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The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead‐times of producing high‐quality peer‐reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health‐related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health‐ and computer‐science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single‐screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health‐related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research. [ABSTRACT FROM AUTHOR]

Liver transplantation ( LT) is a transformative, life-saving procedure with life-long sequale for patients and their caregivers. The impact of LT on the patient's main caregiver can be underestimated. We carried out a systematic review of the impact of LT on the Health-Related Quality of Life ( HRQL) of LT patients' main caregivers. We searched 13 medical databases from 1996 to 2015. We included studies with HRQL data on caregivers of patients following LT then quality assessed and narratively synthesized the findings from these studies. Of 7076 initial hits, only five studies fell within the scope of this study. In general, they showed caregiver burden persisted in the early period following LT. One study showed improvements, however, the other four showed caregiver's levels of stress, anxiety and depression, remained similar or got worse post- LT and remained above that of the normal population. It was suggested that HRQL of the patient impacted on the caregiver and vice versa and may be linked to patient outcomes. No data were available investigating which groups were at particular risk of low HRQL following LT or if any interventions could improve this. The current information about LT caregivers' needs and factors that impact on their HRQL are not adequately defined. Large studies are needed to examine the effects of LT on the patients' family and caregivers to understand the importance of caregiver support to maximize outcomes of LT for the patient and their caregivers. [ABSTRACT FROM AUTHOR]

Abstract: Background: The Geriatric Depression Scale (GDS) has been evaluated in hospital settings but its validity and added value in primary care is uncertain. We therefore conducted a meta-analysis analysing the diagnostic accuracy, clinical utility and added value of the GDS in primary care. Methods: A comprehensive search identified 69 studies that measured the diagnostic validity of the GDS against a semi-structured psychiatric interview and of these 17 analyses (in 14 publications) took place in primary care. Seven studies examined the GDS30 and 10 studies examined the GDS15. Heterogeneity was moderate to high, therefore random effects meta-analysis was used. Results: Diagnostic accuracy of the GDS30 after meta-analytic weighting was given by a sensitivity of 77.4% (95% CI=66.3% to 86.8%) and a specificity=65.4% (95% CI=44.2% to 83.8%). For the GDS15 the sensitivity was 81.3% (95% CI=77.2% to 85.2%) and specificity=78.4% (95% CI=71.2% to 84.8%). The fraction correctly identified (also known as efficiency) by the GDS15 was significantly higher than the GDS30 (77.6% vs 71.2%, Chi2 =24.8 P <0.0001). The clinical utility of both the GDS30 and GDS15 was “poor” for case-finding (UI+ 0.29, UI+ 0.32 respectively). However the GDS15 was rated as “good” for screening (UI− 0.75) whereas the GDS30 was “adequate” (UI− 0.60). Concerning added value, when identification using the GDS was compared with general practitioners'' ability to diagnose late-life depressions unassisted by tools, at a prevalence of 15% the GDS30 had no added benefit whereas the GDS15 helped identify an additional 4 cases per 100 primary care attendees and also helped rule-out an additional 4 non-cases per 100 attendees. Thus we estimate the potential gain of the GDS15 in primary care to be 8% over unassisted clinical detection but at a cost of 3–4 minutes of extra time per appointment. Conclusion: The GDS yields potential added value in primary care. We recommend the GDS15 but not the GDS30 in the diagnosis of late-life depression in primary care. [Copyright &y& Elsevier]

Background: Behavioural and cognitive interventions remain credible approaches in addressing loneliness and depression. There was a need to rapidly generate and assimilate trial-based data during COVID-19., Objectives: We undertook a parallel pilot RCT of behavioural activation (a brief behavioural intervention) for depression and loneliness (Behavioural Activation in Social Isolation, the BASIL-C19 trial ISRCTN94091479). We also assimilate these data in a living systematic review (PROSPERO CRD42021298788) of cognitive and/or behavioural interventions., Methods: Participants (≥65 years) with long-term conditions were computer randomised to behavioural activation (n=47) versus care as usual (n=49). Primary outcome was PHQ-9. Secondary outcomes included loneliness (De Jong Scale). Data from the BASIL-C19 trial were included in a metanalysis of depression and loneliness., Findings: The 12 months adjusted mean difference for PHQ-9 was -0.70 (95% CI -2.61 to 1.20) and for loneliness was -0.39 (95% CI -1.43 to 0.65).The BASIL-C19 living systematic review (12 trials) found short-term reductions in depression (standardised mean difference (SMD)=-0.31, 95% CI -0.51 to -0.11) and loneliness (SMD=-0.48, 95% CI -0.70 to -0.27). There were few long-term trials, but there was evidence of some benefit (loneliness SMD=-0.20, 95% CI -0.40 to -0.01; depression SMD=-0.20, 95% CI -0.47 to 0.07)., Discussion: We delivered a pilot trial of a behavioural intervention targeting loneliness and depression; achieving long-term follow-up. Living meta-analysis provides strong evidence of short-term benefit for loneliness and depression for cognitive and/or behavioural approaches. A fully powered BASIL trial is underway., Clinical Implications: Scalable behavioural and cognitive approaches should be considered as population-level strategies for depression and loneliness on the basis of a living systematic review., Competing Interests: Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests. DE and CCG were committee members for the NICE Depression Guideline (update) Development Group between 2015 and 2022, and SiG was a member between 2015 and 2018. SiG, PC and DM are supported by the NIHR Yorkshire and Humberside Applied Research Collaboration (ARC) and DE is supported by the North East and North Cumbria ARCs. CCG is part funded by West Midland ARC., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Context: Multiple risk behaviors are common and associated with developing chronic conditions such as heart disease, cancer, or Type 2 diabetes. A systematic review, meta-analysis, and meta-regression of the effectiveness of multiple risk behavior interventions was conducted., Evidence Acquisition: Six electronic databases including MEDLINE, EMBASE, and PsycINFO were searched to August 2016. RCTs of non-pharmacologic interventions in general adult populations were selected. Studies targeting specific at-risk groups (such as people screened for cardiovascular risk factors or obesity) were excluded. Studies were screened independently. Study characteristics and outcomes were extracted and risk of bias assessed by one researcher and checked by another. The Behaviour Change Wheel and Oxford Implementation Index were used to code intervention content and context., Evidence Synthesis: Random-effects meta-analyses were conducted. Sixty-nine trials involving 73,873 individuals were included. Interventions mainly comprised education and skills training and were associated with modest improvements in most risk behaviors: increased fruit and vegetable intake (0.31 portions, 95% CI=0.17, 0.45) and physical activity (standardized mean difference, 0.25; 95% CI=0.13, 0.38), and reduced fat intake (standardized mean difference, -0.24; 95% CI=-0.36, -0.12). Although reductions in smoking were found (OR=0.78, 95% CI=0.68, 0.90), they appeared to be negatively associated with improvement in other behaviors (such as diet and physical activity). Preliminary evidence suggests that sequentially changing smoking alongside other risk behaviors was more effective than simultaneous change. But most studies assessed simultaneous rather than sequential change in risk behaviors; therefore, comparisons are sparse. Follow-up period and intervention characteristics impacted effectiveness for some outcomes., Conclusions: Interventions comprising education (e.g., providing information about behaviors associated with health risks) and skills training (e.g., teaching skills that equip participants to engage in less risky behavior) and targeting multiple risk behaviors concurrently are associated with small changes in diet and physical activity. Although on average smoking was reduced, it appeared changes in smoking were negatively associated with changes in other behaviors, suggesting it may not be optimal to target smoking simultaneously with other risk behaviors., (Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)