Toward Sustaining Web-Based Senior Center Programming Accessibility With and for Older Adult Immigrants: Community-Based Participatory Research Cross-Sectional Study.

BACKGROUND: During the COVID-19 pandemic, many community-based organizations serving Asian Americans pivoted to provide web-based care and social services. Asian American community leaders in the United States Pacific Northwest, including Asian Health & Service Center expressed that there are older immigrant adults who experienced backlash from discrimination, fear, and anxiety owing in part to anti-Asian hate and isolation, including from infection precautions. Pivoting supported staying safe from COVID-19 transmission and anti-Asian hate crimes. OBJECTIVE: This study aims to examine the readiness of diverse groups of older Asian American immigrant adults (Chinese, Koreans, and Vietnamese) to use a web-based senior center, including technology access and telehealth use, and to identify the psychosocial health impacts that a web-based senior center could be positioned to meet. METHODS: A community-based participatory research approach was used to conduct a cross-sectional survey study in an Asian-based health and service center in 2022. We selected surveys from the National Institutes of Health-supported PhenX Toolkit. Analyses were performed using R software. RESULTS: There was an 88.2% (216/245) response rate. Overall, 39.8% (86/216) of participants were Chinese, 25% (54/216) were Korean, and 24.5% (53/216) were Vietnamese. There were significant group differences in mobile data plans (P=.0005). Most had an unlimited mobile data plan (38/86, 44% Chinese; 39/54, 72% Koreans; 25/53, 47% Vietnamese). Significant group differences existed regarding whether they started using a new electronic device to communicate with friends or family after the COVID-19 outbreak (P=.0005); most were Korean participants (31/54, 57%). For written text and audio or video apps, most Chinese participants used WeChat (65/85, 76%; 57/84, 68%, respectively), most Koreans used KakaoTalk (49/54, 91%; 49/54, 91%, respectively), and most Vietnamese used Facebook Messenger for written text (32/50, 64%) and Apple Face Time (33/50, 66%) or Facebook Messenger (31/50, 62%) for audio or video. Significant group differences existed regarding whether to try telehealth (P=.0005); most Vietnamese expressed that they would never consider it (41/53, 77%). Significant group differences existed regarding how well they were able to concentrate (χ22=44.7; P<.0001); Chinese participants reported a greater inability (median 5, IQR 4-6). With regard to difficulties in life experiences (χ22=51; P<.0001), the median was 6 (IQR 5-7) for the Vietnamese group. Significant group differences existed in having had a family/household member's salary, hours, and contracts reduced (P=.0005) and having had a family/household member or friend fallen physically ill (P=.0005)-most Vietnamese (15/53, 28%) and Korean participants (10/53, 19%). CONCLUSIONS: To build an efficacious, web-based senior center with web-based care and social service options, more older adults need access to the internet and education about using technology-enabled communication devices. Addressing the unique psychosocial impacts of the COVID-19 pandemic on each group could improve health equity. The strength of the participating older adults was observed and honored.

Family caregiver readiness to adopt smart home technology to monitor care-Dependent older adults: A qualitative exploratory study.

AIMS: The aim of this study was to explore factors that influence family caregiver readiness to adopt health smart home technology for their care-dependent older adult family member. Health smart homes are designed to remotely monitor the health and wellness of community-dwelling older adults supporting independent living for as long as possible. Accordingly, if the health smart home is deployed into the home of a care-depended older adult, it can potentially support family caregivers by facilitating workforce participation and give piece of mind to the family caregiver who may not live close to the older adult. However, wider adoption of health smart home technologies into the homes of community-older adults is low, and little is known about the factors that influence the readiness of family caregivers to adopt smart home technologies for their care-dependent older adults. DESIGN: A qualitative Descriptive study design was utilized. METHODS: Qualitative data were collected between 2019 and 2020 via semi-structured interviews. Thematic analysis of interviews was completed, and data were organized into themes. RESULTS: Study findings show that caregiver readiness (N = 10) to adopt smart home technology to monitor older adult family members were influenced by five primary themes including a 'big brother effect', 'framing for acceptance', 'data privacy', 'burden' and 'cost.' CONCLUSION: Family caregivers were open to adopting smart home technology to support the independent living of their older adult family members. However, the readiness of family caregivers was inextricably linked to the older adults' readiness for smart home adoption. The family caregiver's primary concern was on how they could frame the idea of the smart home to overcome what they viewed as hesitancy to adopt in the older adult. The findings suggest that family caregivers endeavour to balance the hesitancy in their older adult family members with the potential benefits of smart home technology. IMPACT: Family caregivers could benefit if their care-dependent older adults adopt smart home technology. Recognizing the role of caregivers and their perspectives on using smart home technologies with their care-dependents is critical to the meaningful design, use and adoption.

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids.

BACKGROUND: Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks. AIM: This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone. METHODS: Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data. RESULTS: Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%). CONCLUSIONS: Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.

Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series.

Background: Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points. Objective: The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes. Participants: 25 participants with multiple chronic health conditions. Methods: Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored. Results: For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event. Conclusions: We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention. Tweetable abstract: Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.

Factors influencing community-dwelling older adults' readiness to adopt smart home technology: A qualitative exploratory study.

AIMS: Ageing-in-place for older people could be more feasible with the support of smart home technology. Ageing in-place may maximize the independence of older adults and enhance their well-being and quality of life, while decreasing the financial burden of residential care costs, and addressing workforce shortages. However, the uptake of smart home technology is very low among older adults. Accordingly, the aim of this study was to explore factors influencing community-dwelling older adults' readiness to adopt smart home technology. DESIGN: A qualitative exploratory study design was utilized. METHODS: Descriptive data were collected between 2019 and 2020 to provide context of sample characteristics for community-dwelling older adults aged ≥65 years. Qualitative data were collected via semi-structured interviews and focus groups, to generate an understanding of older adult's perspectives. Thematic analysis of interviews and focus group transcripts was completed. The Elderadopt model was the conceptual framework used in the analysis of the findings. RESULTS: Several factors influenced community-dwelling older adults' (N = 19) readiness to adopt smart home technology. Five qualitative themes were identified: knowledge, health and safety, independence, security and cost. CONCLUSION: Community-dwelling older adults were open to adopting smart home technology to support independence despite some concerns about security and loss of privacy. Opportunities to share information about smart home technology need to be increased to promote awareness and discussion. IMPACT: Wider adoption of smart home technology globally into the model of aged care can have positive impacts on caregiver burden, clinical workforce, health care utilization and health care economics. Nurses, as the main providers of healthcare in this sector need to be knowledgeable about the options available and be able to provide information and respond to questions know about ageing-in-place technologies to best support older adults and their families.

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.

With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. All subjects in the first group are diagnosed with cognitive impairment. The second group consists of cognitively healthy, age-matched controls. Using BCD-G, we identify differences between these two groups, such as how impairment affects patterns of performing activities of daily living and how clinically-relevant behavioral features, such as in-home walking speed, differ for cognitively-impaired individuals. With the unobtrusive monitoring of smart home environments, clinicians can use BCD-G for remote identification of behavior changes that are early indicators of health concerns.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. OBJECTIVE: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. METHODS: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. CONCLUSIONS: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

Interpreting Health Events in Big Data Using Qualitative Traditions.

The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant's description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.

Iterative Design of Visual Analytics for a Clinician-in-the-Loop Smart Home.

In order to meet the health needs of the coming "age wave," technology needs to be designed that supports remote health monitoring and assessment. In this study we design clinician in the loop (CIL), a clinician-in-the-loop visual interface, that provides clinicians with patient behavior patterns, derived from smart home data. A total of 60 experienced nurses participated in an iterative design of an interactive graphical interface for remote behavior monitoring. Results of the study indicate that usability of the system improves over multiple iterations of participatory design. In addition, the resulting interface is useful for identifying behavior patterns that are indicative of chronic health conditions and unexpected health events. This technology offers the potential to support self-management and chronic conditions, even for individuals living in remote locations.

A conceptual framework for clinicians working with artificial intelligence and health-assistive Smart Homes.

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and should play a key role in the development and application of Smart Home technology. Accordingly, conceptual frameworks are needed for nurse scientists who are collaborating with multidisciplinary research teams in developing an intelligent Smart Home that assists with managing older adults' health. We present a conceptual framework that is grounded in critical realism and pragmatism, informing a unique mixed methodological approach to generating, analyzing, and contextualizing sensor data for clinician-based machine learning. This framework can guide nurse scientists in knowledge construction as they participate in multidisciplinary health-assistive Smart Home and artificial intelligence research. In this paper, we review philosophical underpinnings and explicate how this framework can guide nurse scientists collaborating with engineers to develop intelligent health-assistive Smart Homes. It is critical that clinical nursing knowledge is integrated into Smart Home and artificial intelligence features. A conceptual framework and practical method will provide needed structure for knowledge construction by nurse scientists.

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.

Using Smart City Technology to Make Healthcare Smarter.

Smart cities use information and communication technologies (ICT) to scale services include utilities and transportation to a growing population. In this article we discuss how smart city ICT can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.

Data Collection via In-Depth Email Interviewing: Lessons From the Field.

This methods article is a reflection on the use of in-depth email interviewing in a qualitative descriptive study. The use of emailing to conduct interviews is thought to be an effective way to collect qualitative data. Building on current methodological literature in qualitative research regarding in-depth email interviewing, we move the conversation toward elicitation of quality data and management of multiple concurrent email interviews. Excerpts are shared from a field journal that was kept throughout one study, with commentary on developing insights. Valuable lessons learned include the importance of (a) logistics and timing related to the management of multiple concurrent email interviews, (b) language and eliciting the data, (c) constructing the email, and (d) processing text-based data and preparing transcripts. Qualitative researchers seeking deeply reflective answers and geographically diverse samples may wish to consider using in-depth email interviews.

Interactive CO-learning for Research Engagement and Education (I-COREE) Curriculum to Build Capacity Between Community Partners and Academic Researchers.

The voice of diverse communities continues to be minimal in academic research. Few models exist for education and training of new research topics and terminology and building partnership capacity in community-engaged research. Little is known about integrative education and training when building participatory research partnerships for sustainability and developing trust and rapport. Community partners at an Asian community-based health and social services center in a large metropolitan area wanted to explore the cultural context of a health-assistive smart home that monitors and auto-alerts with changes in health. With historical and recent rising trends in culturally insensitive research in several diverse communities, the concept of technology-enabled monitoring in the privacy of one's home brings uncertainty. Academic nurse researchers and community partners co-created a culturally safe integrative education and training curriculum, the Interactive CO-learning for Research Engagement and Education (I-COREE). The purpose was to design, implement, and evaluate the curriculum to respond to the community partners' needs to create a culturally safe space through an integrative education and training to facilitate building partnership capacity for research engagement including developing trust and rapport and addressing uncertainties in health-assistive technologies. Popular education tenets informed the curriculum. Twelve academic and community partners participated, four were team teachers who co-led the session. Implementation of the experiential, multimodal co-learning activities were conducted within ahalf-day. The curriculum evaluation indicated that it helped bridge critical conversations about partners' fears of the unknown, approach culturally sensitive topics safely, and trust and rapport. Key elements may be translatable to other partnerships.

Health-Assistive Smart Homes for Aging in Place: Leading the Way for Integration of the Asian Immigrant Minority Voice.

Caring for America's aging population is a complex humanitarian issue. The number of older adults is expected to increase to 98.5 million by 2060 with a 295% growth in foreign-born older adults, including Asian immigrants. Most older adults will have one or more chronic conditions and 95% of healthcare costs will be attributed to caring for these conditions. Among Asian Americans, common chronic conditions include respiratory disease, cancer, cardiovascular disease, and pain. The National Institutes of Health, Institute on Aging, and National Science Foundation call for innovative technologies to be developed by multidisciplinary teams to address these concerns. Asian community leaders at Asian Health & Service Center and community members in Oregon identified the use of health-assistive technologies as a priority for potentially reducing stress and improving quality of life for both older adults and their caregivers. The purpose of this article is to introduce nurses and healthcare workers, advocating for the interests of Asian/Pacific Island community members, to the innovative health-assistive smart home. The health-assistive smart home uses artificial intelligence to identify and predict health events. Inclusion of minority persons' data in the development of artificial intelligence has been generally overlooked. This may result in continued health inequities and is incompatible with the goals of global health. Integration of minority voices while exploring the efficacious use of the health-assistive smart home is of significant value to minority populations. Asian immigrant older adults engaging in smart home research and development will enhance the cultural and technical safety of future devices. Asian families may be particularly interested in smart homes for extending independence because they place an emphasis on collective culture and family-based care. Community engagement of stakeholders and steadfast leadership are needed so that future technologies used in healthcare delivery are both technically and culturally sound. A community-engaged research approach promotes community empowerment that is responsive to community identified priorities and is a good fit for studying adoption of smart home monitoring for health-assistance.

Research education: findings of a study of teaching-learning research using multiple analytical perspectives.

This multimethod, qualitative study provides results for educators of nursing doctoral students to consider. Combining the expertise of an empirical analytical researcher (who uses statistical methods) and an interpretive phenomenological researcher (who uses hermeneutic methods), a course was designed that would place doctoral students in the midst of multiparadigmatic discussions while learning fundamental research methods. Field notes and iterative analytical discussions led to patterns and themes that highlight the value of this innovative pedagogical application. Using content analysis and interpretive phenomenological approaches, together with one of the students, data were analyzed from field notes recorded in real time over the period the course was offered. This article describes the course and the study analysis, and offers the pedagogical experience as transformative. A link to a sample syllabus is included in the article. The results encourage nurse educators of doctoral nursing students to focus educational practice on multiple methodological perspectives.