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Assessing the Feasibility and Acceptability of Smart Speakers in Behavioral Intervention Research With Older Adults: Mixed Methods Study
Abstract
Background
Smart speakers, such as Amazon’s Echo and Google’s Nest Home, combine natural language processing with a conversational interface to carry out everyday tasks, like playing music and finding information. Easy to use, they are embraced by older adults, including those with limited physical function, vision, or computer literacy. While smart speakers are increasingly used for research purposes (eg, implementing interventions and automatically recording selected research data), information on the advantages and disadvantages of using these devices for studies related to health promotion programs is limited.
Objective
This study evaluates the feasibility and acceptability of using smart speakers to deliver a physical activity (PA) program designed to help older adults enhance their physical well-being.
Methods
Community-dwelling older adults (n=18) were asked to use a custom smart speaker app to participate in an evidence-based, low-impact PA program for 10 weeks. Collected data, including measures of technology acceptance, interviews, field notes, and device logs, were analyzed using a concurrent mixed analysis approach. Technology acceptance measures were evaluated using time series ANOVAs to examine acceptability, appropriateness, feasibility, and intention to adopt smart speaker technology. Device logs provided evidence of interaction with and adoption of the device and the intervention. Interviews and field notes were thematically coded to triangulate the quantitative measures and further expand on factors relating to intervention fidelity.
Results
Smart speakers were found to be acceptable for administering a PA program, as participants reported that the devices were highly usable (mean 5.02, SE 0.38) and had strong intentions to continue their use (mean 5.90, SE 0.39). Factors such as the voice-user interface and engagement with the device on everyday tasks were identified as meaningful to acceptability. The feasibility of the devices for research activity, however, was mixed. Despite the participants rating the smart speakers as easy to use (mean 5.55, SE 1.16), functional and technical factors, such as Wi-Fi connectivity and appropriate command phrasing, required the provision of additional support resources to participants and potentially impaired intervention fidelity.
Conclusions
Smart speakers present an acceptable and appropriate behavioral intervention technology for PA programs directed at older adults but entail additional requirements for resource planning, technical support, and troubleshooting to ensure their feasibility for the research context and for fidelity of the intervention.
... Extracted characteristics of each study included design, sample characteristics, technological experience, and intervention. Regarding design, all studies included data from qualitative interviews following up on an intervention with a voice-controlled conversational AI device [7][8][9][10][11][12][13][14][15][16]. Regarding sample characteristics, in total 241 elderly individuals were included, with most studies having between 10 and 20 participants. ...
... Most studies included adults aged 60 years and older [9][10][11][12][13][14][15][16], except for two studies [7,8] which had slightly younger participants (58 and 50 years and older). Most studies included cognitively healthy participants [8][9][10][11]14,15]. Concerning technological experience, half of the studies reported no experience [9,12,13,15,16], three studies reported varying levels of experience [7,8,10], and two studies did not directly inquire technological experience [11,14], meaning in most studies the elderly had little technological experience. ...
... Most studies included adults aged 60 years and older [9][10][11][12][13][14][15][16], except for two studies [7,8] which had slightly younger participants (58 and 50 years and older). Most studies included cognitively healthy participants [8][9][10][11]14,15]. Concerning technological experience, half of the studies reported no experience [9,12,13,15,16], three studies reported varying levels of experience [7,8,10], and two studies did not directly inquire technological experience [11,14], meaning in most studies the elderly had little technological experience. ...
Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare perspective, as these voice-controlled assistants may support the elderly and enable remote health monitoring. The bottleneck for efficacy, however, is how well these devices work in practice and how the elderly experience them, but research on this topic is scant. We review elderly use of voice-controlled AI and highlight various user- and technology-centered issues, that need to be considered before effective speech-controlled AI for elderly care can be realized.
Background
As smart speakers become more popular, there have been an increasing number of studies on how they may benefit older adults or how older adults perceive them. Despite the increasing ownership rates of smart speakers among older adults, studies that examine their integration and the long-term use in older adults’ daily practices are scarce.
Objective
This study aims to uncover the integration of smart speakers into the daily practices of older adults over the long term, contributing to an in-depth understanding of maintained technology use among this demographic.
Methods
To achieve these objectives, the study interviewed 20 older adults who had been using smart speakers for over 6 months. These semistructured interviews enabled participants to share their insights and experiences regarding the maintained use of smart speakers in the long term.
Results
We identified 4 dimensions of the long-term use of smart speakers among older adults, including functional integration, spatial integration, cognitive integration, and semantic integration. For the functional integration of smart speakers, the study reported different types of use, including entertainment, information collection, medication reminders, companionship, environment modification, and emergency calls. For the spatial integration of smart speakers, the study showed older adults’ agency in defining, changing, and reshaping daily practices through the spatial organization of smart speakers. For the cognitive integration of smart speakers, the findings showed the cognitive processes involved in adapting to and incorporating smart speakers into daily habits and routines. For the semantic integration of smart speakers, the findings revealed that older adults’ enjoyable user experience and strong bonds with the device contributed to their acceptance of occasional functional errors. Finally, the study proposed several suggestions for designers and developers to better design smart speakers that promote maintainable use behaviors among older adults.
Conclusions
On the basis of the findings, this study highlighted the importance of understanding how older adults use smart speakers and the practices through which they integrate them into their daily routines. The findings suggest that smart speakers can provide significant benefits for older adults, including increased convenience and improved quality of life. However, to promote maintainable use behaviors, designers and developers should consider more about the technology use contexts and the specific needs and preferences of older adults when designing these devices.
We present "Mystery Agent," an interactive storytelling voice user interface (VUI) equipped with self-regulated learning strategies to deliver informal health-related learning to older adults through a murder mystery story. We conducted a mixed methods user study with 10 older adults to evaluate Mystery Agent, using usability and perception-based questionnaires, followed by a semi-structured interview and co-design activity to generate design insights and identify design priorities. We found older adults had a positive experience interacting with Mystery Agent and considered storytelling to be an appropriate and engaging way of learning health information. However, older adults identified credibility, compassion, and control as crucial factors influencing long-term use. To address this, we present design guidelines using Mystery Agent as an example to help practitioners and researchers devise novel solutions to address the informal health information learning needs of older adults.
Purpose
This study identified the effects of applying information and communication technologies (ICT) to the health management of older adults aged 65 or older.
Methods
Older adults registered at public health centers were provided with the health management app “Health Today” and a smart speaker for 6 months to perform assigned healthcare missions. The program was conducted for 6 months by dividing participants into two groups: one that received both the health management app and the smart speaker, and another that used only the health management app. Depression, self-efficacy, number of days of moderate-intensity exercise, relative grip strength, balance tests, and five-times-sit-to-stand tests were measured during the pre- and post-evaluation.
Results
Both groups showed a positive health status and behavioral changes at post-evaluation. However, no reduced depression was observed due to communication and music listening functions in the group that was additionally provided smart speakers.
Conclusion
ICT use in healthcare can be beneficial for older adults. However, whether these devices meet the purpose of the national health project must be determined, and an effect evaluation must be undertaken prior to providing these ICT devices for the health management of older adults in the public domain.
In this paper, we present Health Buddy, a voice agent integrated into commercially available Voice User Interfaces (VUIs) to support informal self-regulated learning (SRL) of health-related topics through multiple learning strategies and examine the efficacy of Health Buddy on learning outcomes for younger and older adults. We conducted a mixed-factorial-design experiment with 26 younger and 25 older adults, assigned to three SRL conditions (within-subjects): monologue, dialogue-based scaffolding building, and conceptual diagramming. We found that while younger adults benefit more from scaffolding building and conceptual diagramming, both younger and older adults showed equivalent learning outcomes. Furthermore, interaction fluency (operationalized by the number of conversational breakdowns) was associated with learning outcomes regardless of age. While older adults did not experience less fluent conversations, interaction fluency affected their technology acceptance toward VUIs more than younger ones. Our study discusses age-related learning differences and has implications for designing VUI-based learning programs for older adults. Additional
Background:
The rapid diffusion of technology apps may support older adults' independence and improve the quality of their lives. Models for predicting technology acceptance in older adults are sparse, based on broad questions related to general technology acceptance, and largely not grounded in theories of aging.
Objective:
This study aimed to use a mixed methods approach involving 5 technologies to comprehensively assess the causal relationships among factors that influence older adults' willingness to adopt the technologies.
Methods:
In total, 187 men and women aged 65 to 92 years participated in the study. Participants were given presentations on 5 different technologies spanning domains that included transportation, leisure, health, and new learning and provided ratings of each technology on various measures hypothesized to influence adoption. They were also administered other instruments to collect data on their actual and self-assessed cognitive abilities, rates of discounting of the technologies with respect to willingness to invest time to attain higher skills in the technologies, general technology experience, and attitudes toward technology. We used the machine learning technique of k-fold cross-validated regressions to select variables that predicted participants' willingness to adopt the technologies.
Results:
Willingness to adopt technologies was most impacted by 3 variables: perceived value of the technologies (β=.54), perceived improvement in quality of life attainable from the technologies (β=.24), and confidence in being able to use the technologies (β=.15). These variables, in turn, were mostly facilitated or inhibited by the perceived effort required to learn to use the technologies, a positive attitude toward technology as reflected in the optimism component of the technology readiness scale, the degree to which technologies were discounted, and the perceived help needed to learn to use the technologies.
Conclusions:
Our findings demonstrate that participants' willingness to adopt technologies is mainly determined by perceptions of 3 aspects of the technologies; these aspects possibly mediate many relationships with willingness to adopt. We discuss the implications of these findings for the design and marketing of technology products for older consumers.
Context
Few remote care technologies have been able to integrate clinical routines in the health sector, one of the reasons is the non-acceptance of these technologies by end-users who are health professionals.
Objective
Identify from a review of the literature the research that has studied the acceptance behavior of remote care technologies by health professionals and that has used technology acceptance model (TAM) and Unified theory of acceptance and use of technology (UTAUT) as a theoretical framework.
Methods
An electronic search was conducted in the PubMed, Scopus, and Web of Science databases during the period between May and June 2021. The articles were identified using predefined inclusion and exclusion criteria. All search results have been exported to an Excel file. Next, the titles and abstracts of the publications were assessed by two independent reviewers according to the inclusion and exclusion criteria to select relevant studies from those found in the database, Any conflict in decision-making during the selection phase was resolved by discussion among reviewers or, if necessary, with the research coordinator until consensus was reached.
Results
Of 531 potentially relevant titles (duplicates excluded), 32 studies were included in this systematic review. These studies did not test a single model, there was a variety of models tested, 7 studies tested the original TAM model and 6 tested the original UTAUT model. While 13 studies tested an extension of TAM (or modified TAM) and 6 tested an extension of UTAUT (or modified UTAUT). There are studies on a wide variety of remote health care applications in the articles included in this systematic review. In the majority of cases (21 studies 66%), the authors did not specify the type of application they studied, i.e. they used generic terms such as eHealth, mHealth, Telehealth, or telemedicine.
Conclusions
The main finding of this review shows that these two models TAM and UTAUT are still valid and used to predict the acceptance behavior of remote care technologies by health professionals, even if they date back several years and even if they were not originally formulated to be used in the context of remote care technologies.
Background: The use of smart speakers to improve well-being had been trialed in social care by others; however, we were not aware of their implementation in most care homes across a region in the Southwest of the United Kingdom. For the widespread adoption of new technology, it must be locally demonstrable and become normalized. Objective: The aim of this study was to install smart speakers in care homes in a rural and coastal region and to explore if and how the devices were being used, the barriers to their implementation, and their potential benefits. Methods: Email, workshops, drop-in sessions, phone, and cold calling was used to contact all 230 care homes, offering a free smart speaker and some advisory support. Care homes accepting the devices were asked to complete a feedback diary. Nonresponse rate for diary completion was high and was thus supplemented with a telephone survey. Results: Over the course of 7 months, we installed 156 devices in 92 care homes for older people, 50 devices for people with physical or mental health needs, and 8 for others. The devices were used mainly for music but also for poetry, recipes, light controls, jokes, and video calls. Care home managers reported the benefits for the residents, including enhanced engagement with home activities, enjoyment, calming effects, and the acquisition of new skills. Implementation problems included internet connectivity, staff capacity, and skills. Conclusions: Affordable consumer devices such as smart speakers should be installed in all care homes to benefit residents. Voice-activated technologies are easy to use and promote interaction. This study indicates that implementation in care homes was possible and that smart speakers had multifaceted benefits for residents and staff. Most care homes in this region now use smart speakers for their residents, thereby normalizing this practice.
The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection.
Background:
Although smart speaker technology is poised to help improve the health and well-being of older adults by offering services such as music, medication reminders, and connection to others, more research is needed to determine how older adults from lower socioeconomic position (SEP) accept and use this technology.
Objective:
This study aimed to investigate the feasibility of using smart speakers to improve the health and well-being of low-SEP older adults.
Methods:
A total of 39 adults aged between 65 and 85 years who lived in a subsidized housing community were recruited to participate in a 3-month study. The participants had a smart speaker at their home and were given a brief orientation on its use. Over the course of the study, participants were given weekly check-in calls to help assist with any problems and newsletters with tips on how to use the speaker. Participants received a pretest and posttest to gauge comfort with technology, well-being, and perceptions and use of the speaker. The study staff also maintained detailed process notes of interactions with the participants over the course of the study, including a log of all issues reported.
Results:
At the end of the study period, 38% (15/39) of the participants indicated using the speaker daily, and 38% (15/39) of the participants reported using it several times per week. In addition, 72% (28/39) of the participants indicated that they wanted to continue using the speaker after the end of the study. Most participants (24/39, 62%) indicated that the speaker was useful, and approximately half of the participants felt that the speaker gave them another voice to talk to (19/39, 49%) and connected them with the outside world (18/39, 46%). Although common uses were using the speaker for weather, music, and news, fewer participants reported using it for health-related questions. Despite the initial challenges participants experienced with framing questions to the speaker, additional explanations by the study staff addressed these issues in the early weeks of the study.
Conclusions:
The results of this study indicate that there is promise for smart speaker technology for low-SEP older adults, particularly to connect them to music, news, and reminders. Future studies will need to provide more upfront training on query formation as well as develop and promote more specific options for older adults, particularly in the area of health and well-being.
Background
The use of smart speakers to improve well-being had been trialed in social care by others; however, we were not aware of their implementation in most care homes across a region in the Southwest of the United Kingdom. For the widespread adoption of new technology, it must be locally demonstrable and become normalized.
Objective
The aim of this study was to install smart speakers in care homes in a rural and coastal region and to explore if and how the devices were being used, the barriers to their implementation, and their potential benefits.
Methods
Email, workshops, drop-in sessions, phone, and cold calling was used to contact all 230 care homes, offering a free smart speaker and some advisory support. Care homes accepting the devices were asked to complete a feedback diary. Nonresponse rate for diary completion was high and was thus supplemented with a telephone survey.
Results
Over the course of 7 months, we installed 156 devices in 92 care homes for older people, 50 devices for people with physical or mental health needs, and 8 for others. The devices were used mainly for music but also for poetry, recipes, light controls, jokes, and video calls. Care home managers reported the benefits for the residents, including enhanced engagement with home activities, enjoyment, calming effects, and the acquisition of new skills. Implementation problems included internet connectivity, staff capacity, and skills.
Conclusions
Affordable consumer devices such as smart speakers should be installed in all care homes to benefit residents. Voice-activated technologies are easy to use and promote interaction. This study indicates that implementation in care homes was possible and that smart speakers had multifaceted benefits for residents and staff. Most care homes in this region now use smart speakers for their residents, thereby normalizing this practice.
Over the last few years, smart speakers such as Amazon Echo and Google Home have become increasingly present within British households. Yet, privacy remains a prominent concern in the public discourse about smart speakers, as well as in the nascent academic literature. We argue that privacy in the context of smart speakers is more complex than in other settings due to smart speakers' specific technological affordances and due to the axial relationships between users, the device, device manufacturers, application developers, and other third parties such as moderation contractors and data brokers. With survey data from Amazon Echo and Google Home users in the UK, we explore users' privacy concerns and privacy protection behaviors related to smart speakers. We rely on a contextual understanding of privacy, assessing the prevalence of seven distinct privacy concern types as well as three privacy protection behaviors. The results indicate that concerns about third-parties, such as contractors listening to smart speaker recordings, are most pronounced. Privacy protection behaviors are uncommon but partly affected by privacy concerns and motives such as social presence and utilitarian benefits. Taken together, our research paints a picture of privacy pragmatism or privacy cynicism among smart speaker users.
Engaging in positive healthy lifestyle behaviors continues to be a public health challenge, requiring innovative solutions. As the market for voice assistants (Amazon Alexa, Google Assistant, and Apple Siri) grows and people increasingly use them to assist their daily tasks, there is a pressing need to explore how voice assistant (VA) technology may be used in behavioral health interventions. A scoping review of literature was conducted to address a PICO (Population, Intervention, Comparison, and Outcome) question: across populations, how does the use of voice assistants in behavioral health research/interventions influence healthy lifestyle behaviors versus control or comparison interventions? To inform the science, a secondary aim of this review was to explore characteristics of VAs used in behavioral health research. The review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines with scoping review extension (PRISMA-ScR). Ten studies satisfied the inclusion criteria, representing research published through February 2019. Studies spanned pediatric to elderly populations, covering a vast array of self-management and healthy lifestyle behaviors. The majority of interventions were multicomponent, involving more than one of the following behavior change techniques grouped by cluster: shaping knowledge, self-belief, repetition and substitution, feedback and monitoring, goals and planning, antecedents, natural consequences, comparison of behavior, and identification. However, most studies were in early stages of development, with limited efficacy trials. VA technology continues to evolve and support behavioral interventions using various platforms (e.g., Interactive Voice Response [IVR] systems, smartphones, and smart speakers) which are used alone or in conjunction with other platforms. Feasibility, usability, preliminary efficacy, along with high user satisfaction of research adapted VAs, in contrast to standalone commercially available VAs, suggest a role for VAs in behavioral health intervention research. DOI: 10.1093/tbm/ibz141
In this paper we present the results of an exploratory study examining the potential of voice assistants (VA) for some groups of older adults in the context of Smart Home Technology (SHT). To research the aspect of older adults' interaction with voice user interfaces (VUI) we organized two workshops and gathered insights concerning possible benefits and barriers to the use of VA combined with SHT by older adults. Apart from evaluating the participants' interaction with the devices during the two workshops we also discuss some improvements to the VA interaction paradigm.
Behavioral intervention technologies (BITs) are websites, software, mobile apps, and sensors designed to help users address or change behaviors, cognitions, and emotional states. BITs have the potential to transform health care delivery, and early research has produced promising findings of efficacy. BITs also favor new models of health care delivery and provide novel data sources for measurement. However, there are few examples of successful BIT implementation and a lack of consensus on as well as inadequate descriptions of BIT implementation measurement. The aim of this viewpoint paper is to provide an overview and characterization of implementation outcomes for the study of BIT use in routine practice settings. Eight outcomes for the evaluation of implementation have been previously described: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. In a proposed recharacterization of these outcomes with respect to BIT implementation, definitions are clarified, expansions to the level of analysis are identified, and unique measurement characteristics are discussed. Differences between BIT development and implementation, an increased focus on consumer-level outcomes, the expansion of providers who support BIT use, and the blending of BITs with traditional health care services are specifically discussed. BITs have the potential to transform health care delivery. Realizing this potential, however, will hinge on high-quality research that consistently and accurately measures how well such technologies have been integrated into health services. This overview and characterization of implementation outcomes support BIT research by identifying and proposing solutions for key theoretical and practical measurement challenges. ©Eric DA Hermes, Aaron R Lyon, Stephen M Schueller, Joseph E Glass. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.01.2019.
Although much research examines the factors that affect technology adoption and use, less is known about how older adults as a group differ in their ability to use the Internet. The theory of digital inequality suggests that even once people have gone online, differences among them will persist in important ways such as their online skills. We analyze survey data about older American adults’ Internet skills to examine whether skills differ in this group and if they do, what explains differential online abilities. We find that there is considerable variation in Internet know-how and this relates to both socioeconomic status and autonomy of use. The results suggest that attempts to achieve a knowledgeable older adult population regarding Internet use must take into account these users’ socioeconomic background and available access points.
Recognition the needs and acceptance of individuals is the beginning stage of any businesses and this understanding would be helpful to find the way of future development, thus academicians are interested to realize the factors that drive users’ acceptance or rejection of technologies. A number of models and frameworks have been developed to explain user adoption of new technologies and these models introduce factors that can affect the user acceptance. In this paper, an overview of theories and models regarding user acceptance of technology has been provided. The existing review will emphasize literature that tries to show how developers and researchers presage the level of admission any information technology will attain.
Background and Objectives
Numerous technology applications are available that have the potential to improve the quality of life (QoL) of older adults. However, older adults are less likely to adopt new and emerging technologies and reap the potential benefits. This study examines factors that influence older adults’ decisions about the adoption of new technology.
Research Design and Methods
Fifty-two older adults participated in a mixed-method procedure, which entailed: (1) observing presentations detailing nine differing technologies, (2) assessing the technologies using tailored questionnaires, and (3) participating in focus group discussions. Participants were assigned into one of seven groups separated by age (65–74, 75+) and language (English, Spanish). The outcome was willingness to adopt technology. Predictors included self-assessed abilities (e.g., numeric ability), computer/Internet skills and knowledge, technology readiness, age, language, and technology ratings (e.g., perceived value). Analyses included Spearman’s ρ, t-tests, and regression analysis. Focus group discussions were examined for supportive examples.
Results
Self-assessed abilities and computer/Internet skills were predictive of willingness to adopt technologies although the relationship varied according to the technology examined. Technology readiness, age, and language group showed weak associations with the outcome. Of the technology ratings, perceived value, confidence in ability to learn the technology, and the perceived impact on QoL were the most robust predictors of willingness to adopt technology.
Discussion and Implications
Findings indicate that various stakeholders in technology adoption among older adults must be cognizant of a technology’s functionality and complexity as well as the characteristics and abilities of older adults. However, certain factors such as perceptions about the value of the technology and potential impact on QoL are also critically important to decisions regarding technology adoption among older people.
Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today’s voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.
While the field of digital inequality continues to expand in many directions, the relationship between digital inequalities and other forms of inequality has yet to be fully appreciated. This article invites social scientists in and outside the field of digital media studies to attend to digital inequality, both as a substantive problem and as a methodological concern. The authors present current research on multiple aspects of digital inequality, defined expansively in terms of access, usage, skills, and self-perceptions, as well as future lines of research. Each of the contributions makes the case that digital inequality deserves a place alongside more traditional forms of inequality in the twenty-first century pantheon of inequalities. Digital inequality should not be only the preserve of specialists but should make its way into the work of social scientists concerned with a broad range of outcomes connected to life chances and life trajectories. As we argue, the significance of digital inequalities is clear across a broad range of individual-level and macro-level domains, including life course, gender, race, and class, as well as health care, politics, economic activity, and social capital.
This study examined the “Unified Theory of Acceptance and Use of Technology” (UTAUT) in the context of tablet devices across multiple generations. We tested the four UTAUT determinants, performance expectancy, effort expectancy, social influence, and facilitating conditions, to determine their contributions for predicting behavioral intention to use tablets with age, gender, and user experience as moderators. 899 respondents aged 19–99 completed the survey. We found consistent generational differences in UTAUT determinants, most frequently between the oldest and youngest generations. Effort expectancy and facilitating conditions were the only determinants that positively predicted tablet use intentions after controlling for age, gender, and tablet use. We also discuss the implications of ageism and gender discrimination of technology adoption. Finally, we argue that our findings can be extended to create effective training programs for the teaching, learning, and adoption of new technologies in a variety of organizational settings.
In a context of progressive loss of intellectual and interactional capacities for the elderly, the goal of this article is to examine to what extent a new technological environment can improve their quality of life. In this study, we examine the very elderly (mean age 87) who have experienced a loss in functional capacities and are dependent on managed care such as residential home care units. Using qualitative methods amongst a group of 17 residents (semi-structured interviews and longitudinal observations), we examine whether new social practices form and whether subjects feel more socially recognised. Our study shows that information and communications technologies may, to some extent, play an instrumental role in interconnectedness and social stimulation, and can also be seen as a ‘boundary object’ that communicates between the residents’ world (who are rather isolated) and their families’ world (including grandchildren).
Objective:
Regular physical activity (PA) is important for maintaining long-term physical, cognitive, and emotional health. However, few older adults engage in routine PA, and even fewer take advantage of programs designed to enhance PA participation. Though most managed Medicare members have free access to the Silver Sneakers and EnhanceFitness PA programs, the vast majority of eligible seniors do not utilize these programs. The goal of this qualitative study was to better understand the barriers to and facilitators of PA and participation in PA programs among older adults.
Design:
This was a qualitative study using focus group interviews.
Setting:
Focus groups took place at three Group Health clinics in King County, Washington.
Participants:
Fifty-two randomly selected Group Health Medicare members between the ages of 66 to 78 participated.
Methods:
We conducted four focus groups with 13 participants each. Focus group discussions were audio-recorded, transcribed, and analyzed using an inductive thematic approach and a social-ecological framework.
Results:
Men and women were nearly equally represented among the participants, and the sample was largely white (77%), well-educated (69% college graduates), and relatively physically active. Prominent barriers to PA and PA program participation were physical limitations due to health conditions or aging, lack of professional guidance, and inadequate distribution of information on available and appropriate PA options and programs. Facilitators included the motivation to maintain physical and mental health and access to affordable, convenient, and stimulating PA options.
Conclusion:
Older adult populations may benefit from greater support and information from their providers and health care systems on how to safely and successfully improve or maintain PA levels through later adulthood. Efforts among health care systems to boost PA among older adults may need to consider patient-centered adjustments to current PA programs, as well as alternative methods for promoting overall active lifestyle choices.
Sedentary behavior is a cluster of behaviors adopted in a sitting or lying posture where little energy is being expended. Sedentary behavior is a risk factor for health independent to inactivity. Currently, there are no published systematic reviews on the prevalence of sedentary behavior objectively measured in, or subjectively reported by, older adults. The aim of this systematic review was to collect and analyze published literature relating to reported prevalence of sedentary behavior, written in English, on human adults, where subjects aged 60 years and over were represented in the study. 23 reports covered data from 18 surveys sourced from seven countries. It was noted that sedentary behavior is defined in different ways by each survey. The majority of surveys included used self-report as a measurement of sedentary behavior. Objective measurements were also captured with the use of body worn accelerometers. Whether measurements are subjective or objective, the majority of older adults are sedentary. Almost 60% of older adult's reported sitting for more than 4 h per day, 65% sit in front of a screen for more than 3 h daily and over 55% report watching more than 2 h of TV. However, when measured objectively in a small survey, it was found that 67% of the older population were sedentary for more than 8.5 h daily.
Objective:
A technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health was charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs refers to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes.
Method:
This study on the findings of the technical expert panel.
Results:
Videoconferencing and standard telephone technologies to deliver psychotherapy have been well validated. Web-based interventions have shown efficacy across a broad range of mental health outcomes. Social media such as online support groups have produced disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received little work in mental health.
Conclusion:
Research focused on understanding reach, adherence, barriers and cost is recommended. Improvements in the collection, storage, analysis and visualization of big data will be required. New theoretical models and evaluation strategies will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention is required.
People with mild cognitive impairment or dementia in a mild phase who live at home are expected to manage the everyday technology that is common in this context. However, the knowledge of how technology use may interfere with the performance of daily activities is sparse. The purpose of this study was to evaluate if a new instrument measuring relevance and competence in everyday technology use, the Everyday Technology Use Questionnaire, could generate linear measures of competence in a valid manner when used in a population of 157 older adults with and without cognitive impairment or dementia. The results from this study indicate that the Everyday Technology Use Questionnaire demonstrates acceptable levels of scale validity and person response validity, supporting researchers and clinicians with a tool that generates a valid measure of competence in use of everyday technology for people with mild cognitive impairment or dementia in a mild phase who live at home.
Smart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of
elderly to continue to live in their own home as long as possible. However, this calls for technological solutions that suit
their specific needs and capabilities. The Sweet-Home project aims at developing a new user friendly technology for home automation based on voice command. This paper reports
a user evaluation assessing the acceptance and fear of this new technology. Eight healthy persons between 71 and 88years
old, 7 relatives (child, grandchild or friend) and 3 professional carers participated in a user evaluation. During about 45
min, the persons were questioned in co-discovery in the Domus smart home alternating between interview and wizard of Oz periods followed by a debriefing. The experience aimed at testing
four important aspects of the project: voice command, communication with the outside world, domotics system interrupting a
person’s activity, and electronic agenda. Voice interface appeared to have a great potential to ease daily living for elderly
and frail persons and would be better accepted than more intrusive solutions. By considering still healthy and independent
elderly people in the user evaluation, an interesting finding that came up is their overall acceptance provided the system
does not drive them to a lazy lifestyle by taking control of everything. This particular fear must be addressed for the development
of smart homes that support daily living by giving them more ability to control rather than putting them away from the daily
routine.
KeywordsVoice interface–Smart home–Ubiquitous computing–User evaluation–Gerontechnology
Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empiri- cally compare the eight models and their exten- sions, (3) formulate a unified model that integrates elements across the eight models, and (4) empiri- cally validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models ex- plained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Tech- nology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted
To describe a modification of the computer self efficacy scale for use in clinical settings and to report on the modified scale's reliability and construct validity.
The computer self efficacy scale was modified to make it applicable for clinical settings (for use with older people or people with disabilities using everyday technologies). The modified scale was piloted, then tested with patients in an Australian inpatient rehabilitation setting (n = 88) to determine the internal consistency using Cronbach's alpha coefficient. Construct validity was assessed by correlation of the scale with age and technology use. Factor analysis using principal components analysis was undertaken to identify important constructs within the scale.
The modified computer self efficacy scale demonstrated high internal consistency with a standardised alpha coefficient of 0.94. Two constructs within the scale were apparent; using the technology alone, and using the technology with the support of others. Scores on the scale were correlated with age and frequency of use of some technologies thereby supporting construct validity.
The modified computer self efficacy scale has demonstrated reliability and construct validity for measuring the self efficacy of older people or people with disabilities when using everyday technologies. This tool has the potential to assist clinicians in identifying older patients who may be more open to using new technologies to maintain independence.
Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes. Here we outline how LLM applications such as ChatGPT are developed, and we discuss how they are being leveraged in clinical settings. We consider the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. LLM chatbots have already been deployed in a range of biomedical contexts, with impressive but mixed results. This review acts as a primer for interested clinicians, who will determine if and how LLM technology is used in healthcare for the benefit of patients and practitioners.
In recent years, there has been an explosion of research focused on using technology in healthcare, including Web and mobile health assessment and intervention tools, as well as smartphone sensors and smart environments for monitoring and promoting health behavior. This work has shown that technology-based therapeutic tools offer considerable promise for monitoring and responding to individuals’ health behavior in real time. They may extend the reach of clinicians or serve as stand-alone tools, may be cost-effective and offer countless opportunities for tailoring behavioral interventions that are optimally responsive to individuals’ needs and preferences. Technology may enable entirely new models of healthcare both within and outside of formal systems of care. This book defines the state of scientific research related to the development, experimental evaluation, and effective dissemination of technology-based therapeutic tools targeting behavioral health. It provides an overview of evidence-based approaches to harnessing technology in areas of substance use, mental health, diet and exercise, medication adherence, and chronic disease management. The book also defines the state of implementation research examining models for integrating technology-based behavioral healthcare systems to increase the quality and reach of evidence-based and cost-effective behavioral healthcare, and discusses public health and policy implications of this work. This book consists of contributions from an interdisciplinary group of authors who are leaders in fields of paramount importance to the arena of technology and behavioral health, including the science of behavior change, emerging technologies, health economics, novel methodologies and analytics, implementation science, regulation, privacy and security, and public policy.
The use of digital technology by older adults has improved in recent years in response to the need for their functional adaptation to an increasingly technological social context. Understanding this type of technological adaptation has recently become an important field of inquiry in both social and gerontological studies. Working within this framework, the aim of this study is to identify the main determinants that influence the intention of older people to use digital technology in their daily lives, using the Technological Acceptance Model. A study was carried out with the participation of 1155 people over 65 years of age in Spain. Confirmatory Factor Analysis and structural equation models (SEM) were performed. The results show that the TAM is a useful model to explain the intention of older adults to use Digital Technology, showing a high predictive power, highlighting Perceived Usefulness and Perceived Ease of Use as the main predictor variables.
Thanks to their conversational capabilities, smart speaker-based voice assistants are gaining attention for their potential to support the aging population, though the empirical evidence is still scarce. This paper aims to obtain empirical evidence on older adults' experiences with a voice assistant. We especially focused on how their perception and use change over time as they progress from novice to more experienced users through a longitudinal field deployment study. We deployed Google Home devices in the homes of twelve older adults aged 65 and above and studied their use for sixteen weeks. Results show that the benefits our participants perceived have incrementally changed from enjoying simplicity and convenience of operation in the early phase of the study to not worrying about making mistakes and building digital companionship as they got used to using it. Results also show that participants confronted several challenges that evolved from the unfamiliarity with a voice assistant in their first interactions to coping with the functional errors due to limited speech technology as they got used to using it. Based on the results, we discuss design implications that could foster better user experiences with a voice assistant among older adults.
The implementation of evidence-based physical activity (PA) programs for older adults is limited in part due to the administration-related personnel costs. The rapid growth of the off-the-shelf smart speakers, conversational agents (CAs), demonstrates the potential of scalable delivery of PA programs to older adults at home. We implemented a PA virtual coach based on an evidence-based PA program on a Google Home device, and conducted a user study to examine how older adults interact with the virtual coach. Results suggested that all older adults were able to complete the PA program with guidance from the virtual coach, and showed high acceptance and intentions to use CAs in the future. Analyses on conversation turn-taking further suggested that all older adults (including 80% novice CAs users) experienced minimum difficulty talking with the PA virtual coach. Relationships among age, technology acceptance, conversation patterns and the perceived sociability of CAs are also discussed.
Older adults are increasingly crossing the digital divide and becoming users of a range of technologies. This chapter provides an overview of research on technology use and its impacts, including identification of critical insights into contemporary research and gaps in knowledge. I focus on information and communication technologies, as well as select emerging technologies, that are likely to have significant impacts on older adults’ lives in the coming years; in addition, I discuss impacts of technology use in relation to psychosocial and physical health, as well as unanticipated outcomes. Though research on older adults and technology use is increasing, I call for increased longitudinal studies focusing on in-depth examinations of technology use among older adults, and that more fully exploit behavioral and health data. As research progresses over time, better collaboration between device manufacturers and researchers will allow for collection of objective use and variation data from devices and applications, as well as enable a symbiotic relationship whereby research results are translated into better-designed devices that accommodate the changing nature of older adults’ lives.
Smart Home Personal Assistants (SPA) are an emerging innovation that is changing the means by which home users interact with technology. However, several elements expose these systems to various risks: (i) the open nature of the voice channel they use, (ii) the complexity of their architecture, (iii) the AI features they rely on, and (iv) their use of a wide range of underlying technologies. This article presents an in-depth review of SPA’s security and privacy issues, categorizing the most important attack vectors and their countermeasures. Based on this, we discuss open research challenges that can help steer the community to tackle and address current security and privacy issues in SPA. One of our key findings is that even though the attack surface of SPA is conspicuously broad and there has been a significant amount of recent research efforts in this area, research has so far focused on a small part of the attack surface, particularly on issues related to the interaction between the user and the SPA devices. To the best of our knowledge, this is the first article to conduct such a comprehensive review and characterization of the security and privacy issues and countermeasures of SPA.
Technology has the ability to foster social engagement, but a sizable divide exists between older and younger adults in the use of social communication technologies. The goal of the current study was to gain a better understanding of older adults' perspectives on social communication technologies, including those with higher adoption rates such as email and those with lower adoption rates such as social networking sites (e.g., Facebook, Instagram). Semi-structured group interviews were conducted with either users or non-users of social networking sites to gain insight into issues of adoption and non-adoption of social communication technologies. The Unified Theory of Acceptance and Use of Technology model (UTAUT) was adapted and used to categorize the interview content. We found support for a benefit-driven account of social communication technology acceptance and usage, with participants most frequently discussing the degree social communication technologies would or would not help them attain gains in social connectedness, entertainment, and/or information sharing. However, the UTAUT was not sufficient in fully capturing the group-interview content, with additional categories being necessary. For instance, trust in social networking sites (privacy and security concerns) was frequently discussed by both users and non-users. The current results broaden theories of technology acceptance by identifying facilitators and barriers to technology use in the older adult population.
Identifying the difference in barriers and motivators between middle-age and older adults could contribute toward the development of age-specific health promotion interventions. The aim of this review was to synthesize the literature on barriers and motivators for physical activity in middle-aged (50-64 years) and older adults (65-70 years). The review examined qualitative and quantitative studies using the theoretical domain framework as the guiding theory. The search generated 9400 results from seven databases. A total of fifty-five articles meeting the inclusion criteria. Results indicate that barriers are comparable across the two age groups with environmental factors and resources being the most commonly identified barriers. In older adults, social influences, reinforcement and assistance in managing change were the most identified motivators. Middle-aged identified goals settings, believe that activity will be beneficial and social influences were most important. Findings can be used by professionals to encourage engagement with and adherence to physical activity.
The recent breakthroughs in Artificial Intelligence (AI) have allowed individuals to rely on automated systems for a variety of reasons. Some of these systems are the currently popular voice-enabled systems like Echo by Amazon and Home by Google that are also called as Intelligent Personal Assistants (IPAs). Though there are rising concerns about privacy and ethical implications, users of these IPAs seem to continue using these systems. We aim to investigate to what extent users are concerned about privacy and how they are handling these concerns while using the IPAs. By utilizing the reviews posted online along with the responses to a survey, this paper provides a set of insights about the detected markers related to user interests and privacy challenges. The insights suggest that users of these systems irrespective of their concerns about privacy, are generally positive in terms of utilizing IPAs in their everyday lives. However, there is a significant percentage of users who are concerned about privacy and take further actions to address related concerns. Some percentage of users expressed that they do not have any privacy concerns but when they learned about the "always listening" feature of these devices, their concern about privacy increased.
Voice-operated smart speakers may be an effective tool to support healthy aging and wellness of older adults. A smart speaker primarily designed for usage in a home (e.g. Amazon’s Alexa®) executes a user’s voice commands through an integrated digital personal assistant powered by artificial intelligence. This study explored older adults’ attitudes, needs, challenges, and preferences of using commercially available smart speakers to assist in maintaining their independence at home. As part of a larger Internet of Things feasibility study, nineteen older adult participants (65+) chose to have a smart speaker installed in their home for a 2-month deployment study. We conducted three semi-structured interview sessions, one at baseline, one at 1 month, and another at exit to gather participant perspectives pertaining to overall obtrusiveness, needs and preferences and overall attitudes towards this technology. During the exit interview, older adults were asked what kind of improvements or features they would want to have in the future based on their experience with the smart speaker technology. Interview sessions were audio-recorded and transcribed. We conducted a content analysis of these transcripts to identify themes pertaining to participants’ perceptions of smart speakers. In general, participants expressed a positive experience with hands-free voice interactions with the smart speaker. The two most frequently used features were 1) asking practical questions (e.g. weather) and 2) managing tasks such as setting a reminder to take medication. Participants discussed its potential to be used as a health management device to keep track of their blood pressure or blood sugar.
Research regarding source orientation has demonstrated that when interacting with computers, people direct their communication toward and react toward the technology itself. Users perceive technology to be a source in human-machine communication (HMC). This study provides a new dimension to those findings with regard to source orientation with voice-based, mobile virtual assistants enabled by artificial intelligence (AI). In qualitative interviews regarding their conceptualizations of mobile conversational agents (Apple's Siri, Google Voice, Samsung S-Voice) and their perceptions of interactions with these specific technologies, some participants describe the agent they can hear but not see as a voice in the mobile phone (assistant as distinct entity) while others perceive the technology that they command to be the voice of the phone (assistant as the device). Therefore, congruent with existing research, users of mobile assistants orient toward a technology, instead of thinking they are interacting with a human, but, in contrast to existing research, attend to different technologies. When technologies possess a disembodied voice and are designed with various social cues and degrees of intelligence, the locus and nature of the digital interlocutor is not uniform in people's minds. Link for free access until Jan. 7, 2019: https://authors.elsevier.com/a/1Y4hh2f~UW4lja
Voice has become an increasingly popular User Interaction (UI) channel, with voice-activated devices becoming regular fixtures in our homes. The popularity of voice-based assistants (VAs), however, have brought along significant privacy and security threats to their users. Recent revelations have indicated that some VAs record user's private conversations continuously and innocuously. With the VAs being connected to the Internet, they can leak the recorded content without the user's authorization. Moreover, these devices often do not pack authentication mechanisms to check if the voice commands are issued by authorized users. To address both shortcomings, we propose a framework to impose a security and privacy perimeter around the user's VA. Our proposed framework continuously jams the VA to prevent it from innocuously recording the user's speech, unless the user issues a voice command. To prevent unauthorized voice commands, our framework provides a scheme similar to two-factor authentication to only grant access when the authorized user is in its vicinity. Our proposed framework achieves both objectives through a combination of several techniques to (a) continuously jam one (or many) VA's microphones in a manner inaudible to the user, and (b) provide only authenticated users easy access to VAs.
This paper explores how seniors perceive Voice Enabled User Interfaces (VUIs) and the factors that shape those perceptions. An experiment was administered to 15 seniors (over age of 65), in which the participants searched for information using a traditional keyboard/mouse interface and an experimental voice/touch interface. An analysis of the data collected showed that seniors perceive meaningful differences between the two interfaces in terms of learnability, usability, ease of understanding and helpfulness.
Background:
Older adults are increasingly using information and communication technologies (ICTs). Recent studies show beneficial effects of using ICTs for older adults, particularly in terms of reducing loneliness and depression. However, little is known about the factors that may prevent discontinued ICT use in populations that may be at greater risk, such as those in continuing care retirement communities (CCRCs).
Objectives:
The purpose of this study is to examine a range of factors that may influence discontinued (1) ICT use, (2) searching for health information, and (3) searching for general information over time among CCRC residents.
Methods:
We use longitudinal data from a randomized controlled trial conducted with residents of 19 CCRCs. We use flexible parametric models to estimate the hazard ratio or hazard rate over 5 waves of data to determine what factors significantly predict discontinued (1) ICT use, (2) health information searching, and (3) general information searching.
Results:
The analysis reveals that independent living residents who took part in an 8-week ICT training intervention were less likely to stop using ICTs. Age and the number of instrumental activities of daily living (IADL) impairments significantly predicted an increased likelihood of stopping ICT use. When examining specific ICT-related activities, the analysis reveals that independent living residents who took part in the ICT training intervention were less likely to stop searching for health information and general information online. In addition, age and the number of IADL impairments were associated with increased likelihood of discontinued health information searches and discontinued general information searches.
Conclusion:
ICT training interventions may motivate residents of CCRCs to stay connected by increasing the ICT skill level and promoting confidence, thus decreasing the probability that they will discontinue using ICTs and searching for general information. However, the effects of ICT training on motivating continued ICT usage may be more pronounced among independent living residents. Limitations in the number of IADL impairments is a key factor leading to discontinued use of ICTs among CCRC residents, suggesting that designers of ICTs should be cognizant of the cognitive and physical limitations among this group.
Maintaining functional independence is a high priority for many older adults. Often, staying in their own homes is key to such independence. Computer technology has the potential to assist in this goal by supporting the everyday tasks of older individuals, as well as by aiding caregivers and family members. Our research in advanced computing technologies explores how computational capabilities can enhance day-to-day activities. The computer is not a tool to be picked up, used, and then set aside; it is a constant partner in daily activities. The challenge is to design interfaces that reflect and support ongoing activities of daily life but not be inappropriately intrusive. The purpose of this paper is to provide an overview of the issues that must be considered to ensure success of these development efforts, including older adults’ attitudes towards technology as well as their needs and capabilities. We present two illustrative example projects: one designed to meet daily awareness needs of adult children concerned about the well-being of a senior parent, and another that can provide surrogate memory aids for household tasks. These examples demonstrate the complexity of the issues involved in designing the computationally capable home of the future and provide direction for future research and development efforts.
ObjectivesWe sought a richer understanding of the determinants and complexities of physical activity (PA) behavior by examining how early life experiences and beliefs were manifested over time to determine PA behavior in late life.MethodWe interviewed 23 inactive older adults regarding past and current attitudes and beliefs regarding PA in a two-study format. Study 1 participants responded to a semi-structured interview and data were analyzed inductively for emergent themes. Study 2 participants responded to an unstructured, narrative set of three interviews and data were analyzed using narrative analytical techniques.ResultsStudy 1 descriptively confirmed previously reported barriers and motivators and shed light on how experiences across the lifespan may affect current perceptions. Study 2 extended these findings by demonstrating how beliefs and experiences formed at a young age were manifested over time to determine current behaviors and attitudes.ConclusionImplications include how the findings presented and the narrative method itself can be used to inform contemporary theoretical perspectives and promote PA behavior in older adults.