The mobile phone ranks among the most usedmodern-day devices with both children and adults owning thesedevices. The existence of these devices has allowed people tocommunicate while on the move with text messages being the mostcommon form of communication used by mobile users. Communicatingthrough text messaging has however resulted in various problemsincluding the occurrence of road accidents which are mainly as aresult of distractions that may occur while an individual is texting.According to Stavrinos, Byington, and Schwebel (2009) individuals whooperate vehicles while texting experience an increased risk ofaccidents. However, little is known with regards to the effects oftexting while walking. This study aims at investing how the use ofmobile phones affects the walking behavior of different individuals.
Recentinnovations in the telephony industry have resulted in an increasedpopularity of the mobile phone among individuals of all ages.However, the increase in mobile phone usage has resulted in variouschanges in human behavior including reduced awareness of theimmediate environment as well as augmented cognitive distraction,therefore, resulting in reduced safety while on the road (Lamberg &Muratori, 2012). Texting or talking on the phone while walking mayalso result in errors in walking and affect an individual’s workingmemory negatively. Lopresti-Goodman, Rivera, and Dressel (2012)conducted a study on the impact of texting on people’s walkingbehaviour and explained that texting while walking resulted inincreased caution as well as reduced walking speed. However, despitebeing overcautious, text messages are often at an increased risk ofaccidents especially at busy intersections, characterised by bothmotorised and non-motorised means of transport (Schwebel et al.,2012).
People who use cell phone devices while walking through the intersection will be in more danger.
People will have an increased awareness of safety when they walk through a busy intersection.
Older people over 50 will take longer to walk through the intersection when using mobile devices
Understandinghow the use of mobile devices affects the walking behavior ofdifferent individuals will require the researcher to observe howpeople behave on both a busy and non-busy intersection in New YorkCity. The study participants will be individual using either Beekmanstreet or Ann Street. The study will consider the behavior of thedifferent participants. Some of the behaviors considered by theresearcher include, how many people look down at their phone whilecrossing the intersection, the percentage of people who look up whilecrossing, the number of individuals who trip or hit a hazard, as wellas the number of persons who almost trip over. The age of theparticipants will range from those below the 18 years to individualsabove 50 years.
Datacollection will employ diverse materials including pencils, whichwill be used in recording the different behaviors observed. Theresearcher will also use observation sheets and a timer. Using atimer in the study will allow equal allocation of time in both thebusy and non-busy intersection. The researcher will employ the directobservation technique to collect data on how cell phone use whilewalking is likely to affect the behavior of different people whilecrossing an intersection. The researcher will spend one hour at eachintersection.
Tounderstand how texting affects the behavior of people while crossingan intersection, the researcher would have to observe pedestriansfrom both a busy and a not so busy intersection. The busyintersection chosen by the researcher was Beekman Street while thenot so busy intersection was Ann Street. Both intersections arelocated in Manhattan New York City. Beekman Street is characterisedby different activities including the existence of Temple Court,which is a historic site in New York. The observation was carried outon a Tuesday from 11:30-12:30 am. During the observation, the weatherwas mostly cloudy with the temperatures quite low. However, despitethe weather, the intersection was busy due to the tenant and officeusers constantly using it to go to different areas. Low kerbsseparated the main road and pedestrian walkway. There was also acyclist lane crossing the intersection, which, resulted in anincreased conflict among the different users. The road was alsocharacterised by a high number of vehicles. However, the conflictbetween vehicular and pedestrian circulation at the intersection wasprevented using traffic lights. The intersection also has a zebracrossing.
TheAnn Street intersection is characterised by mixed residential andcommercial buildings. The street is mainly a one-way traffictherefore, is characterized by reduced vehicular activity. Theobservation was carried out on a Tuesday between 11:30-12:30 am oneweek after carrying out the observation on Beekman Street. During theobservation, the weather was partially sunny though it was stillcold. Reduced human traffic characterised the street. There were nostreet lights at the intersection. The road and pedestrian walkwaywere separated using kerbs. A unique aspect noted at the intersectionwas the garbage papers located at one end of the intersection. Duringthe observation, several individuals ran into the garbage bags withone almost falling over. The intersection did not have any streetlights. Zebra crossings characterised the intersection. However, thezebra crossings were not properly maintained as was noted due to thefaded white paint.
After one hour observation on both the busy andnon-busy street, the data collected revealed the average number ofpeople who looked up at the busy intersection was higher (M1 = 67.26,SD = 28.677) than of those who looked up at Ann Street (M2 = 47.34,SD = 27.572). An inspection of the data on the number of people whoeither look up from their phones revealed a homogeneity of varianceas evaluated by the Levine’s equality Variance test. This resultprompted the running of an independent t-test on the data collectedat the two intersections using a 95% CI of the mean difference. Thetest revealed that the results were statistically significant, t(127.803) = 4.038, p < .05. Theaverage percentage of people who walked during don’t walk signs washigher in Ann Street (M2 = 68.22, SD = 28.542) than those who crossedwhile lights were flashing at Beekman Street (M1= 51.46, SD = 25.625.We found the results to be statistically significant.t (127.842) = -3.661, p < .05
An analysis of the different ages of people whomade it across a road without looking down revealed a statisticallysignificant difference between the different age groups as calculatedusing a one-way ANOVA, F (3, 396) = 5.563,p = .001. The average number of individuals who made it across theintersection without looking at their phone was highest forindividuals aged between 18-30 years (M =1.43, SD = 1.760) and lowestamong individuals aged over 50 years (M = .46, SD = .926). A closeanalysis of the data on the number of people who looked down revealeda statistically significant difference between the various age groupsas examined using a one-way ANOVA, F (3,395) = 13.453. p = .000. Theaverage number of people who looked down at their phones whilecrossing was highest among people aged between 18-30 years (M = 1.19,SD = 1.116) and lowest among individuals aged above 50 years (M =.18, SD = .479).
The analysis of the results involved testing twoindependent variables namely Age and intersection. Participants ofdifferent ages either used the busy or not so busy intersection.There was a statistical significant interaction between age andintersection F (3) = 1.869, p = .134. At the busy intersection, ahigher number persons aged between 18-30 years (M = 1.24, SD = 1.188)looked down at their phones at the busy intersection as compared topersons aged above 50 years (M = .24, SD = .591). The number ofpeople looking down at their phones at the not so busy intersectionwas also highest among individuals aged between 18-30 years (M =1.14, SD = 1.050) and lowest among persons aged above 50 years (M =.12, SD = .328).
Themobile device has become a part of the human lives. People use thesedevices everywhere, which has resulted in several safety concerns.The use of mobile phones while walking has previously been explainedto result in an increased risk of accidents especially for users(Basch, Ethan, Raian, & Basch, 2014). However, a study on twoManhattan intersections revealed the use of mobile phones whilewalking to result in increased caution among pedestrians, especiallythose aged between 18-30 years. Caution was noted by the rate atwhich persons of different ages looked up from their phones whilecrossing the road. Individuals aged above 50 years moved slower whenusing mobile phones. These individuals also used their phones lesswhen crossing the road therefore, illustrating increased caution.
Basch, C. H., Ethan, D., Raian, S., & Basch,C. E. (2014). Technology Related Distracted Walking Behaviours inManhattan`s Most Dangerous Intersections. InjuryPrevention, 20(5).doi:10.1136/injuryprev-2013-041063
Lamberg, E. M., & Muratori, L. M. (2012). CellPhones Change the way we walk. ShortCommunication, 688-690.
Lopresti-Goodman, S. M., Rivera, A., &Dressel, C. (2012). Practicing Safe Text: the Impact of Texting onWalking Behavior. Applied CognitivePsychology, 644-648.
Schwebel, D. C., Stavrinos, D., Byington, K. W.,Davis, T., O`Neal, E. E., & De Jong, D. (2012). Distraction andpedestrian safety: How talking on the phone, texting, and listeningto music impact crossing the stree. AccidentAnalysis And Preventio, 266-271.
Stavrinos, D., Byington, K. W., & Schwebel, D.C. (2009, February). Effect of Cell Phone Distraction on PediatricPedestrian Injury Risk. PADIATRICS,23(2), e179-e185. Retrieved fromwww.pediatrics.org/cgi/doi/10.1542/ peds.2008-1382