Research in Gerontological Nursing

State of the Science Review 

Technology for Long-Term Care

Sunghee H. Tak, PhD, RN; Lazelle E. Benefield, PhD, RN, FAAN; Diane Feeney Mahoney, PhD, APRN, BC, FAAN


Severe staff shortages in long-term care (LTC) make it difficult to meet the demands of the growing aging population. Further, technology-savvy Baby Boomers are expected to reshape the current institutional environments toward gaining more freedom and control in their care and lives. Voices from business, academia, research, advocacy organizations, and government bodies suggest that innovative technological approaches are the linchpin that may prepare society to cope with these projected demands. In this article, we review the current state of aging-related technology, identify potential areas for efficacy testing on improving the quality of life of LTC residents in future research, and discuss barriers to implementation of LTC technology. Finally, we present a vision of future technology use that could transform current care practices.


Severe staff shortages in long-term care (LTC) make it difficult to meet the demands of the growing aging population. Further, technology-savvy Baby Boomers are expected to reshape the current institutional environments toward gaining more freedom and control in their care and lives. Voices from business, academia, research, advocacy organizations, and government bodies suggest that innovative technological approaches are the linchpin that may prepare society to cope with these projected demands. In this article, we review the current state of aging-related technology, identify potential areas for efficacy testing on improving the quality of life of LTC residents in future research, and discuss barriers to implementation of LTC technology. Finally, we present a vision of future technology use that could transform current care practices.

By 2030, almost one in five Americans will be age 65 or older (National Institute on Aging [NIA], 2006), straining both the long-term care (LTC) system and the caregiving capacity of most American families. Nearly 60% of families will provide caregiving assistance (National Family Caregivers Association, 2000), and with one in three households caring for a family member with cognitive impairment (Gaudet, 2005), needs for formal LTC will increase dramatically. Most (80% to 90%) nursing home residents have both physical and cognitive impairments (Centers for Medicare & Medicaid Services, 2004; Maslow & Ory, 2001; Teresi, Morris, Mattis, Mattis, & Reisberg, 2000), and nursing home costs, having grown from $28 billion in 1983 to more than $200 billion in 2005 (Institute of Medicine, 1986; U.S. Department of Health and Human Services, National Clearinghouse for Long-Term Care Information, 2009), will continue to escalate.

The LTC industry is facing unprecedented challenges to address resident acuity, while providing assistance in daily activities and personal care. Amid a nationwide nursing shortage (Freedman, Calkins, DeRosiers, & Van Haitsma, 2005) and low wages associated with nursing assistant positions (Hospital and Healthcare Compensation Service, 2002), approximately 400,000 fewer nurses will provide care by 2020 (National Center for Health Workforce Analysis, 2002).

To address these challenges, new approaches to services for older adults and institutional LTC are required. Service providers, government policy makers, manufacturers, and researchers assert that technology-based interventions have the potential to control these pressures, revolutionize the care of older adults, and improve the quality of life of LTC residents (Morgan, 2005). This article reviews the current state of aging-related technology, identifies areas for future research to determine efficacy to improve the quality of life of LTC residents, discusses barriers to implementing LTC technology, and presents a vision for technology use that radically reforms current care practices.

Current State of Aging-Related Technology

Government, academic research, and business efforts to deploy technology in aging services are growing. Reports from U.S. government offices emphasize the essential role of technology in future health care for the aging population (Brailer, 2005; Freedman et al., 2005; Office of Technology Policy, Technology Administration, U.S. Department of Commerce, 2005). The American Health Information Management Association (2008) suggested that adopting health information technology will facilitate coordination, improve quality of care, and enhance efficiency in LTC and across the health care delivery continuum. Private initiatives, including the Center for Aging Services Technologies (CAST), developed under the auspices of the American Association of Homes and Services for the Aging, actively promote the role of technology in supporting older adult care via professional and family care networks (CAST, 2003, 2005). The U.S. Department of Veterans Affairs (VA), the nation’s largest network of aging-related services, uses technology-based home monitoring interventions for its older veteran population (Linkous, 2005).

Universities nationwide are organizing programs of study and research in gerontology and technology. The NIA established Edward R. Roybal Centers for Research on Applied Gerontology to facilitate translation of basic science into practical outcomes, including new technologies to benefit older adults. Rehabilitation Engineering Research Centers (RERCs) exist at 22 universities, with approximately 25% addressing technology and aging research in the concept-for-test, prototype, and development phases. Table 1 presents selected research programs in U.S.-based universities.

Selected Research Programs in U.S.-Based University Research Centers

Table 1: Selected Research Programs in U.S.-Based University Research Centers

Technology companies (e.g., Intel®, Microsoft®) investigate a broad range of technology products and solutions to address cognitive and functional disabilities. Although specialized retail outlets and catalogs offer some products for the older population, most technologies have not been customized or widely tested for their efficacy with older adults.

Areas for Future Research

Most current and emerging technologies for the older population are in the concept-for-test, prototype, and development phases. There is a striking lack of evidence about the efficacy of the technologies in general, and in particular, on improving the quality of life of LTC residents. Table 2 identifies the areas for future research in which current and emerging technologies should be tested for their effects on improving LTC residents’ quality of life.

Areas for Efficacy Testing of Potential Long-Term Care Technology and ExamplesAreas for Efficacy Testing of Potential Long-Term Care Technology and ExamplesAreas for Efficacy Testing of Potential Long-Term Care Technology and Examples

Table 2: Areas for Efficacy Testing of Potential Long-Term Care Technology and Examples

The current and emerging technologies have primarily been used for older adults in community or acute care settings. Thus, little is known about the potential benefits in residential care settings. Technologies relevant for LTC may include products and services that enable older adults to perform tasks or functions related to activities of daily living, promote a sense of control and dignity, and improve the quality of life in an institutionalized setting. The technology products can be worn (“on”), embedded (“in”), and placed in the person’s environment (“around”) (Barrett, 2002). Optimally, they are unobtrusive, preventive, personalized, and remote. All three kinds of products may connect to each other and work across an expanded health care network.

LTC-related technology should support daily life activities and enrich resident quality of life, addressing safety (e.g., falls, wandering), self-care activities (e.g., bathing, taking medication, eating, mobility, sleeping), communication (e.g., social interaction and connection), and entertainment (e.g., recreation, leisure). Ideally, technology products and services should support facility staff and family members (e.g., electronic medical records and recording devices, distance connectivity to enhance family caregiving).

Future research to guide the development and use of technology must include assessing while helping, adapting assistance to variability in cognitive and functional abilities, catalyzing instead of replacing social interactions, and using familiar interfaces (Morris & Lundell, 2003). In addition, it is important to examine the efficacy of the technologies in assisting residents and nursing home staff related to safety and monitoring, management of everyday activities, cognitive stimulation, and social connectedness (Dishman, 2004; McClendon, Bass, Brennan, & McCarthy, 1998; Morris & Lundell, 2003; Mynatt, Rowan, Craighill, & Jacobs, 2001; Rowe, Lane, & Phipps, 2007; Tak & Beck, 2005).

One of the greatest potential benefits of current and emerging technologies would be the possibility to provide a new person-centered environment in LTC settings (Morgan, 2003). Person-centered LTC technology allows high-tech options where residents are primary consumers of products used to improve their function and quality of life. High-tech approaches undertake calm technology, which recedes into the background of residents’ lives (Weiser, 1996). Some high-tech options may include wireless broadband access, biosensors, activity sensors, information fusion systems, ambient displays and actuator networks, and remote community and collaboration (Dishman, Matthews, & Dunbar-Jacob, 2004).

In the person-centered nursing home of the future, ubiquitous computing and embedded technology may assist in the environment by integrating computer technology into the physical structure and architecture, furniture, and social environment surrounding the resident. Infrared- and radio frequency-based elopement alarm systems can monitor many doors, elevators, and outdoor areas, accompanied by tracking systems that enable staff to locate residents who have left the facility (e.g., the Versus™ Information System, Computer- and communication-based Internet technology can address residents’ psychosocial needs by connecting them to their families, friends, and communities (“Care for People with Dementia,” 2004; Intel®, 2007; Mynatt et al., 2001). Two-way video connections adapted for the older adult’s level of physical and cognitive ability can provide social and cognitive stimulation through communication with family and friends in other locations, Internet chat sites, and access to news articles or information on topics of interest.

Intelligent assistive technology, such as activity cueing, automatic reminders, and televideo monitoring will assist in wellness checking, provide information and decision support, and assess changes in health or functional status (Brennan, Moore, & Smyth, 1995; “Care for People with Dementia,” 2004; Czaja & Rubert, 2002; Czaja, Sharit, Charness, Fisk, & Rogers, 2001; Morris & Lundell, 2003). Use of family portraits, ambient displays, and customized two-way video and computers offer methods to connect with others through use of familiar devices (Caregiver Technologies, n.d.; Dishman et al., 2004; Mahoney, Tarlow, Jones, Tennstedt, & Kasten, 2001; Mankoff et al., 2003; Morris & Lundell, 2003; Mynatt et al., 2001).

However, as stated above, the efficacy of these technologies in LTC is not known because these technologies have only recently been developed or are still in development. Thus, this article focuses on identifying areas for research, providing detailed examples of potential technology use in LTC, and proposing the efficacy testing of the technologies in future research. In future research, researchers need to not only test the efficacy of the technologies but also examine potential detriments to the introduction and use of technologies in LTC. Further, it is critical that future research explores residents’ perception about the high-tech LTC environment and examines “high-touch,” humanistic care from nursing staff in LTC.

Barriers to Implementation of LTC Technologies

Major barriers to identifying and implementing technology in LTC settings include awareness; access; acceptance and adoption; and lack of regulatory standards, reimbursement, and evaluation processes (Freedman et al., 2005; Office of Technology Policy, Technology Administration, U.S. Department of Commerce, 2005). First, older adults, their family caregivers, and LTC providers are often unaware of the availability of emerging LTC-related technology products and lack information about where to find these technologies. Table 3 provides related resource websites for technologies in aging and includes advocacy and interest groups that promote awareness and knowledge of emerging technologies for older adults.

Related Resource Websites for Technologies in Aging

Table 3: Related Resource Websites for Technologies in Aging

Second, making technology available for older adults in LTC with different cognitive, perceptual, and physical abilities is challenging. Universal design in technology products has been emphasized to improve their accessibility. Third, LTC industries may not see the importance of technology or be ready to accept and adopt technologies in delivering care. Factors that affect acceptance of LTC technologies include usefulness and usability, efficiency of care delivery, cost effectiveness, and improvement of resident quality of life. LTC providers are concerned about the applicability of the technology to their situations and settings, the stability of the manufacturer, the cost effectiveness of the technology, and whether it will integrate and operate with other technologies (i.e., interoperability). They also may lack experience implementing and managing technological changes. Further, restrictions of financial and human resources can prevent them from purchasing and implementing residential LTC technologies. Finally, failure of the regulatory system to keep pace with technological advances stands in the way of implementing such technologies. Regulatory agencies lack experience in evaluating technological applications in LTC. Currently, few regulatory standards and reimbursement policies are associated with LTC technology.

A report by Freedman et al. (2005) suggests that the educational and exploratory strategies to address the barriers require improving knowledge and awareness about technologies in residential LTC settings, developing and implementing cost-effective technological innovations in these settings, reducing regulatory barriers to innovation, developing industry standards for technologies salient to the LTC setting, and educating LTC providers about implementing and managing technological change. In regards to interoperability issues, the Continua® Health Alliance (2009) recently issued its first set of industry standards that technology developers must meet to place the Continua Health Alliance seal of approval on their products, thereby assuring purchasers of compatibility.

A Preferred Future and New Vision

What is the future of technology use in nursing homes? Given our review of the state of the art in current technologies and prototypes, we suggest these developments are becoming near-term options. A plethora of opportunities exist to integrate a wide range of new technologies into LTC within a decade. At present, a handful of nursing homes are serving as test sites for specific technology interventions. In the future, nursing homes will not be limited to retrofitting single-focus technology into their facilities for testing but will integrate multiple technologies incorporated into new buildings or through major renovations (Mahoney, Mahoney, & Liss, 2009).

Why will such development efforts be initiated? The reasons are primarily to entice prospective residents to choose one facility over another, to survive competition from the increasing number of LTC housing options, to maintain financial viability amid regulatory compliance, and to reduce liability given the escalating shortage of nursing staff. Technology provides the solution to not only address labor, regulatory, and competition issues but also improve quality of care for residents. Savvy consumers will be comparing facilities’ technologies, thereby generating a competitive drive among facility administrators to upgrade their services.

Can facilities afford new technology? Used prudently, technologies offer the possibility of performing more services with less manpower and lower administrative costs. While we are not suggesting technology can substitute for hands-on care, we do imply that hands that previously completed paperwork and performed non-skilled tasks can be re-deployed to direct care. Efficiencies gained in better organizational activities and deployment of skilled workers will translate into cost savings. In addition, because technologies generally rapidly drop in price every 18 months, over a short period of time, they become affordable and thereby increase access.

How can staff resistance to technology be overcome? Technology must serve a useful purpose, productively relieve staff from non-essential tasks, and have a strong potential to increase staff satisfaction and morale. As organizational change theory predicts, not only will there be staff wedded to tradition but also champions who must be found, cultivated, and supported to direct the change process in a positive manner. We need these champions to provide input to the technology design team so products are chosen that can fit and be molded to the facility’s needs. Prior research suggests the key agents to change practices in nursing homes are the administrator who legitimizes and advocates the change with a director of nursing who directs the process through nurse champions who implement it on each shift (Mahoney, 1995). This team should be involved in the technology planning, design, and usability testing for their organization.

Federal and state regulations drive LTC practices as the governmental payer and aims to ensure resident safety and financial accountability. While these aims appear logical and simple, trying to achieve them in an industry with wide variability in residential administrative practices has been elusive. In our envisioned future, these aims will be reached through technology. Governmental regulations will favor those who adopt and use electronic records in LTC settings. Nursing home surveyors will no longer have to visit every home to ensure quality but will receive sentinel alert reports from systems that automatically identify facility criteria lapses or unrectified deficiencies. Nursing home administrators will receive notification ahead of time to address deficiencies before they are posted publicly. Staff time and efforts will be recorded via signals from their badges to the nursing home’s computer, thereby eliminating narrative documentation. Smart appliances will send reports on their temperature and functioning for environmental and kitchen safety. Overall, the embedded technology in the everyday environment will serve as the recorder and documenter of many of today’s tasks. Most important, technology will uniquely add a critical missing quality oversight piece—“real time” monitoring with proactive sentinel alerts to negative patterns within and across facilities—that is unfeasible today.

Once “unfrozen” from current negative stereotypes of institutional warehousing of residents who submit to daily schedules based on institutional routines, with staff focused on paperwork and administrative regulations, what would daily life be like? Foremost, residents would drive care through their needs, wants, and preferences. Technology would enable individualized care tailored to the resident as shown in the following example.

Mr. Jones likes to watch late-night television until 2:00 a.m. and then sleep until 10:00 a.m. Bed sensors indicate two episodes of urinary leakage that trigger an automated disposable bed pad change that leaves him undisturbed, asleep, and dry. Upon arising, he prefers to shave first, then have breakfast, take his pills, read the paper, wash, and get dressed. Robere is there to help him with these tasks. Mr. Jones makes a video visit with his daughter at noon and changes the channel to start his personalized therapeutic exercise program to strengthen his muscle tone. Robere accompanies him when he ambulates as directed on the walking path and guides him back to his room. Mr. Jones has his lunch at 3:00 p.m. He plays games with Robere and takes his pills from him at 5:00 p.m. His nurse practitioner checks in with him on the video screen and reviews his vital signs and metabolic readings. She notes from the sensor reports that Mr. Jones’ walking pattern and distance have deteriorated, and he reports his hip pain is worsening. She adjusts his medications to decrease the discomfort from arthritis, and the changes are automatically sent to the dispensing pharmacy, Robere, and the resident record. Mr. Jones prefers to eat late, sometimes after 8:00 p.m., which is no problem because Robere can prepare dinner whenever requested. Robere also helps Mr. Jones to get ready for bed around 11:00 p.m. and offers the new arthritis medication, so it will be effective by bedtime to reduce nighttime awakenings due to joint discomfort and hip pain.

Robere works 24 hours per day, 7 days per week; is culturally competent; can communicate in any resident’s preferred language; and “knows” the resident’s usual patterns of behaviors and personal care preferences. How can that be? Robere is a robotic aide. In fact, one robot designed to help nurses lift and move patients is called RONA (robotic nursing assistant) and is currently in prototype development (Hstar Technologies), while another is in the battlefield helping remove wounded soldiers in the field under enemy fire (Vecna Robotics®, n.d.). In our futuristic scenario, we will name it RoBear, a miniaturized personal robot aide, always ready, never tired or off duty with affective computing ability. That is, it can respond to emotional expressions and convey features on its face and in its intonations that range from happiness to sympathy. RoBear’s eyes also transmit video, its belly is a screen that displays two-way communication, and its hands sense and transmit physiological reports. Residents can now decrease their social isolation by having virtual visits with their friends and family any time, any day in between onsite visits via RoBear or their two-way communication screen in their room. This video can also transmit health care data and send images to health care providers. RoBear integrates and processes all of the signals sent per assigned resident and emits sentinel alerts.

RoBears will be the around-the-clock companions of the most disturbed residents with Alzheimer’s disease, learning their patterns of behaviors, what triggers their agitation, and adjusting the environment to become a soothing place for them rather than a source of stress. For those who are physically impaired, RoBears will safely lift and transport them via their embedded seat. If the person is completely dependent and bedridden, RoBears will gently lift and move residents every 2 hours, or more frequently if desired, thereby reducing the incidence of pressure ulcers. For residents who are quadriplegic, RoBears will respond to voice commands and provide a new means of independence and preservation of personal dignity. They will also take diagnostic x-rays and transmit digital pictures of skin changes or wounds to specialists, thereby decreasing uncomfortable, disorienting, and costly ambulance rides. No longer will it be problematic to have physicians respond to telephone calls from a nursing home or make an onsite visit. Virtual visits will be commonplace, with all data posted simultaneously to authorized information accounts. Research has documented that frail spouses often travel daily to maintain vigilance over their institutionalized loved ones with Alzheimer’s disease, resulting in high caregiver stress (Mahoney, 2003; Mahoney et al., 2003). As authorized account holders, spouses will gain access to daily updates and maintain interactive video vigilance without risking travel and falls in adverse weather conditions. While the practical aspects of building such holistic robotic assistance remains quite formidable, research to date has demonstrated that remote monitoring of older adults is acceptable and reduces caregiver anxiety when they cannot be onsite (Mahoney, 2004; Mahoney, Mutschler, Tarlow, & Liss, 2008).

What will nursing staff be doing if they are not charting and administering medications? In our future world, they will specialize in overseeing and ensuring residents’ quality of care. Nurses will provide the high-touch, humanistic aspect of care to offset the possibility of a high-tech atmosphere without any warmth or sensitivity. They will have the time and the charge to make “touch” rounds where they will hold a resident’s hands or give a massage while they listen to their concerns and assess the match between their needs and the current plan of care. They will easily reprogram the technology or adapt it from the bedside to fit the resident’s needs and wants. Nurses will direct the use of technologies to enhance communication among residents, families, and providers. On the basis of their assessment and review of video logs, nurses will prescribe the diversionary and therapeutic activity technologies best suited for residents with cognitive impairment who experience sundowning, continually seek exit, or exhibit acting out behaviors related to Alzheimer’s disease. Nurses will assess the sentinel alerts for reliability and validity and initiate remedial actions when warranted. They will leverage their professional assessment and management skills, making this position more attractive to recruit new nurses. And, because of RoBear’s lifting abilities, back injuries will be eliminated among nursing staff, thereby helping not only retain older staff but also reduce workers’ compensation claims.

Overall, technology—if well designed and interoperable—has the potential to be a powerful integrator of health information across all sectors of the health care industry (Mahoney, 2000, 2008). It can address the notorious gaps in medical record information, medications, therapeutics, and discharge planning that occur for residents who become patients in acute care and then cycle through rehabilitation, LTC, and around again. It offers the potential means to transform inefficient patterns of care and documentation into support for professional caregivers and providers to improve the quality of care, tailor it to an individual’s needs, improve consumer satisfaction, and reduce health care costs.

To reach this potential, users and purchasers of technology need to demand products that fit their needs and provide practical solutions that integrate seamlessly into the environment without disrupting normal activities. There remains a critical and immediate need to study technology prototypes to discern potential problems in design, usability, and functioning in real-world settings. Evaluation of new technologies does not match expectations for traditional research grant funding from the National Institutes of Health (Alwan & Mahoney, 2006). Yet, commercial sponsorship of evaluative studies can create a financial bias to report positive findings. Confirmatory research by non-affiliated external evaluators, such as academic researchers, is needed. After a given technology has matured and been deployed enough to enable large-scale comparative studies, future research to examine its efficacy, including cost and quality of life outcomes, is warranted.


The preferred future for older adult-centered care in LTC expands the current model of computer technology and other emerging technologies from a conscious presence to becoming embedded in the environment to assist in daily life and enrichment activities. If designed and implemented appropriately, technology can be an important instrument to critically improve the quality of care for the older adult population.


  • Alwan, M. & Mahoney, D. (2007). Position paper on federal government grants for aging services technology research: Problems and recommendations. Retrieved from the Center for Aging Services Technologies website:
  • American Health Information Management Association. (2008). A road map for health IT in long term care 2008–2010. Retrieved from the Center for Aging Services Technologies website:
  • Barrett, M.J., Holmes, B.J. & McAulay, S.E. (with ). (2002, December17). Healthcare unbound. Retrieved from the Forrester website:,1317,15452,00.htm
  • Brailer, D. (2005). Remarks by David Brailer, MD PhD, National Coordinator for Health Information Technology, HIMSS. Retrieved from the Center for Aging Services Technologies website:
  • Brennan, P.F., Moore, S.M. & Smyth, K.A. (1995). The effects of a special computer network on caregivers of persons with Alzheimer’s disease. Nursing Research, 44, 166–172. doi:10.1097/00006199-199505000-00007 [CrossRef]
  • Caregiver Technologies. (n.d.). AttentiveCare. Retrieved from
  • Center for Aging Services Technologies. (2003). Progress and possibilities: State of technology and aging services 2003. Retrieved from
  • Center for Aging Services Technologies. (2005). Leading change: An opportunity to transform healthcare services. Retrieved from
  • Centers for Medicare & Medicaid Services. (2004). MDS quality indicator report. Retrieved from
  • Continua® Health Alliance. (2009). The Continua version one design guidelines. Retrieved from
  • Czaja, S.J. & Rubert, M.P. (2002). Telecommunications technology as an aid to family caregivers of persons with dementia. Psychosomatic Medicine, 64, 469–476.
  • Czaja, S.J., Sharit, J., Charness, N., Fisk, A.D. & Rogers, W. (2001). The Center for Research and Education on Aging and Technology Enhancement (CREATE): A program to enhance technology for older adults. Gerontechnology, 1, 50–59. doi:10.4017/gt.2001. [CrossRef]
  • Dishman, E. Chair. . (2004, July17). Care for people with dementia. Perspectives from technology: A research planning workshop for ETAC. Workshop conducted at the 9th International Conference on Alzheimer’s Disease. , Philadelphia. .
  • Dishman, E. (2004). Written testimony of Eric Dishman, Director and Senior Research Scientist, Proactive Health Research, Intel Corporation, Chair, Center for Aging Services Technologies (CAST), Presented to U.S. Senate Special Committee on Aging hearing on assistive technology for aging populations. Retrieved from the Center for Aging Services Technologies website:
  • Dishman, E., Matthews, J. & Dunbar-Jacob, J. (2004). Everyday health: Technology for adaptive aging. In Pew, R. & Van Hemel, S. (Eds.), Technology for adaptive aging (pp. 179–208). Retrieved from the National Academies Press website:
  • Freedman, V.A., Calkins, M., DeRosiers, R. & Van Haitsma, K. (2005). Barriers to implementing technology in residential long-term care settings: Executive summary. Retrieved from the U.S. Department of Health and Human Services website:
  • Gaudet, L. (2005). Advancing health and health care for the ageing through technology. Retrieved from the Center for Aging Services Technologies website:
  • Hospital and Healthcare Compensation Service. (2002). HCS reports: 2002–2003 salary and benefits reports for nursing home, assisted living, and continuing care retirement communities. Oakland, NJ: Author.
  • Institute of Medicine. (1986). Toward a national strategy for long-term care of the elderly: A study plan. Retrieved from the National Academies Press website:
  • Intel®. (2007). Intel’s proactive health lab. Retrieved from
  • Linkous, J.D. (2005). The American Telemedicine Association testimony before the House Committee on Veterans Affairs, Health Subcommittee. Retrieved from the House Committee on Veterans’ Affairs website:
  • Mahoney, D.F. (1995). Analysis of restraint-free nursing homes. Image, 27, 155–160.
  • Mahoney, D.F. (2003). Vigilance: Evolution and definition for caregivers of family members with Alzheimer’s disease. Journal of Gerontological Nursing, 29(8), 24–30.
  • Mahoney, D.F. (2004). Linking home care and the workplace through innovative wireless technology: The Worker Interactive Networking (WIN) project. Home Health Care Management & Practice, 16, 417–428. doi:10.1177/1084822304264616 [CrossRef]
  • Mahoney, D.F. (2008). Technologies for informal caregivers. Gerontechnology, 7, 347–348. doi:10.4017/gt.2008. doi:10.4017/gt.2008. [CrossRef]
  • Mahoney, D.F., Jones, R.N., Coon, D.W., Mendelsohn, A.B., Gitlin, L.N. & Ory, M. (2003). The Caregiver Vigilance Scale: Application and validation in the Resources for Enhancing Alzheimer’s Caregiver Health (REACH) project. American Journal of Alzheimer’s Disease and Other Dementias, 18, 39–48. doi:10.1177/153331750301800110 [CrossRef]
  • Mahoney, D.F., Mahoney, E.L. & Liss, E. (2009). AT EASE: Automated technology for elder assessment, safety, and environmental monitoring. Gerontechnology, 8, 11–25. doi:10.4017/gt.2009. [CrossRef]
  • Mahoney, D.M. (2000). Developing technology applications for intervention research: A case study. Computers in Nursing, 18, 260–264.
  • Mahoney, D.M., Mutschler, P.H., Tarlow, B. & Liss, E. (2008). Real world implementation lessons and outcomes from the Worker Interactive Networking (WIN) project: Workplace-based online caregiver support and remote monitoring of elders at home. Telemedicine Journal and e-Health, 14, 224–234. doi:10.1089/tmj.2007.0046 [CrossRef]
  • Mahoney, D.M., Tarlow, B., Jones, R.N., Tennstedt, S. & Kasten, L. (2001). Factors affecting the use of a telephone-based intervention for caregivers of people with Alzheimer’s disease. Journal of Telemedicine and Telecare, 7, 139–148. doi:10.1258/1357633011936291 [CrossRef]
  • Mankoff, J., Dey, A., Hsieh, G., Kientz, J., Lederer, S. & Ames, M. (2003, April). Heuristic evaluation of ambient displays. Paper presented at the CHI 2003 Conference on Human Factors in Computing Systems. , Fort Lauderdale, FL. .
  • Maslow, K. & Ory, M. (2001). Review of a decade of dementia special care unit research: Lessons learned and future directions. Alzheimer’s Care Today, 2(3), 10–16.
  • McClendon, M.J., Bass, D.M., Brennan, P.F. & McCarthy, C. (1998). A computer network for Alzheimer’s caregivers and use of support group services. Journal of Mental Health & Aging, 4, 403–420.
  • Morgan, R. (2003). Computer-based technology and caregiving for older adults: Exploring the range of possibilities and beyond. Public Policy and Aging Report, 14(1), 1–32.
  • Morgan, R.E. Jr.. (2005). Technology greets the age wave [Review of the books Gerotechnology: Research and practice in technology and aging, by Burdick, D.C. & Kwon, S. (Eds.); Impact of technology on successful aging, by Charness, N. & Warner Schaie, K. (Eds.); & Technology for adaptive aging, by Pew, R.W. & Van Hemel, S.B. (Eds.)]. The Gerontologist, 45, 704–710.
  • Morris, M. & Lundell, J. (2003). Ubiquitous computing for cognitive decline: Findings from Intel’s proactive health research. Retrieved from the Alzheimer’s Association website:
  • Mynatt, E.D., Rowan, J., Craighill, S. & Jacobs, A. (2001, March–April). Digital family portraits: Providing peace of mind for extended family members. Paper presented at the CHI 2001 Conference on Human Factors in Computing Systems. , Seattle, WA. .
  • National Center for Health Workforce Analysis. (2002). Projected supply, demand, and shortages of registered nurses: 2000–2020. Retrieved from the American Health Care Association website:
  • National Family Caregivers Association. (2000). Facts about family caregivers. Retrieved from the ALS Association website:
  • National Institute on Aging. (2006). Dramatic changes in U.S. aging highlighted in new census, NIH report. Retrieved from
  • Office of Technology Policy, Technology Administration, U.S. Department of Commerce. (2005). Technology and innovation in an emerging senior/boomer marketplace. Retrieved from the Interagency Committee on Disability Research website:
  • Rowe, M., Lane, S. & Phipps, C. (2007). CareWatch: A home monitoring system for use in homes of persons with cognitive impairment. Topics in Geriatric Rehabilitation, 23, 3–8.
  • Tak, S. & Beck, C. (2005). Computer-assisted stimulating activities for persons with dementia. Proceedings of the 5th International Gerontechnology Conference. , Nagoya, Japan [CD-ROM]. .
  • Teresi, A., Morris, J., Mattis, S., Mattis, B. & Reisberg, B. (2000). Cognitive impairment among SCU and non-SCU residents in the United States: Prevalence estimates from the National Institute on Aging collaborative studies of special care units for Alzheimer’s disease. In Holmes, D., Teresi, J.A. & Ory, M. (Eds.), Research and practice in Alzheimer’s disease (Vol. 4, pp. 117–150). New York: Springer.
  • U.S. Department of Health and Human Services, National Clearinghouse for Long-Term Care Information. (2009). Paying for LTC. Retrieved from
  • Vecna Robotics®. (n.d.). The BEAR™: Battlefield extraction-assist robot. Retrieved from
  • Weiser, M. (1996). Ubiquitous computing. Retrieved from

Selected Research Programs in U.S.-Based University Research Centers

Research Center Website Research Areas
Center for Research and Education on Aging and Technology Enhancement (CREATE) at the University of Miami Research on human interaction with technology
Georgia Institute of Technology, The Aware Home Research Initiative Social communication, memory aids, and everyday home assistants
Massachusetts Institute of Technology (MIT) AgeLab Electronic toy pets for medication taking, biosensors to monitor health, safe return for wandering in Alzheimer’s disease
Oregon Center for Aging and Technology (ORCATECH) Intelligent pill box, home sensors and tracking devices, intelligent walkers and canes
University of Colorado Coleman Institute for Cognitive Disabilities Assistive technology for people with cognitive disabilities
University of Florida Mobile & Pervasive Computing Laboratory Gator-Tech Smart House program with smart technologies
University of Pittsburgh/Carnegie Mellon University Nursebot Project Memory aids, activity assistant, cognitive orthotics, and robotics
University of Rochester Center for Future Health Medication assistance and automated health assessment systems
University of Virginia Medical Automation Research Center Automation and robotic solutions for in-home monitoring and sleep monitoring systems, intelligent automated assistive walking device
University of Washington Laboratory for Assisted Cognition Environments Assisted cognition and system for human activity recognition and prediction

Areas for Efficacy Testing of Potential Long-Term Care Technology and Examples

Area Technology Examples of Potential Technology Use
Falls Gait and motion sensors, GPS devices, ambient displays, actuator networks

Sensors installed in the facility to monitor gaits and motions of residents. Example: Activity sensors are placed in a wall or within each living space in the facility. The sensors track the older adult’s gait, stability, and movement pattern and identify pattern changes that reflect an increased risk for falls. They also detect any fall and alert staff immediately for intervention.

A wireless pendant or wristband with a personal help button. Example: Pushing the button alerts staff to send help.

Wandering Wireless motion sensors and a GPS location system

Location, object, and person tracking around the facility. Example: Sensors send alert to staff if older adults with AD exit doors and wander out of facility. Alternatively, a resident at risk for wandering may wear a small necklace or carry a key chain or other device embedded with GPS. Such devices can be attached to clothing and other items to track and locate a resident who has left the facility.

Pressure ulcers Biosensors

Sensors installed on personal items. Example: Sensors are attached to a pair of socks and detect swelling in a resident’s feet and relay the change to staff.

Skin images are sent for dermatological analysis. Example: Sensor attached to a mattress monitors pressure distribution, detects any change in a resident’s skin, and alerts staff.

Wellness monitoring Biosensors, behavioral sensors, bodily diagnostics, information fusion and inference engines

Real-time, routine chemical analysis. Example: The real-time biosensors track routine blood chemistry analysis and monitor any changes. Sensors in the toilet perform chemical analysis and track any changes.

Functional and cognitive ability measurement and assessment of personal baselines and alerts to meaningful deviations. Example: Physiological and behavioral changes are monitored through sensors and assessed for depression, cognitive decline, and dementia. The data supplied by the sensors are evaluated for changes over time, ideally enabling trend analysis and assessment of residents’ physiological, cognitive, and psychosocial well-being.

Bathing Sensors, information transfer, decision guidance systems Remodeling and redesigning bath environment; monitoring and heath promotion with sensors. Examples: (a) Handrails in the bathtub not only prevent residents from a slip or fall but also check vital signs and skin status. Sensors in the tub measure the water temperature and self-regulate the inflow water temperature. (b) A sensor in a toothbrush analyzes saliva and identifies any vitamin, mineral, or enzyme deficiencies, along with a resident’s current blood sugar levels. The information is sent to staff for possible adjustment of dosages of vitamins and prescription drugs.
Eating Sensors, wireless information transfer, decision guidance systems Sensors for assessment, monitoring, and detection; information transfer and decision guidance systems for evaluation and planning. Example: The bathroom scale detects weight changes of a resident and sends the information to rehabilitation equipment, such as a treadmill, which customizes the resident’s weekly exercise program. A menu-planning program simultaneously increases or decreases the daily calories and fat in a resident’s customized daily menu plan and sends the information to the kitchen. Weight and oral intake information is sent directly to the resident’s electronic medical record.
Mobility Sensors, robotics, mechanical engineering Assisted mobility. Examples: (a) Ceiling-mounted lifts are activated by the resident pushing a wall button. They move from room to room and attach to multiple overhead track systems to lift, transfer, and transport residents with physical limitations to and from the bed, toilet, bathtub, chair, or floor. (b) An intelligent walker or autonomous robotic wheelchair with adaptive guidance may tell a resident with low vision directions to avoid any obstacles; allow the resident to power across gravel, grass, and other uneven terrain; and hold a conversation on the move.
Sleeping Wireless sensors Information on sleep behaviors Examples: (a) A mattress pad in the bed can detect the sleeping position of a resident, the minutes of nighttime sleep, the number of awakenings, the count of breaths per minute, percentage of time spent in bed, and the level of room light. The information is sent to staff for monitoring. (b) A care system for use in the homes of people with cognitive disabilities, such as AD, includes a security system control pad, a wireless receiver, motion sensors, door-opening sensors, and a bed occupancy sensor. No alarms go off after the person goes to bed. This allows caregivers to move around in the home without triggering the alarm. The system automatically activates once the client rises.
Medication Biosensors, medication dispensers with motion sensors Targeted drug delivery and effects analysis. Examples: (a) Real-time, non-intrusive biosensors track medication dosage and frequency and fluid and solid nutritional intake, which could be modified based on the analysis. Then, the effects on blood chemistry could be quickly and easily assessed. (b) The amount, frequency, and kind of medications are tracked and monitored through sensors on automated medication dispensers. Automated medication dispensers improve safety and reduce errors. These dispensers are equipped with audio and visual reminders and personal emergency response systems.
ADLs helper Artificial intelligence, robotic engineering; adaptive, distributed interfaces

Reminding and assisting in ADLs. Example:A robot aids residents with dementia by reminding them about ADLs, measuring vital signs, and obtaining items.

Personalized interactive experience for ADLs. Example:Residents use their desktop or overbed table as the screen for selecting meals, adjusting their personal schedule for the day, and connecting with others.

Cognition • Ambient displays and actuator networks • Cognitive orthosis technology Assistive cognition and mental fitness. Example:For the older adult with cognitive impairment, a digital portrait will display reminders, such as time, date, and family members’ faces and names. The automatic reminders and prompts are customized to individual specifications, using strategies to retain and stimulate cognitive reserve. The older adult can call up virtual reels from a personal library of family portraits, videos of past pictures or family events of significance, movies, documentaries, books, and plays for viewing. Adaptive cognitive orthotics. Example:An intelligent cognitive orthotic that provides adaptive reminders to residents with dementia on the basis of their evolving needs and actions.
Social connectivity and communication Wireless broadband, remote community and collaboration

Multiple modes and media for communicating across distances. Example:Wireless broadband access enables older adults to engage in contacts both within and outside the institutional environment, virtually connecting with family, friends, and colleagues around the globe.

Ways of representing and feeling “presence” at lonely times. Examples: (a) Using a digital monitor projected on the wall and voice commands or other prompts, older adults can virtually connect via videoconferencing with friends, family, and colleagues at a distance. (b) A computer face lights up when a friend or family member has checked in or is available via the Internet.

Rich and multiple streams of information delivery. Example:Older adults may choose to have health information sent to family members or other professional providers, including independently hired geriatric case managers, who advocate on their behalf.

Recreation Computer technology and virtual reality systems Desktop, laptop, handheld computers; simulated room/environment created by a virtual reality system. Examples: (a) Residents use computers to watch a large-screen display of scenery with music, create a greeting card, or play computer games for enjoyment and mental stimulation. (b) Residents with mobility limitations may “walk” around a virtual garden or bike trail created by a virtual reality system. (c) Residents use publishing software to write their life stories and publish them on the Internet.
Companionship • Robotics • Computer technology Artificial intelligence pets may help decrease feelings of isolation or depression. Example:A robotic cat that may calm agitated individuals with Alzheimer’s disease by purring at the bedside or responding with movement when held and stroked. Example:Residents use computers to visit with pastors of their churches via videoconferencing.
Resident monitoring Sensors and robotics Facility driven by technology. Example:Staff monitors residents’ medical conditions using a robot equipped with sensors and that is linked to a large screen at the nurses station.
Electronic health records Information technology Central repository for personal and professional health information; tools for easy visualization of long-term trends and care evaluation and planning. Example:Personal and longitudinal health records across settings can allow staff to assess comprehensive health information about an individual’s biobehavioral patterns and evaluate and plan for care.
Family involvement • Wireless broadband networking • Personal health informatics Mobile communication and computing devices; Internet access between family member and facilities. Examples: (a) Facilities provide family members with password-protected access to data reflecting residents’ daily routine. This allows family members to have greater involvement in their loved one’s care. (b) Family members join virtual caregiving networks, exchange information about publicly and privately provided health care services, and receive formal or informal support. Central repository for personal and professional health information; tools for easy visualization of long-term trends. Example:Personal health information is projected to designated family caregivers, independently hired personal care managers, or friends who are serving as advocates. Information is translated into a language and format understandable to laypeople. When needed, best evidence-based care protocols may be included with each projection of information.

Related Resource Websites for Technologies in Aging

Group Website Description
AARP Collaborates with numerous government, nonprofit, and for-profit organizations on a wide range of matters related to aging, including technology.
Aging and Disability Resource Centers, U.S. Administration on Aging Includes information about in-home services and nursing facility care.
Alzheimer’s Association Organizes conferences and a workgroup on technology use in Alzheimer’s disease. Provides research grants through Everyday Technologies for Alzheimer Care, a cooperative research funding initiative.
Center for Aging Services Technologies Offers a user-friendly clearinghouse for current technology products, pilot projects, research and development, and emerging technologies.
Centers for Independent Living Pathfinder for Services & Programs for Older Americans Includes a comprehensive reference manual on federal programs and legislation, as well as a source of useful information and references on topics such as assistive technology, home modification, transportation, and housing.
Gerontological Society of America, Formal Interest Group on Technology & Aging Promotes the conduct of multi- and interdisciplinary research in aging and promotes and supports research and practice of applying technology to improve quality of life of older adults.
Illinois Assistive Technology Program and Pennsylvania’s Assistive Technology Lending Library and Provide information on the availability of assistive technology services and programs for people with disabilities.
National Institute of Standards and Technology, Health Care Standards Landscape Publishes information on health information technology standards, organizations, and related references.
SPRY (Setting Priorities for Retirement Years) Foundation Nonprofit foundation for research and educational activities in the aging population. Develops consumer-oriented educational brochures and information on technology use. Carries out research and educational activities that emphasize planning and prevention-oriented strategies. Interested in enabling people to better access and understand new information by translating research findings into consumer-friendly language.
Technology for Long Term Care Provides information to professionals on available long-term care technologies.

Dr. Tak is Associate Professor, University of Arkansas for Medical Sciences, College of Nursing, Little Rock, Arkansas; Dr. Benefield is Professor and Parry Chair in Gerontological Nursing, University of Oklahoma Health Sciences Center, College of Nursing, Oklahoma City, Oklahoma; and Dr. Mahoney is Jacques Mohr Research Professor in Geriatric Nursing and Director of Gerontechnology, Massachusetts General Hospital Institute of Health Professions, School of Nursing, Boston, Massachusetts.

The authors disclose that they have no significant financial interests in any product or class of products discussed directly or indirectly in this activity. Dr. Tak and Dr. Benefield are fellows in the Building Academic Geriatric Nursing Capacity (BAGNC) program, supported by the John A. Hartford Foundation and Atlantic Philanthropies. This work was supported in part by the National Institutes of Health, National Institute on Aging, Alzheimer’s Disease Centers (grant 5 P 30 AG 19606) and Beverly Healthcare, Inc. The authors thank Cornelia Beck, PhD, RN, FAAN, for consultation.

Address correspondence to Sunghee H. Tak, PhD, RN, Associate Professor, University of Arkansas for Medical Sciences, College of Nursing, 4301 West Markham, # 529, Little Rock, AR 72205; e-mail:

Received: January 09, 2008
Accepted: June 15, 2009
Posted Online: January 27, 2010


Sign up to receive

Journal E-contents