Measuring the Switch Cost of Smartphone Use While Walking
April 30th, 2020
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.
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.
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Overview
This study investigates the task-switching cost associated with using a smartphone while walking. Participants engage in both a control condition (walking) and a multitasking condition (texting while walking), with EEG data and behavioral measures recorded.
Key Study Components
Area of Science
- Neuroscience
- Behavioral Psychology
- Human Factors
Background
- The rise in pedestrian accidents linked to smartphone use.
- The psychological implications of multitasking in public spaces.
- The challenges of studying this behavior safely.
- Development of a method to investigate texting while walking.
Purpose of Study
- To measure the cognitive costs of multitasking while walking.
- To understand the impact of texting on walking performance.
- To explore the relationship between task-switching and accident risk.
Methods Used
- Participants perform tasks on a treadmill.
- EEG data is collected to monitor brain activity.
- Behavioral measures are recorded during task performance.
- Synchronization software is used to align data collection.
Main Results
- Increased task-switching costs observed in multitasking condition.
- EEG data reveals changes in cognitive load while texting.
- Behavioral performance declines when participants text while walking.
- Findings highlight risks associated with smartphone use in pedestrians.
Conclusions
- Texting while walking significantly impairs walking performance.
- Multitasking increases cognitive load and task-switching costs.
- Understanding these effects can inform public safety initiatives.
Frequently Asked Questions
Executive Industry Relevance
Quantifying cognitive switch costs during smartphone use while walking provides a controlled framework for understanding multitasking impacts on behavioral and neurophysiological performance. This protocol enables precise measurement of task-switching effects, supporting mechanistic de-risking and predictive confidence in human factors research. The approach is foundational for translational studies on cognitive load and multitasking in real-world scenarios relevant to device safety and neuroergonomics.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of cognitive mechanisms underlying multitasking and task inhibition.
- Supports functional validation of neurophysiological biomarkers linked to task-switching costs.
- Facilitates predictive confidence in behavioral and EEG-based endpoints for human factors studies.
Screening & Assay Development
- Establishes a validated behavioral and EEG assay for quantifying cognitive load during multitasking.
- Provides standardized, reproducible measures of response accuracy and reaction time under controlled conditions.
- Enables reliable comparison of intervention effects on cognitive switching performance.
Translational & Preclinical Research
- Aligns behavioral and neurophysiological readouts for translational continuity in cognitive workload studies.
- Supports risk-adjusted advancement of interventions targeting cognitive flexibility and multitasking capacity.
- Offers mechanistic insight into neural correlates of task-switching relevant to neuropsychiatric and neuroergonomic research.
Pipeline & Workflow Integration
This protocol integrates into the discovery-to-preclinical continuum for cognitive workload and multitasking research, bridging behavioral assays and neurophysiological analytics.
- Discovery Biology: Enables hypothesis testing on cognitive inhibition and task-switching mechanisms using EEG and behavioral outputs.
- Screening: Provides assay readiness with standardized response accuracy and reaction time metrics.
- Analytics: Delivers quantitative EEG alpha oscillation data and behavioral performance measures for condition comparison.
- Translational Research: Supports alignment of behavioral and neural endpoints for preclinical and applied studies.
- Enterprise Reuse: Serves as a reusable platform for evaluating multitasking interventions and cognitive load across studies.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in cognitive workload and multitasking research.
- Operational Value: Standardizes behavioral and EEG data collection for reproducibility and scalability.
- Strategic Value: Informs go/no-go decisions for interventions targeting cognitive flexibility and multitasking safety.
- Portfolio Impact: Enables risk-adjusted prioritization of neuroergonomic and cognitive enhancement programs.
Implementation Considerations
- Requires expertise in EEG setup, behavioral assay design, and synchronized data acquisition.
- Needs access to treadmill, EEG amplifier, synchronization software, and visual stimulus presentation tools.
- Demands cross-team standardization of experimental protocols and data analysis pipelines.
- Adaptation may be needed for ecological validity or different multitasking scenarios.
- Limitations include controlled laboratory setting and potential differences from real-world pedestrian environments.
