaction time was found. EEG recordings were used to determine the neurophysiological activity involved in task switching by observing alpha oscillations and cognitive engagement. The alpha ratio reflects the process of task inhibition.A higher alpha ratio led to better performance during the walking condition, while in the texting condition, performance was hindered by a higher alpha ratio. These results show that when texting while walking, performance was negatively impacted by the amount of resources needed to inhibit the previous task set. We're confident that this technique will enable our community to pursue research in this field, as it allowed to study the phenomena of mobile multitasking in a safe way.

measuring-the-switch-cost-of-smartphone-use-while-walking

Research

JoVE Journal

Behavior

1.7K vues.  HEC Montréal. We all know that a number of pedestrians involved in accidents has risen worldwide, and one reason for this is this, people texting while walking, everywhere. Studying this phenomenon and the psychology involved in it is critical, but doing so in a safe manner is a challenge. We developed a method to do so that we'll present in this video.Begin by turning on the four recording computers, the EEG amplifier, the projector, the treadmill, the speakers, and the smartphone. To set up the re...