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Aim: To explore the health beliefs and health behaviours of older Vietnam-born people living with chronic disease in Western Australia. Design: This study was designed as a focused ethnography guided by the interpretative research paradigm and Leininger's Theory of Culture Care. Methods: Data were collected through participant observation and interviews undertaken at participants' homes in Western Australia over 7 months in 2019. Data were analysed using Wolcott's approach for transferring qualitative data, comprising three phases: description, thematic analysis and interpretation. Results: This study included 12 participants. The health perspectives and practices of older Vietnam-born Australians significantly reflected the traditional Vietnamese values. These included the stigma towards chronic disease as an inevitable consequence of ageing; self-blame thoughts about chronic disease causations and maintained traditional dietary practices. However, some aspects of their health behaviours such as beliefs and practices in traditional medicine, Western medicine and physical exercise reflected a blended approach combining both Vietnamese-oriented and Westernized-orientated practices. Individual factors were also identified as contributing to chronic disease self-care among participants. Conclusion: This study emphasizes culture played a significant role in shaping the way that older Vietnam-born Australians believed and behaved while living with chronic conditions. However, it also indicates that culture is not a stationary concept, it evolves gradually and is socially constructed. Implications: A better understanding of the health beliefs and practices of older Vietnam-born Australians is expected to contribute to the delivery of culturally safe and effective support for this population. The achievement of culturally safe care requires a systemic approach and collaboration of strategies across sectors. Patient and public contribution: This study encompassed the contri

Background: Whilst theoretical grounding is considered important for sound research methodology, consensus on the application of theory in qualitative research remains elusive. Novice researchers may experience challenges in applying theory in qualitative research and these may contribute to the under-use, over-reliance or inappropriate application of theory. Practical guidance on how theory can inform and guide the conduct of qualitative research is needed. Purpose: The purpose of this paper was to propose a framework for the application of theory in qualitative research and provide an exemplar. Methods: The Theoretical Application Framework for Qualitative Studies (TAF-QS) was developed from the synthesis of existing literature and the authors' own experience of the application of theory. Results: The TAF-QS encourages researchers to articulate which theoretical framework or conceptional framework they are drawing on and how this will be applied by reflecting on the purpose and the context of the study. Conclusion: The TAF-QS provides support for researchers to explore how theory can be applied and how to achieve this in qualitative research. Tweetable abstract: The use of theory in qualitative research.

Most older adults desire to be as independent as possible and remain living in their ancestral home as they age. Aging-in-place maximizes the independence of older adults, enhancing their wellbeing and quality of life while decreasing the financial burden of residential care costs. However, due to chronic disease, multimorbidity, and age-related changes, appropriate conditions are required to make aging-in-place possible. Remote monitoring with smart home technologies could provide the infrastructure that enables older adults to remain living independently in their own homes safely. The health-assistive smart home shows great promise, but there are challenges to integrating smart homes on a larger scale. The purpose of this discussion paper is to propose a Design Thinking (DT) process to improve the possibility of integrating a smart home for health monitoring more widely and making it more accessible to all older adults wishing to continue living independently in their ancestral homes. From a nursing perspective, we discuss the necessary stakeholder groups and describe how these stakeholders should engage to accelerate the integration of health smart homes into real-world settings.

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.

Background: Virtual reality (VR) delivered through immersive headsets creates an opportunity to deliver interventions to improve physical, mental, and psychosocial health outcomes. VR app studies with older adults have primarily focused on rehabilitation and physical function including gait, balance, fall prevention, pain management, and cognition. Several systematic reviews have previously been conducted, but much of the extant literature is focused on rehabilitation or other institutional settings, and little is known about the effectiveness of VR apps using immersive headsets to target health outcomes among community-dwelling older adults. Objective: The objective of this review was to evaluate the effectiveness of VR apps delivered using commercially available immersive headsets to improve physical, mental, or psychosocial health outcomes in community-dwelling older adults. Methods: Peer-reviewed publications that included community-dwelling older adults aged ≥ 60 years residing in residential aged care settings and nursing homes were included. This systematic review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for systematic reviews of effectiveness evidence. The title of this review was registered with JBI, and the systematic review protocol was registered with the International Prospective Register of Systematic Reviews. Results: In total, 7 studies that specifically included community-dwelling older adults were included in this review. VR apps using a head-mounted display led to improvements in a number of health outcomes, including pain management, posture, cognitive functioning specifically related to Alzheimer disease, and a decreased risk of falls. A total of 6 studies reported a statistically significant difference post VR intervention, and 1 study reported an improvement in cognitive function to reduce navigational errors. Only one study reported on the usability and acceptability of the interventions delivered through

© The Author(s) 2020. 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.

©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. 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.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.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 cli

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. © 2018 John Wiley & Sons Ltd

Hospitalized older adults who do not receive sufficient mobility are more likely to sustain negative health outcomes, including higher rates of mortality and institutionalization. Accordingly, the purpose of the current secondary data analysis was to examine the nurse-promoted mobility of hospitalized older adults and the association between nurses’ barriers and nurse-promoted mobility. In addition, the relationship among patient severity of illness, proxy levels for function, and nurse-promoted mobility was examined. The final study sample included 61 nurses working in medical units caring for a total of 77 older adults. Findings suggest nurse knowledge gaps and attitude barriers could potentially influence the type and frequency of mobility they promote in older patients. A relationship was found between older patients with impaired mobility using assistive devices for mobility at home, and those at high risk for falls and nurses promoting more sedentary activity (e.g., chair sitting, walking in the room). Interestingly, nurses promoted significantly more sedentary mobility for patients with physical therapy orders.

This is an Authors Accepted Manuscript version of an article published by Wiley, at https://doi.org/10.1111/jan.15053

Dermody, G., Fritz, R., Glass, C., Dunham, M., & Whitehead, L. (2021). Factors influencing community‐dwelling older adults’ readiness to adopt smart home technology: A qualitative exploratory study. Journal of Advanced Nursing, 77(12), 4847-4861. https://doi.org/10.1111/jan.14996

Danazumi, M. S., Ibrahim, S. U., Yakasai, A. M., Dermody, G., Bello, B., & Kaka, B. (2021). A comparison between the effect of combined chain exercises plus Kinesio taping with combined chain exercises alone in knee osteoarthritis: A randomized clinical trial. American Journal of Physical Medicine & Rehabilitation, 100(11), 1070-1077. https://doi.org/10.1097/PHM.0000000000001705

Kang, J. J., Yang, W., Dermody, G., Ghasemian, M., Adibi, S., & Haskell-Dowland, P. (2020). No soldiers left behind: An IoT-based low-power military mobile health system design. IEEE Access, 8, 201498-201515. https://doi.org/10.1109/ACCESS.2020.3035812

Dermody, G., Whitehead, L., Wilson, G., & Glass, C. (2020). The Role of Virtual Reality in Improving Health Outcomes for Community-Dwelling Older Adults: Systematic Review. Journal of Medical Internet Research, 22(6), Article e17331. http://doi.org/10.2196/17331

Fritz, R. L., Wilson, M., Dermody, G., Schmitter-Edgecombe, M., & Cook, D. J. (2020). Automated smart home assessment to support pain management: Multiple methods analysis. Journal of Medical Internet Research, 22(11), article e23943. https://doi.org/10.2196/23943

Fritz, R. L., & Dermody, G. (2020). Interpreting health events in big data using qualitative traditions. International Journal of Qualitative Methods, 19, 1609406920976453. https://doi.org/10.1177/1609406920976453

Danazumi, M. S., Yakasai, A. M., Dermody, G., & Nuhu, J. M. (2021). Knowledge and current practices of Nigerian physiotherapists on the physical activity and exercise training in the rehabilitation of patients with asthma. Journal of Asthma. Advance online publication. https://doi.org/10.1080/02770903.2020.1868500

This is an Author's Accepted Manuscript of: Dermody, G., & Kovach, C. R. (2018). Barriers to Promoting Mobility in Hospitalized Older Adults. Research in gerontological nursing, 11(1), 17-27. Available here.

This is a non-final version of an article published in final form in (Dermody, G., Odom-Maryon, T., Zimmerman, J., & Glass, C. (2020). Hospitalized adult patient characteristics associated with participation in a volunteer-assisted mobility program: A feasibility study. Journal of Nursing Care Quality, 35(4), 341-347. doi:10.1097/NCQ.0000000000000460) Dermody, G., Odom-Maryon, T., Zimmerman, J., & Glass, C. (2020). Hospitalized adult patient characteristics associated with participation in a volunteer-assisted mobility program: A feasibility study. Journal of Nursing Care Quality, 35(4), 341-347. https://doi.org/10.1097/NCQ.0000000000000460

Fritz, R. L., & Dermody, G. (2019). A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place. Nursing Outlook, 67(2), 140-153. Available here

Dermody, G., & Fritz, R. (2019). A conceptual framework for clinicians working with artificial intelligence and health-assistive smart homes. Nursing Inquiry, 26(1). Available here

Dermody, G., & Tiedt, J. (2019). Enhancing Post‐Baccalaureate Doctor of Nursing Practice students' analytic skills. Nursing Forum, 54(2), 232-237. Available here