Barriers to Nurses’ Promoting Mobility in Hospitalized Older Adults

Objectives: To examine the association between nurses’ knowledge, attitude and external barriers and the nurse’s mobility-promoting behavior. Nurse perception of the priority organizations place on mobility, and the relationship of nurses’ level of experience to nurse prioritization for promoting mobility was also investigated. Design: Cross-sectional, descriptive, correlation study with convenience sampling. Setting: Two community-based hospitals in the Pacific Northwest of the U.S. Participants: Eighty-five nurses caring for 98 inpatients 65 and older. Measurement: Nurses’ knowledge, attitude and external barriers were examined with a validated 5-point Likert Scale. Patient-related and other clinical barriers and the nurses mobility-promoting behavior was obtained with the validated self-recorded mobility log. Patient Basic Metabolic Index (BMI) and severity of illness was obtained though data extraction. Results: Nurses viewed the promotion of mobility as important, yet mobilizing older patients was infrequent. Nurses perceived a number of barriers to promoting mobility: Patient condition, the perception that patients could be harmed during mobilization, perceptions of heavy workload, difficulty prioritizing nursing care, and staffing shortages. While novice nurses had lower priority to promote mobility compared to more experienced nurses, novice nurses tended to promote more mobility. Conclusion: As nurses care for hospitalized older adults the convergence of interpersonal, patient, and environmental complexities acting as barriers to mobility need to be considered. It is important to understand the needs of beginning, less experienced nurses to overcome the barriers to promoting mobility. This study shows that even experienced nurses need to overcome barriers to promoting mobility. Hospitals need to address the needs of the novice nurse while enhancing the practice of more experienced nurses in order to support nurse-promoted mobility. The findings from this study show that nurses knowledge, attitude, and external barriers could play a role in the low levels of mobility in hospitalized older adults

Interpreting health events in big data using qualitative traditions

© 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

The role of virtual reality in improving health outcomes for community-dwelling older adults : systematic review

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 VR. While one study used a distraction mechanism for pain management, none of the studies used gaming technology to promote enjoyment. Conclusions: Interventions to improve health outcomes through VR have demonstrated potential; however, the ability to synthesize findings by primary outcome for the older adult population is not possible. A number of factors, especially related to frailty, usability, and acceptability, also need to be explored before more substantial recommendations on the effectiveness of VR interventions for older adults can be made

Barriers to promoting mobility in hospitalized older adults

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

Automated smart home assessment to support pain management: Multiple methods analysis

©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 clinician guidance (0.16 accuracy achieved; P \u3c .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

No soldiers left behind: An IoT-based low-power military mobile health system design

© 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\u27s 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

Perspectives on wider integration of the health-assistive smart home

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

Perspectives on Wider Integration of the Health-Assistive Smart Home

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