Participants

All methods and procedures performed were carried out in accordance with the relevant guidelines and regulations and were approved by the University of Texas at Austin Institutional Review Board. Prior to participating, all participants reviewed and signed a written informed consent form approved by the University of Texas IRB. Thirty young healthy adults, age 18–29 years, participated (Table 1). All participants were screened to ensure that they had no history of orthopedic, visual or neurological impairments or medications that would have affected their ability to perform the required walking and texting tasks.

Baseline Cognitive Testing

The battery of cognitive tests consisted of the Kaufman Brief Intelligence Test – 2 (KBIT-2), PEBL Perceptual Vigilance Task (PPVT) and Berg’s Card Sorting Test (BCST). KBIT-2 was administered according to the standard protocol48. PPVT and BCST were administered on a laptop using Psychology Experiment Building Language (PEBL, Version 0.14) software45.

KBIT-2 measured Intelligence Quotient (IQ)48. The test consists of three sections: verbal knowledge, matrices, and riddles. Each section had multiple questions that required participants to respond in a word or select one of the multiple pictures presented to them. The verbal knowledge and riddles section measured verbal intelligence. The matrices portion of the test measured non-verbal intelligence.

PPVT is a simple reaction time task used to measure attention49. The test consisted of 6 practice trials followed by 25 testing trials. At the beginning of each trial, a fixation cross ‘ + ’ (ready signal) was presented for 100 msec. After that, a stimulus (red circle; Fig. 6a) was presented at a random time interval. Participants responded to each stimulus by pressing the space bar as quickly as they could. The time lag between each stimulus and its response was recorded as a reaction time and was displayed after each response.

Figure 6 Cognitive Tests. (a) PEBL Perceptual Vigilance Task (PPVT) consisted of a single stimulus that appeared at randomly-varying time intervals. Participants responded to the stimulus as quickly as they could and Reaction Time (RT) was calculated. (b) Berg’s Card Sorting Test (BCST) consisted of a card on the lower row that had to be matched to one of the four cards in the top row, based on the rules (explained in the Method section). Perseverative Error, Perseverative Response, and Failure to Maintain Set were calculated based on the performance. Full size image

BCST is a PEBL version of the Wisconsin Card Sorting Test (WCST)45 that measures different components of executive function. The test was validated by Fox, et al.46. During the test, participants were presented with one card at a time. They had to use a mouse to match the card to one of the four cards that were different from each other in terms of color, shape, and number (Fig. 6b). The card had to be matched based on one of the three rules: i.e. same color, same shape, or same number. For each trial, only one rule was correct. After each response, participants received visual feedback indicating if their match was correct or not. To match correctly, participants had to figure out the correct rule. Participants first completed 10–12 practice trials where the rule changed after every 4 correct responses. After this acclimation, participants performed the full test that included approximately 120 trials where the rule changed after every 10 correct responses.

Experimental Protocol

The walking experiments were carried out in a Motek V-Gait Virtual Reality system (Fig. 7a) that consists of a 180° semi-cylindrical visual display in front of an instrumented 1 m wide × 2 m long dual-belt treadmill (Motekforce Link, Amsterdam, Netherlands). Participant movements (kinematics) were tracked by an integrated 10-camera VICON MX motion capture system (Oxford Metrics, Inc., Oxford, UK).

Figure 7 Object Negotiation and Cell Phone Tasks. (a) Photo of a person (not a study participant) walking in the virtual environment. The virtual environment included buildings on both sides of the road. The photo shows an approaching obstacle on the left lane and the person changing lanes to avoid the obstacle. (b) Schematic representation of a typical screen image participants migh have seen while performing the cell phone task. Fish and bubbles appear at random locations and times and and move horizontally (fish) orr vertically (bubbles) across the screen. When an object (fish or bubble) is tapped by the person playing the game, this object wil disappear and a score will be recorded. The scores (separately for number of bublles and fish tapped) is shown on the top left. Total Game Score was computed as the sum of these two numbers. Full size image

Before stepping on the treadmill, 4 markers each were placed on the participant’s head, feet and pelvis, defining a 16 marker set previously established in our lab50. To prevent falls, participants wore a commercially available safety harness attached to an overhead support frame. This harness did not interfere with their normal movements. During all trials, the treadmill was set to run at a pre-determined comfortable speed (v w ) for each participant (Table 1), calculated as \({v}_{w}=\sqrt{Fr\cdot g\cdot l}\), where Fr = 0.16 is the Froude number, g = 9.81 m/s2 is gravitational acceleration, and l is the leg length in meters, as we have done previously51,52.

There were six conditions that combined three different negotiation tasks paired with either texting or no texting. The six conditions were – No Texting-No Object Negotiation (NN), No Texting-Simple Object Negotiation (NS), No Texting-Complex Object Negotiation (NC), Texting-No Object Negotiation (TN), Texting-Simple Object Negotiation (TS), and Texting-Complex Object Negotiation (TC). Participants walked under NS, NC, TS, and TC trials for approximately 3 minutes each, whereas they walked for about 1 min during NN and TN trials, as there were no objects. Each of these six different experimental conditions were performed twice. To minimize learning effects, the order of presentation of the trials was randomized for each participant, using a counter-balanced Latin square design that balanced presentation order across all participants. Participants rested at least one minute between each trial to minimize fatigue.

Individuals performed the texting task for 20 sec while standing, after stepping on the treadmill to get acclimated to the cell phone task. During non-texting trials (NN, NS, NC), participants were instructed to hold an android touch screen smart phone (Motorola, Moto G) in landscape mode near their belly button, but the phone was turned off and participants were not required to look at the phone. During the experimental texting trials (TN, TS, TC), participants held the phone in the same position and played a standardized game (“Fish Farts”, Version 1.2) (Fig. 7b) on the cell phone that mimicked a typical texting task (game sounds were disabled). This cell phone game presented to participants randomly appearing fish and bubbles that moved across the screen. To earn points, participants had to tap them. The game tracked each user’s score in terms of the number of fish and bubbles tapped. We chose this game based on several key features. First, it is played continuously (there are no “levels” to advance through and the game never ends), thus it did not interrupt trials. Second, the game required visual attention because the fish and bubbles appeared on the screen at random locations at random time intervals. Third, the game required a “texting-like” response as it required participants to actively touch the fish and bubbles to earn points. Fourth and most importantly, Fish Farts was very easy to learn and as minimally cognitively challenging as possible (i.e., ‘don’t think – just tap the fish and bubbles’).

For the walking tasks, each participant initially practiced walking on the treadmill for 10 min, during which they were acclimated to each of the six conditions. Participants walked in a virtual environment depicting a road with two lanes (0.5 m wide each, corresponding to each treadmill belt) in a city with buildings on either side of the road (Fig. 7a). Participants were instructed to walk on one lane at a time (i.e. either left or right) and to change lanes appropriately to respond to approaching obstacles and targets. For Simple Object Negotiation (NS & TS), participants were randomly presented with obstacles (red balls) only in either lane and were instructed to always avoid these red obstacles by moving laterally to the lane that did not have the obstacle. During Complex Object Negotiation (NC & TC), participants were randomly presented with either obstacles (red balls) or targets (green balls) in either lane and were instructed to always avoid obstacles and hit targets by changing lanes as needed. Thus, Complex Object Negotiation was cognitively more challenging compared to Simple Negotiation, as individuals had to make an additional decision based on the color of the ball during Complex Negotiation.

Individuals could respond to approaching objects at any time from when they first appear to when they passed them. If a participant had not made the correct response in the time allowed, it was recorded as a failure (i.e. collision). Objects reached the center of the treadmill in two seconds after they appeared. Thus, depending on individual’s exact location on the treadmill, this gave participants approximately 2 sec to respond to each object (i.e., a little less time if they were in front of the center, or a little more if they were behind it). This ~2 sec window provided participants enough time to see the objects and alter their gait accordingly53,54. Feedback was provided when each object crossed the participant. Positive auditory feedback (a pleasant sound) was provided for each successful response and a combined visual-plus-auditory negative feedback (a bright flash on the screen and a loud noise resembling a collision) were provided following each failure.

Data Collection and Processing

IQ was calculated based on the scores of the 3 sections of KBIT-2 to determine the participants skills and knowledge acquired through education and acculturation. Mean and standard deviation of IQ score was calculated across participants (Table 1).

For the PPVT test, the PEBL software calculated reaction time, defined as the amount of time taken to respond to a stimulus. Reaction times in the range of 150 and 500 msec are typically considered to be accurate responses55. Therefore, mean of reaction times between 150 to 500 msec was defined as Reaction Time (RT), which corresponds to a delay in the response. During the PPVT test, all participants were offered 25 stimuli, except one individual who was accidently offered seven stimuli. However, this did not affect further analysis.

For the BCST test, the PEBL software calculated several measures of executive function, including Perseverative Error, Perseverative Response, and Failure to Maintain Set. Failure to Maintain Set quantified failure to follow a rule after 5 correct responses for the same rule and thus indicated a measure of attention56. Perseverative Response was defined as the number of correct and incorrect response in which previous rule was followed as a percent of the total number of trials. Similarly, Perseverative Error was quantified as the number of incorrect response which would be correct for previous rule as a percent of the total number of trials. Perseverative Error and Perseverative Responses are measures of cognitive flexibility44, as they quantify how well participants are able to follow the previous rule.

During walking, kinematic data were recorded from markers placed on individuals. Raw kinematics data were processed using Vicon Nexus software. Additional, data processing and analyses were performed using Matlab (MathWorks, Inc., Natick, MA).

During the texting conditions (TN, TS and TC), the total number of fish and bubbles tapped and the duration of the trials were recorded for each trial. Game Score was calculated as the average number of fish and bubbles tapped per second across two trials. This was calculated by dividing the total number of fish and bubbles tapped across both trials by the total time for both trials. Game Score was used to determine the performance on the cell phone task, where higher Game Score corresponded better performance.

The task of avoiding an obstacle required participants to view and perceive the object, decide to avoid or hit it based on the color of the object, and to plan and execute the lateral shift from their current lane (i.e., current treadmill belt) to the other lane as needed. For each object encounter, Movement Time (MT) (in sec) was defined as the time taken to move laterally from the lane the participant was currently walking in to the other lane. Participant lateral movement was determined by the location of the center of their pelvis, which was calculated as the geometric centroid of the 4 markers placed on the pelvis. Movement time was calculated as the time between the appearance of the object and the shifting of the geometric centroid of the 4 pelvis markers to the other lane (i.e. across the midline of the treadmill). Responses were pooled across all objects encountered across both trials for each condition. Mean of Movement Time (Mean MT), Standard Deviation of MT (SD MT), and Percent Collision were calculated for each participant for each experimental condition involving objects (i.e. NS, NC, TS, TC). Percent Collision was defined as the total number of failures divided by the total number of (correct and incorrect) responses × 100%.

Data Analysis

Cell phone task performance was determined on the basis of Game Score, where higher scores corresponded better performance. There were no game scores for the conditions that did not involve texting. Therefore, we compared Game Score for the three tasks that involved texting, (i.e. TN, TS and TC) using a single-factor Analysis of variance (ANOVA) with ‘Negotiation’ (No vs. simple vs. complex) as the factor. We hypothesized that Game Score would decrease with an increase in the complexity of the object negotiation task. For all statistical analyses, comparisons were considered “statistically significant” if they reached p < 0.05.

Percent Collision measured failure to avoid an obstacle. We hypothesized that Percent Collision would be higher while texting and would also increase with an increase in the complexity of the negotiation task. To test these hypotheses, we compared Percent Collision across all of the experimental conditions that involved objects (i.e. NS, NC, TS, TC), using a 2 × 2 ANOVA with Texting and Negotiation as factors.

We compared Mean MT and SD MT across conditions to quantify the effects of both the cognitive complexity of the object negotiation task and of texting. An increase in the Mean MT corresponds to a longer delay in the response, whereas an increase in the SD MT corresponds to more variable responses. We hypothesized that the Mean MT would increase due to texting and also due to an increase in the cognitive complexity of object negotiation. To test these hypotheses, we conducted two separate 2 × 2 ANOVAs for Mean MT and SD MT with Texting and Negotiation as factors.

Cognitive test statistics included mean and standard deviation of reaction times, Failure to Maintain set, Perseverative Response and Perseverative Error. Linear correlation analyses of cognitive variables with performance on cell phone (during TN, TS and TC) and negotiation tasks (during NS, NC, TS and TC) were used to quantify the extent to which the Baseline cognitive measures could predict subsequent performance on both the texting (Game Score) and object negotiation (% Collision) tasks.