Today’s society thrives on services of convenience. In the midst of this digital age, mobile devices and digital applications (apps) have radically transformed cultural routines and are commonplace in the daily lives of people everywhere. At the tap of one button, an individual is capable of virtually anything, whether they are transferring money, requesting services or products, sending or receiving messages, or utilizing an alternate function among the multiple ones offered on their device. These digital apps are moving society forward, and some of these apps, such as Uber and Lyft, are literally moving consumers from place to place.
Uber and Lyft
Both Uber and Lyft are apps designed to provide quick and reliable transportation services to their users. Unlike taxi services, Uber and Lyft are more accommodating to the consumers; users are granted with the power of choosing their driver and vehicle, given more information regarding the estimated arrival time to their destination, and provided with relatively accurate predictions of the potential costs of their trips (“Uber vs Lyft”, 2017). The process of acquiring access to each app is relatively simple; the consumer must download the free app onto any of their mobile devices and create an account. When creating the account, the user will input their payment information, and this allows the financial transactions for their future rides to remain completely paperless. Both Uber and Lyft locate the current location of the user, identify the nearest drivers in their surrounding area, and offer the estimated arrival time of the driver to the potential rider’s location. The apps provide additional information regarding the driver’s competence by displaying a customer satisfaction rating that ranges from zero to five, a picture of the driver as well as the make and model of their vehicle, and a map with the Global Positioning System (GPS) coordinates of the drivers. The rider is free to examine their available options and choose the driver and vehicle that they believe will best cater to their needs (“Uber vs Lyft”, 2017).
Technological Need in Older Adult Cohort
Despite the prevailing stereotypes surrounding the older adult cohort’s use of technology, this population could benefit immensely from the adoption and incorporation of these transportation apps into their daily schedules. The biological normative age-graded influences associated with aging could diminish an older adult’s ability to function a vehicle safely and properly (Burzynska, 2017). For example, joint conditions, including osteoporosis, osteoarthritis, and rheumatoid arthritis, all contribute to increasing amounts of stiffness, pain, and mobility issues that could greatly inhibit an individual’s ability to operate a vehicle. Also, the older adult cohort generally struggles with structural vision problems that negatively alter vision, such as Presbyopia, Cataracts, and Glaucoma, which can potentially lead to blindness. Psychological normative age-graded influences could also play a role in the lessening of an older adult’s ability to drive because of decreased sense of self-confidence due to the internalization of negative stereotype threats and degenerative conditions that impair cognitive functioning (Burzynska, 2017).
Limitations for Technology Use in Older Adult Cohort
Although Uber and Lyft could positively influence the lives of individuals in the older adult age-group, some of them may have insufficient knowledge about the technology, possess inadequate physical capabilities, or lack desire to use the services. For example, the sociocultural normative history-graded influences in the older adult cohort may be the cause for the lack of emphasis and reluctance to the utilize the services offered through the apps (Burzynska, 2017). In addition, the biological normative age-graded influences may be impacting older adults’ physical abilities to access the services provided by Uber and Lyft. As stated earlier, conditions associated with old age often result in the disintegration of mobility and functionality, and these disorders aid in providing a possible explanation as to why the older adult cohort would not be using the transportation apps (Burzynska, 2017).
First Research Study
Driving cessation in older adults has been linked to multiple health problems, including the lessening of physical and social functioning as well as an increased risk of death (Choi, Lohman, & Mezuk, 2014). A research study with a longitudinal mixed effect design was performed to examine the relationship between driving cessation and changes in cognitive functioning. There were 9,135 participants above the age of 65 included the study without any memory disease or cognitive impairment, and the data was collected from six waves over a period of ten years. The individuals completed an interview that assessed their cognitive status, and they self-reported information regarding their driving status and health characteristics. The participants’ driving statuses, cognitive functioning, sociodemographic characteristics, and health statuses were measured and examined using chi-square tests and F-tests. The findings indicated that active drivers displayed less depressive symptoms, exhibited fewer functional limitations, and achieved higher scores on the cognitive test than the former drivers and remaining participants who had never driven before. Additionally, the results suggested that the former drivers experienced more cognitive decline in comparison to the active drivers after the 10-year follow-up. Overall, this study concluded that driving cessation is, indeed, a valid indicator of accelerated cognitive decline in the older adult cohort. This is likely due to driving cessation being linked to living in small spaces, having reduced amounts of physical and social activities, and experiencing higher levels of social isolation (Choi et al., 2014).
Second Research Study
Within the second study, researchers focused on the relationship between stereotype threat and the driving-related cognitive performance in older adults (Chapman, Sargent-Cox, Horswill, & Anstey, 2016). There were 86 participants included in the study who were 65 years of age and above. Individuals were randomly assigned to a positive or negative stereotype prime condition regarding the driving of older adults, required to complete questionnaires on their driving capabilities, and instructed to participate in a simulated driving performance and hazard perception test. To analyze the data, independent t-tests and Analysis of Variance (ANOVA) tests were applied. The results of the study suggested drivers subjected to the negative stereotype prime prior to their driving simulation displayed lower confidence levels. These findings indicated that stereotype threat can affect an individual’s emotional well-being, self-confidence and, ultimately, their performance on specific tasks (Chapman et al., 2016).
In both studies, it is apparent that the effects of aging can provide physical, psychological, and cultural hardships for older adults, often resulting in compromised cognitive performance, increased risk of mortality, and lessened physical and social health. The services provided by Uber and Lyft could allow this older adult cohort to maintain a sense of autonomy and self-sufficiency, preserve their psychological well-being through remaining mobile and socially engaged, and, ultimately, serve as a protective factor against the harmful effects associated with driving cessation. With a culture that embraces the use of digital devices and the continuing advancements in technology, it is in the best interest of older adults to adopt these instruments that can assist in minimizing the vast amount of issues that arise in old age.
Choi, M., Lohman, M.C., Mezuk, B. (2014). Trajectories of cognitive decline by driving mobility: evidence from the Health and Retirement Study. International Journal of Geriatric Psychiatry, 29, 447-453.
Burzynska, A. (2017). Lifespan Development [PowerPoint slides].
Chapman, L., Sargent-Cox, K., Horswill, M., Anstey, K.J. (2016). The Impact of Age Stereotypes on Older Adults’ Hazard Perception Performance and Deriving Confidence. Journal of Applied Gerontology, 35(6), 642-652.
Ridester. (2017, January 8). Uber vs Lyft: A Side-by-Side Comparison. Ridester.com. Retrieved April 16, 2017, from http://www.ridester.com/uber-vs-lyft/