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This study aimed to (1) evaluate the effectiveness of e-health interventions in improving physical activity and associated health outcomes during pregnancy, (2) compare the e-health functions employed across interventions and (3) systematically identify the behaviour change techniques (BCTs) used and examine their interrelationships.
A systematic review and meta-analysis following the PRISMA 2020 guidelines.
Randomised controlled trials were included. Meta-analyses and subgroup analyses were performed using RevMan 5.3. Social network analysis was conducted to determine the most central BCTs within the intervention landscape.
Ten databases were searched, including PubMed, Embase, Web of Science, Cochrane Library, ProQuest, Scopus, SinoMed, China National Knowledge Infrastructure, WanFang and the China Science and Technology Journal Database, from inception to April 22, 2024.
Thirty-five studies were included. Pooled analyses indicated that e-health interventions significantly improved both total (SMD: 0.19; 95% CI: 0.10 to 0.27; I 2 = 55%) and moderate-to-vigorous physical activity (SMD: 0.16, 95% CI: 0.06 to 0.26; I 2 = 53%) in pregnant women. Subgroup analyses revealed that interventions based on theoretical frameworks and those not specifically targeting overweight or obese women demonstrated greater effectiveness. Additionally, e-health interventions were associated with significant reductions in both total and weekly gestational weight gain. Six of the twelve e-health functions were utilised, with ‘client education and behaviour change communication’ being the most prevalent. Thirty unique BCTs were identified; among them, ‘instruction on how to perform the behaviour’, ‘self-monitoring’, ‘problem solving’, and ‘goal setting’ showed the highest degree of interconnectedness.
E-health interventions are effective in enhancing physical activity and reducing gestational weight gain during pregnancy. Incorporating theoretical frameworks and well-integrated BCTs is recommended to optimise intervention outcomes.
Integrating e-health interventions into existing perinatal care models holds promise for enhancing physical activity among pregnant women and improving maternal health outcomes.
This study adhered to the PRISMA checklist.
No patient or public involvement.
The study protocol was preregistered in the International Prospective Register of Systematic Reviews (CRD42024518740)
To refine fall risk assessment scale among older adults with cognitive impairment in nursing homes.
A cross-sectional survey.
Mokken analysis was conducted to refine the assessment scale based on unidimensionality, local independence, monotonicity, dimensionality, and reliability. Data were gathered from cognitively impaired older adults in a nursing home from January to February 2023. Trained nursing assistants conducted face-to-face assessments and reviewed medical records to administer the scale.
Emotion and State Dimension did not meet unidimensionality criteria (H = 0.14), particularly item Q9, which also violated local independence. Monotonicity analysis showed all items exhibited monotonic increases. After refinement at c = 0.3, the scale consists of nine items. With increasing c-values, the first seven items were ultimately retained to form the final version of the scale. Both optimised scales (9-item and 7-item) satisfied reliability requirements, with all coefficients (Cronbach's α, Guttman's lambda-2, Molenaar-Sijtsma, Latent Class Reliability Coefficient) ≥ 0.74.
The scale is suitable for assessing fall risk among older adults with cognitive impairment, with a unidimensional scale of the first seven items recommended for practical use. Future efforts should refine the scale by exploring additional risk factors, especially emotion-related ones.
The refined 7-item scale provides nursing home staff with a practical, reliable tool for assessing fall risk in cognitively impaired older adults, enabling targeted prevention strategies to enhance safety and reduce injuries.
The refined 7-item scale provides nursing home staff with a reliable, practical, and scientifically validated tool specifically designed for assessing fall risk in older adults with cognitive impairment. Its simplicity enables efficient integration into routine clinical workflows, empowering caregivers to proactively identify risk factors and implement timely, targeted interventions. This approach directly enhances resident safety by translating assessment results into actionable prevention strategies within daily care practices.
This study was reported in accordance with the STROBE guidelines.
No Patient or Public Contribution.
In recent years, the critical role of health literacy in diabetes management has become increasingly prominent. The aim of this study was to investigate the impact of social support on health literacy among patients with diabetes, to test the mediating role of self-efficacy and empowerment between social support and health literacy, and the moderating role of eHealth literacy.
A cross-sectional study conducted between August 2023 and June 2024.
This study adopted the cluster sampling method and conducted a questionnaire survey among 251 patients with diabetes in a tertiary hospital in Wuhu City, Anhui Province. The questionnaires included the Social Support Rating Scale, the Self-Efficacy for Diabetes scale, the Health Empowerment Scale, the eHealth Literacy Scale and the Diabetes Health Literacy Scale.
Social support was positively associated with health literacy in patients with diabetes. Self-efficacy and empowerment mediated the relationship and formed chained mediation pathways respectively. eHealth literacy has a moderating role between self-efficacy and empowerment.
The results revealed that social support influences health literacy among patients with diabetes through the mediating pathways of self-efficacy and empowerment, and that this process is moderated by eHealth literacy. These findings provide a theoretical basis and practical insights for improving health literacy among patients with diabetes.
Enhancing health literacy among people with diabetes by strengthening social support, self-efficacy and empowerment levels, while focusing on the technology-enabling role of eHealth literacy in this context.
This study adheres to the relevant EQUATOR guidelines based on the STROBE cross-sectional reporting method.
We thank all patients who participated in the study for their understanding and support.
To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms.
This was a cross-sectional study.
Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models—random forest, gradient boosting tree, support vector machine, and logistic regression—were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist.
Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min walking distance, globulin, white blood cells, aspartate aminotransferase, blood urea, MDASI score, depression score, and SRAHP score. The random forest model performed best among the four algorithms, achieving AUC-ROC values of 0.86, 0.70, 0.72, and 0.67, respectively, and was used to develop an online predictive tool (URL: http://10.60.32.178:5000).
This study developed a machine learning model to predict physical activity levels in lung cancer survivors, with the random forest model demonstrating the highest accuracy and clinical utility. This tool enables the early identification of low-activity survivors, facilitating timely, personalised rehabilitation and health management.
The development of a predictive model for physical activity levels in lung cancer survivors can help clinical medical staff identify survivors with relatively low physical activity levels as early as possible. Thus, personalised rehabilitation plans can be formulated to optimise quality of life during their survival period.
Physical activity has been used as a nonpharmacological intervention in cancer patient rehabilitation plans. However, a review of past studies has shown that lung cancer survivors generally have low physical activity levels. In this study, we identified the key factors influencing physical activity among lung cancer survivors through a literature review. We constructed a prediction model for their physical activity levels using machine learning algorithms. Clinical medical staff can use this model to identify patients with low physical activity levels early and to develop personalised intervention plans to improve their quality of life during survival.
The study adhered to the relevant EQUATOR reporting guidelines, the TRIPOD Checklist for Prediction Model Development and Validation.
During the data collection phase, participants were recruited to complete the questionnaires.
Individuals with systemic lupus erythematosus (SLE) often suffer from sleep disturbance, which exhibits heterogeneity. Whether it could be grouped into different clusters remains unknown, posing challenges to the development of personalised interventions to address sleep disturbance.
To examine clusters of sleep disturbance and associated factors in people with SLE.
Cross-sectional design.
From November 2023 to January 2024, people diagnosed with SLE were recruited by a convenience sampling approach. Data were collected via an online platform Wenjuanxing. Sleep disturbance was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Other information, such as disease activity, pain, fatigue, depression and anxiety was also collected using validated instruments. Latent profile analysis was performed to reveal the distinct clusters of sleep disturbance. Multiple logistic regression analysis was performed to investigate factors associated with the clusters.
A total of 538 participants were included, with a response rate of 85.1% (538/632). Only those with sleep disturbance (PSQI > 5) were included in the final analyses. Participant mean age was 32.9 (SD = 8.4) years and 402 (92.6%) were females. All had sleep disturbance (PSQI > 5) and their mean PSQI was 8.8 (SD = 2.9). Three distinct clusters were identified: mild sleep disturbance with poor sleep quality, adequate sleep duration and good daytime functioning (50.7%), mild sleep disturbance with poor sleep quality, adequate sleep duration and poor daytime functioning (30.9%) and moderate sleep disturbance with poor sleep quality, inadequate sleep duration and impaired daytime functioning (18.4%). There are both overlaps and unique aspects in terms of factors associated with each cluster of sleep disturbance, including age, body mass index, cardiovascular system damage, musculoskeletal system damage, depression and anxiety.
Sleep disturbance in patients with SLE showed three distinct clusters, with each cluster having slightly different predisposing factors.
In clinical practice, nurses are recommended to prioritise assessment and interventions for those at-risk subgroups. They could also use the above information to develop and provide personalised interventions to address the unique needs of each cluster of sleep disturbance.
Checklist for reporting of survey studies.
No patient or public contribution.
To explore the heterogeneity of disease-specific anxiety profiles among patients with chronic obstructive pulmonary disease (COPD) using latent profile analysis (LPA), and to identify the associations between distinct anxiety subtypes and inhaler medication adherence in patients with COPD.
Adherence to inhaled medication among patients with COPD continues to be suboptimal. Anxiety, a common comorbidity, may exacerbate this issue. However, the specific relationship between anxiety and adherence to inhaled medications remains unclear.
A prospective cohort study was conducted following the STROBE Checklist.
A prospective observational study employed the Anxiety Inventory for Respiratory Disease (AIR) to assess disease-specific anxiety in patients with COPD. Inhaler medication adherence was evaluated using the Test of Adherence to Inhalers (TAI) 6 months after initiating treatment. Latent Profile Analysis (LPA) was performed to identify distinct anxiety subtypes. Multiple linear regression analysis was conducted to examine the associations between identified anxiety subtypes and adherence dimensions, adjusting for sociodemographic and clinical variables.
Among 298 COPD patients, the overall AIR score was 5 (IQR: 2–11). Using LPA, three distinct anxiety subtypes were identified: Low Anxiety—Irritable Subtype (57.05%), Moderate Anxiety—Tense Subtype (26.85%) and High Anxiety—Anticipatory Subtype (16.10%). Through multiple linear regression analysis, the High Anxiety—Anticipatory Subtype was significantly associated with lower inhaler medication adherence among COPD patients.
This study revealed three latent profiles of disease-specific anxiety among COPD patients. The High Anxiety–Anticipatory Subtype was associated with a lower inhaler medication adherence in individuals with COPD after initiating treatment.
Identifying the relationship between disease-specific anxiety and inhaler medication adherence in patients with COPD after initiating treatment underscores the need for healthcare providers to assess anxiety during patient visits and prioritise patients with high anticipatory anxiety. When high anxiety adversely affects inhaler medication adherence, targeted interventions should be developed to improve adherence and prognosis.
No patient or public contribution.
Deep vein thrombosis (DVT) is a frequent complication following endovascular thrombectomy (EVT) in patients with acute ischaemic stroke (AIS), potentially leading to fatal pulmonary embolism (PE). Identifying patients early at high risk for DVT is clinically important. This study developed and validated a nomogram combining laboratory findings and clinical characteristics to predict the risk of lower-extremity DVT after EVT in patients with AIS.
This retrospective multicentre observational study was conducted in two tertiary hospitals in China, enrolling 640 patients who underwent ultrasonography for DVT diagnosis within 10 days following EVT. Data on medical history, examination and laboratory results were collected for logistic regression analyses to develop a DVT risk nomogram.
Logistic regression analyses identified critical predictors of DVT: lower limb National Institutes of Health Stroke Scale (NIHSS) score ≥ 2, elevated D-dimer levels (≥ 1.62 mg/L) and prolonged puncture-to-recanalization time (PRT ≥ 66 min). The nomogram demonstrated good discriminative ability (AUC 0.741–0.822) and clinical utility across internal and external validation cohorts. Additionally, the presence of DVT was significantly associated with reduced functional independence at 90 days post-EVT, highlighting the negative impact of DVT on patient recovery (OR = 3.85; 95% CI: 2.18–6.78; p < 0.001).
The study provides a practical clinical tool for early detection and intervention in patients with AIS at high risk for DVT following EVT. Early identification and intervention may help improve outcomes in patients with AIS undergoing EVT.
This nomogram helps in the early detection and proactive management of DVT in AIS patients, which can reduce severe complications and improve patient recovery outcomes.
No patient or public contributions were involved in this study due to its retrospective design, where data were utilised from existing medical records without direct patient interaction.
To evaluate the effectiveness of nurse-led care (NLC) in patients with rheumatoid arthritis on disease activity, physical function, fatigue, satisfaction, pain, and quality of life.
Rheumatoid arthritis is a chronic autoimmune disease, which may not respond to insufficient rheumatology care capacity and workforce shortage. NLC is a care delivery model that can help address this shortage and improve disease management.
Systematic review and meta-analysis.
Nine databases were independently searched by two reviewers for eligible studies. Randomised controlled studies evaluating the effects of NLC on disease activity, physical function, fatigue, satisfaction, and other outcomes were included. The cochrane risk of bias tool was used to assess the risk of bias.
A total of nine studies involving 1447 participants were included. The pooled results indicated that no significant difference in disease activity was found at 0.5 years of follow-up (SMD: −0.33, 95% CI [−0.70, 0.04]), and a significant difference was seen in favour of NLC at 1 year (SMD: −0.35, 95% CI [−0.48, −0.10]), and 2 years (SMD: −0.29, 95% CI [−0.48, −0.10]). Moreover, no significant difference was found in fatigue and satisfaction at 0.5 years of follow-up, whereas differences in favour of NLC were seen at 1 year. In addition, no significant difference was found in physical function, pain, and quality of life.
This review indicated that NLC was not inferior to other types of care, and even had a better positive impact on disease activity, fatigue, and satisfaction for patients with rheumatoid arthritis.
Our study demonstrates that NLC is an effective approach to managing rheumatoid arthritis and recommends medical practitioners be well-versed in its importance.
Patients or public members were not directly involved in this study.
ClinicalTrials.gov identifier: CRD42022355963
To explore the effectiveness of dyadic intervention on the psychological distress of cancer patients and their partners.
Cancer patients and their partners demonstrated high levels of psychological distress. However, the effects of dyadic intervention on psychological distress were unclear.
A systematic review and meta-analysis of randomised controlled trials was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement.
A systematic search on couple-based dyadic intervention for cancer patients and their partners was carried out across eight databases. Our review adhered to the Cochrane risk-of-bias tool as its foundational framework, and data extraction and analysis followed standardised checklists for quantitative research studies.
No statistically significant effects were reported on patients' anxiety, depressive symptoms, or cancer-related distress. However, subgroup analysis revealed that interventions lasting 6 or 12 weeks had positive effects on patients' cancer-related distress. Significant reductions in cancer-related distress scores were only observed when interventions included communication and support (CS) and skill building (SB) components, however. Additionally, patients experienced higher distress levels with less than six interventions or session durations shorter than 6 h. For partners, couple-based dyadic interventions significantly reduced their anxiety and depressive symptom levels.
Couple-based dyadic interventions, with either 6- or 12-week durations, or encompassing both CS and SB components, demonstrated significantly positive effectiveness on patients' psychological distress. Couple-based dyadic interventions also exhibited a propensity for alleviating psychological distress in both cancer patients and their partners, with a more pronounced impact observed among partners.
This meta-analysis highlights the effectiveness of dyadic interventions in reducing psychological distress in cancer patients and their partners. Healthcare professionals should incorporate these interventions into their care practices.
Direct contributions from patients or the public were not included in this review.
PROSPERO number: CRD42023418978; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418978
To explore the potential risk factors contributing to inadequate bowel preparation in middle-aged and elderly patients (aged 40 and above) undergoing colonoscopy, and to subsequently devise and validate a comprehensive risk assessment tool and nomogram model for accurately predicting such preparation.
A retrospective observational study was conducted at three campuses from January 2023 to December 2023.
Twenty-three clinical indicators derived from colonoscopy records were leveraged to inform the predictive models. By using multivariate and stepwise logistic regression analyses, a risk-scoring model and a nomogram prediction model were devised. Calibration curves were used to evaluate the model's accuracy, while decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to evaluate the model's clinical applicability and discriminatory power, encompassing the Hosmer–Lemeshow χ2 test for goodness-of-fit. TRIPOD was used to guide this study.
A total of 6860 outpatients who met the criteria were selected and divided into a training set (n = 4116) and a validation set (n = 2744) according to the bowel preparation quality. BMI, comorbidity, history of constipation, frailty degree, prescribing doctor's specialty, nonpatient's own prescribing, anxiety level and abdominal surgery history were independent risk factors for inadequate bowel preparation. The corresponding risk scores were 2, 0, 2, 4 3, 3, 2 and 1 respectively; with a total score of ≥ 8.5 classifying patients into a high-risk group. The area under the curve for the training and validation sets were 0.740 and 0.730, respectively, and an optimal critical value threshold of 34%. DCA findings demonstrated that the nomogram model was clinically helpful throughout a broad threshold probability range.
The risk prediction nomogram model and assessment tool constructed in this study can help clinicians identify individuals at high risk for inadequate bowel preparation at an early stage, which is a guideline for personalised prevention and treatment.
The name of the trial register was outpatient discharge management after general intravenous anaesthesia. The clinical trial registration was 2024–0116, and the link to the trial at the registration website was https://ctms.z2hospital.com:8443/.
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist.
Data for this study were provided by 7724 outpatients over 40 years old who underwent their first colonoscopy between January 2023 and December 2023 at three campuses of a medical centre.
Increases in cancer survivorship negatively impact patients and family caregivers, decreasing quality of life. Previous dyadic interventions involved them as a unit and focused on their outcomes, but inconsistent results existed in influencing quality of life.
To assess dyadic intervention effect on quality of life for cancer patients and family caregivers across different cancer types and intervention durations.
A systematic review and meta-analysis based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
Six databases were searched from establishment until 14 January 2024. Two authors independently performed the search process, literature screening, and data extraction. The ROB version 2 and GRADE were respectively used to check the methodology and evidence quality. The data were analysed via RStudio, and intervention effects were estimated with 95% CIs and SMDs. The statistical heterogeneity was explored through the I2 statistic, P values, and Egger's test, and differences in overall effects were deemed statistically significant, having a P value < 0.05. Subgroup analysis was also conducted.
13 RCTs with 1625 participants, published from 2005 to 2021, were included. The results demonstrated that dyadic interventions enhanced quality of life for both cancer patients and family caregivers. Subgroup analysis suggested that family-centred interventions for patients with specific cancer types, which lasted for a long period (> 6 weeks), enhanced quality of life for cancer patients and family caregivers. The evidence and methodology were of a moderate quality.
Nurses are important practitioners of culture-oriented dyadic interventions. Long-term (> 6 weeks) and family-centred dyadic interventions for patients with a specific cancer type can enhance cancer patients' and family caregivers' quality of life, along with digital intelligence approaches to promote mutual communication and strengthen family relationships, thereby optimising oncology clinical nursing and enhancing the quality of life, health, and welfare of the entire family.
Dyadic interventions emphasising the involvement of both cancer patients and family caregivers should be considered and tailored by professionals and oncology nurses to establish harmonious family relationships, improve family coping techniques and decision-making to enhance the whole family's quality of life and well-being according to their cultural contexts, and promote more efficient, targeted, and economical oncology care.
No Patient or Public Contribution because all the involved participants were from existing studies, and the design, conduction, analysis, and interpretation of the data were completed by the authors in this article.
International Prospective Register of Systematic Reviews: CRD42024519432; https://www.crd.york.ac.uk/PROSPERO/#recordDetails
To explore the complete decision-making process and action logic of nurses making autonomous decisions that result in missed nursing care.
The complex characteristics of patients in Intensive Care Units place higher demands on the allocation of nursing resources, as well as on the professional skills, resilience and ethics of nursing staff. Preventing missed nursing care is particularly crucial in Intensive Care Units.
A theory construction qualitative study using grounded theory.
Semistructured face-to-face interviews were conducted with 20 nurses, including three head nurses and 17 bedside nurses. Head nurses provided insights into counselling and management practices.
The theoretical model of nurses' decision-making processes comprise four strategies: setting priorities, seeking help, delaying nursing care and omitting nursing care. The latter two constitute missed nursing care. Inadequate staffing, task urgency and negative emotions can lead to omitting nursing care.
This study proposes an original concept: grassroots arrangement of nursing care (GANC). Grassroots arrangement of nursing care includes the autonomous and adaptive decision-making process used by bedside nurses to optimise workflow in busy environments. It includes specific strategies and quality implications, enabling a nuanced balance between limited nursing resources, increasing patient needs and maintaining the best possible quality of care.
Nursing managers should consider the dual aspects of grassroots arrangement of nursing care, support nurses' grassroots autonomy and streamline decision-making processes.
This study follows the Consolidated Criteria for Reporting Qualitative Studies (COREQ).
No patient or public contribution.
This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).
A cross-sectional design.
From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).
The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.
This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.
Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.
Some patients were involved in data interpretation. No public contribution.
Although several models have been developed to predict postoperative pneumonia in elderly hip fracture patients, no systematic review of the model quality and clinical applicability has been reported.
To systematically review and critically appraise existing models for postoperative pneumonia in elderly hip fracture patients.
Systematic review and meta-analysis.
10 databases were systematically searched from inception to April 15, 2024, updated on August 26. Two reviewers independently performed literature selection, information extraction and quality assessment. A narrative synthesis was employed to summarise the characteristics of the models. Meta-analysis was performed using Stata 17.0.
13 studies containing 25 models were included. The prevalence of pneumonia was 9.62% (95% CI: 7.62%–11.62%). Age (53.8%), hypoproteinemia (46.2%), chronic obstructive pulmonary disease (COPD, 30.8%), gender (30.8%), activity of daily living score (ADL, 30.8%) and American Society of Anesthesiologists (ASA, 30.8%) score were the top six predictors. All models reported area under curve (AUC: 0.617–0.996). 9 studies (69.2%) used the Hosmer-Lemeshow (H-L) test, calibration curves, or Brier scores to evaluate the calibration. 5 studies (38.5%) performed internal validation, 4 studies (30.8%) performed external validation. All studies had a high risk of bias due to single sample source, inappropriate data processing, inadequate model evaluation, and negligence of calibration and validation. 10 studies (76.9%) had good applicability.
Prediction models for postoperative pneumonia in elderly hip fracture patients are still in the developing stage. The validation and evaluation of existing models are poor. Future studies should focus on robust external validation and updating. Additionally, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + artificial intelligence (TRIPOD+AI) statement should be followed.
Prediction models are effective in discriminating postoperative pneumonia in elderly hip fracture patients, but further external validation and adjustment are still warranted.
To evaluate the prevalence of frailty and its impact on quality of life (QoL) in older Chinese breast cancer (BC) patients, which have not been thoroughly reported in this population.
A prospective multi-centre cross-sectional registry study.
Data were collected from Cancer Hospital of the Chinese Academy of Medical Sciences, Peking University Third Hospital and Beijing Chaoyang District San Huan Cancer Hospital between October 2021 and July 2023.
BC patients aged over 65 years were enrolled in this study. They completed three assessment scales including the FRAIL scale, Hospital Anxiety and Depression Scale (HADS) and European Organization for Research and Treatment of Cancer Quality of Life questionnaire Core 30 (EORTC QLQ-C30), to screen for frailty, related factors and QoL. Clinical and pathological data were also collected. Analysis of frailty and prefrailty risk factors was performed via logistic regression. A multivariable linear regression model was used to evaluate the mean differences in scores for each QoL domain between patients with different frailty statuses.
A total of 946 patients were enrolled from three hospitals in Beijing between October 2021 and July 2023. Their median age was 69 years and 73.6% of them had early-stage breast cancer. Further, 37.2% of these patients had ≥ 1 comorbidity. The prevalence of frailty was 8.8% and frailty was more common in those with aged ≥ 75 years (22.3%), those with advanced tumours (15.6%), those with anxiety (31.3%) and those with depression (29.3%). More than half (57.2%) of the patients were prefrail. Regression analysis revealed that older age (odds ratio [OR] 1.12 [95% CI 1.07–1.17], p < 0.001), an advanced tumour (OR 2.27 [1.33–3.89], p = 0.003), anxiety (OR 2.74 [1.37–5.48], p = 0.004) and depression (OR 3.84 [1.97–7.49], p < 0.001) were significantly associated with frailty. After adjusting for other factors, different frailty states were shown to be independent influencing factors for QoL in both the functional and the symptom domains (all p < 0.05).
Our study provides data on the prevalence of frailty and prefrailty in older Chinese patients with BC. Both conditions are closely related to poor QoL. It is helpful for oncologist and clinical care to making intervention and better treatment decisions.
The study adhered to the STROBE checklist.
This study provides detailed data on the prevalence of frailty in older Chinese patients with BC and correlative factors. It suggests that clinical care should fully assess patients' frailty before making treatment decisions and provide early intervention for related factors.
Patients participated in the implementation of the project (including the informed consent and questionnaire process). No other public contribution to this research.
This study provides data on the prevalence of frailty in Chinese older BC patients and correlative factors. It indicates that clinicians should fully assess patients' frailty before making treatment decisions and provide early intervention for related factors.
ChiCTR2200056070
To synthesise up-to-date research evidence for non-pharmacological interventions to improve various sleep outcomes (e.g., sleep quality, duration) in postsurgical cardiac patients.
Sleep disturbances are common amongst postsurgical cardiac patients, yet the effectiveness of non-pharmacological interventions in improving various sleep outcomes has not been comprehensively reviewed.
A systematic review and meta-analysis guided by the PRISMA protocol.
CINAHL, PubMed, PsycINFO, Embase, Web of Science, and Cochrane Library were searched for relevant research in May 2023. Included studies used a randomised controlled trial design that applied a non-pharmacological intervention for postsurgical cardiac patients and reported sleep as an outcome. For the meta-analysis, mean effect sizes were separately calculated for studies with regular and reverse-scored scales.
Of 37 studies included, the most common cardiac surgery was coronary artery bypass graft. Most interventions were performed within the first postoperative week and assessed sleep quality outcomes using the Pittsburgh Sleep Quality Index. The interventions are categorised into five types. Human resource-based strategies emerged as the most effective. The meta-analysis of 27 eligible studies showed a mean effect size of 0.76 for studies with regular scoring scales and − 1.04 for those with reverse-scored scales, indicating medium to large effect sizes.
Our findings provide strong evidence that non-pharmacological interventions, particularly human resource-based strategies, significantly improve sleep quality in postsurgical cardiac patients. The medium to large effect sizes underscore the clinical significance of these findings.
Healthcare professionals should consider incorporating non-pharmacological interventions, especially human resource-based strategies, in care plans for postsurgical cardiac patients to improve sleep outcomes and promote recovery. These interventions should be tailored to individual physical and cultural differences for maximum effectiveness. Future research should evaluate the long-term effects of these interventions on various sleep outcomes, using both objective and subjective measures to provide a comprehensive assessment of their efficacy.
This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.
Patient and public contributions were not required for this review.
Our study aims to assess the effectiveness of horticultural therapy in improving outcomes in older patients with dementia.
A systematic review and meta-analysis.
The included studies comprised randomised controlled trials (RCTs) that aimed to assess the effectiveness of horticultural therapy on cognitive function in older patients with dementia. The study design and data extraction were independently conducted by two investigators, who also evaluated the risk of bias using RoB 2.0. The meta-analysis was carried out using Stata 15.1 software.
On November 2023, we searched relevant English and Chinese publications in PubMed, Web of Science, Cochrane Library, Embase, CNKI and Wanfang databases.
The meta-analysis included a total of 9 RCTs, involving 655 older patients diagnosed with dementia. The findings from these studies demonstrated that horticultural therapy had a significant positive impact on various aspects of the patients' well-being when compared to conventional care. Specifically, it was found to improve cognitive function scores, alleviate symptoms of depression, enhance daily activities and enhance overall quality of life. When conducting a subgroup analysis, it was observed that horticultural therapy had a statistically significant effect on cognitive function in older patients with dementia when the intervention frequency was at least two times per week. Furthermore, interventions with a duration of less than 6 months were found to be more effective than those lasting 6 months or longer. Additionally, outdoor horticultural therapy was found to be superior to indoor interventions. Moreover, structured interventions were observed to yield better outcomes compared to non-structured interventions.
More high-quality studies are needed to further corroborate these findings due to the low quality of the included studies. Horticultural therapy has been found to have a significantly positive impact on the cognitive function, depression status, ADL, and quality of life of older patients with dementia.
We provide references for non-pharmacologic treatment of older patients with dementia.
This study aimed to measure the efficacy of horticultural therapy in older patients with dementia across four dimensions: cognitive function, depression levels, daily living activities and overall quality of life.
In older patients with dementia, horticultural therapy has been proven to have a significant positive impact on cognitive function, depressive status, activities of daily living and quality of life.
This study will inform non-pharmacological interventions for older patients with dementia worldwide.
No Patient or Public Contribution.
FreshRSS 