The Prediction of Quality of Life by Frailty and Disability among Dutch Community-Dwelling People Aged 75 Years or Older.

The present study aimed to examine the prediction of quality of life by frailty and disability in a baseline sample of 479 Dutch community-dwelling people aged 75 years or older using a follow-up period of 8 years. Regarding frailty, we distinguish between physical, psychological, and social frailty. Concerning physical disability, we distinguish between limitations in performing activities in daily living (ADL) and instrumental activities in daily living (IADL). The Tilburg Frailty Indicator (TFI) and the Groningen Activity Restriction Scale (GARS) were used to assess frailty domains and types of disability, respectively. Quality of life was determined by the WHOQOL-BREF containing physical, psychological, social, and environmental domains. In our study, 53.9% of participants were woman, and the mean age was 80.3 years (range 75-93). The study showed that psychological frailty predicted four domains of quality of life and physical frailty three. Social frailty was only found to be a significant predictor of social quality of life and environmental quality of life. ADL and IADL disability proved to be the worst predictors. It is recommended that primary healthcare professionals (e.g., general practitioners, district nurses) focus their interventions primarily on factors that can prevent or delay psychological and physical frailty, thereby ensuring that people's quality of life does not deteriorate.

Application of machine learning in predicting frailty syndrome in patients with heart failure.

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables.

Towards an operational definition of oral frailty: A e-Delphi study.

OBJECTIVES: Building upon our recently developed conceptual definition of oral frailty (the age-related functional decline of orofacial structures), this e-Delphi study aims to develop an operational definition of oral frailty by identifying its components. METHODS: We used a modified e-Delphi study to reach a consensus among international experts on the components of oral frailty. Twelve out of fifteen invited experts in the field of gerodontology participated. Experts responded to three rounds of an online 5-point scale questionnaire of components to be included or excluded from the operational definition of oral frailty. After each round, scores and rationales were shared with all experts, after which they could revise their position. A consensus was reached when at least 70% of the experts agreed on whether or not a component should be included in the operational definition of oral frailty. RESULTS: The experts achieved a high level of agreement (80 - 100%) on including eight components of oral frailty and excluding nineteen. The operational definition of oral frailty should include the following components: 1) difficulty eating hard or tough foods, 2) inability to chew all types of foods, 3) decreased ability to swallow solid foods, 4) decreased ability to swallow liquids, 5) overall poor swallowing function, 6) impaired tongue movement, 7) speech or phonatory disorders, and 8) hyposalivation or xerostomia. CONCLUSION: This e-Delphi study provided eight components that make up the operational definition of oral frailty. These components are the foundation for the next stage, which involves developing an oral frailty assessment tool.

Associations of individual chronic diseases and multimorbidity with multidimensional frailty.

OBJECTIVE: To examine the associations between individual chronic diseases and multidimensional frailty comprising physical, psychological, and social frailty. METHODS: Dutch individuals (N = 47,768) age ≥ 65 years completed a general health questionnaire sent by the Public Health Services (response rate of 58.5 %), including data concerning self-reported chronic diseases, multidimensional frailty, and sociodemographic characteristics. Multidimensional frailty was assessed with the Tilburg Frailty Indicator (TFI). Total frailty and each frailty domain were regressed onto background characteristics and the six most prevalent chronic diseases: diabetes mellitus, cancer, hypertension, arthrosis, urinary incontinence, and severe back disorder. Multimorbidity was defined as the presence of combinations of these six diseases. RESULTS: The six chronic diseases had medium and strong associations with total ((f2 = 0.122) and physical frailty (f2 = 0.170), respectively, and weak associations with psychological (f2 = 0.023) and social frailty (f2 = 0.008). The effects of the six diseases on the frailty variables differed strongly across diseases, with urinary incontinence and severe back disorder impairing frailty most. No synergetic effects were found; the effects of a disease on frailty did not get noteworthy stronger in the presence of another disease. CONCLUSIONS: Chronic diseases, in particular urinary incontinence and severe back disorder, were associated with frailty. We thus recommend assigning different weights to individual chronic diseases in a measure of multimorbidity that aims to examine effects of multimorbidity on multidimensional frailty. Because there were no synergetic effects of chronic diseases, the measure does not need to include interactions between diseases.

What If the Clinical and Older Adults' Perspectives about Frailty Converge? A Call for a Mixed Conceptual Model of Frailty: A Traditional Literature Review.

Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults' perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled "by others" as compared to "self-labeling", from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term "frail", and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult's perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.

External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study.

Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.

'They're not doing too much are they?' How the socialization of registered nurses perpetuates status differences with certified nursing assistants: A qualitative study.

BACKGROUND: Limited knowledge exists about how the socialization of vocationally trained registered nurses both at school and during internships in the community of practice influences their perception of, and working relationship with certified nursing assistants. OBJECTIVES: This paper studies, first, how registered nurse students internalize the perceptions and discourses about certified nursing assistants conveyed by teachers, mentors and other students during their socialization at school and in the community of practice. Second, it examines how this socialization forms student's perception of, and actual working relationship with certified nursing assistants. DESIGN: Qualitative descriptive and exploratory study using an interpretative framework. METHODS: Individual in-depth interviews were conducted with 15 registered nurse students that were in their third or fourth year of training. RESULTS: The findings reveal that at school the division of tasks and working relationship between registered nurse students and certified nursing assistants was very rarely discussed explicitly. However, teachers and students implicitly and explicitly conveyed that certified nursing assistants have lower status, describing the latter's role as inferior and as assisting to the role of registered nurses. During internships in the community of practice, some students initially adjust this perception when directly working with certified nursing assistants, who generally are their mentor in the first years of training, consider certified nursing assistants as equal and highlight the interdependence of the two occupational groups. Yet, further in their training, registered nurse students start to relate more to graduated registered nurses and reproduce the dominant perception and discourse that certified nursing assistants are inferior and supposed to support registered nurses, thereby perpetuating pervasive status differences and inequality. CONCLUSION: Findings will assist nurse educators both in training centers and in the community of practice to understand how education can be used to end pervasive status differences and foster mutual respect and equity between different designations in nursing.

The Role of Nursing Staff Regarding Goal Setting and Achieving in Geriatric Rehabilitation: A Focus Group Study.

PURPOSE: The aim of this study was to explore and clarify the role of nursing staff in geriatric rehabilitation on supporting patients in goal setting and achieving, through reflecting on rehabilitation interventions. DESIGN: A descriptive qualitative study was conducted. METHODS: We conducted four online focus group interviews with 23 members of the nursing staff working in geriatric rehabilitation. They reflected on six interventions, preclassified into three types: setting goals in the admission phase, increasing patient participation in order to personalize the rehabilitation trajectory, and supporting patients in working on short-term goals. Data were analyzed using thematic content analysis. RESULTS: Setting goals in the admission phase is primarily the task of the multidisciplinary team rather than the nursing staff. Interventions to increase patient participation align with the coordinating role of nursing staff in the rehabilitation team. Working on short-term goals is of great value to patients. CLINICAL RELEVANCE TO THE PRACTICE OF REHABILITATION NURSING: The connection between the patient's personal goals and professional treatment aimed at functional recovery can be enhanced by strengthening the position of nursing staff working in geriatric rehabilitation. CONCLUSION: Members of nursing staff in geriatric rehabilitation see themselves playing a coordinating role in the multidisciplinary team, supporting the patient in goal work. Interventions aimed at advancing patient participation and providing support for short-term goals reinforce this role.

The effects of psychosocial interventions using generic photos on social interaction, mood and quality of life of persons with dementia: a systematic review.

BACKGROUND: Although family photos are often used in the psychosocial care for people with dementia, little is known about the use and effectiveness of generic photos. This systematic literature review explored psychosocial interventions using generic photos for people with dementia, and the effects they have on their social interaction and/or mood and/or quality of life. In addition, it was investigated whether these interventions made use of technology in its implementation. METHODS: A systematic search on the following databases was performed: PubMed, Embase, APA PsychInfo, Cinahl, Web of Science, Scopus and Cochrane Central. Inclusion and exclusion criteria were based on the PICO model (Population, Intervention, Comparison, Outcome), and quality assessment was undertaken using the Weight of Evidence Framework. Narrative synthesis was undertaken to summarize study characteristics- settings and designs, type of psychosocial interventions identified, type of photos and technology used, outcome measures, and results. RESULTS: A total of 2,035 results were found, however after title, abstract and full-text screening, a total of 8 studies were included. The most common psychosocial intervention using generic photos was found to be reminiscence therapy, followed by art-viewing activities. In studies that used technology, it was reported that viewing digitalized photos were either similar or better to conventional printed photos. Despite photos being generic, it was found that generic photos could still hold personal significance to the person with dementia. Some positive and significant effects were found for the outcomes social interaction, mood and quality of life, though no study evaluated all three outcomes. Two studies were rated as having high overall quality, 4 were rated as fair, and 2 studies had a low quality assessment rating. CONCLUSION: Studies found using generic photos were limited, showing varying outcomes and methodological quality. Firm conclusions on the effectiveness of interventions using generic photos are not possible. However, the use of generic photos in psychosocial interventions is a promising area for future research. Researchers should consider studies with better methodological quality and larger samples; and qualitative studies where the intention is to get better insight into successful implementation and impact mechanisms of such psychosocial interventions. TRIAL REGISTRATION: n/a.

Translation and Validation of the Swedish Version of the Tilburg Frailty Indicator.

The Tilburg Frailty Indicator (TFI) is a questionnaire with 15 questions designed for screening for frailty in community-dwelling older people. TFI has a multidimensional approach to frailty, including physical, psychological, and social dimensions. The aim of this study was to translate TFI into Swedish and study its psychometric properties in community-dwelling older people with multimorbidity. A cross-sectional study of individuals 75 years and older, with ≥3 diagnoses of the ICD-10 and ≥3 visits to the Emergency Department in the past 18 months. International guidelines for back-translation were followed. Psychometric properties of the TFI were examined by determining the reliability (inter-item correlations, internal consistency, test-retest) and validity (concurrent, construct, structural). A total of 315 participants (57.8% women) were included, and the mean age was 83.3 years. The reliability coefficient KR-20 was 0.69 for the total sum. A total of 39 individuals were re-tested, and the weighted kappa was 0.7. TFI correlated moderately with other frailty measures. The individual items correlated with alternative measures mostly as expected. In the confirmatory factor analysis (CFA), a three-factor model fitted the data better than a one-factor model. We found evidence for adequate reliability and validity of the Swedish TFI and potential for improvements.

Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach.

The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients' responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the non-physical origins of HF.

Supporting older patients in working on rehabilitation goals: A scoping review of nursing interventions.

BACKGROUND: Nurses are consistently present throughout the rehabilitation of older patients but are apprehensive about performing goal-centred care in the multidisciplinary team. OBJECTIVES: The aim of this review was to explore working interventions on setting goals and working with goals designed for nurses in geriatric rehabilitation, and to describe their distinctive features. METHODS: We performed a scoping review. We searched MEDLINE and CINAHL through August 4, 2021. Search terms related to the following themes: nurses, rehabilitation, geriatric, goal and method. We used snowballing to find additional. From the selected studies, we systematically extracted data on means, materials and the nursing role and summarized them in a narrative synthesis, using intervention component analysis. RESULTS: The study includes 13 articles, describing 11 interventions which were developed for six different aims: improving multidisciplinary team care; increasing patient centredness; improving disease management by patients; improving the psychological, and emotional rehabilitation; increasing the nursing involvement in rehabilitation; or helping patients to achieve goals. The interventions appeal to four aspects of the nursing profession: assessing self-care skills incorporating patient's preferences; setting goals with patients, taking into account personal needs and what is medically advisable; linking the needs of the patient with multidisciplinary professional treatment and vice versa; and thus, playing an intermediate role and supporting goal achievement. CONCLUSIONS: The interventions show that in goal-centred care, the nurse might play an important unifying role between patients and the multidisciplinary team. With the support of nurses, the patient may become more aware of the rehabilitation process and transfer of ownership of treatment goals from the multidisciplinary team to the patient might be achieved. Not many interventions were found meant to support the nursing role. This may indicate a blind spot in the rehabilitation community to the additional value of its contribution.

Frailty Viewed From a Nursing Perspective.

Introduction: More and more researchers are convinced that frailty should refer not only to physical limitations but also to psychological and social limitations that older people may have. Such a broad, or multidimensional, definition of frailty fits better with nursing, in which a holistic view of human beings, and thus their total functioning, is the starting point. Purpose: In this article, which should be considered a Practice Update, we aim at emphasizing the importance of the inclusion of other domains of human functioning in the definition and measurement of frailty. In addition, we provide a description of how district nurses view frailty in older people. Finally, we present interventions that nurses can perform to prevent or delay frailty or its adverse outcomes. We present, in particular, results from studies in which the Tilburg Frailty Indicator, a multidimensional frailty instrument, was used. Conclusion: The importance of a multidimensional assessment of frailty was demonstrated by usually satisfactory results concerning adverse outcomes of mortality, disability, an increase in healthcare utilization, and lower quality of life. Not many studies have been performed on nurses' opinions about frailty. Starting from a multidimensional definition of frailty, encompassing physical, psychological, and social domains, nurses are able to assess and diagnose frailty and conduct a variety of interventions to prevent or reduce frailty and its adverse effects. Because nurses come into frequent contact with frail older people, we recommend future studies on opinions of nurses about frailty (e.g., screening, prevention, and addressing).

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands.

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BACKGROUND: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. OBJECTIVE: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. METHODS: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. RESULTS: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 µm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. CONCLUSIONS: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Illness Perceptions and Self-Management among People with Chronic Lung Disease and Healthcare Professionals: A Mixed-Method Study Identifying the Local Context.

Self-management interventions (SMIs) may fail if they misalign with the local context. To optimize the implementation of SMIs in Chinese people with chronic lung disease (CLD), the local context was identified in Chinese primary care (PC) and secondary care (SC). A mixed-method study using semi-structured interviews and quantitative surveys was conducted on people with CLD and healthcare professionals (HCPs). The qualitative data was collected until data saturation was reached, and participants were invited to complete the survey after the interview. The qualitative data-analyzed with the framework approach-was triangulated with the quantitative data. A total of 52 participants completed the interviews, and 48 also finished the survey. Four themes were identified; (a) illness perceptions (e.g., patients had poor CLD knowledge and SM, inadequate resources lead to suboptimal disease control in PC); (b) self-management skills (e.g., most patients delayed exacerbation recognition and action, and some were admitted at the crisis point); (c) factors influencing self-management skills (e.g., (in)adequate disease knowledge and medical expenditure affordability); and (d) needs for self-management (e.g., increased disease knowledge, individualized self-management plan, eHealth, (healthcare insurance) policy support). Identified themes were dependent on each other and should be leveraged when implementing SMIs. Ultimately, such SMIs can optimize patient health outcomes.

Frailty at Risk Scale (FARS): development and application.

The aim of this cross-sectional study was to develop a Frailty at Risk Scale (FARS) incorporating ten well-known determinants of frailty: age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. In addition, a second aim was to develop an online calculator that can easily support healthcare professionals in determining the risk of frailty among community-dwelling older people. The FARS was developed using data of 373 people aged ≥ 75 years. The Tilburg Frailty Indicator (TFI) was used for assessing frailty. Multivariate logistic regression analysis showed that the determinants multimorbidity, unhealthy lifestyle, and ethnicity (ethnic minority) were the most important predictors. The area under the curve (AUC) of the model was 0.811 (optimism 0.019, 95% bootstrap CI = -0.029; 0.064). The FARS is offered on a Web site, so that it can be easily used by healthcare professionals, allowing quick intervention in promoting quality of life among community-dwelling older people.

REducing delay through edUcation on eXacerbations for people with chronic lung disease: Study protocol of a single-arm pre-post study.

AIM: This study protocol aims to examine the effectiveness and preconditions of a self-management program-named REducing Delay through edUcation on eXacerbations (REDUX)-in China. BACKGROUND: The high disease burden in people with chronic lung disease is mainly due to exacerbations. There is a need for effective exacerbation-management interventions. A nurse-led program, REDUX, helped patients self-manage exacerbations. DESIGN: A single-arm pre-post study. METHODS: Fifty-four patients and 24 healthcare professionals (HCPs) in Chinese primary care will be included. The core element of the program is a personalized action plan. HCPs will receive training in using the action plan to help patients manage exacerbations. The intervention will start when a patient is referred to the nurse for a post-exacerbation consultation and ends when the patient presents for the second post-exacerbation consultation. During the first post-exacerbation consultation, the patient and nurse will create the action plan. The primary outcomes in patients will include the delays between the onset of exacerbation and recognition, between exacerbation recognition and action, between exacerbation recognition and consultation with a doctor, and when the patients feel better after receiving medical help from HCPs. The secondary outcomes will include preconditions of the program. The ethics approval was obtained in September 2021. DISCUSSION: This study will discuss a culturally adapted nurse-led self-management intervention for people with chronic lung disease in China. The intervention could help Chinese HCPs provide efficient care and reduce their workload. Furthermore, it will inform future research on tailoring nurse-led self-management interventions in different contexts. IMPACT: The study will contribute to the evidence on the effectiveness and preconditions of REDUX in China. If effective, the result will assist the nursing of people with chronic lung disease. TRIAL REGISTRATION: Registered in the Chinese clinical trial registry (ID: 2100051782).

Exploring crucial programme characteristics and group mechanisms of an empowerment programme for certified nursing assistants-A qualitative study.

AIMS: To identify crucial programme characteristics and group mechanisms of, and lessons learned from hindrances in an empowerment programme for certified nursing assistants and contribute to the development of similar programmes in other care settings. DESIGN: Exploratory qualitative study. METHODS: Between May 2017 and September 2020, we used in-depth interviews and participant observations to study four groups participating in an empowerment programme for certified nursing assistants (N = 44). RESULTS: We identified three crucial empowerment-enhancing programme characteristics: (1) inviting participants to move outside their comfort zone of caregiving; (2) stimulating the use of untapped talents, competencies and interests; (3) supporting the rediscovery of participants' occupational role and worth. Crucial group mechanisms encompassed learning from and with each other, as well as mechanisms of self-correction and self-motivation. Hindrances included a perceived lack of direction, and a lack of organizational support and facilitation. CONCLUSION: We showed the significance of creating an inviting and stimulating environment in which participants can explore and function in ways they otherwise would not. Likewise, we identified how this can help participants learn from, critically correct and motivate one another. IMPACT: The programme under study was uniquely aimed to empower certified nursing assistants. Our insights on crucial programme characteristics and group mechanisms may benefit those who develop empowerment programmes, but also policymakers and managers in supporting certified nursing assistants and other nursing professions in empowerment endeavours. Such empowerment may enhance employee retention and make occupational members more likely to address challenges affecting their occupational group and the long-term care sector.

Physical, Psychological and Social Frailty Are Predictive of Heart Failure: A Cross-Sectional Study.

BACKGROUND: Little is known about frailty among patients hospitalized with heart failure (HF). To date, the limited information on frailty in HF is based on a unidimensional view of frailty, in which only physical aspects are considered when determining frailty. The aims of this study were to study different dimensions of frailty (physical, psychological and social) in patients with HF and the effect of different dimensions of frailty on the incidence of heart failure. METHODS: The study used a cross-sectional design and included 965 patients hospitalized for heart failure and 164 healthy controls. HF was defined according to the ESC guidelines. The Tilburg Frailty Indicator (TFI) was used to assess frailty. Probit regression analyses and chi-square statistics were used to examine associations between the occurrence of heart failure and TFI domains of frailty. RESULTS: Patients diagnosed with frailty were 15.3% more likely to develop HF compared to those not diagnosed with frailty (p < 0.001). An increase in physical, psychological and social frailty corresponded to an increased risk of HF of 2.9% (p < 0.001), 4.4% (p < 0.001) and 6.6% (p < 0.001), respectively. CONCLUSIONS: We found evidence of the association between different dimensions of frailty and incidence of HF.