Independent aging with the help op smart technology : investigating the acceptance of ambient assisted living technologies

Jaschinski, Christina

doi:10.3990/1.9789036546348

Cited by 8 publications

(11 citation statements)

References 254 publications

(686 reference statements)

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“…Participants were presented with a short (2.25 minutes) video animation that explained the concept of AAL [ 65 ]. Previous research has shown that animated content with spoken text works well to communicate complex health-related information [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

Acceptance of Technologies for Aging in Place: A Conceptual Model

Jaschinski¹,

Allouch²,

Peters³

et al. 2021

J Med Internet Res

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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.

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Section: Methodsmentioning

confidence: 99%

Acceptance of Technologies for Aging in Place: A Conceptual Model

Jaschinski¹,

Allouch²,

Peters³

et al. 2021

J Med Internet Res

Self Cite

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“… 3 Personal innovativeness The willingness of persons to try out new ICT. 4 The questions have been adapted from existing questionnaires [ 29 , 30 ] …”

Section: Methodsmentioning

confidence: 99%

“…The constructs included based on the UTAUT were: Intention to use ( n = 3), Outcome expectancy (n = 3), Effort expectancy ( n = 4), Attitude (n = 4), Social influence (n = 3), Facilitating conditions (n = 4), Anxiety ( n = 3) and Self-efficacy ( n = 3). Another validated construct about Personal innovativeness (4 items) based on DOI was added [ 27 , 30 , 31 ]. The wording of individual items were adapted to fit the research focus on video communication in terminal care.…”

Section: Methodsmentioning

confidence: 99%

“…However, no other items were excluded as Cronbach’s alpha did not increase sufficiently. In addition, all constructs were kept in the analysis as these were based on validated questionnaires [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, no other items were excluded as Cronbach's alpha did not increase sufficiently. In addition, all constructs were kept in the analysis as these were based on validated questionnaires [29,30]. Descriptive analysis of the demographics (age, gender, function, and healthcare institute) included calculation of frequencies, percentages, means (M) and standard deviation (SD) (Table 2).…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

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Intention of healthcare providers to use video-communication in terminal care: a cross-sectional study

et al. 2022

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Background Interdisciplinary collaboration between healthcare providers with regard to consultation, transfer and advice in terminal care is both important and challenging. The use of video communication in terminal care is low while in first-line healthcare it has the potential to improve quality of care, as it allows healthcare providers to assess the clinical situation in real time and determine collectively what care is needed. The aim of the present study is to explore the intention to use video communication by healthcare providers in interprofessional terminal care and predictors herein. Methods In this cross-sectional study, an online survey was used to explore the intention to use video communication. The survey was sent to first-line healthcare providers involved in terminal care (at home, in hospices and/ or nursing homes) and consisted of 39 questions regarding demographics, experience with video communication and constructs of intention to use (i.e. Outcome expectancy, Effort expectancy, Attitude, Social influence, Facilitating conditions, Anxiety, Self-efficacy and Personal innovativeness) based on the Unified Theory of Acceptance and Use of Technology and Diffusion of Innovation Theory. Descriptive statistics were used to analyze demographics and experiences with video communication. A multiple linear regression analysis was performed to give insight in the intention to use video communication and predictors herein. Results 90 respondents were included in the analysis.65 (72%) respondents had experience with video communication within their profession, although only 15 respondents (17%) used it in terminal care. In general, healthcare providers intended to use video communication in terminal care (Mean (M) = 3.6; Standard Deviation (SD) = .88). The regression model was significant (F = 9.809, p-value<.001) and explained 44% of the variance in intention to use video communication, with ‘Outcome expectancy’ (beta .420, p < .001) and ‘Social influence’ (beta .266, p = .004) as significant predictors. Conclusions Healthcare providers have in general the intention to use video communication in interprofessional terminal care. However, their actual use in terminal care is low. ‘Outcome expectancy’ and ‘Social influence’ seem to be important predictors for intention to use video communication. This implicates the importance of informing healthcare providers, and their colleagues and significant others, about the usefulness and efficiency of video communication.

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