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Background Screening is often recommended as a first step in frailty management. Many guidelines call to implicate frailty screening into practice in the primary care setting. However, few countries or organizations implement it. Understanding and clarifying the stakeholders’ views and issues faced by the implementation is essential to the successful implementation of frailty screening. However, the systematic review on stakeholders’ views of frailty screening in primary care is decidedly limited. Our objective was to explore the perspective of older adults, caregivers, and healthcare providers on frailty screening and determine the enablers and barriers to implementing frailty screening in primary care. Methods A systematic search of six databases and other resources was conducted following JBI’s three-step search strategy. The search resulted in 7362 articles, of which 97 were identified for further assessment according to the inclusion criteria. After the full-text screening, quality assessment and data extraction were carried out using the tools from Joanna Briggs Institute (JBI). Moreover, reviewers used the approach of meta-aggregative of JBI to analyze data and synthesis the findings. Results Six studies were included. A total of 63 findings were aggregated into 12 categories and then further grouped into three synthesized findings:1) capacity of healthcare providers and older adults; 2) opportunity in the implementation of frailty screening; 3) motivation in the implementation of frailty screening. These themes can help identify what influences the implementation of screening from the perspective of stakeholders. Conclusions This meta-synthesis provides evidence on the barriers and enablers of frailty screening in primary care, from the aspects of psychological, physical, social, material, etc. However, stakeholder perspectives of frailty screening have not been adequately studied. More research and efforts are needed to explore the influencing factors and address the existing barriers.

BackgroundSeveral prediction models for cognitive frailty (CF) in older adults have been developed. However, the existing models have varied in predictors and performances, and the methodological quality still needs to be determined.ObjectivesWe aimed to summarize and critically appraise the reported multivariable prediction models in older adults with CF.MethodsPubMed, Embase, Cochrane Library, Web of Science, Scopus, PsycINFO, CINAHL, China National Knowledge Infrastructure, and Wanfang Databases were searched from the inception to March 1, 2022. Included models were descriptively summarized and critically appraised by the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 1,535 articles were screened, of which seven were included in the review, describing the development of eight models. Most models were developed in China (n = 4, 50.0%). The most common predictors were age (n = 8, 100%) and depression (n = 4, 50.0%). Seven models reported discrimination by the C-index or area under the receiver operating curve (AUC) ranging from 0.71 to 0.97, and four models reported the calibration using the Hosmer–Lemeshow test and calibration plot. All models were rated as high risk of bias. Two models were validated externally.ConclusionThere are a few prediction models for CF. As a result of methodological shortcomings, incomplete presentation, and lack of external validation, the models’ usefulness still needs to be determined. In the future, models with better prediction performance and methodological quality should be developed and validated externally.Systematic review registrationwww.crd.york.ac.uk/prospero, identifier CRD42022323591.

Background: Non-pharmacological interventions (NPIs) are important in cognitive decline prevention in individuals with mild cognitive impairment (MCI). However, the dose-response relationship remains unclear.Design: Systematic review and meta-analysis of randomised controlled trials.Methods: Seven databases were searched until April 2020. RCTs of NPIs in individuals with MCI were eligible for inclusion. Hedge's g was used to calculate the effect size.A random-effect meta-analysis was used to explore the impact of NPIs on cognition.Subgroup analysis was used to investigate the moderates. The dose was measured by prescription (frequency, intensity, type, time and volume) and intervention characteristics (period, energy expenditure, delivery mode and setting) in NPIs. Results: Forty-two studies with 4401 participants were included. In the NPIs, cognitive intervention (g = 0.167), physical exercise (g = 0.536) and multicomponent intervention (g = 0.386) had significant effect on cognition in individuals with MCI. Dose-response results showed cognition could be significantly improved in 1-2 times/week (p < .05), 60-120 min/session (p < .05), ≥12 weeks (p < .05), supervised (p < .05), clinical setting (p < .05) in cognitive intervention. In physical exercise, cognition could be improved in ≥3 times/week (p < .05), vigorous-intensity (p < .05), muscle-strengthening activity (p < .05), 30-60 min/session (p < .05), 6-12 weeks (p < .05), unsupervised (p < .05), community setting (p < .05). In the multicomponent intervention, cognition could be improved in 1-2 times/week (p < .05), 30-60 min/ session (p < .05), 8-16 weeks (p < .05), clinical (p < .05). In nutrition intervention, cognition could be better improved DHA (p < .05), >1000 mg/day (p < .05).

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