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

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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 participants walk on a treadmill throughout the experiment. Certain participant&#8217;s data will be unusable due to artefacts caused by excessive facial, head, and body movements, as well as due to the potential of excessive sweating and equipment malfunction. To avoid biasing or impacting the results, data exclusions should be determined prior to the behavioral analysis. Since conducting this study, our laboratory has acquired the capability of localizing electrode position and we hope to use this technology in future studies to better analyze source activity. We recommend that future studies take advantage of electrode localization technology to permit source estimation of related EEG signals.
A critical step to pay attention to in this protocol is the script for the participant&#8217;s texting conversation with the research assistant. It is important that the texting conversations be guided with predefined topics and some open-ended questions. There is much value in following such a script. First, we ensure that all participants have similar types of conversation, so we remove the variability that would exist in a naturally occurring conversation. In this way we ensure that the level of distraction does not vary due to the conversation being excessively different between participants. Secondly, we can ensure that the conversation does not lead to strong emotional reactions by choosing the topics wisely. Emotionally charged interactions may alter EEG analysis and distractibility levels, which would in turn complicate the interpretation of both the behavioral and neurophysiological results. All texting conversations will inevitably vary to some extent, but having a script allows us a certain amount of control over this variability. To further limit the variability in the conversation, it would be preferable to have one specified research assistant responsible for this task throughout the duration of the research project. Nonetheless, by adhering to a script we also lose the ecological validity of such a conversation. When individuals have conversations with their friends, for example, these conversations may be emotionally charged, and this may in fact alter the task-switching cost. Yet it is important to consider that to analyze the impact of conversation types on the task-switching cost, the goal of the study would have to focus on that aspect, due to the complexity of such an analysis. Hence, for our purposes the use of a script was more appropriate.
There should also be caution when creating the database file where the participants&#8217; responses will be noted. The formula we used in Excel to test accuracy (i.e., =IF(A1=B1,1,0)) is format dependent (e.g., it will be influenced by extra blank spaces and capitalized letters). It is therefore recommended to write R for right or L for left, in the same format than that used in the output extracted from the visual experiment presentation software. Any error in the writing of the file may cause false negatives in the accuracy rating. Finally, for this kind of study, where visual processing plays a big role, it is important that all participants have normal or corrected-to-normal vision. Because we are using EEG tools, it is also relevant to screen for epilepsy and neurological, as well as psychiatric, diagnoses, which could impact the brain signals of the participants. It is wise to exclude those participants from the study, as differences in brain activity may bias the results.
This methodology can be modified to test multiple smartphone uses (e.g., reading, social media, gaming, viewing images, etc.)28. Questionnaires can also be added in between experimental conditions, or at the end of the experiment, to gain more insight into the participants&#39; characteristics and perceptions (see Mourra29). Questionnaires in between the tasks should not be time consuming to avoid increasing unnecessarily the participants&#39; fatigue for the following conditions. This moment is quite useful to test different task-related constructs, such as the perception of time, the interest in the task the participant just completed, and the perceived difficulty. Questionnaires at the end of the experiment can be more time consuming, but the fatigue of finalizing the conditions must be taken into account. The timing of the questionnaires should be done in a way to avoid participants&#8217; answers being biased by their experience during the task, and to avoid participants&#39; behavior being biased due to the questions asked previously.
This method is limited in that real pedestrian environments have many stimuli presented simultaneously, so the cognitive load required in these environments is probably much higher than in this study (see Pourchon et al.7). Nonetheless, to truly to be able to pinpoint the underlying neurophysiological mechanisms, it seemed necessary to make such a trade-off. Depending on the purpose of the particular study, the visual stimulus may be modified to test different factors that may impact the task-switching cost of using a smartphone while walking. In this methodology, the point-light walker figure was used instead of an actual human figure because this point-light walker is less prone to bias. The appearance of an actual human walker could be more pleasing or displeasing to certain participants and this may impact the attention attributed to it. By using a group of dots representing a human form and human movement, we can bypass this potential extraneous variable of the human walker&#8217;s gender, clothing, body image, among other variables that may skew the results. For example, participants who find the human walker more attractive may be more prone to focusing their attention on the walker than they would have otherwise.
This methodology can be used for different applications in future studies. By modifying, for example, the visual stimulus to have different characteristics, it would be possible to study how the characteristics of the object in an environment can influence the task-switching cost. It may also be interesting to use this method with a manual treadmill, where the action of the participants&#39; feet against the deck moves the treadmill belt. In this way, we could determine how speed fluctuates during the experiment due to multitasking or due to the task switching. This would increase the ecological validity while adding a new variable to consider in the analysis (e.g., does stopping, or walking slower or faster influence the participants&#39; performance?). Thus, both in terms of stimuli and subject movement, there are many other possibilities than the ones proposed in this method (i.e., point-light walker and automatic treadmill) to investigate texting while walking behaviors (Pourchon et al.7, Schabrun et al.30). This would increase the internal or external validity of future studies. Also, it must be noted that our decision to use EEG data from only two electrodes comes with some limitations. Future research should try to extend the analysis to regions of interest encompassing multiple electrodes. It would also be possible to not use a conversation script and let conversation occur naturally. In such instances the content of the conversation could be analyzed with a content analysis, and the impact of different types of conversations could be studied in a natural way. In sum, this methodology can be the base on which more complex studies can build on to grow the knowledge of the different factors that may impact our capacity to multitask with a smartphone while walking.&#160;

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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><tbody><tr><td>The Observer XT</td><td>Noldus</td><td></td><td>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><td>MediaRecorder</td><td>Noldus</td><td></td><td>Audio and video recording software</td></tr><tr><td>FaceReader</td><td>Noldus</td><td></td><td>Software for automatic analysis of the 6 basic facial expressions</td></tr><tr><td>E-Prime</td><td>Psychology Software Tools, Inc.</td><td></td><td>Software for computerized experiment design, data collection, and analysis</td></tr><tr><td>BrainVision Recorder</td><td>Brain Vision</td><td></td><td>Software used for recording neuro-/electrophysiological signals (EEG in this case)</td></tr><tr><td>Analyzer</td><td></td><td></td><td>EEG signal processing software</td></tr><tr><td>Qualtrics</td><td>Qualtrics</td><td></td><td>Online survey environment</td></tr><tr><td>Tapis Roulant</td><td>ThermoTread GT Office Treadmill</td><td></td><td></td></tr><tr><td>Syncbox</td><td>Noldus</td><td></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><td>Move2actiCAP</td><td>Brain Vision</td><td></td><td>Add-on for a digital wireless system for EEG</td></tr><tr><td>iPhone 6s</td><td>Apple</td><td></td><td></td></tr><tr><td>iMessage</td><td>Apple</td><td></td><td></td></tr><tr><td>iPad</td><td>Apple</td><td></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-switching costs. Participants&#8217; responses were analyzed with two response variables: 1) Correct identification; and 2) response time. The two experimental conditions represented the two groups: 1) Texting while walking; and 2) simply walking before responding to the stimulus. Response times were calculated at the end of the experiment. The video recordings of the experiment were converted into audio files and then analyzed with a sound software that marked the peaks in sound wavelengths. Once the sound of the cue and the sound of the participant&#8217;s verbal response were marked, the time between the two was determined. Correct response times were analyzed by exporting the participant&#8217;s correct direction for the 88 trials, from the experimental presentation software, and adding it to the database file containing the participants&#39; responses. In the program used (Excel), a formula to test accuracy (=IF(A1=B1,1,0)) was used to determine whether the information contained in the first data column (i.e., participants&#39; response) was the same as the second column.
Because each participant had to repeatedly determine the orientation of the stimulus, a t-test could not be used to analyze the differences in performance means across conditions. Instead, to account for intra-subject correlation between trials, a generalized linear regression model was used. This analysis was run using Proc Glimmix with the SAS 9.4 software. The group variable was the explanatory variable for the response variables and a random Gaussian intercept was added for each subject. The accuracy of the response variables (correct or incorrect response) was binary, and as such, a logit link function was appropriate for this regression model.
We found that 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 (Odds Ratio = 0.77; T = &#8722;3.12; p = 0.001; 95% confidence interval (.657;.908)). No significant difference in reaction time was found (&#946; = &#8722;0.005; T = &#8722;.26; p = 0.799; 95% confidence interval (-.047;. 036)) (see Figure 3).
In order to combine accuracy with response time, the Inverse Efficiency Score (IES)24 was used. The probability of being accurate on the direction trials were modeled using a logistic regression with response time as the control variable. Again, an individual random intercept was added for each subject to account for potential intra-subject correlations between trials. The results of this mixed effect regression showed a significant effect of experimental condition, where the estimated probability of accurately responding to the stimulus was 18.9% smaller in the condition where participants texted while walking, as compared to when they simply walked prior to the appearance of the stimulus (Odds ratio = 0.811; T = &#8722;2.46; p = 0.014; 95% confidence interval 0.686&#8211;0.959; see Figure 3). This showed that regardless of the response time, the accuracy of the stimulus&#8217; direction was consistently lower when participants texted while walking.
Neurophysiological data 
EEG recordings were used to determine the neurophysiological activity involved in task switching by observing alpha oscillations and cognitive engagement. Using EEG during movement led to more artefacts. In order to ensure the quality of the data, several steps were taken. First, to allow recording during walking, new active electrode technology with a noise subtraction circuit (i.e., pre-amplified electrodes) was used. Second, the EEG data were filtered offline with a lowpass IIR filter at 20 Hz, to isolate the alpha waves, and a highpass IIR filter at 1 Hz, was used to reduce noise. Third, an Independent Component Analysis (ICA) was applied in order to attenuate the artefacts caused by eye blinks and ocular saccades in the EEG data25. Fourth, an automatic artefact rejection was used to exclude epochs with voltage differences over 50 &#956;V between two neighboring sampling points and a difference over 50 &#956;V in a 75 ms interval.
Data analysis was performed with Vision Analyzer 2. Based on Luck26, data were re-referenced to the common average reference. Furthermore, the data were segmented to isolate the 2 s after the presentation of the walker stimulus as well as a 2 s baseline. For each stimulus presentation a baseline representing the activity occurring when the participant solely walked or texted while walking was determined. This baseline was obtained during a 2 s time point, occurring 12 s before the auditory cue of each stimulus appearance. Both segments were analyzed separately with a Fast-Fourier Transform on 1 s epochs to obtain power values in the frequency domain. All epochs were averaged separately by experimental condition.
The aim of this analysis was to determine whether the two sub-steps of task-set inhibition and task-set activation impact the behavioral switch cost (i.e., performance measures) differently. In order to do this, the EEG data were analyzed based on two indexes: 1) alpha oscillations; and 2) cognitive engagement. All the calculations were done using the Cz and Pz sites because their data contained less noise and fewer artefacts. The changes in alpha oscillations, due to the stimulus presentation, were analyzed with alpha ratios by comparing the baseline alpha power with the alpha power occurring with the stimulus presentation18,19. Using the cognitive engagement index developed by Pope et al.20, a ratio was created of the combined power in the beta (14&#8211;20 Hz) divided by total power in alpha (8&#8211;12 Hz) and theta (4&#8211;8 Hz) components. In order to calculate the combined power, the sums of powers used were at Cz and Pz locations.
The alpha ratio and its effect on performance were compared between the two conditions. The alpha ratio reflects the processes of task inhibition. Because the alpha ratio was measured for each participant, it was necessary to compare the ratio with the aggregated performance during that condition (i.e., the correct response percentage of the 44 trials of that condition). To compare the correlation coefficient of both conditions, the z-test proposed by Steiger27 was used as a means to compare correlation coefficients measured from the same individual. At the Pz site, it was found that the correlation between performance and the alpha ratio was statistically different between the two conditions (p = 0.032; 95% confidence interval = 0.054&#8211;1.220) (see Figure 4). Because the correlations of each condition were of opposite signs, it was shown that the inhibition processes impacted performance differently in the two conditions, with a higher alpha ratio leading 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, the amount of resources needed to inhibit the previous task set negatively impacted performance. Thus, the extent to which participants engaged resources in task-set inhibition had more effect on upcoming performance when were texting. With regards to the Cz site, no significant differences were found, suggesting that the effect was mostly located in the parietal region of the scalp.
The cognitive engagement ratio and its effect on performance were also compared between the two conditions. As for the alpha ratio, the z-test proposed by Steiger27 was also used for this analysis. The results showed a statistically significant difference between the two conditions, where the engagement on the task done immediately before the appearance of the stimulus (i.e., walking or texting while walking) impacted performance differently in each condition (p = 0.027; 95% confidence interval = -1.062 &#8211; -0.061). Here again the correlations were of opposite signs. Our results suggest that when participants were walking before the task switch, a higher ratio of cognitive engagement was related to a decrease in performance, whereas when participants were texting while walking before the task switch a higher ratio of cognitive engagement was related to an increase in performance. This shows that the higher task-switching cost of texting while walking was not due to a higher cognitive engagement in that task.
<img alt="Movie 1" src="/files/ftp_upload/60555/60555movie1.jpg" /> 
Figure 1: In this video, a figure walking towards the right side of the subject is visible. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60555/Fig1_Towards_right_side.mpeg" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60555/60555fig2.jpg" /> 
Figure 2: Experimental setup of the room. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60555/60555fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60555/60555fig3.jpg" /> 
Figure 3: Effect of texting on accuracy and response time. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60555/60555fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60555/60555fig4v2.jpg" /> 
Figure 4: Correlation between Alpha at Fz and performance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60555/60555fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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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 ...

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

Nanyang Technological University

Research

JoVE Journal

Behavior

1.8K Views.  HEC Montréal. Dit onderzoeksontwerp meet de taakwisselingskosten van het gebruik van een smartphone tijdens het lopen. Deelnemers ondergaan twee experimentele condities: een controleconditie (lopen) en een multitasking conditie (sms'en tijdens het lopen). Deelnemers schakelen tussen deze taken en een richting bepalende taak. EEG-gegevens en gedragsmaatregelen worden geregistreerd.

Video: Measuring the Switch Cost of Smartphone Use While Walking

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Environment

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the fundamental objects of studies on smart devices. With the development of screen technology, more precise data and indicators could be collected by smartphones to in-depth evaluate the keyboard design. The enlargement of the phone screen has led to unsatisfactory input experience and finger pain, especially for one-handed input. The input efficiency and comfort have attracted the attention of researchers and designers, and the curved keyboard with size-adjustable buttons, which roughly accorded with the physiological structure of thumbs, was proposed to optimize the one-handed usage on large-screen smartphones. However, its real effects remained ambiguous. Therefore, this protocol demonstrated a general and summarized method to evaluate the effect of curved QWERTY keyboard design on a 5-inch smartphone through a self-developed software with detailed variables, including objective behavioral data, subjective feedback, and the coordinate data of each touchpoint. There is sufficient existing literature on evaluating virtual keyboards; however, only a few of them systematically summarized and took reflection on the evaluation methods and processes. Therefore, this protocol fills in the gap and presents a process and method of the systematic evaluation of keyboard design with available codes for analysis and visualization.&#160;It needs no additional or expensive equipment&#160;and is easy to conduct and operate. In addition, the protocol also helps to get potential reasons for the disadvantages of the design and enlightens the optimization of designs. In conclusion, this protocol with the open-source resources could not only be an in-class demonstrative experiment to inspire the novice to start their studies but also contributes to improving the user experience and the revenue of input method editor companies.

The presented protocol integrates various evaluation methods and demonstrates a method to evaluate the keyboard design on smartphones. Pairs matched by English characters are proposed as the input material, and the transition time between two keys is used as the dependent variable.

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the ...

Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in physical activity and social connectivity during the pandemic. Physical activity, specifically moderate-intensity walking, has been shown to have multiple health benefits for older adults. This methodology was created during the COVID-19 pandemic, contributing to lower physical activity levels and social isolation in older adults. Both the in-person and virtual classes utilize technology such as fitness tracking apps and video platforms. Data are presented from two groups of older adults with neurodegenerative disease: people with prodromal Alzheimer's disease and people with Parkinson's disease. Participants in the virtual classes were screened for balance impairments before the walk, and any person deemed at risk for falls was not eligible to participate virtually. As COVID vaccines became available and restrictions were lifted, in-person walking groups became feasible. Staff and caregivers were trained in balance management, roles/responsibilities, and delivering walking cues. Both walks, virtual and in-person, consisted of a warm up, a walk, and a cool down, and posture, gait, and safety cues were given throughout. Measures of the rate of perceived exertion (RPE) and heart rate (HR) were taken pre-warm up, post-warm up, and at 15 min, 30 min, and 45 min time points. Participants also used a walking application (app) on their phones to record the distance walked and the number of steps taken. The study showed a positive correlation between HR and RPE in both groups. The participants in the virtual group rated the walking group favorably in terms of improving their quality of life during a period of social isolation and contributing to physical, mental, and emotional well-being. The methodology shows a safe and feasible way to implement virtual and in-person walking groups for older adults with neurological diseases.

The present protocol aims to inform rehabilitation specialists and fitness instructors in safe, feasible, and evidence-based methods of delivering virtual and in-person walking classes to older adults with neurodegenerative diseases.

This study presents a protocol for virtual and in-person walking groups for older adults with neurodegenerative diseases and addresses the decline in ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Medicine

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. 
In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. 
Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application.
The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments ...

Bioengineering

Smartphone App for Measuring Refractive Errors and Inter-Pupillary Distance

Subjective Refraction Test Using a Smartphone for Vision Screening

To improve access to vision care and to enable mass vision screening, a smartphone app has been developed to measure refractive errors. Without needing any external attachment, the app running on a standalone phone can be used by lay personnel to measure subjective refraction. Its validity has been pilot-tested in clinical settings and underserved communities. The app estimates the refractive error by measuring the distances of far points for discerning visual stimuli. Spherical equivalent refraction and astigmatism can be measured using Tumbling E letters and grating patterns, respectively. The purpose of this paper is to describe the measurement protocols for performing subjective refraction using the app. Experimental results with 34 subjects (30 eyes for spherical equivalent and 38 eyes for astigmatism assessment) are presented. Measurement with the app was compared with standard clinical methods. The average absolute error of spherical equivalent refraction was 0.63D, and the average absolute error of astigmatism measurement was 0.28D. In addition, 22 subjects were enrolled to evaluate inter-pupillary distance (IPD) measurement with the app. The average absolute error in IPD measurement with the app was 1.2 mm. The protocol for measuring IPD with the app is also described.

This paper presents the protocols and clinical validation data for using a smartphone app to subjectively measure refractive error.

To improve access to vision care and to enable mass vision screening, a smartphone app has been developed to measure refractive errors. Without needing ...

Supply

Supply is a fundamental concept in economics that refers to the quantity of goods and services that producers are willing and able to offer for sale at various prices within a given period.
It represents the relationship between the price of a product and the quantity supplied. Generally as prices rise, producers are typically motivated to supply more goods or services to the market, increasing the quantity supplied. Conversely, when prices fall, producers may reduce the quantity supplied as it becomes less profitable.
Several factors affect supply, including input costs, technology, government policies, the number of suppliers, and future price expectations. Input costs, like raw materials and labor, significantly impact production expenses. Higher input costs tend to decrease supply, while lower input costs can increase it. Technological advancements can enhance production efficiency, leading to an increase in supply. Government policies, such as subsidies, taxes or regulations, shape supply dynamics. The number of suppliers in the market increases or decreases overall supply levels. Additionally, producers&#39; expectations about future prices can influence current supply decisions. If higher price anticipated, producers might reduce current supply to sell more later, and vice versa. All these factors interact to determine how much producers are willing and able to supply at different price levels.
Understanding supply is essential as it helps individuals comprehend how businesses make production decisions and how changes in supply affect market equilibrium.

Supply is the amount of a good or service that producers are willing and able to sell at a particular price during a specific period.
It depends on ...

Business

JoVE Business

Microeconomics

Unit 1

Supply and its Elasticities

KEY TERMS AND CONCEPTS

Effect of Shift in Supply Curve on Market Equilibrium

The dynamics of market equilibrium, defined by the balance between supply and demand, play a pivotal role in determining the prices and quantities of goods. A fascinating example of this principle can be observed in the technology sector, particularly in smartphone manufacturing.
Supply Increase: Imagine a scenario where a major technological breakthrough lowers production costs for smartphones. This innovation acts similarly to a government subsidy by increasing the supply of smartphones, represented by a rightward shift in the supply curve, assuming demand remains constant.
<ul>
	<li>&#8226; Result: With more smartphones available at lower production costs, competition between manufacturers may reduce prices to stimulate sales, leading to a lower equilibrium price and a higher equilibrium quantity.</li>
</ul>
Supply Decrease: On the flip side, a scarcity of lithium, a vital component for smartphone batteries, would constrict the supply. This limitation causes a leftward shift in the supply curve seen with material shortages without altering demand.
<ul>
	<li>&#8226; Result: To manage the dwindling supply, prices are driven up. The higher prices discourage some consumers from purchasing, thus decreasing the equilibrium quantity while increasing the equilibrium price.</li>
</ul>
These hypothetical scenarios underscore how shifts in supply can significantly influence market equilibrium.

A change in supply or shift in the supply curve can influence the equilibrium price and quantity.
Consider a bicycle market.
If the government subsidizes ...

Market Equilibrium

Mobile Marketing

As consumers increasingly rely on smartphones and other mobile devices for browsing, communication, and shopping, mobile marketing has become essential in today&#39;s digital world. Brands must adjust their strategies to engage users where they spend most of their time, which is on mobile screens. Understanding mobile user behavior is crucial for successful mobile marketing. Mobile users typically interact with their devices briefly and frequently throughout the day. They prefer content that is quick and easy to consume. This means that brands must provide short, impactful content that loads quickly, looks appealing, and is easy to navigate on mobile devices.
A mobile-friendly website design is not only important but vital for maintaining user engagement. Responsive design, which adjusts the layout to fit different screen sizes, ensures a seamless user experience across all devices. Fast loading times are essential, as delays can lead to site abandonment. Additionally, the content should be easily accessible without excessive scrolling or zooming, allowing users to find information quickly and efficiently.
SMS marketing is a highly effective tool within mobile marketing, offering a direct channel to reach consumers with open rates as high as 98%. According to a study by Gartner, SMS messages are typically opened within minutes, highlighting their immediate impact and high engagement rate. Messages should be brief, featuring clear calls to action and personalized content that reflects user preferences and behaviors. This method of communication strengthens the personal connection between brands and consumers, making it a valuable asset in mobile marketing strategies.
Mobile apps present another opportunity for brands to interact with their audience in a more personalized and engaging way. These apps can offer exclusive content, push notifications, and customized user experiences. A well-designed app can drive customer loyalty by incorporating features such as rewards programs, personalized promotions, and streamlined interfaces. Additionally, integrating location-based services within mobile apps allows brands to provide geographically relevant offers, enhancing the overall effectiveness of their marketing efforts.

Mobile marketing is essential for engaging today&#8217;s on-the-go consumers. It involves delivering targeted messages and promotions through mobile ...

Measuring the Switch Cost of Smartphone Use While Walking

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