Conectar
Cognitive decline, including subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia, significantly affects social participation, leading to social isolation and reduced quality of life. Enhancing social participation through interventions may mitigate these effects, yet evidence on intervention effectiveness and mechanisms remains inconsistent.
To evaluate the effectiveness of social participation interventions for individuals with cognitive decline and identify effective behavior change techniques (BCTs) supporting social participation.
Our search using the following databases—PubMed, Web of Science, Embase, Cochrane Library, CINAHL, Scopus, CNKI, and Wanfang—was conducted until October 2024. The quality of the included studies was assessed using the Cochrane risk of bias tool for randomized trials. Meta-analyses were conducted using Review Manager 5.4 and Stata18, and the certainty of evidence was rated using the Grading of Recommendations Assessment, Development, and Evaluation approach.
Sixteen RCTs involving 2190 participants were included. Music therapy (SMD = 0.62, 95% CI [0.15, 1.10]) and reminiscence therapy (SMD = 0.34, 95% CI [0.02, 0.66]) demonstrated significant positive effects on social participation. Group-based interventions were particularly effective (SMD = 0.23, 95% CI [0.04, 0.43]). Commonly used BCTs included goal setting, behavioral practice/rehearsal, and social support. However, substantial heterogeneity and limited data on SCD and MCI restricted generalizability.
Interventions promoting social participation may enhance engagement for individuals with cognitive decline, particularly through music therapy, reminiscence therapy, and group-based formats. The complexity and dynamic nature of social interaction require individuals to engage and integrate various cognitive functions and skills, which can present significant challenges for older adults with cognitive impairments in their daily social participation. Further research is needed to optimize intervention components and address gaps in targeting early cognitive decline stages.
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.
This study explored the impact of different care modes on the outcome of hospitalized patients with acute ischaemic stroke (AIS) during hospitalization and 3 months after discharge.
This was a prospective cohort study comparing the outcomes at hospitalization, at discharge, and at 3 months post discharge among AIS patients with different caregiving arrangements from 9, December 2022 to 20, August 2023. The general information questionnaire, Modified Barthel Index, Shortened General Comfort Questionnaire, Perceived Social Support scale, Herth Hope Index, modified Rankin scale and EQ-5D-5L were utilized for the investigation.
The psychological evaluation scores during hospitalization, including comfort, perceived social support, and hope, did not significantly differ between the two groups of AIS patients (p > .05). Moreover, there were no significant impacts observed in terms of length of stay (LOS) at the hospital or hospitalization expense (p > .05). The proportion of patients with intact functionality was greater in the family caregiver group 3 months after discharge (16.5%). However, when stratified based on prognosis, the difference in outcomes between the two groups of patients did not reach statistical significance (p > .05). The analysis of ADL, quality of life and stroke recurrence in 276 surviving ischaemic stroke patients 3 months post discharge indicated no differences between the two groups across all three aspects (p > .05).
Older and divorced or widowed AIS patients tend to prefer professional caregivers. The psychological state during hospitalization, length of hospital stay and hospitalization expenses are not influenced by the caregiving model. Three months post discharge, a greater proportion of patients in the family caregiving group had intact mRS functionality, but this choice did not impact patient prognosis, stroke recurrence, quality of life or independence in ADL.
FreshRSS