Technology and Aging: An Evolving Partnership

Research at the intersection of technology and aging has grown dramatically over the past few years. Two trends are responsible for this growth: first, the unsustainable cost of providing care for older people with disabilities, and second, the rapid pace of technological advances in health care. Baltes (1997), in his seminal article, argued for the “fundamental incompleteness of the biological and cultural architecture of the life span” and the need for cultural/technological evolution to support successful aging in the “fourth age” of life. The 21 articles in this special issue of The Gerontologist highlight state-of-the-art conceptual and empirical work regarding the relationship between aging and technology. Each article identifies gaps in the knowledge base and suggests strategies for moving forward. Computers are an integral part of everyday life. Nearly 75% of U.S. households include a desktop or laptop, 77% of U.S. adults own a handheld device such as a smartphone, and 51% of adults have a computer tablet (Ryan & Lewis, 2015). Despite increases in technology use among older people, many lag behind. In 2015, 65% of people aged 65 and older owned a computer, 42% owned a smartphone, and 32% owned a tablet. One-third of adults aged 65 and older have never used the Internet (Andersen & Perrin, 2017). Although age is a key determinant of the digital divide, other factors contribute to digital disparities, including education, income, gender, and generational status. A systematic analysis of 55 articles published between 2006 and 2016 by Fang and colleagues (2018) highlights the significant inequities that exist with respect to who can access, use, and benefit from digital interventions. Findings highlight the unequal access to emerging information and communication technologies within and among countries. In the United States, ethnic minority status intersects with age to create disparities. Using data from the Health and Retirement Study, Mitchell, Chebli, Ruggiero, and Muramatsu (2018) highlight the digital disparities in health-related technology. They found that compared with older whites, older blacks and Hispanics, after accounting for demographic characteristics, education, and health, are less likely to use technology for health-related purposes. Peine and Neven (2018) contend that an interventionist logic in which aging is a target for technological interventions has both sparked and fueled the digital divide. The authors encourage aging and technology scholars to move beyond the interventionist logic and study how design, socio-material practices, and policy discourses can influence innovation. This perspective demands that older people be seen as a heterogeneous group of actors who use, modify, and produce technologies in varied ways, rather than as a homogeneous group of technologically incompetent laggards. In a survey of 3,917 adults, Lee and colleagues (2018) examined differences in computer attitudes as a function of age, time, and cohort over a 20-year period. They found that, with age, people have less comfort with and efficacy about using computers. They also found a cohort effect in which attitudes toward computers are more positive among recent birth cohorts. The authors suggest that these findings point to the need for product developers and marketers to identify strategies that can engender confidence among older adults so that they will be able to master and use new technologies. Postulating that rates of technology adoption are lower among older than younger adults because many technologies are not relevant for older people, Mitzner and colleagues (2018) examined adoption of PRISM (Personal Reminder Information & Social Management), a computer system specifically designed for older users. They found that early use, executive function, and gender (women) predicted mid-term adoption of technology and that mid-term adoption and computer efficacy predicted long-term adoption. These findings provide insight for models of technology acceptance that can guide design, instruction, and deployment of other technology products intended for older people. Providing access and support for using a technology, especially in the early weeks of its deployment, may be the most important steps to take in order to facilitate technology adoption. A systematic literature review of 26 studies by Merkel and Kucharski (2018) revealed the importance of integrating older users into all phases of the innovation process. To increase market penetration and encourage older people to adopt, implement, and use technology, designers and developers need to focus more on user characteristics, needs, and preferences. A promising way to address this issue is to integrate users into the innovation process following a participatory approach. The methods for integrating users into the innovation process depend on the context. The authors suggest that participatory design should follow a comprehensive strategy, starting with the users’ needs and ending with an evaluation to determine the effectiveness of the technology. The authors conclude that partnerships among researchers, designers, developers, and older people, throughout the whole innovation process, are critical. Grates, Heming, Vukoman, Schabasky, and Sorgalla (2018) warn that although user participation has gained importance, participants in research studies are frequently not representative of the diversity of potential user groups. Consequently, designers often overlook the needs of the target group, exacerbating digital inequalities. Using the example of a digital district platform designed to provide networking, information, and support services for older people in Germany, the authors demonstrate the problems that developed when they excluded people with limited mobility, those with low socioeconomic status, people who are immigrants, and those who are very old and single. Recognizing their exclusion problem, the team of authors developed an interdisciplinary, methodical approach that complemented the initial participatory design and led to solutions that better served their heterogeneous population. While there is evidence that Internet use can support well-being, not every form of engagement affects well-being the same way. Using data from three waves of the New Zealand Health, Work, and Retirement Study, Szabo, Allen, Stephens, and Alpass (2018) tested a longitudinal mediation model focused on how loneliness and social engagement affect the relationship between Internet use and well-being. They found that using the Internet to connect with family and friends indirectly affected well-being by decreasing loneliness and increasing social engagement. Using the Internet for financial tasks or for acquiring health-related information had indirect effects on well-being through engagement in a wider range of activities, but was unrelated to loneliness. Findings from this study suggest that being online is not sufficient to benefit well-being. Rather, what is key is how people engage with the digital space. The Internet is an important source of health-related information. Nam, Han, and Gillgan (2018) postulated that the interdependence of married couples in later life would make one spouse’s Internet use affect the preventive health behaviors of the other spouse. Using data from the Health and Retirement Study, the team found that Internet use was associated with a higher likelihood of husbands’ receiving prostate exams and cholesterol tests, net of demographic and health characteristics and insurance status. Wives’ Internet use was associated with a higher likelihood of their husbands’ receiving flu shots and prostate exams. Husbands’ Internet use, however, was not associated with wives’ preventive health behaviors. A systematic review of 80 studies by Zafrani and Nimrod (2018) concludes that to reduce the social and economic impact associated with eldercare, technological solutions are critical and that robotic technology will play a significant role. The authors group assistive robots into service-type robots (designed to assist frail older people with specific activities of daily living including bathing, dressing, and eating or to assist with communication and transportation) and companion-type robots (designed to offer friendly and enjoyable interactions). The review finds that the majority of existing studies are cross-sectional, focus on community-dwelling adults, and lack theoretical underpinning. Moreover, most studies fail to consider user characteristics and the physical, social, and cultural context of the human–robot interaction. Zafrani and Nimrod highlight the importance for future studies to better examine human–robot interaction in later life. Social robots hold the potential to promote the health of older people. In a systematic review and meta-analysis of 13 randomized controlled studies, Pu, Moyle, Jones, and Todorovic (2018) found that social robots have positive impacts on agitation, anxiety, and quality of life of older people, but the meta-analysis showed that these trends were not statistically significant. Results from a narrative review indicated that social robot interactions could improve engagement, interaction, and medication use, and could decrease stress and loneliness. Conclusions, however, are limited given the lack of high-quality studies. Several articles in this special issue describe pilot studies that demonstrate the diversity of ways in which technology can address problems experienced by older people living in the community. Gaugler and colleagues (2018) evaluated the effects of remote activity monitoring (RAM) system on caregiver outcomes in families providing care for persons with Alzheimer’s disease or a related dementia. Using a mixed-method design, the investigators found the RAM system did not have statistically significant effects on caregiving outcomes over a 6-month period. Qualitative analyses, however, identified potential moderators of RAM technology effectiveness. Post hoc analyses revealed that caregivers who used RAM technology and cared for relatives with less severe cognitive impairment had statistically significant increases in competence and that people who provided care for those who had difficulty navigating around the home experienced statistically significant increases in self-efficacy. Schmidt and Wahl (2018) contrasted the performance of people with and without mild cognitive impairment (MCI) on tasks including using a blood pressure monitor, a mobile phone, and an eBook reader. They found that cognitively healthy participants outperformed those with MCI in both completion time and errors. An interaction effect of device and study group indicated larger differences in completion time for tasks with multilayered interfaces. Findings suggest that training programs may benefit from considering individual resources and limitations in the cognitive, personality-related, or emotional-motivational domains. Braley, Fritz, Van Son, and Schmitter-Edgecomb (2018) studied 15 people with dementia who completed eight instrumental activities of daily living in a smart home testbed and received a hierarchy of verbal auto-prompts (indirect, direct, multimodal). Using qualitative descriptive methods, the team reviewed transcripts and organized data into themes using content analysis. Context and communication emerged as major themes, suggesting that positive user experiences will require auto-prompting systems to account for contextual factors including level of cognitive impairment, previous exposure to task, and familiarity of environment. Findings indicate that communicating with another human rather than an automated prompting system may be important if people exhibit signs of stress while completing activities. Choi, Kasko, and Feng (2018) used driving simulation to examine associations between attentional functions of older drivers and crash risks in various hazardous situations. They used the Attention Network Test, a computerized assessment that measures efficiencies of three distinct attentional functions (alerting, orienting, and executive), and examined specific attentional functioning underlying older drivers’ crash risks. They found significant associations between executive attentional efficiency and crash risks in situations that demand a driver quickly resolve conflicts among multiple competing tasks or information. Using a mixed-methods design to explore the feasibility and acceptability of a tablet-based research consent process with older adults, Jayasinghe and colleagues (2018) found that focus group participants were interested in the novel format, but cautioned that older people would need comprehensive orientation to use the technology. Although participants took longer to complete the tablet-based consent than a paper-based version, user-friendliness, immediate comprehension, and retention of the tablet-based consent were similar to those of the paper-based consent. Life review is an effective, evidence-based treatment for depression in later life. Westerhof, Lamers, Postel, and Bohlmeijer (2018) conducted a pilot randomized controlled trial to examine the effectiveness of an online life review program. The investigators randomly assigned 58 people to one of three groups: (a) online life review supported with online counseling, (b) online life review with online peer support, or (c) waitlist control. They found that participants in all conditions improved significantly in depressive symptoms, engaged living, mastery, and vitality, but not in ego integrity and despair, social support, loneliness, and well-being. Adherence, user experience, and acceptability were better in the counselor condition than in the peer condition. Croff and colleagues (2018) report the effectiveness of a multimodal intervention—the Sharing History through Active Reminiscence and Photo-imagery (SHARP) program—on 19 older African Americans. The application takes an integrative approach, blending walking, reminiscence, and social engagement. Croff and colleagues report that technology acceptance was influenced by group learning, paper-copy replicas, ease of navigation, usefulness for integrating and engaging health behaviors, relevance to integrating individual benefit, and the community priority of preserving history amid gentrification. Perceived community benefits sustained acceptance despite intermittent technology failure. A pilot study examining the feasibility and effectiveness of nutritional telemonitoring among Dutch home care clients by van Doom-van Atten Haveman-Nies, Heery, de Vries, and de Groot (2018) revealed that participant acceptance of the technology was low, due mainly to the low usability of the telemonitoring device. Adherence to the telemonitoring measurements, on the other hand, was good, although participants needed more help than anticipated from nurses. As a result of the program, participants increased compliance to several dietary guidelines though no effects on nutritional status, physical functioning, and quality of life were observed. As person-centered care has become a goal in long-term care, the role of technology has increased. Following Netflix’ recommender system, Gannod, Abbott, Van Haitsma, Martindale, and Heppner (2018) used the Preferences for Everyday Living inventory (PELI-NH) to develop a machine learning recommender system that could identify additional experiences that may be important to nursing home residents. They found that the recommender system successfully provided guidance on how to tailor the preference items asked of residents and that the system can support preference captured in busy clinical environments. Finally, Moyle, Bramble, Jones, and Murfield (2018) explored the perceptions of family members when their institutionalized relative with dementia interacted with either a therapeutic robot (Paro the baby harp seal) or a look-alike, nonrobotic plush toy. Investigators found that family members expressed positive perceptions of Paro, perceiving that it improved mood, reduced agitation, and provided opportunity for communication for their relative. However, family members had negative perceptions of the plush toy, primarily noting the lack of movement and engagement. Together the articles in this special issue show us the state-of-the-art regarding aging and technology and point us to the future. There is much work to be done. Going forward, we must remember that the current generation of older people came of age when an “apple” was a piece of fruit and a “tablet” was something Moses delivered the Ten Commandments on, a pad of paper, or something you took when you felt ill. When people wanted information, they used an encyclopedia, not Google. While today’s voice command uses “Alexa” to change the television station, when I was a child, the voice command came from my father who said, “Rachel, change the channel.” Although we often lament older people’s inability to understand technology and to use it correctly, we need to remember that, unlike children today, Baby Boomers and people in the generations before them did not grow up knowing how to text or how to download apps. Technology has the potential to improve the lives of older people. However, for technology to be useful, gerontologists must be engaged in every step of its development. Gerontologists understand the aging process and can facilitate the experiences of older users. Engineers and marketing managers usually do not have this expertise. When technology and marketing proceed without gerontologists, a great deal of money is invested in useless technology. On the other hand, gerontologists do not have the skills to develop or design new technologies. Success will depend on evolving partnerships that include gerontologists, engineers, marketing experts, and older people working together and listening to one another.