Background: Most older adults prefer to age in place, and supporting older adults to remain in their own homes and communities is also favored by policy makers. Technology can play a role in staying independent, active and healthy. However, the use of technology varies considerably among older adults. Previous research indicates that current models of technology acceptance are missing essential predictors specific to community-dwelling older adults. Furthermore, in situ research within the specific context of aging in place is scarce, while this type of research is needed to better understand how and why community-dwelling older adults are using technology. Objective: To explore which factors influence the level of use of various types of technology by older adults who are aging in place and to describe these factors in a comprehensive model. Methods: A qualitative explorative field study was set up, involving home visits to 53 community-dwelling older adults, aged 68-95, living in the Netherlands. Purposive sampling was used to include participants with different health statuses, living arrangements, and levels of technology experience. During each home visit: (1) background information on the participants' chronic conditions, major life events, frailty, cognitive functioning, subjective health, ownership and use of technology was gathered, and (2) a semistructured interview was conducted regarding reasons for the level of use of technology. The study was designed to include various types of technology that could support activities of daily living, personal health or safety, mobility, communication, physical activity, personal development, and leisure activities. Thematic analysis was employed to analyze interview transcripts. Results: The level of technology use in the context of aging in place is influenced by six major themes: challenges in the domain of independent living; behavioral options; personal thoughts on technology use; influence of the social network; influence of organizations, and the role of the physical environment. Conclusion: Older adults' perceptions and use of technology are embedded in their personal, social, and physical context. Awareness of these psychological and contextual factors is needed in order to facilitate aging in place through the use of technology. A conceptual model covering these factors is presented.
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BACKGROUND: Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs.OBJECTIVE: The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors.METHODS: A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships.RESULTS: Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73).CONCLUSIONS: This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users' needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation.
Purpose: To provide an overview of factors influencing the acceptance of electronic tech-nologies that support aging in place by community-dwelling older adults. Since technologyacceptance factors fluctuate over time, a distinction was made between factors in the pre-implementation stage and factors in the post-implementation stage. Methods: A systematic review of mixed studies. Seven major scientific databases (includingMEDLINE, Scopus and CINAHL) were searched. Inclusion criteria were as follows: (1) originaland peer-reviewed research, (2) qualitative, quantitative or mixed methods research, (3)research in which participants are community-dwelling older adults aged 60 years or older,and (4) research aimed at investigating factors that influence the intention to use or theactual use of electronic technology for aging in place. Three researchers each read the articlesand extracted factors. Results: Sixteen out of 2841 articles were included. Most articles investigated acceptance oftechnology that enhances safety or provides social interaction. The majority of data wasbased on qualitative research investigating factors in the pre-implementation stage. Accep-tance in this stage is influenced by 27 factors, divided into six themes: concerns regardingtechnology (e.g., high cost, privacy implications and usability factors); expected benefits oftechnology (e.g., increased safety and perceived usefulness); need for technology (e.g., per-ceived need and subjective health status); alternatives to technology (e.g., help by family orspouse), social influence (e.g., influence of family, friends and professional caregivers); andcharacteristics of older adults (e.g., desire to age in place). When comparing these results to qualitative results on post-implementation acceptance, our analysis showed that some factors are persistent while new factors also emerge. Quantitative results showed that a small number of variables have a significant influence in the pre-implementation stage. Fourteen out of the sixteen included articles did not use an existing technology acceptance framework or model. Conclusions: Acceptance of technology in the pre-implementation stage is influenced by multiple factors. However, post-implementation research on technology acceptance by community-dwelling older adults is scarce and most of the factors in this review have not been tested by using quantitative methods. Further research is needed to determine if and how the factors in this review are interrelated, and how they relate to existing models of technology acceptance.
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