Conectar
To synthesise evidence from qualitative studies on community-dwelling older adults' experiences of social interactions in group interaction programmes.
A systematic review and meta-synthesis based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines.
Two independent researchers screened the titles and abstracts of all retrieved studies based on eligibility criteria. Quality appraisal was conducted based on the comprehensive Critical Appraisal Skills Programme checklist. Data were extracted through an adapted Data Extraction Tool from the Joanna Briggs Institute Qualitative Assessment and Instrument. Findings were synthesised through the JBI meta-aggregation approach.
A literature search for peer-reviewed studies was conducted across seven databases from inception to September 2023.
Our review included the experiences of 395 community-dwelling older adults across 25 studies, yielding the overarching theme of ‘Navigating relationships and facilitating growth in a supportive community’. Five themes were identified: feeling safe in a familiar environment, bringing people together, establishing meaningful relationships, improving personal well-being and facing socialising challenges.
Our findings provided not just theoretical insights but also practical implications. They can serve as a solid foundation for formulating or enhancing similar programmes, directly addressing the pervasive issues of loneliness and social isolation among the ageing population worldwide.
This review improves our understanding of the social, emotional and personal benefits for older adults upon engaging in group interaction programmes. The insights into the elements of successful group interaction programmes for older adults, include providing a safe and familiar setting and bringing people together for socialisation. Future interventions should incorporate these elements to promote social interactions and community engagement more effectively to mitigate loneliness among older adults. Finally, the diverse range of programmes explored in this review demonstrates that future programmes can be flexibly formulated to suit different interests.
The authors have nothing to report.
The review protocol has been registered on PROSPERO.
Digital Creative Art Interventions (DCAIs) are innovative approaches to art interventions using digital technology, which can improve older adults' health. However, a comprehensive summary of the implementation of this intervention among older adults is lacking.
To summarise the deliveries and categories of DCAIs, review their feasibility and roles in older adults' healthcare, and explore the barriers and facilitators to implementing DCAIs in older adults.
Scoping review.
This scoping review followed Arksey and O'Malley's framework, and PRISMA-ScR was used to guide the report.
PubMed, Embase, EBSCOhost, Web of Science and Cochrane Library on 26 February 2024.
Thirty-one studies were selected in this review. We summarised the deliveries and categories of DCAIs in older adults. Besides, we cleared DCAIs to offer music, dance, museum, photo collage, drama, visual art interaction and mixed art intervention to older adults, primarily through videoconferencing or mobile applications. The five health promotion roles were physiological health enhancer, psychological caregiver, socialisation supporter, cognitive promoter and life optimiser. Most older adults believed DCAIs were not only feasible and acceptable, but they also met some barriers such as technological problems, problems brought about by older adults' decline in functioning, experience and privacy.
Despite the unique advantages of DCAIs, continuous improvements are needed. In the future, researchers and healthcare workers should focus on platform improvements, increasing interactivity, diversifying formats and ensuring security and privacy.
This review found that DCAIs offered new approaches to treatment options for older adults' physical and mental health. Therefore, it is recommended that they be continuously optimised and put into clinical practice.
No patient or public contribution.
This study summarised the DCAIs and provides the new approach for health promotion in older adults.
OSF (https://osf.io/m62x9/, registration DOI: https://doi.org/10.17605/OSF.IO/4ZGE6)
Virtual reality-reminiscence therapy (VR-RT) has increasingly been applied to older adults to improve psychological well-being and cognition.
This review aims to identify (1) the design characteristics of conducting a VR-RT and (2) the effects of VR-RT on the user experience, cognitive outcomes and psychological well-being.
Systematic review.
Eligible studies were sourced across nine electronic databases, trial registries, grey literature and hand-searching of the reference list. A narrative synthesis was conducted. Twenty-two studies were included, and most were appraised as high quality. Most of the VR-RTs were highly immersive and personalised, with participants having the autonomy of control. VR-RT has the potential to improve anxiety and depression, and cognitive outcomes for older adults. Overall, VR-RT was reported to be an enjoyable experience for older adults.
VR-RT is a promising innovation that can improve older adults' psychological well-being and cognition without significant side effects, including cybersickness and with the potential for scalability across various settings. More randomised controlled studies are needed to evaluate the effectiveness of VR-RT and its features and treatment dosage. These studies could also examine the effectiveness of VR-RT as an intervention to promote independence in activities of daily living and physical rehabilitation.
VR-RT is a promising intervention for older adults in community settings to enhance psychological well-being and cognition. VR's versatility enables personalised experiences within dynamic virtual environments, possibly enhancing engagement and therapeutic outcomes.
This systematic review did not directly involve patient or public contribution to the manuscript.
This study aimed to explore the perceived preparedness and psychosocial well-being of general ward nurses prior to their deployment into the outbreak intensive care units (ICUs) during the COVID-19 pandemic.
With the surge in COVID-19 cases requiring ICU care, non-ICU nurses maybe deployed into the ICUs. Having experienced through SARS, hospitals in Singapore instituted upskilling programs to secure general ward nurses’ competency in providing critical care nursing. However, no studies have explored the perceptions of general ward nurses on deployment into the ICUs during the COVID-19 pandemic.
Qualitative descriptive study.
The study was conducted at Singapore’s epicentre of COVID-19 management. Five focus groups were conducted following purposive sampling of 30 general ward nurses identified for outbreak ICU deployment. Focus groups were audio-recorded, transcribed verbatim and data thematically analysed. This study was conducted and reported in accordance with the COREQ checklist.
Three salient themes arose, exemplifying the transition from clinical experts in the general wards to practising novices in the outbreak ICUs. Firstly, ‘Into the deep end of the pool’ described general ward nurses’ feelings of anxiety and stress associated with higher exposure risk and expanded responsibilities to nurse critically ill patients. Secondly, ‘Preparing for “war”’ illustrated deployed nurses’ need for clear communication and essential critical care nursing training. Lastly, ‘Call of duty’ affirmed the nurses’ personal and professional commitment to embrace this transition into the ICUs, and their desire for greater psychosocial support.
The study findings highlight that though general ward nurses perceived their impending ICU deployment positively, they require ongoing support to facilitate a smoother transition.
Findings provided an evidence base to improve the preparedness of general ward nurses deployed into the ICUs during the COVID-19 pandemic within key areas of training, information dissemination and psychosocial resilience.
To explore the latent categories and influencing factors of dyadic decision self-efficacy among stroke patients and their caregivers.
A cross-sectional survey involving 305 patient-caregiver pairs was conducted using standardised questionnaires. Latent profile analysis was used to identify dyadic self-efficacy categories and multinomial logistic regression was employed to analyse influencing factors.
The dyadic decision self-efficacy of stroke patients and their caregivers was classified into three categories: low common decision self-efficacy group (35.6%), patients' high decision self-efficacy and caregivers' moderate decision self-efficacy group (38.6%), and high common decision self-efficacy group (25.8%). Influencing factors included patients' education level, income and health literacy, as well as caregivers' education, caregiving duration and social support.
The levels of dyadic decision self-efficacy among stroke patients and their caregivers are heterogeneous. Clinicians can develop targeted interventions involving both patients and caregivers, based on the population's characteristics and influencing factors, to improve their dyadic decision self-efficacy.
This study was conducted and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.
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.
FreshRSS 