Challenges and needs in cerebrovascular disease pathway: A qualitative descriptive study from the patients' and healthcare professionals' perspectives.

AIM(S): To understand patients' and healthcare professionals' experiences to improve care in and across different domains of the cerebrovascular disease pathway. DESIGN: A qualitative descriptive study. METHODS: Semi-structured in-person interviews were conducted among 22 patients diagnosed with acute cerebrovascular disease and 26 healthcare professionals taking care of them in a single tertiary-level hospital from August 2021 to March 2022. Data were analysed using deductive and inductive content analysis. The consolidated criteria for reporting qualitative research was used to ensure reliable reporting. RESULTS: Overall, 19 generic and 79 sub-categories describing perceived challenges with 17 generic and 62 sub-categories describing perceived needs were identified related to primary prevention, organization of stroke services, management of acute stroke, secondary prevention, rehabilitation, evaluation of stroke outcome and quality assessment, and life after stroke. CONCLUSION: Several challenges and needs were identified in and across the different domains of the cerebrovascular disease pathway. There is a requirement for adequate resources, early initiation of treatment, early diagnostics and recanalization, dedicated rehabilitation services, long-term counselling and support, and impact evaluation of services to improve cerebrovascular disease care. Future research on caregivers', and clinical leadership experiences in and across the cerebrovascular disease pathway is needed to explore the provision of services. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The results of this study can be applied by organizations, managers and research for developing and improving services in the cerebrovascular disease pathway. IMPACT: This study identified several patient-related, organizational and logistical needs and challenges, with suggestions for required actions, that can benefit the provision of effective, high-quality cerebrovascular disease care. REPORTING METHOD: We have adhered to relevant EQUATOR guidelines with the COREQ reporting method. PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Association between chronic diseases and falls among a sample of older people in Finland.

BACKGROUND: Falls are a major problem for older people and recurrent fallers are especially prone to severe consequences due to falls. This study investigated the association between chronic conditions and falls. METHODS: Responses from 872 older persons (age 65-98) to a health questionnaire were used in the analyses. Characteristics and disease prevalence between recurrent fallers, one-time fallers and non-fallers were compared. A hierarchical clustering method was applied to find combinations of chronic conditions that were associated with recent recurrent falling. RESULTS: The results showed that recurrent fallers had a higher number of diseases (median 4, interquartile range, IQR = 2.0-5.0) compared to non-fallers (median 2, IQR = 1.0-3.0). Eight clusters were formed based on the data. The participants in the low chronic disease cluster were younger, more physically active, not frail, and had fewer geriatric conditions. Multiple chronic disease cluster participants were older, less physically active, overweight (body mass index, BMI > 30), at risk of malnutrition, and had more geriatric conditions. Significantly increased risk of recurrent falls relative to the low chronic cluster was found for respondents in the osteoporosis cluster and multiple chronic disease cluster (OR = 5.65, 95% confidence interval CI: 1.23-25.85, p = 0.026, and OR = 13.42, 95% CI: 2.47-72.96, p = 0.002, respectively). None of the clusters were associated with increased risk of one-time falling. CONCLUSIONS: The results implicate that the number of chronic diseases is related with risk of recurrent falling. Furthermore, the results implicate the potential of identifying certain combinations of chronic diseases that increase fall risk by analyzing health record data, although further studies are needed with a larger population sample.

Mobile Fall Risk Assessment Solution for Daily-Life Settings.

Prevention of falls requires accurate means for fall risk assessment in order to identify persons at risk. This paper introduces a novel mobile fall risk assessment solution for daily-life settings. The solution contains an Android application that uses acceleration sensor data received via Bluetooth LE connection. The application guides through a simple walk test, analyzes the acceleration data measured from the acceleration sensor attached to the lower back and gives feedback about the fall risk for the user. Preliminary user tests with 12 healthy subjects were conducted to evaluate the feasibility of the solution. Each test subject performed three walks demonstrating normal, dragging and slow gait. The results showed that the acceleration features calculated by the application distinguish normal gait from dragging and slow gaits. Further collection of comprehensive data set with older adults is needed to adjust the application parameters appropriately for the target group.

Comparison of Accelerometry-Based Features for Fall Risk Assessment Measured From Two Sensor Locations.

Falls are an unfortunate problem for older adults, their relatives and societies. Continuous gait monitoring for fall risk assessment during daily lives would allow early interventions to prevent injurious falls. Continuous gait monitoring is possible using technological solutions such as inertial sensors; for example accelerometers. Current solutions require attaching the sensor to a certain location on the body and many of them to the lower back, which is not convenient for the user. The objective of this study was to find out whether gait variables calculated from the acceleration signal measured during walk from two different locations on waist area differ from each other. Forty two older adult subjects were measured during walk test with a triaxial acceleration sensor worn on an elastic belt at the lower back and frontal hip area. Most of the analyzed gait features from the two locations have a strong correlation, indicating that these features are not sensitive to sensor location around waist level. A subsequent study is needed to confirm other locations for the sensors to allow analyzing gait during everyday lives.

Feasibility of mobile mental wellness training for older adults.

Mobile technology has been increasingly adopted in promotion of mental health among older people. This study assessed the feasibility of a mobile mental wellness training application for individual use and for group work from the perspectives of older adults and social care professionals. The older individuals recruited for the study were participants in a Circle of Friends group and family caregivers' peer support group offered by the communal senior services. The qualitative and quantitative results of interviews, questionnaires, observation, and application usage were reported. Seven older adults started using the application independently at home in parallel with the group activity. This study revealed new information regarding the barriers to the older adults' full adoption of such mobile technologies. The results indicated that there may be potential in the incorporation of mobile technologies in promotion of mental health of older people at group settings.

Accelerometry-based assessment and detection of early signs of balance deficits.

Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals' fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4m walk. The follow-up tests with the same protocol were performed after one year. Altogether 43 features were extracted from the accelerometer signals. Sequential forward floating selection and ten-fold cross-validation were applied to determine models for 1) estimating the outcomes of BBS, TUG and 4m walk tests and 2) predicting decline in balance during one-year follow-up indicated as decline in BBS total score and one leg stance. Normalized root-mean-square errors (RMSE) of the assessment scale result estimates were 0.28 for BBS score, 0.18 for TUG time, and 0.22 for 4m walk test. Area under curve (AUC) was 0.78 for predicting decline in BBS total score and 0.82 for one leg stance, respectively. The results suggest that the gait features can be used to estimate the result of a clinical balance assessment scale and predict decline in balance. A simple walk test with wearable monitoring could be applicable as an initial screening tool to identify people with early signs of balance deficits.

Use of Information and Communication Technologies Among Older People With and Without Frailty: A Population-Based Survey.

BACKGROUND: Use of information and communication technologies (ICT) among seniors is increasing; however, studies on the use of ICT by seniors at the highest risk of health impairment are lacking. Frail and prefrail seniors are a group that would likely benefit from preventive nutrition and exercise interventions, both of which can take advantage of ICT. OBJECTIVE: The objective of the study was to quantify the differences in ICT use, attitudes, and reasons for nonuse among physically frail, prefrail, and nonfrail home-dwelling seniors. METHODS: This was a population-based questionnaire study on people aged 65-98 years living in Northern Finland. A total of 794 eligible individuals responded out of a contacted random sample of 1500. RESULTS: In this study, 29.8% (237/794) of the respondents were classified as frail or prefrail. The ICT use of frail persons was lower than that of the nonfrail ones. In multivariable logistic regression analysis, age and education level were associated with both the use of Internet and advanced mobile ICT such as smartphones or tablets. Controlling for age and education, frailty or prefrailty was independently related to the nonuse of advanced mobile ICT (odds ratio, OR=0.61, P=.01), and frailty with use of the Internet (OR=0.45, P=.03). The frail or prefrail ICT nonusers also held the most negative opinions on the usefulness or usability of mobile ICT. When opinion variables were included in the model, frailty status remained a significant predictor of ICT use. CONCLUSIONS: Physical frailty status is associated with older peoples' ICT use independent of age, education, and opinions on ICT use. This should be taken into consideration when designing preventive and assistive technologies and interventions for older people at risk of health impairment.

Gait analysis and estimation of changes in fall risk factors.

Falls are a major problem for older adults. A continuous gait monitoring that provides fall risk assessment would allow timely interventions aiming for preventing falls. The objective of this work was to find out whether gait variables calculated from the acceleration signal measured during walk task in the baseline assessment can predict changes in commonly used fall risk assessment scales after 12 months follow-up. Forty two subjects were measured during walk test with a triaxial acceleration sensor worn on a waist belt at the lower back near the centre of mass. The fall risk was assessed using a test protocol, which included several assessment methods. Gait analysis was able to predict a decline in ABC, BBS and GDS total scores and slower time in STS-5 after twelve-months follow-up. A subsequent study is needed to confirm the model's suitability for data recorded in everyday lives.

Disease state fingerprint for fall risk assessment.

Fall prevention is an important and complex multifactorial challenge, since one third of people over 65 years old fall at least once every year. A novel application of Disease State Fingerprint (DSF) algorithm is presented for holistic visualization of fall risk factors and identifying persons with falls history or decreased level of physical functioning based on fall risk assessment data. The algorithm is tested with data from 42 older adults, that went through a comprehensive fall risk assessment. Within the study population the Activities-specific Balance Confidence (ABC) scale score, Berg Balance Scale (BBS) score and the number of drugs in use were the three most relevant variables, that differed between the fallers and non-fallers. This study showed that the DSF visualization is beneficial in inspection of an individual's significant fall risk factors, since people have problems in different areas and one single assessment scale is not enough to expose all the people at risk.

Severe acute respiratory syndrome coronavirus fails to activate cytokine-mediated innate immune responses in cultured human monocyte-derived dendritic cells.

Activation of host innate immune responses was studied in severe acute respiratory syndrome coronavirus (SCV)-infected human A549 lung epithelial cells, macrophages, and dendritic cells (DCs). In all cell types, SCV-specific subgenomic mRNAs were seen, whereas no expression of SCV proteins was found. No induction of cytokine genes (alpha interferon [IFN-alpha], IFN-beta, interleukin-28A/B [IL-28A/B], IL-29, tumor necrosis factor alpha, CCL5, or CXCL10) or IFN-alpha/beta-induced MxA gene was seen in SCV-infected A549 cells, macrophages, or DCs. SCV also failed to induce DC maturation (CD86 expression) or enhance major histocompatibility complex class II expression. Our data strongly suggest that SCV fails to activate host cell cytokine gene expression in human macrophages and DCs.