Older Adults’ Adoption of Technology-Mediated Mobility Solutions: A Review and Agenda

Figures & data

Figure 1 Review procedure using the SPAR-4-SLR protocol.

Assembling, arranging, and assessing of research in the field.

Figure 2 Publication trend of research on older adults’ adoption of technology-mediated mobility solutions.

Trend of research over the years in the field.

Table 1. Ten Most-Cited Articles on Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Author(s)	Total citations (n = 519)	Author(s)	Citations per year (n = 184.2)
Peek et al. (2019)	160	Peek et al. (2019)	80.0
Wolf and Seebauer (2014)	112	Wolf and Seebauer (2014)	56.0
Silveira et al. (2013)	66	Leger et al. (2019)	12.5
Govercin et al. (2010)	40	Rahman et al. (2019)	12.5
Seelye et al. (2012)	30	Silveira et al. (2013)	8.3
Bailey et al. (2011)	26	Govercin et al. (2010)	3.6
Leger et al. (2019)	25	Seelye et al. (2012)	3.3
Rahman et al. (2019)	25	Marschollek et al. (2014)	2.7
Marschollek et al. (2014)	19	Russell et al. (2015)	2.7
Russell et al. (2015)	16	Bailey et al. (2011)	2.6

Citations per year = Total citations ÷ [Current year (2021) – Year of publishing].

Table 2. List of Journals for Research on Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Journal title	Article(s) (n = 21)	Citation(s) (n = 597)	Scopus field (Highest quartile 2021)	Scopus quartile (2021)
Gerontology	1	160	Geriatrics and gerontology	Q1
Transportation Research Part A: Policy and Practice	2	137	Transportation	Q1
Journal of Medical Internet Research	1	66	Health informatics	Q1
Informatics for Health and Social Care	2	59	Health informatics	Q2
Telemedicine and e-Health	1	30	Health informatics	Q1
Ageing and Society	1	26	Geriatrics and gerontology	Q2
Transportation Research Part F: Traffic Psychology and Behaviour	1	25	Transportation	Q1
BMC Geriatrics	2	20	Geriatrics and gerontology	Q1
Journal of Telemedicine and Telecare	1	16	Health informatics	Q1
International Journal of Older People Nursing	1	16	Gerontology	Q2
Assistive Technology	1	11	Rehabilitation	Q3
Transportation	1	10	Transportation	Q1
JMIR mHealth and uHealth	1	5	Health informatics	Q1
Journal of Safety Research	1	5	Safety, risk, reliability and quality	Q1
Preventive Medicine Reports	1	4	Health Informatics	Q1
Universal Access in the Information Society	1	3	Information systems	Q2
Accident Analysis and Prevention	1	3	Safety, risk, reliability and quality	Q1
Traffic Injury Prevention	1	1	Safety research	Q1

Table 3. List of Theories Used in Research on Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Theory	Area	Article(s)	Source(s)
Automation Acceptance Model (AAM)	Technology	1	Rahman et al. (2019)
Dynamics in Technology Use by Seniors (DITUS) Framework	Technology	1	Peek et al. (2019)
Knowledge, Attitudes, and Practices (KAP) Theory	Behavior	1	Robertson et al. (2019)
Needs Analysis Framework	Needs	1	Seaborn et al. (2016)
Technology Acceptance Model (TAM)	Technology	4	Haghzare et al. (2021), Rahman et al. (2019), Russell et al. (2015), and Tural et al. (2020)
Theory of Planned Behavior (TPB)	Behavior	1	Rahman et al. (2019)
Transtheoretical Model (TTM)	Behavior	1	Silveira et al. (2013)
Unified Theory of Adoption and Use of Technology (UTAUT)	Technology	1	Wolf and Seebauer (2014)
Webber’s Comprehensive Mobility Framework	Mobility	1	Leger et al. (2019)
With theory		10	Haghzare et al. (2021), Leger et al. (2019), Peek et al. (2019), Rahman et al. (2019), Robertson et al. (2019), Russell et al. (2015), Seaborn et al. (2016), Silveira et al. (2013), Tural et al. (2020), and Wolf and Seebauer (2014)
Without theory		11	Bailey et al. (2011), Chung et al. (2017), Govercin et al. (2010), Isaacson et al. (2016), Marschollek et al. (2014), Nikitina et al. (2018), Oxley et al. (2019), Park et al. (2019), Peek et al. (2016), Seelye et al. (2012), and Wang et al. (2011)

Figure 3 Research design distribution for older adults’ adoption of technology-mediated mobility solutions research.

Distribution of research designs in the field.

Table 4. Research Data on Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Data type	Article(s)	Source(s)
Qualitative (n = 14)
In-depth interviews	7	Bailey et al. (2011), Chung et al. (2017), Leger et al. (2019), Peek et al. (2016), Peek et al. (2019), Marschollek et al. (2014), and Wang et al. (2011).
Focus group interviews	7	Govercin et al. (2010), Leger et al. (2019), Park et al. (2019), Robertson et al. (2019), Seaborn et al. (2016), Tural et al. (2020), and Wang et al. (2011).
Quantitative (n = 18)
Activity logs and diaries	4	Bailey et al. (2011), Chung et al. (2017), Isaacson et al. (2016), and Marschollek et al. (2014).
Offline survey	7	Haghzare et al. (2021), Nikitina et al. (2018), Park et al. (2019), Seaborn et al. (2016), Silveira et al. (2013), Tural et al. (2020), and Wang et al. (2011).
Online survey	5	Rahman et al. (2019), Robertson et al. (2019), Russell et al. (2015), Seelye et al. (2012), and Tural et al. (2020).
Postal survey	1	Wolf and Seebauer (2014)
Telephone survey	1	Oxley et al. (2019)

Table 5. Types of Technology-Mediated Mobility Solutions Available for Older Adults’ Adoption.

Technology-mediated mobility solution	Source(s)
Indoor
Home-based sensor system	Chung et al. (2017), Govercin et al. (2010), and Marschollek et al. (2014).
Mobile app (active lifestyle, virtual gym)	Nikitina et al. (2018) and Silveira et al. (2013).
Power wheelchair	Seaborn et al. (2016) and Wang et al. (2011).
Robot (active space, telecommunication)	Park et al. (2019) and Seelye et al. (2012).
Stair mobility	Tural et al. (2020).
Telehealth service	Russell et al. (2015).
Outdoor
Electric bicycle	Leger et al. (2019) and Wolf and Seebauer (2014).
Fully automated and self-driving vehicle	Haghzare et al. (2021), Rahman et al. (2019), and Robertson et al. (2019).
Vehicle safety	Oxley et al. (2019).

Three articles considered technology-mediated mobility solution in general (Bailey et al., 2011; Peek et al., 2016; Peek et al., 2019).

FIGURE 4 State-of-the-art overview of the antecedents, decisions, and consequences of older adults’ adoption of technology-mediated mobility solutions and its supporting theories, contexts, and methods.

Antecedents, decisions, and outcomes as well as theories, contexts, and methods of research in the field.

Table 6. Antecedents of Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Antecedents	Source(s)
Ethical considerations
Autonomy or sovereignty	Park et al. (2019) and Wolf and Seebauer (2014).
Dignity	Park et al. (2019) and Tural et al. (2020).
Privacy	Chung et al. (2017), Govercin et al. (2010), and Isaacson et al. (2016).
Liability	Peek et al. (2016)
Stigmatization	Leger et al. (2019) and Tural et al. (2020).
Trustworthiness	Rahman et al. (2019) and Russell et al. (2015).
Social influences
Social adherence	Nikitina et al. (2018) and Silveira et al. (2013).
Social connectedness	Seelye et al. (2012).
Social interaction	Nikitina et al. (2018) and Silveira et al. (2013).
Social motivation	Nikitina et al. (2018), Peek et al. (2019), and Silveira et al. (2013).
Personal factors
Adventure seeking	Seaborn et al. (2016).
Coping ability	Bailey et al. (2011), Peek et al. (2019), and Wolf and Seebauer (2014).
Digital literacy	Peek et al. (2019) and Seaborn et al. (2016).
Health condition	Isaacson et al. (2016) and Wang et al. (2011).
Open-mindedness	Park et al. (2019).
Self-efficacy	Bailey et al. (2011) and Haghzare et al. (2021).
Self-imposed limitation	Chung et al. (2017) and Russell et al. (2015).
Technology anxiety	Haghzare et al. (2021) and Russell et al. (2015).
Usability elements
Adjustment or compatibility	Bailey et al. (2011), Oxley et al. (2019), Peek et al. (2016), and Rahman et al. (2019).
Ease of use or effort expectancy	Leger et al. (2019), Peek et al. (2016), Peek et al. (2019), Russell et al. (2015), and Wolf and Seebauer (2014).
Enjoyment	Nikitina et al. (2018), Seaborn et al. (2016), and Wolf and Seebauer (2014).
Usefulness or performance expectancy	Govercin et al. (2010), Nikitina et al. (2018), Rahman et al. (2019), Tural et al. (2020), Wang et al. (2011), and Wolf and Seebauer (2014).
Reliability	Oxley et al. (2019).
Safety	Govercin et al. (2010), Haghzare et al. (2021), Oxley et al. (2019), Rahman et al. (2019), and Wang et al. (2011).
Environmental conditions
Accessibility	Leger et al. (2019).
Environmental conduciveness	Haghzare et al. (2021), Park et al. (2019), Peek et al. (2016), Peek et al. (2019), and Wolf and Seebauer (2014).
Facility	Robertson et al. (2019).

Table 7. Decisions Reflecting Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Decisions	Source(s)
Adoption
Purchase of technology-mediated mobility solutions	Govercin et al. (2010) and Oxley et al. (2019).
Use of technology-mediated mobility solutions	Bailey et al. (2011), Chung et al. (2017), Govercin et al. (2010), Haghzare et al. (2021), Isaacson et al. (2016), Leger et al. (2019), Marschollek et al. (2014), Nikitina et al. (2018), Oxley et al. (2019), Park et al. (2019), Peek et al. (2016), Peek et al. (2019), Rahman et al. (2019), Robertson et al. (2019), Russell et al. (2015), Seaborn et al. (2016), Seelye et al. (2012), Silveira et al. (2013), Tural et al. (2020), Wang et al. (2011), and Wolf and Seebauer (2014).
Willingness to pay more for technology-mediated mobility solutions	Oxley et al. (2019)

Table 8. Consequences of Older Adults’ Adoption of Technology-Mediated Mobility Solutions.

Consequences	Source(s)
Quality of life
Connect with the outside world	Nikitina et al. (2018) and Silveira et al. (2013).
Experience quality health	Nikitina et al. (2018) and Silveira et al. (2013).
Get involved in activities of interest	Chung et al. (2017), Haghzare et al. (2021), Isaacson et al. (2016), Leger et al. (2019), and Rahman et al. (2019).
Self-manage and live independently	Peek et al. (2016)
Socialize with others	Russell et al. (2015) and Seelye et al. (2012).

Table

Study	Scope	Sample(s)	Method(s)	Findings(s)
Haghzare et al. (2021)	Exposure to automated vehicle technology (pre-post exposure), driving style (manual versus automatic type), driving circumstances (clear, rain, traffic ally), and age are used to examine the acceptability of fully autonomous vehicles by older individuals.	36 healthy older adults aged 65 to 90 years.	Quantitative	Age and driving style influence the acceptance of fully automated vehicles by older adults.
Tural et al. (2020)	Older adults’ attitudes toward and intentions to use stair assistive mobility devices.	Quantitative: 56 participants aged 50 years and above. Qualitative: 15 older adults (three participants aged between 65 and 74 years, 11 participants aged between 74 to 84 years, and one participant aged 85 years and above).	Mixed	Survey findings showed that perceived usefulness consistently predicts older adults’ attitudes toward three examined stair mobility products and their intentions to use them in their homes. Most survey participants held positive attitudes toward stair assistive products, but intentions to use them in their own homes were low. Interviews show ongoing stigma against disability and the value of aesthetics regarding home modifications.
Oxley et al. (2019)	Use of safer automobiles among older adults.	501 active drivers aged 65 to 92 years.	Quantitative	The driver’s safety features were less known, especially among oldest-old drivers. Only 1/4 of drivers were open to additional security characteristics, and they had no interest to pay more for driverless vehicles.
Robertson et al. (2019)	Older drivers’ knowledge, attitudes, and perceptions toward limited self-driving vehicles (LSDVs).	Quantitative: 2,662 participants aged 16 to 93 years. Qualitative: 38 older adults consisting of 20 females and 18 males who are licensed drivers over the age of 65, with a mean age of 73 years.	Mixed	Survey findings indicated a favorable relationship between security sentiments and knowledge of LSDVs with perceived ease of use and technology adoption. Focus groups showed that the safety and expertise of LSDVs are essential to the likelihood of adopting this technology and revealed a high receptivity among older drivers to educational strategies and tools to increase their understanding of LSDVs.
Peek et al. (2019)	Changes and stability in using technologies by independent-living seniors.	33 community-dwelling older adults.	Qualitative	A core of six interrelated factors was closely linked to the frequency of technology use: emotional attachment, need compatibility, cues to use, proficiency to use, the input of resources, and support. Disruptive forces (e.g., social influences, competition with alternative means, changes of personal needs) induce change by affecting these six factors. Long-term technology use was in some cases more resilient to disruption than in other cases.
Park et al. (2019)	Critical demands of community-dwelling older adults regarding living robotics (Connected Active Space Technology) to support daily living.	Quantitative: 234 older adults aged 65 years and above. Qualitative: 23 older adults aged 65 years and above.	Mixed	Strong outlook for robotic services to support daily live performance. Priority was given to fast response to emergencies, early detection of emergencies, aid to find items, mobility support, and help in memory recall. The most wanted feature of a robot service was the attribute of being a friend or assistant.
Rahman et al. (2019)	Older adults’ perception of self-driving vehicles from the perspective of users and pedestrians.	173 older adults aged 60 and above.	Quantitative	Older adults perceive self-driving vehicles positively alongside perceived usefulness, trust, social norm, and acceptance as users. However, their perception was neutral or negative as pedestrians, except for perceived usefulness.
Leger et al. (2019)	Potential for e-bikes to support independent mobility and active aging among older adults.	17 community stakeholders and 37 older adults.	Qualitative	Constraints included road safety, safety, and cycling infrastructure, legislation, and hurdles to stigmatization.
Nikitina et al. (2018)	Technology usage and adherence to training, as well as the effects of technology-supported intervention.	44 older adults aged 59 to 83 years.	Quantitative	The study established the feasibility and effectiveness of technology-supported physical interventions, particularly online group exercise, among older adults. Results in terms of subjective well-being are promising but enabling interaction has proved inadequate to observe a decrease in loneliness.
Chung et al. (2017)	Feasibility of a home-based sensor system designed to assess mobility and daily activity patterns among older adults and sensor technology acceptance.	Six older adults aged 65 years and above.	Mixed	Sensor data had natural fluctuation over time, and different 24-hour patterns were observed across homes. The GLM model estimates showed that effect sizes of the time variables vary across individuals. Overall, sensors were acceptable for all participants, despite some privacy concerns.
Seaborn et al. (2016)	Mixed reality gaming platform that explores the impact of technology and interaction paradigms on older powered chair users’ well-being.	11 older adults aged 55 and above.	Mixed	Powered chair users’ community-dwelling older adults are a technology progressive population. They welcomed synchronizing assistive and un-assistive technology to promote social enjoyment, advanced training, and the promotion of comprehension and empathy in others.
Peek et al. (2016)	Factors influencing the use of various types of aging-in-place technology by older adults.	53 older adults aged 68 to 95 years.	Qualitative	Six factors affect the degree of technology usage in the context of aging: difficulties in the area of self-employment; behavioral alternatives; unique ideas about the use of technologies; the effect of social network; the influence of organizations; and the function of the physical environment.
Isaacson et al. (2016)	GPS for mobility compliance monitoring.	89 older adults aged 63 to 92 years.	Quantitative	Healthy older men and women with cognitive impairment have typically been found to conform better to GPS mobility monitoring.
Russell et al. (2015)	Older adults’ intention to adopt telehealth.	306 participants aged 50 to 68 years.	Quantitative	Telehealth trust, technology acceptance, health habits, and traditional healthcare discontent motivate telehealth adoption intention.
Wolf and Seebauer (2014)	Characteristics of early adopters of e-bikes, how they reshape their mobility patterns due to the adopted technology, and what motivations or product attributes drive their behavioral change.	1,398 older adults aged 60 years and above who own e-bikes.	Quantitative	Early adopters predominantly comprised of older adults who mainly use e-bikes for leisure trips.
Marschollek et al. (2014)	Acceptance of home health monitoring using a set of ambient and wearable sensors among older adults with functional deficits.	14 older adults aged 71 to 90 years.	Mixed	Older adults showed strong acceptance of technology, especially ambient components of home health monitoring devices.
Silveira et al. (2013)	IT-mediated motivation strategies adherence to physical exercise training plans in older adults and the impact of Active Lifestyle app on physical activity behavior change and health outcome.	44 older adults aged 65 years and above.	Quantitative	App demonstrated its ability to help and inspire independent living and healthy older adults to engage in strength – balance workouts.
Seelye et al. (2012)	Feasibility of using and accepting a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults.	Eight older adults aged 64 to 92 years.	Quantitative	Older adults responded positively to a remote-controlled robot with video-communication capabilities.
Wang et al. (2011)	Anti-collision power wheelchair’s ability to enable safe, independent mobility in nursing home residents with dementia.	Six older adults aged 82 to 91 years.	Mixed	Power wheelchairs improve the mobility and well-being of nursing home residents with complex physical and cognitive impairments.
Bailey et al. (2011)	Ethical implications of new care and support technologies, such as how well they fit with everyday challenges and needs without reducing an older adult to just a faller or someone with a fear of falls.	Eight older adults aged 70 to 87 years.	Qualitative	Acceptance among some older adults, while others struggle to get through the day and live comfortably.
Govercin et al. (2010)	Ergonomic and functional requirements for assistive devices and user-centered development process.	22 older adults aged 50 years and above.	Qualitative	Fall prediction system considered to be as important as a fall detection system. Although the ambient, unobtrusive character of the optical sensor system was appreciated, wearable inertial sensors were preferred because of their wide range of use, which provides higher levels of security. Security and mobility were two significant reasons people at risk of falling buy a wearable and optical fall prediction and fall detection device.

Peek, S. T. M., Luijkx, K. G., Vrijhoef, H. J. M., Nieboer, M. E., Aarts, S., van der Voort, C. S., Rijnaard, M. D., & Wouters, E. J. M. (2019). Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: A dynamical framework. BMC Geriatrics, 19(1), 1–13. https://doi.org/10.1186/s12877-019-1241-9

 PubMed Web of Science ®Google Scholar

Wolf, A., & Seebauer, S. (2014). Technology adoption of electric bicycles: A survey among early adopters. Transportation Research Part A: Policy and Practice, 69, 196–211. https://doi.org/10.1016/j.tra.2014.08.007

 Web of Science ®Google Scholar

Silveira, P., van de Langenberg, R., van Het Reve, E., Daniel, F., Casati, F., & de Bruin, E. D. (2013). Tablet-based strength-balance training to motivate and improve adherence to exercise in independently living older people: A phase II preclinical exploratory trial. Journal of Medical Internet Research, 15(8), e159. https://doi.org/10.2196/jmir.2579

 PubMed Web of Science ®Google Scholar

Leger, S. J., Dean, J. L., Edge, S., & Casello, J. M. (2019). “If I had a regular bicycle, I wouldn’t be out riding anymore”: Perspectives on the potential of e-bikes to support active living and independent mobility among older adults in Waterloo, Canada. Transportation Research Part A: Policy and Practice, 123, 240–254. https://doi.org/10.1016/j.tra.2018.10.009

 Web of Science ®Google Scholar

Govercin, M., Koltzsch, Y., Meis, M., Wegel, S., Gietzelt, M., Spehr, J., Winkelbach, S., Marschollek, M., & Steinhagen-Thiessen, E. (2010). Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Informatics for Health and Social Care, 35(3–4), 177–187. https://doi.org/10.3109/17538157.2010.528648

 PubMed Web of Science ®Google Scholar

Rahman, M. M., Deb, S., Strawderman, L., Burch, R., & Smith, B. (2019). How the older population perceives self-driving vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 242–257. https://doi.org/10.1016/j.trf.2019.08.002

 Web of Science ®Google Scholar

Seelye, A. M., Wild, K. V., Larimer, N., Maxwell, S., Kearns, P., & Kaye, J. A. (2012). Reactions to a remote-controlled video-communication robot in seniors’ homes: A pilot study of feasibility and acceptance. Telemedicine and E-Health, 18(10), 755–759. https://doi.org/10.1089/tmj.2012.0026

 PubMed Web of Science ®Google Scholar

Bailey, C., Foran, T. G., Scanaill, C. N., & Dromey, B. E. N. (2011). Older adults, falls and technologies for independent living: A life space approach. Ageing & Society, 31(5), 829–848. https://doi.org/10.1017/S0144686X10001170

 Google Scholar

Marschollek, M., Becker, M., Bauer, J. M., Bente, P., Dasenbrock, L., Elbers, K., Hein, A., Kolb, G., Künemund, H., Lammel-Polchau, C., Meis, M., Zu Schwabedissen, H. M., Remmers, H., Schulze, M., Steen, E. E., Thoben, W., Wang, J., Wolf, K. H., & Haux, R. (2014). Multimodal activity monitoring for home rehabilitation of geriatric fracture patients–feasibility and acceptance of sensor systems in the GAL-NATARS study. Informatics for Health and Social Care, 39(3–4), 262–271. https://doi.org/10.3109/17538157.2014.931852

 PubMed Web of Science ®Google Scholar

Russell, T. G., Gillespie, N., Hartley, N., Theodoros, D., Hill, A., & Gray, L. (2015). Exploring the predictors of home telehealth uptake by elderly Australian healthcare consumers. Journal of Telemedicine and Telecare, 21(8), 485–489. https://doi.org/10.1177/1357633X15606264

 PubMed Web of Science ®Google Scholar

Robertson, R. D., Woods-Fry, H., Vanlaar, W. G., & Hing, M. M. (2019). Automated vehicles and older drivers in Canada. Journal of Safety Research, 70, 193–199. https://doi.org/10.1016/j.jsr.2019.07.003

 PubMed Web of Science ®Google Scholar

Seaborn, K., Pennefather, P., & Fels, D. I. (2016). “Learn what we’re going through”: Attitudes of older powered chair users towards mixed reality games that involve power mobility. Universal Access in the Information Society, 15(4), 699–711. https://doi.org/10.1007/s10209-015-0450-z

 Web of Science ®Google Scholar

Haghzare, S., Campos, J. L., Bak, K., & Mihailidis, A. (2021). Older adults’ acceptance of fully automated vehicles: Effects of exposure, driving style, age, and driving conditions. Accident Analysis & Prevention, 150, 105919. https://doi.org/10.1016/j.aap.2020.105919

 PubMed Web of Science ®Google Scholar

Tural, E., Lu, D., & Cole, D. A. (2020). Factors predicting older adults’ attitudes toward and intentions to use stair mobility assistive designs at home. Preventive Medicine Reports, 18, 101082. https://doi.org/10.1016/j.pmedr.2020.101082

 PubMedGoogle Scholar

Chung, J., Demiris, G., Thompson, H. J., Chen, K. Y., Burr, R., Patel, S., & Fogarty, J. (2017). Feasibility testing of a home‐based sensor system to monitor mobility and daily activities in Korean American older adults. International Journal of Older People Nursing, 12(1), e12127. https://doi.org/10.1111/opn.12127

 Web of Science ®Google Scholar

Isaacson, M., Shoval, N., Wahl, H. W., Oswald, F., & Auslander, G. (2016). Compliance and data quality in GPS-based studies. Transportation, 43(1), 25–36. https://doi.org/10.1007/s11116-014-9560-3

 Web of Science ®Google Scholar

Nikitina, S., Didino, D., Baez, M., & Casati, F. (2018). Feasibility of virtual tablet-based group exercise among older adults in Siberia: Findings from two pilot trials. Journal of Medical Internet Research MHealth and UHealth, 6(2), e7531. https://doi.org/10.2196/mhealth.7531

 Web of Science ®Google Scholar

Oxley, J., Charlton, J., Logan, D., O’Hern, S., Koppel, S., & Meuleners, L. (2019). Safer vehicles and technology for older adults. Traffic Injury Prevention, 20(Sup2), S176–S179. https://doi.org/10.1080/15389588.2019.1661712

 PubMed Web of Science ®Google Scholar

Park, Y. H., Chang, H. K., Lee, M. H., & Lee, S. H. (2019). Community-dwelling older adults’ needs and acceptance regarding the use of robot technology to assist with daily living performance. BMC Geriatrics, 19(1), 1–9. https://doi.org/10.1186/s12877-019-1227-7

 PubMed Web of Science ®Google Scholar

Peek, S. T., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J. M., & Wouters, E. J. (2016). Older adults’ reasons for using technology while aging in place. Gerontology, 62(2), 226–237. https://doi.org/10.1159/000430949

 PubMed Web of Science ®Google Scholar

Wang, R. H., Gorski, S. M., Holliday, P. J., & Fernie, G. R. (2011). Evaluation of a contact sensor skirt for an anti-collision power wheelchair for older adult nursing home residents with dementia: Safety and mobility. Assistive Technology, 23(3), 117–134. https://doi.org/10.1080/10400435.2010.541406

 Web of Science ®Google Scholar

Data availability statement

A data availability statement is not applicable because this study is based exclusively on published literature.