Prevalence of physical inactivity and its determinants among older adults living in nursing homes: A cross-sectional study based on COM-B model.

AIMS: To investigate the prevalence of physical inactivity in older adults living in nursing homes and explore the determinants of physical inactivity by using the Capability, Opportunity, Motivation-Behaviour model. DESIGN: A multisite, cross-sectional study was performed by convenience sampling and questionnaire survey. METHODS: A total of 390 nursing home residents were recruited from three nursing homes in Southern China from May 2022 to April 2023. The participants completed a self-designed general information questionnaire, Physical Activity Scale for the Elderly, Self-Efficacy for Exercise Scale, Exercise Benefits Scale, Patient Health Questionnaire-9 and the Short Physical Performance Battery test. Descriptive statistics, univariate analysis, Spearman correlation analysis, and ordinal logistic regression were applied for data analysis. RESULTS: The prevalence of physical inactivity among the nursing home residents reached 88.46%. Ordinal logistic regression results showed that exercise self-efficacy, perceived exercise benefits, physical function, availability of physical activity instruction, having depression, number of chronic diseases and living with spouse were the main influencing determinants of physical inactivity and explained 63.7% of the variance. CONCLUSIONS: Physical inactivity was considerable in nursing home residents in China and influenced by complex factors. Tailored measures should be designed and implemented based on these factors to enhance physical activity while considering the uniqueness of Chinese culture. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Healthcare professionals should enhance physical activity of residents by increasing benefits understanding, boosting self-efficacy, improving physical function, alleviating depression and integrating personalized physical activity guidance into routine care services. And more attention should be paid to the residents who had more chronic diseases or did not live with spouse. IMPACT: Physical inactivity is a significant problem in nursing home residents. Understanding physical inactivity and its determinants enables the development of tailored interventions to enhance their physical activity level. REPORTING METHOD: This study was reported conforming to the STROBE statement. PATIENTS OR PUBLIC CONTRIBUTION: Nursing home residents who met the inclusion criteria were recruited.

Adverse event reporting attitude and its individual and organizational predictors among nursing staff: A multisite study in Chinese nursing homes.

This study explored the status of adverse event reporting attitudes and its predictors among nursing staff in Chinese nursing homes. A cross-sectional study was conducted with 475 nursing staff, and they completed sociodemographic and facility-related questionnaire, Incident Reporting Attitude Scale, Adverse Event Reporting Awareness Scale, and Nursing Home Survey on Patient Safety Culture. Univariate analysis and multiple linear regression models were performed. The mean score for adverse event reporting attitude was 125.87 (SD=15.35). The predictors included individual variables, such as education level (ß=0.129, p = 0.001) and working years (ß=-0.102, p = 0.007), and organizational variables, such as patient safety culture (ß=0.503, p < 0.001) and adverse event reporting awareness (ß=0.261, p < 0.001). These factors explained 35.3 % of total variance. Managers in nursing homes should strengthen team-targeted education and training for nursing staff with longer working years and lower educational backgrounds. Meanwhile, a simplified and non-punitive reporting system should be established to create positive safety management climate.

A qualitative meta-synthesis of patient dignity from the perspective of caregivers.

BACKGROUND: The concept of dignity remains disputed, with most studies defining dignity based on its external dimension. Although its inherent dimension is a rooted attribute of dignity, it has received scarce attention. Caregivers have close relationships with their care recipients and thus may perceive their patient's inherent as well as external dimensions of dignity. Therefore, in this study, we aimed to identify, analyze, and synthesize evidence on human dignity presented in qualitative studies from the perspective of caregivers to gain a deeper comprehension of the preservation of patients' dignity by their caregivers. METHODS: A qualitative meta-synthesis was performed by searching for relevant qualitative literature via systematic electronic databases, including MEDLINE, PsycINFO, ProQuest, CINAHL, Embase, Health Source, and Web of Science, from inception to March 15, 2022. RESULTS: Nine studies were eligible for inclusion and included in the meta-synthesis. Three overarching categories were identified: integrated person, "rootedness" and "growth" atmosphere, and balanced state. CONCLUSIONS: Dignity is rooted in its inherent dimension, whereas its external dimension may promote individual dignity. Furthermore, caregiver-patient relationships may be a key factor linking the inherent dimension of dignity with its external dimension. Thus, further studies should focus on the mechanism of relationships in preserving dignity.

Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study.

OBJECTIVES: To develop and externally validate a machine learning-based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. METHODS: Potential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). RESULTS: The 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. CONCLUSIONS AND IMPLICATIONS: The machine learning-based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Incidence and Risk Factors of Falls Among Older People in Nursing Homes: Systematic Review and Meta-Analysis.

OBJECTIVES: Falls are common among older people in nursing homes, and the assessment of fall risk factors is critical for the success of fall prevention interventions. This study aimed to systematically assess the incidence and risk factors of falls in older people living in nursing homes. DESIGN: Systematic review and meta-analysis. SETTING AND PARTICIPANTS: Older people living in nursing homes. METHODS: Literature searches were conducted independently by 2 researchers in 8 databases. Qualities of included studies were assessed using the Newcastle-Ottawa Scale. The prevalence and risk factors of falls were analyzed with a random effects model. All analyses were performed by R software, x64 4.2.2. RESULTS: In 18 prospective studies addressing older adults living in nursing homes, the pooled incidence of falls was 43% (95% CI 38%-49%), and the meta-regression analysis indicated that the incidence generally decreased from 1998 to 2021. The following risk factors had a strong association with all falls: fall history, impaired ADL performance, insomnia, and depression. Risk factors with low to moderate correlation were vertigo, walking aids, poor balance, use of antidepressants, use of benzodiazepines, use of antipsychotics, use of anxiolytics, polypharmacy, dementia, unsteady gait, hearing problems, and gender (being male). Having bed rails was identified as a protective environmental factor. CONCLUSIONS AND IMPLICATIONS: The results from our meta-analysis suggest that the incidence of falls of older adults living in nursing homes is high, and the risk factors for falls are various. Assessments of balance and mobility, medical condition, and use of medications should be included as key elements in the fall risk assessments of older people in nursing homes. Environmental risk factors still need to be explored in future studies. Tailored fall prevention strategies should be implemented by addressing the modifiable risk factors.