Willingness of older adults to share data and privacy concerns after exposure to unobtrusive in-home monitoring

Experiences with home monitoring technology in older adults with traumatic brain injury: a qualitative study

BACKGROUND

Home monitoring systems utilising artificial intelligence hold promise for digitally enhanced healthcare in older adults. Their real-world use will depend on acceptability to the end user i.e. older adults and caregivers. We explored the experiences of adults over the age of 60 and their social and care networks with a home monitoring system installed on hospital discharge after sustaining a moderate/severe Traumatic Brain Injury (TBI), a growing public health concern.

METHODS

A qualitative descriptive approach was taken to explore experiential data from older adults and their caregivers as part of a feasibility study. Semi-structured interviews were conducted with 6 patients and 6 caregivers (N = 12) at 6-month study exit. Data were analysed using Framework analysis. Potential factors affecting acceptability and barriers and facilitators to the use of home monitoring in clinical care and research were examined.

RESULTS

Home monitoring was acceptable to older adults with TBI and their caregivers. Facilitators to the use of home monitoring were perceived need for greater support after hospital discharge, the absence of sound and video recording, and the peace of mind provided to care providers. Potential barriers to adoption were reliability, lack of confidence in technology and uncertainty at how data would be acted upon to improve safety at home.

CONCLUSIONS

Remote monitoring approaches are likely to be acceptable, especially if patients and caregivers see direct benefit to their care. We identified key barriers and facilitators to the use of home monitoring in older adults who had sustained TBI, which can inform the development of home monitoring for research and clinical use. For sustained use in this demographic the technology should be developed in conjunction with older adults and their social and care networks.

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists

OBJECTIVE

Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS.

METHODS

Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis.

RESULTS

The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions.

DISCUSSION

The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship.

CONCLUSION

This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.

Evaluating Mobile Apps Targeting Older Adults: Descriptive Study

BACKGROUND

Smartphone use has increased dramatically and, in parallel, a market for mobile apps, including health apps, has emerged. The business model of targeted mobile app advertisements allows for the collection of personal and potentially sensitive information, often without user knowledge. Older adults comprise a rapidly growing demographic that is potentially vulnerable to exploitation by those accessing data collected via these apps.

OBJECTIVE

This research examined apps that claimed to be useful to older adults with a goal of (1) classifying the functionality of each app, (2) identifying whether a privacy policy existed and was accessible, and (3) evaluating evidence that could support claims of value to older adults.

METHODS

An environmental scan was conducted using the Google search engine and typing "apps for older adults." The first 25 sites that this search returned comprised the primary data for this study. Data were organized by descriptive features of purpose (eg, health, finance, and utility), the existence of an electronically accessible privacy policy, price, and evidence supporting each recommended mobile app.

RESULTS

A total of 133 mobile apps were identified and promoted as being the best "apps for older adults." Of these 133 mobile apps, 83% (n=110) included a privacy policy. Fewer apps designated in the "medical" category included a privacy policy than those classified otherwise.

CONCLUSIONS

The results suggest that most mobile apps targeting older adults include a privacy policy. Research is needed to determine whether these privacy policies are readable, succinct, and incorporate accessible data use and sharing practices to mitigate potential risks, particularly when collecting potentially sensitive health information.

Older adults' experience with virtual conversational agents for health data collection

Introduction

Virtual conversational agents (i.e., chatbots) are an intuitive form of data collection. Understanding older adults' experiences with chatbots could help identify their usability needs. This quality improvement study evaluated older adults' experiences with a chatbot for health data collection. A secondary goal was to understand how perceptions differed based on length of chatbot forms.

Methods

After a demographic survey, participants (≥60 years) completed either a short (21 questions), moderate (30 questions), or long (66 questions) chatbot form. Perceived ease-of-use, usefulness, usability, likelihood to recommend, and cognitive load were measured post-test. Qualitative and quantitative analyses were used.

Results

A total of 260 participants reported on usability and satisfaction metrics including perceived ease-of-use (5.8/7), usefulness (4.7/7), usability (5.4/7), and likelihood to recommend (Net Promoter Score = 0). Cognitive load (12.3/100) was low. There was a statistically significant difference in perceived usefulness between groups, with a significantly higher mean perceived usefulness for Group 1 than Group 3. No other group differences were observed. The chatbot was perceived as quick, easy, and pleasant with concerns about technical issues, privacy, and security. Participants provided suggestions to enhance progress tracking, edit responses, improve readability, and have options to ask questions.

Discussion

Older adults found the chatbot to be easy, useful, and usable. The chatbot required low cognitive load demonstrating it could be an enjoyable health data collection tool for older adults. These results will inform the development of a health data collection chatbot technology.

Passive Remote Monitoring and Aging in Place: A Scoping Review

Passive remote monitoring is a relatively new technology that may support older adults to age in place. However, current knowledge about the effectiveness of this technology in extending older adults' independence is lacking. Therefore, we conducted a scoping review of studies examining passive remote monitoring to systematically synthesize evidence about the technology's effectiveness as an intervention. Our initial search of Embase, CINAHL, PubMed, and Scopus databases identified 486 unique articles. Of these, 14 articles met our inclusion criteria. Results show that passive remote monitoring technologies are being used in innovative and diverse ways to support older adults aging in place and their caregivers. More high-quality research on this topic is needed.

Community-dwelling older adults' acceptance of smartwatches for health and location tracking

BACKGROUND

Despite rapid growth in the popularity of smartwatches, evidence lacks regarding older adults' acceptance of smartwatches. Since most wearable sensors are not designed specifically for older adults, there is a need to examine wearability and usability challenges of wearable sensing devices faced by older adults to facilitate the use of objective measurements of health and mobility.

OBJECTIVES

We aimed to examine older adults' perceptions of GPS-enabled smartwatches and to identify potential barriers and facilitators of smartwatch and sensor data use.

METHODS

As part of a larger feasibility study, we conducted a mixed-methods study that included a descriptive content analysis of interviews and a brief usability survey with 30 participants aged 60 years and older after they had used a smartwatch for 3 days.

RESULTS

Most participants perceived wearable activity trackers including smartwatches and sensor-based data as useful for tracking health, finding activity patterns and promoting healthy behaviours. Privacy was of little concern, leading to willingness to share activity and location data with others. Participants identified barriers to usability as clumsy design, lack of aesthetic appeal, and difficulty reading the display and using the GPS tracking function. In contrast, identified facilitators of adoption included a big display, high-tech look, self-awareness and possible behaviour change.

CONCLUSIONS

Smartwatches have the potential of personalised detection of health deterioration and disability prevention, based on analysis of older adults' activities in free-living environments. The usefulness of this technology for older adults can be significantly increased by addressing usability issues and providing instructions on challenging features.

IMPLICATIONS FOR PRACTICE

To support sustained self-monitoring behaviours through wearable sensor devices in older adults, it is critical to examine how they perceive those devices and identify factors affecting technology acceptance that can maximise adoption.

Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living technologies: Scoping Review (Preprint)

BACKGROUND

The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized.

OBJECTIVE

With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology.

METHODS

A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review.

RESULTS

Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies.

CONCLUSIONS

This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user- and real life-targeting research is needed to assess the acceptance of proposed solutions.

How to create value with unobtrusive monitoring technology in home-based dementia care: a multimethod study among key stakeholders

BACKGROUND

There is a growing interest to support extended independent living of people with dementia (PwD) via unobtrusive monitoring (UM) technologies which allow caregivers to remotely monitor lifestyle, health, and safety of PwD. However, these solutions will only be viable if developers obtain a clear picture of how to create value for all relevant stakeholders involved and achieve successful implementation. The aim of this study was therefore to explore the value proposition of UM technology in home-based dementia care and preconditions for successful implementation from a multi-stakeholder perspective.

METHODS

We conducted an expert-informed survey among potential stakeholders (n = 25) to identify key stakeholders for UM technology in home-based dementia care. Subsequently, focus groups and semi-structured interviews were conducted among 5 key stakeholder groups (n = 24) including informal caregivers (n = 5), home care professionals (n = 5), PwD (n = 4), directors and managers within home care (n = 4), and policy advisors within the aged care and health insurance sector (n = 6). The sessions addressed the value proposition- and business model canvas and were analyzed using thematic analysis.

RESULTS

Stakeholders agreed that UM technology should provide gains such as objective surveillance, timely interventions, and prevention of unnecessary control visits, whereas pains mainly included information overload, unplannable care due to real-time monitoring, and less human interaction. The overall design-oriented need referred to clear situation classifications including urgent care (fall- and wandering detection), non-urgent care (deviations in eating, drinking, sleeping), and future care (risk predictions). Most important preconditions for successful implementation of UM technology included inter-organizational collaboration, a shared vision on re-shaping existing care processes, integrated care ICT infrastructures, clear eligibility criteria for end-users, and flexible care reimbursement systems.

CONCLUSIONS

Our findings can guide the value-driven development and implementation of UM technology for home-based dementia care. Stakeholder values were mostly aligned, although stakeholders all had their own perspective on what UM technology should accomplish. Besides, our study highlights the complexity of implementing novel UM technology in home-based dementia care. To achieve successful implementation, organizational and financial preconditions, as well as digital data exchange between home care organizations, will be important.

The survey for memory, attention, and reaction time (SMART): Preliminary normative online panel data and user attitudes for a brief web-based cognitive performance measure

ObjectiveThe Survey for Memory, Attention, and Reaction Time (SMART) was recently introduced as a brief (<5 min), self-administered, web-based measure of cognitive performance in older adults. The purpose of this study was threefold: (1) to develop preliminary norms on the SMART; (2) to examine the relationship between demographic variables (i.e. age, sex, education), device type used, and SMART performance; and (3) to assess user attitudes of the SMART. Method A sample of 1,050 community-dwelling adults (M age =59.5 (15.2), M education = 16.5 (2.1), 67.1% female, 96% white) was recruited from an ongoing web-based research cohort. Participants completed the SMART, which consists of four face-valid cognitive tasks assessing visual memory, attention/processing speed, and executive functioning. SMART performance outcome metrics were subtest completion time (CT), click count, and total CT. Participants provided demographic information and completed a survey of user attitudes toward the SMART (i.e. usability, acceptability). Results Older age was the only demographic variable associated with slower SMART total CT (r = .60, p <.001). Education was not associated with SMART CT or click counts overall (p > .05). Male sex was generally associated with longer SMART CT (p < .001, partial eta squared = .14) on all sub-tests. Regarding acceptability, 97.3% indicated willingness to take the SMART again, with more than half willing to complete it on a weekly basis. Conclusion The preliminary normative data on the SMART indicates that it is a feasible and well-accepted web-based cognitive assessment tool that can be administered on multiple device platforms.

Control Matters in Elder Care Technology:: Evidence and Direction for Designing It In

Studies find that older adults want control over how technologies are used in their care, but how it can be operationalized through design remains to be clarified. We present findings from a large survey (n=825) of a well-characterized U.S. online cohort that provides actionable evidence of the importance of designing for control over monitoring technologies. This uniquely large, age-diverse sample allows us to compare needs across age and other characteristics with insights about future users and current older adults (n=496 >64), including those concerned about their own memory loss (n=201). All five control options, which are not currently enabled, were very or extremely important to most people across age. Findings indicate that comfort with a range of care technologies is contingent on having privacy- and other control-enabling options. We discuss opportunities for design to meet these user needs that demand course correction through attentive, creative work.

Do You Still Trust?

Recently, data breaches, especially insider data breaches, have become increasingly common. However, there is a limited amount of research on the factors associated with the decrease in users' trust in response to these events. In this study, social role theory and socioemotional selectivity theory are applied to understand the role of age (younger and older), biological (male and female) and cultural (masculinity and femininity) gender, and the four dimensions of privacy concern-collection, secondary use, unauthorized access, and error-on initial trust and the corresponding decrease in trust associated with the three trust beliefs of ability, benevolence, and integrity. A scenario-based approach is used to focus on a case study of an insider breach. The findings also provide helpful insights into the comparative roles of trust builders (e.g., reputation and design) and trust crashers (e.g., privacy concerns) in the process of trust building and trust decrease in different demographics (e.g., older and younger, males and females) for overall trust and trusting beliefs. Theoretical, managerial, and social implications are discussed.

Factors Influencing Intention to Technological Use in Older Adults. The TAM Model Aplication

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.

Old-age diversity is underrepresented in digital health research: findings from the evaluation of a mobile phone system for post-operative progress monitoring in Sweden

Much research is conducted to evaluate digital-based solutions for health-care services, but little is known about how such evaluations acknowledge diversity in later life. This study helps fill this gap and analyses participation in the evaluation of a web-based mobile phone system for monitoring the post-operative progress of patients after day surgery. Participation is conceptualised as resulting from three processes: pre-screening, recruitment and self-selection. Based on field information and survey data, this study models (a) the (non-)participation in a sample of 498 individuals aged 60 and older that includes non-screened, non-recruited, decliners and participants in the evaluation, and (b) the individual decision to participate in a sample of 210 individuals aged 60 and older who were invited to take part in the evaluation. Increasing age enhances the likelihood of not being screened, not being recruited or declining the invitation. Those not recruited were most often ineligible because of technology-related barriers. Decliners and participants differed by age, gender, job, health status, digital skills, but not by social participation. Results suggest that highly specific groups of older people are more likely to be involved than others. Old-age diversity is not properly represented in digital health research, with implications for the inclusivity of new digital health technologies. This has implications for increased risks of old-age exclusion and exacerbation of social and digital inequalities in ageing societies.

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident's house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F₁ score on the same dataset.

Use of in-home activity monitoring technologies in older adult veterans with mild cognitive impairment: The impact of attitudes and cognition

BACKGROUND

As researchers incorporate in-home technologies to identify and track changes in older adults' cognitive and daily functioning that could lead to early interventions, the attitudes of older adults across the continuum from normal cognitive aging to mild cognitive impairment (MCI) must be assessed to ensure technology adoption and adherence in each unique group.

OBJECTIVE

This exploratory pilot study incorporated both quantitative and qualitative approaches to examine mild cognitive impairment (MCI) and cognitively intact older adults' attitudes (i.e., usability, acceptability, digital readiness, barriers) and adherence to in-home technologies after undergoing 7 months of in-home activity monitoring.

METHOD

Participants were 30 older adult veterans who were classified as cognitively intact (n = 15) or having mild cognitive impairment (MCI) (n = 15) and participated in a longitudinal aging and technology study that monitored their physical activity and computer use.

RESULTS

While MCI older adults endorsed reduced digital readiness (p =.041) and required more in-home technology maintenance visits (p =.041) from staff as compared to cognitively intact older adults, there was no difference in adherence to the study technology (p >.05). Usability and acceptability attitudes in the entire sample predicted adherence to the physical activity monitoring technology employed in the study (p =.008).

CONCLUSION

Findings highlight the potential gap between technology developers and older adult end users, and technologies designed specifically for older adults with MCI should be developed with direct input from older adults with MCI to promote usability and long-term adoption in this clinical population. Larger studies are needed to replicate and increase the generalizability of the current findings.

Requirements for Unobtrusive Monitoring to Support Home-Based Dementia Care: Qualitative Study Among Formal and Informal Caregivers

BACKGROUND

Due to a growing shortage in residential care, people with dementia will increasingly be encouraged to live at home for longer. Although people with dementia prefer extended independent living, this also puts more pressure on both their informal and formal care networks. To support (in)formal caregivers of people with dementia, there is growing interest in unobtrusive contactless in-home monitoring technologies that allow caregivers to remotely monitor the lifestyle, health, and safety of their care recipients. Despite their potential, these solutions will only be viable if they meet the expectations and needs of formal and informal caregivers of people with dementia.

OBJECTIVE

The objective of this study was to explore the expected benefits, barriers, needs, and requirements toward unobtrusive in-home monitoring from the perspective of formal and informal caregivers of community-dwelling people with dementia.

METHODS

A combination of semistructured interviews and focus groups was used to collect data among informal (n=19) and formal (n=16) caregivers of people with dementia. Both sets of participants were presented with examples of unobtrusive in-home monitoring followed by questions addressing expected benefits, barriers, and needs. Relevant in-home monitoring goals were identified using a previously developed topic list. Interviews and focus groups were transcribed and inductively analyzed. Requirements for unobtrusive in-home monitoring were elicited based on the procedure of van Velsen and Bergvall-Kåreborn.

RESULTS

Formal and informal caregivers saw unobtrusive in-home monitoring as a support tool that should particularly be used to monitor (the risk of) falls, day and night rhythm, personal hygiene, nocturnal restlessness, and eating and drinking behavior. Generally, (in)formal caregivers reported cross-checking self-care information, extended independent living, objective communication, prevention and proactive measures, emotional reassurance, and personalized and optimized care as the key benefits of unobtrusive in-home monitoring. Main concerns centered around privacy, information overload, and ethical concerns related to dehumanizing care. Furthermore, 16 requirements for unobtrusive in-home monitoring were generated that specified desired functions, how the technology should communicate with the user, which services surrounding the technology were seen as needed, and how the technology should be integrated into the existing work context.

CONCLUSIONS

Despite the presence of barriers, formal and informal caregivers of people with dementia generally saw value in unobtrusive in-home monitoring, and felt that these systems could contribute to a shift from reactive to more proactive and less obtrusive care. However, the full potential of unobtrusive in-home monitoring can only unfold if relevant concerns are considered. Our requirements can inform the development of more acceptable and goal-directed in-home monitoring technologies to support home-based dementia care.

Harnessing Mobile Health Technology for Secondary Cardiovascular Disease Prevention in Older Adults: A Scientific Statement From the American Heart Association

Secondary prevention of cardiovascular disease (CVD), the leading cause of morbidity and mortality, is critical to improving health outcomes and quality of life in our aging population. As mobile health (mHealth) technology gains universal leverage and popularity, it is becoming more user-friendly for older adults and an adjunct to manage CVD risk and improve overall cardiovascular health. With the rapid advances in mHealth technology and increasing technological engagement of older adults, a comprehensive understanding of the current literature and knowledge of gaps and barriers surrounding the impact of mHealth on secondary CVD prevention is essential. After a systematic review of the literature, 26 studies that used mHealth for secondary CVD prevention focusing on lifestyle behavior change and medication adherence in cohorts with a mean age of ≥60 years were identified. Improvements in health behaviors and medication adherence were observed, particularly when there was a short message service (ie, texting) component involved. Although mobile technologies are becoming more mainstream and are starting to blend more seamlessly with standard health care, there are still distinct barriers that limit implementation particularly in older adults, including affordability, usability, privacy, and security issues. Furthermore, studies on the type of mHealth that is the most effective for older adults with longer study duration are essential as the field continues to grow. As our population ages, identifying and implementing effective, widely accepted, cost-effective, and time-efficient mHealth interventions to improve CVD health in a vulnerable demographic group should be a top health priority.

Acceptance of Technologies for Aging in Place: A Conceptual Model

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.

Domain Experts on Dementia-Care Technologies: Mitigating Risk in Design and Implementation

There is an urgent need to learn how to appropriately integrate technologies into dementia care. The aims of this Delphi study were to project which technologies will be most prevalent in dementia care in five years, articulate potential benefits and risks, and identify specific options to mitigate risks. Participants were also asked to identify technologies that are most likely to cause value tensions and thus most warrant a conversation with an older person with mild dementia when families are deciding about their use. Twenty-one interdisciplinary domain experts from academia and industry in aging and technology in the U.S. and Canada participated in a two-round online survey using the Delphi approach with an 84% response rate and no attrition between rounds. Rankings were analyzed using frequency counts and written-in responses were thematically analyzed. Twelve technology categories were identified along with a detailed list of risks and benefits for each. Suggestions to mitigate the most commonly raised risks are categorized as follows: intervene during design, make specific technical choices, build in choice and control, require data transparency, place restrictions on data use and ensure security, enable informed consent, and proactively educate users. This study provides information that is needed to navigate person-centered technology use in dementia care. The specific recommendations participants offered are relevant to designers, clinicians, researchers, ethicists, and policy makers and require proactive engagement from design through implementation.

Understanding Technology Preferences and Requirements for Health Information Technologies Designed to Improve and Maintain the Mental Health and Well-Being of Older Adults: Participatory Design Study

Background

Worldwide, the population is aging rapidly; therefore, there is a growing interest in strategies to support and maintain health and well-being in later life. Although familiarity with technology and digital literacy are increasing among this group, some older adults still lack confidence in their ability to use web-based technologies. In addition, age-related changes in cognition, vision, hearing, and perception may be barriers to adoption and highlight the need for digital tools developed specifically to meet the unique needs of older adults.

Objective

The aim of this study is to understand the use of technology by older adults in general and identify the potential barriers to and facilitators of the adoption of health information technologies (HITs) to support the health and well-being of older adults to facilitate implementation and promote user uptake. In addition, this study aims to co-design and configure the InnoWell Platform, a digital tool designed to facilitate better outcomes for people seeking mental health services, to meet the needs of adults 50 years and older and their supportive others (eg, family members, caregivers) to ensure the accessibility, engagement, and appropriateness of the technology.

Methods

Participants were adults 50 years and older and those who self-identified as a supportive other (eg, family member, caregiver). Participants were invited to participate in a 3-hour participatory design workshop using a variety of methods, including prompted discussion, creation of descriptive artifacts, and group-based development of user journeys.

Results

Four participatory design workshops were conducted, including a total of 21 participants, each attending a single workshop. Technology use was prevalent, with a preference indicated for smartphones and computers. Factors facilitating the adoption of HITs included personalization of content and functionality to meet and be responsive to a consumer’s needs, access to up-to-date information from reputable sources, and integration with standard care practices to support the relationship with health professionals. Concerns regarding data privacy and security were the primary barriers to the use of technology to support mental health and well-being.

Conclusions

Although HITs have the potential to improve access to cost-effective and low-intensity interventions at scale for improving and maintaining mental health and well-being, several strategies may improve the uptake and efficacy of technologies by the older adult community, including the use of co-design methodologies to ensure usability, acceptability, and appropriateness of the technology; support in using and understanding the clinical applications of the technology by a digital navigator; and ready availability of education and training materials.

Advances in Sensor Monitoring Effectiveness and Applicability: A Systematic Review and Update

BACKGROUND AND OBJECTIVES

To provide an updated review article studying the applicability and effectiveness of sensor networks in measuring and supporting activities of daily living (ADLs) among non-demented older adults.

RESEARCH DESIGN AND METHODS

Systematic review following PRISMA guidelines.

Systematic search of PubMed, Embase, PsycINFO, INSPEC, and the Cochrane Library, from October 26, 2012 to January 3, 2018 for empirical studies, measuring and supporting ADLs among independently living, non-demented older adults, investigating wireless sensor monitoring networks.

RESULTS

The search queries yielded 10,782 hits of which 162 articles were manually reviewed. Following exclusion criteria, 13 relevant articles were retained. Although various types of sensor networks with different analyzing algorithms were proposed, from simple video monitoring to complex sensor networks distributed throughout a house, all articles supported the use of wireless sensors for identifying changes in activity patterns.

DISCUSSION AND IMPLICATIONS

Wireless sensor networks appear to be developing into an effective solution for measuring ADLs and for identifying changes in their patterns. They offer a promising solution to support older adults living independently at home. However, there is too much focus on technology, and practical usefulness still needs to be further elaborated. Sensors should focus on ADLs that are sensitive to the earliest signs of cognitive decline, as well as quantitative markers, such as errors in the execution of ADLs.

Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors

Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose. This type of sensor offers both visual and depth sensing modalities from the scene where the information can be fused and coded for better protection of privacy. For this purpose, we introduce the novel notion of passive observer. This observer is only triggered by detecting the absence of movements of older adults in the scene. This is accomplished by classifying and localizing objects in the monitoring scene from both before and after the detection of movements. A deep learning tool is utilized for visual classification of known objects in the physical scene followed by virtual reality reconstructing of the scene where the shape and location of objects are recreated. Such reconstruction can be used as a visual summary in order to identify objects which were handled by an older adult in-between observation. The simplified virtual scene can be used, for example, by caregivers or monitoring personnel in order to assist in detecting any anomalies. This virtual visualization can offer a high level of privacy protection without having any direct visual access to the monitoring scene. In addition, using the scene graph representation, an automatic decision-making tool is proposed where spatial relationships between the objects can be used to estimate the expected activities. The results of this paper are demonstrated through two case studies.

Long-Term Impact of Intelligent Monitoring Technology on People with Cognitive Impairment: An Observational Study

BACKGROUND

Interactive smart home systems are particularly useful for people with cognitive impairment.

OBJECTIVE

To investigate the long-term effects of Assistive Technology (AT) combined with tailored non-pharmacological interventions for people with cognitive impairment.

METHODS

18 participants (12 with mild cognitive impairment and 6 with Alzheimer's disease) took part in the study that we evenly allocated in one of three groups: 1) experimental group (EG), 2) control group 1 (CG1), and 3) control group 2 (CG2). EG received the system installed at home for 4 to 12 months, during which they received tailored non-pharmacological interventions according to system observations. CG1 received tailored interventions for the same period, but only according to state-of-the-art self-reporting methods. Finally, CG2 neither had a system installation nor received interventions. All groups underwent neuropsychological assessment before and after the observational period.

RESULTS

After several months of continuously monitoring at home and deployment of tailored interventions, the EG showed statistically significant improvement in cognitive function, compared to the CG1 and CG2. Moreover, EG participants, who received the sensor-based system, have shown improvement in domains such as sleep quality and daily activity, as measured by the multi-sensor system. In addition, the feedback collected from the participants concludes that the long-term use of the multi-sensor system by people with cognitive impairment can be both feasible and beneficial.

CONCLUSION

Deploying a sensor-based system at real home settings of people with cognitive limitations living alone and maintaining its use long-term is not only possible, but also beneficial for clinical decision making in order to tackle cognitive, functional, and behavioral related problems.

Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study

Background

Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.

Objective

The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.

Methods

We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered.

Results

Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system.

Conclusions

We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.

Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

Investigating the concept of participant burden in aging technology research

BACKGROUND

Research participation burden, despite being an integral concept in research ethics, is not well-conceptualized in the context of the use of technology in research. This knowledge gap is especially critical for the older adult population as new technology solutions are increasingly embedded in clinical trials for this demographic. Our objective was to investigate how older adults conceptualize participation burden in contact for research participation and research trials using technology.

METHODS

We developed and conducted an Internet-based survey consisting of 22 multiple choice and Likert-scale type questions investigating older adults' preferred means and frequency of being contacted about research opportunities, their willingness to use specific kinds of technology and their concerns regarding technology use in clinical trials. We received a total of 273 completed surveys from eligible participants aged 50 or older.

RESULTS

Older adults preferred to be contacted about research opportunities monthly, over email. Survey participants were least willing to use monitoring devices and their biggest concern was the security of the storage of information gathered by technology. This concern was positively correlated with age. Participants indicated a preference to use technology daily, in short sessions, preferably in a way that can be incorporated into their daily routine.

CONCLUSIONS

Results from this work provide insights for the design of effective recruitment campaigns as well as technology interventions in clinical trials through minimizing the burden of research participation.

Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods

This paper investigates the utility of unsupervised machine learning and data visualisation for tracking changes in user activity over time. This is done through analysing unlabelled data generated from passive and ambient smart home sensors, such as motion sensors, which are considered less intrusive than video cameras or wearables. The challenge in using unlabelled passive and ambient sensors data for activity recognition is to find practical methods that can provide meaningful information to support timely interventions based on changing user needs, without the overhead of having to label the data over long periods of time. The paper addresses this challenge to discover patterns in unlabelled sensor data using kernel density estimation (KDE) for pre-processing the data, together with t-distributed stochastic neighbour embedding and uniform manifold approximation and projection for visualising changes. The methodology is developed and tested on the Aruba CASAS smart home dataset and focusses on discovering and tracking changes in kitchen-based activities. The traditional approach of using sliding windows to segment the data requires a priori knowledge of the temporal characteristics of activities being identified. In this paper, we show how an adaptive approach for segmentation, KDE, is a suitable alternative for identifying temporal clusters of sensor events from unlabelled data that can represent an activity. The ability to visualise different recurring patterns of activity and changes to these over time is illustrated by mapping the data for separate days of the week. The paper then demonstrates how this can be used to track patterns over longer time-frames which could be used to help highlight differences in the user’s day-to-day behaviour. By presenting the data in a format that can be visually reviewed for temporal changes in activity over varying periods of time from unlabelled sensor data, opens up the opportunity for carers to then initiate further enquiry if variations to previous patterns are noted. This is seen as an accessible first step to enable carers to initiate informed discussions with the service user to understand what may be causing these changes and suggest appropriate interventions if the change is found to be detrimental to their well-being.

Understanding the use and non-use of social communication technologies by older adults: A qualitative test and extension of the UTAUT model

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.

Why Older Adults and Their Children Disagree About In-Home Surveillance Technology, Sensors, and Tracking

Background and Objectives

Despite the surveilling nature of technologies that allow caregivers to remotely monitor location, movements, or activities, the potential differences in comfort with remote monitoring between caregivers and care recipients have not been examined in depth. On the dyad and aggregate level, we compare preferences of older adult women and their adult children for three remote monitoring technologies. Their assessments of each technology’s impact on privacy, safety, independence, freedom, relationship with family member, social life, and identity are also compared.

Research Design and Methods

This dyadic study used cognitive-based interview probing and value-centered design methods. Twenty-eight individual, in-depth, structured interviews were conducted with 18 women who are Meals on Wheels clients and 10 of their adult children.

Results

Meals on Wheels participants reported multiple chronic conditions and an average of 1.7 ADL and 3.3 IADL difficulties; two thirds were enrolled in Medicaid. Adult children preferred each technology more than their mothers did and underestimated both their mothers’ ability to comprehend the functions of the technologies and the importance of engaging them fully in decision making. Most were confident that they could persuade their mothers to adopt. For both groups, privacy was the most-cited concern, and participants perceived significant overlap between values of privacy, independence, identity, and freedom.

Discussion and Implications

Studying privacy in isolation overlooks privacy’s instrumental role in enabling other values. Shared decision-making tools are needed to promote remote monitoring use consistent with older adults’ values and to prevent conflict and caregiver overreach.

Mobile and Connected Health Technology Needs for Older Adults Aging in Place: Cross-Sectional Survey Study

BACKGROUND

An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear.

OBJECTIVE

This study aimed to assess older adults' perceptions of using mobile and connected health technologies.

METHODS

Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone.

RESULTS

The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device (P<.001) and joining an online health community (P=.05).

CONCLUSIONS

Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.

Technology for Behavioral Change in Rural Older Adults with Obesity

BACKGROUND

Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population.

METHODS

We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics.

RESULTS

All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure.

CONCLUSIONS

Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.

Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities

Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults.

Acceptance and Use of Innovative Assistive Technologies among People with Cognitive Impairment and Their Caregivers: A Systematic Review

Cognitive impairments (CI), associated with the consequences of Alzheimer's disease and other dementias, are increasingly prevalent among older adults, leading to deterioration in self-care, mobility, and interpersonal relationships among them. Innovative Assistive Technologies (IAT) such as electronic reminders and surveillance systems are considered as increasingly important tools to facilitate independence among this population and their caregivers. The aim of this study is to synthesise knowledge on facilitators and barriers related to acceptance of and use of IAT among people with CI and their caregivers. This systematic review includes original papers with quantitative, qualitative, or mixed methods design. Relevant peer-reviewed articles published in English between 2007 and 2017 were retrieved in the following databases: CINAHL; PubMed; Inspec; and PsycINFO. The Mixed Method Appraisal Tool (MMAT) was used for quality assessment. We retrieved thirty studies, including in total 1655 participants from Europe, USA/Canada, Australia, and Asia, enrolled in their homes, care-residences, day-care centres, or Living Labs. Two-thirds of the studies tested technologies integrating home sensors and wearable devices for care and monitoring CI symptoms. Main facilitators for acceptance and adherence to IAT were familiarity with and motivation to use technologies, immediate perception of effectiveness (e.g., increase in safety perceptions), and low technical demands. Barriers identified included older age, low maturity of the IAT, little experience with technologies in general, lack of personalization, and support. More than 2/3 of the studies met 80% of the quality criteria of the MMAT. Low acceptance and use of IAT both independently and with caregivers remains a significant concern. More knowledge on facilitators and barriers to use of IAT among clients of health care and social services is crucial for the successful implementation of innovative programmes aiming to leverage innovative technologies for the independence of older people with CI.

A nurse-driven method for developing artificial intelligence in "smart" homes for aging-in-place

OBJECTIVES

To offer practical guidance to nurse investigators interested in multidisciplinary research that includes assisting in the development of artificial intelligence (AI) algorithms for "smart" health management and aging-in-place.

METHODS

Ten health-assistive Smart Homes were deployed to chronically ill older adults from 2015 to 2018. Data were collected using five sensor types (infrared motion, contact, light, temperature, and humidity). Nurses used telehealth and home visitation to collect health data and provide ground truth annotation for training intelligent algorithms using raw sensor data containing health events.

FINDINGS

Nurses assisting with the development of health-assistive AI may encounter unique challenges and opportunities. We recommend: (a) using a practical and consistent method for collecting field data, (b) using nurse-driven measures for data analytics, (c) multidisciplinary communication occur on an engineering-preferred platform.

CONCLUSIONS

Practical frameworks to guide nurse investigators integrating clinical data with sensor data for training machine learning algorithms may build capacity for nurses to make significant contributions to developing AI for health-assistive Smart Homes.

Ethical Design of Intelligent Assistive Technologies for Dementia: A Descriptive Review

The use of Intelligent Assistive Technology (IAT) in dementia care opens the prospects of reducing the global burden of dementia and enabling novel opportunities to improve the lives of dementia patients. However, with current adoption rates being reportedly low, the potential of IATs might remain under-expressed as long as the reasons for suboptimal adoption remain unaddressed. Among these, ethical and social considerations are critical. This article reviews the spectrum of IATs for dementia and investigates the prevalence of ethical considerations in the design of current IATs. Our screening shows that a significant portion of current IATs is designed in the absence of explicit ethical considerations. These results suggest that the lack of ethical consideration might be a codeterminant of current structural limitations in the translation of IATs from designing labs to bedside. Based on these data, we call for a coordinated effort to proactively incorporate ethical considerations early in the design and development of new products.

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Digital technologies for social inclusion of individuals with disabilities

Information technology can be an important facilitator of social inclusion for people with disabilities into society. However, the goals specified in this area by organizations such as the European Commission have not yet been achieved in their totality. The aim of this paper is to explore which types of information communication technology-based applications and/or digital services have been suggested to facilitate the social integration of people who suffer from different types of disabilities. We performed a literature review that included studies published during a period of 6 years (2010–2016). The results show that, in the data we have had access to, no concrete patterns can be identified regarding the type of technology or technological trends that can be used to support the social integration of individuals with disabilities. This literature review is of relevance to the identification of further research areas and to the identification of issues which have to be considered in the context of the development and implementation of technological innovations that are aimed at promoting or facilitating social inclusion of individuals with disabilities.

A randomized trial using telehealth technology to link caregivers with dementia care experts for in-home caregiving support: FamTechCare protocol

The number of persons with dementia (PWD) in the United States is expected to reach 16 million by 2050. Due to the behavioral and psychological symptoms of dementia, caregivers face challenging in-home care situations that lead to a range of negative health outcomes such as anxiety and depression for the caregivers and nursing home placement for PWD. Supporting Family Caregivers with Technology for Dementia Home Care (FamTechCare) is a multisite randomized controlled trial evaluating the effects of a telehealth intervention on caregiver well-being and PWD behavioral symptoms. The FamTechCare intervention provides individualized dementia-care strategies to in-home caregivers based on video recordings that the caregiver creates of challenging care situations. A team of dementia care experts review videos submitted by caregivers and provide interventions to improve care weekly for the experimental group. Caregivers in the control group receive feedback for improving care based on a weekly phone call with the interventionist and receive feedback on their videos at the end of the 3-month study. Using linear mixed modeling, we will compare experimental and control group outcomes (PWD behavioral symptoms and caregiver burden) after 1 and 3 months. An exploratory descriptive design will identify a typology of interventions for telehealth support for in-home dementia caregivers. Finally, the cost for FamTechCare will be determined and examined in relation to hypothesized effects on PWD behavioral symptoms, placement rates, and caregiver burden. This research will provide the foundation for future research for telehealth interventions with this population, especially for families in rural or remote locations.

Passive Assessment of Routine Driving with Unobtrusive Sensors: A New Approach for Identifying and Monitoring Functional Level in Normal Aging and Mild Cognitive Impairment

BACKGROUND

Driving is a key functional activity for many older adults, and changes in routine driving may be associated with emerging cognitive decline due to early neurodegenerative disease. Current methods for assessing driving such as self-report are inadequate for identifying and monitoring subtle changes in driving patterns that may be the earliest signals of functional change in developing mild cognitive impairment (MCI).

OBJECTIVE

This proof of concept study aimed to establish the feasibility of continuous driving monitoring in a sample of cognitively normal and MCI older adults for an average of 206 days using an unobtrusive driving sensor and demonstrate that derived sensor-based driving metrics could effectively discriminate between MCI and cognitively intact groups.

METHODS

Novel objective driving measures derived from 6 months of routine driving monitoring were examined in older adults with intact cognition (n = 21) and MCI (n = 7) who were enrolled in the Oregon Center for Aging and Technology (ORCATECH) longitudinal assessment program.

RESULTS

Unobtrusive continuous monitoring of older adults' routine driving using a driving sensor was feasible and well accepted. MCI participants drove fewer miles and spent less time on the highway per day than cognitively intact participants. MCI drivers showed less day-to-day fluctuations in their driving habits than cognitively intact drivers.

CONCLUSION

Sensor-based driving measures are objective, unobtrusive, and can be assessed every time a person drives his or her vehicle to identify clinically meaningful changes in daily driving. This novel methodology has the potential to be useful for the early detection and monitoring of changes in daily functioning within individuals.

A Novel and Intelligent Home Monitoring System for Care Support of Elders with Cognitive Impairment

BACKGROUND

Assistive technology, in the form of a smart home environment, is employed to support people with dementia.

OBJECTIVES

To propose a system for continuous and objective remote monitoring of problematic daily living activity areas and design personalized interventions based on system feedback and clinical observations for improving cognitive function and health-related quality of life.

METHODS

The assistive technology of the proposed system, including wearable, sleep, object motion, presence, and utility usage sensors, was methodically deployed at four different home installations of people with cognitive impairment. Detection of sleep patterns, physical activity, and activities of daily living, based on the collected sensor data and analytics, was available at all times through comprehensive data visualization solutions. Combined with clinical observation, targeted psychosocial interventions were introduced to enhance the participants' quality of life and improve their cognitive functions and daily functionality. Meanwhile, participants and their caregivers were able to visualize a reduced set of information tailored to their needs.

RESULTS

Overall, paired-sample t-test analysis of monitored qualities revealed improvement for all participants in neuropsychological assessment. Moreover, improvement was detected from the beginning to the end of the trial, in physical condition and in the domains of sleep. Detecting abnormalities via the system, for example in sleep quality, such as REM sleep, has proved to be critical to assess current status, drive interventions, and evaluate improvements in a reliable manner.

CONCLUSION

It has been proved that the proposed system is suitable to support clinicians to reliably drive and evaluate clinical interventions toward quality of life improvement of people with cognitive impairment.

Older Adults' Perceptions of and Preferences for a Fall Risk Assessment System: Exploring Stages of Acceptance Model

Aging in place is a preferred and cost-effective living option for older adults. Research indicates that technology can assist with this goal. Information on consumer preferences will help in technology development to assist older adults to age in place. The study aim was to explore the perceptions and preferences of older adults and their family members about a fall risk assessment system. Using a qualitative approach, this study examined the perceptions, attitudes, and preferences of 13 older adults and five family members about their experience living with the fall risk assessment system during five points in time. Themes emerged in relation to preferences and expectations about the technology and how it fits into daily routines. We were able to capture changes that occurred over time for older adult participants. Results indicated that there was acceptance of the technology as participants adapted to it. Two themes were present across the five points in time-safety and usefulness. Five stages of acceptance emerged from the data from preinstallation to 2 years postinstallation. Identified themes, stages of acceptance, and design and development considerations are discussed.

Smart homes and home health monitoring technologies for older adults: A systematic review

BACKGROUND

Around the world, populations are aging and there is a growing concern about ways that older adults can maintain their health and well-being while living in their homes.

OBJECTIVES

The aim of this paper was to conduct a systematic literature review to determine: (1) the levels of technology readiness among older adults and, (2) evidence for smart homes and home-based health-monitoring technologies that support aging in place for older adults who have complex needs.

RESULTS

We identified and analyzed 48 of 1863 relevant papers. Our analyses found that: (1) technology-readiness level for smart homes and home health monitoring technologies is low; (2) the highest level of evidence is 1b (i.e., one randomized controlled trial with a PEDro score ≥6); smart homes and home health monitoring technologies are used to monitor activities of daily living, cognitive decline and mental health, and heart conditions in older adults with complex needs; (3) there is no evidence that smart homes and home health monitoring technologies help address disability prediction and health-related quality of life, or fall prevention; and (4) there is conflicting evidence that smart homes and home health monitoring technologies help address chronic obstructive pulmonary disease.

CONCLUSIONS

The level of technology readiness for smart homes and home health monitoring technologies is still low. The highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs.

HEALTH PROFESSIONALS' USER EXPERIENCE OF THE INTELLIGENT BED IN PATIENTS' HOMES

BACKGROUND

The intelligent bed is a medical bed with several home healthcare functions. It includes, among others, an "out of bed" detector, a moisture detector, and a catheter bag detector. The design purpose of the intelligent bed is to assist patients in their daily living, facilitate the work of clinical staff, and improves the quality of care. The aim of this sub-study of the iCare project was to explore how health professionals (HPs) experience and use the intelligent bed in patients' homes.

METHODS

The overall research design is inspired by case study methodology. A triangulation of data collection techniques has been used: log book, documentation study, participant observations (n = 45 hr), and qualitative interviews (n = 23). The data have been analyzed by means of Nvivo 9.0.

FINDINGS

We identified several themes: HP transformation from passive technology recipient to innovator; individualized care; work flow redesign; and sensor technology intruding on patient privacy.

CONCLUSIONS

It is suggested that functions of the intelligent bed can result in more individualized care, workflow redesign, and time savings for the health professionals in caring for elderly patients. However, the technology intruded on patients' privacy.