Objectives:To examine the impact of providing healthcare during or after health emergencies caused by viral epidemic outbreaks on healthcare workers´(HCWs) mental health, and to assess the available evidence base regarding interventions to reduce such impact.Design: Systematic rapid review and meta-analysis.Data sources: MEDLINE, Embase, and PsycINFO, searched up to 23 March 2020. Method:We selected observational and experimental studies examining the impact on mental health of epidemic outbreaks on HCWs. One reviewer screened titles and abstracts, and two reviewers independently reviewed full texts. We extracted study characteristics, symptoms, prevalence of mental health problems, risk factors, mental health interventions, and its impact. We assessed risk of bias for each individual study and used GRADE to ascertain the certainty of the evidence. We conducted a narrative and tabulated synthesis of the results. We pooled data using random-effects meta-analyses to estimate the prevalence of specific mental health problems. Results:We included 61 studies (56 examining impact on mental health and five about interventions to reduce such impact). Most were conducted in Asia (59%), in the hospital setting (79%), and examined the impact of the SARS epidemic (69%). The pooled prevalence was higher for anxiety (45%, 95% CI 21 to 69%; 6 studies, 3,373 participants), followed by

Background: This study aimed at examining the impact of providing healthcare during health emergencies caused by viral epidemic outbreaks on healthcare workers' (HCWs) mental health; to identify factors associated with worse impact, and; to assess the available evidence base regarding interventions to reduce such impact.Method: Rapid systematic review. We searched MEDLINE, Embase, and PsycINFO (inception to August 2020). We pooled data using random-effects meta-analyses to estimate the prevalence of specific mental health problems, and used GRADE to ascertain the certainty of evidence.Results: We included 117 studies. The pooled prevalence was higher for acute stress disorder (40% (95%CI 39 to 41%)), followed by anxiety (30%, (30 to 31%)), burnout (28% (26 to 31%)), depression (24% (24 to 25%)), and post-traumatic stress disorder (13% (13 to 14%)). We identified factors associated with the likelihood of developing those problems, including sociodemographic (younger age and female gender), social (lack of social support, stigmatization), and occupational (working in a high-risk environment, specific occupational roles, and lower levels of specialised training and job experience) factors. Four studies reported interventions for frontline HCW: two educational interventions increased confidence in pandemic self-efficacy and in interpersonal problems solving (very low certainty), whereas one multifaceted intervention improved anxiety, depression, and sleep quality (very low certainty).Limitations: We only searched three databases, and the initial screening was undertaken by a single reviewer. Conclusion:Given the very limited evidence regarding the impact of interventions to tackle mental health problems in HCWs, the risk factors identified represent important targets for future interventions. Providing frontline healthcare during infectious outbreaks increases the

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Background Drug-related problems and potentially inappropriate prescribing impose a huge burden on patients and the health-care system. The most widely used tools for appropriate prescription in older adults in England and in other European countries are the Screening Tool of Older People’s Prescriptions (STOPP)/Screening Tool to Alert to the Right Treatment (START) tools. STOPP/START tools support medicines optimisation for older adults. Objectives To identify, test and refine the programme theories underlying how interventions based on the STOPP/START tools are intended to work, for whom, in what circumstances and why, as well as the resource use and cost requirements or impacts. Design A realist synthesis. Setting Primary care, hospital care and nursing homes. Patients Patients aged ≥ 65 years. Interventions Any intervention based on the use of the STOPP/START tools. Review methods Database and web-searching was carried out to retrieve relevant evidence to identify and test programme theories about how interventions based on the use of the STOPP/START tools work. A project reference group made up of health-care professionals, NHS decision-makers, older people, carers and members of the public was set up. In phase 1 we identified programme theories about STOPP/START interventions on how, for whom, in what contexts and why they are intended to work. We searched the peer-reviewed and grey literature to identify documents relevant to the research questions. We interviewed experts in the field in our reference group to gain input on our list of candidate context–mechanism–outcome configurations, to identify additional context–mechanism–outcome configurations and to identify additional literature and/or relevant concepts. In phase 2 we reviewed and synthesised relevant published and unpublished empirical evidence and tested the programme theories using evidence from a larger set of empirical studies. Results We developed a single logic model structured around three key mechanisms: (1) personalisation, (2) systematisation and (3) evidence implementation. Personalisation: STOPP/START-based interventions are based on shared decision-making, taking into account patient preferences, experiences and expectations (mechanisms), leading to increased patient awareness, adherence, satisfaction, empowerment and quality of life (outcomes). Systematisation: STOPP/START tools provide a standardised/systematic approach for medication reviews (mechanisms), leading to changes in professional and organisational culture and burden/costs (outcomes). Evidence implementation: delivery of STOPP/START-based interventions is based on the implementation of best evidence (mechanisms), reducing adverse outcomes through appropriate prescribing/deprescribing (outcomes). For theory testing, we identified 40 studies of the impact of STOPP/START-based interventions in hospital settings, nursing homes, primary care and community pharmacies. Most of the interventions used multiple mechanisms. We found support for the impact of the personalisation and evidence implementation mechanisms on selected outcome variables, but similar impact was achieved by interventions not relying on these mechanisms. We also observed that the impact of interventions was linked to the proximity of the selected outcomes to the intervention in the logic model, resulting in a clearer benefit for appropriateness of prescribing, adverse drug events and prescription costs. Limitations None of the available studies had been explicitly designed for evaluating underlying causal mechanisms, and qualitative information was sparse. Conclusions No particular configuration of the interventions is associated with a greater likelihood of improved outcomes in given settings. Study registration This study is registered as PROSPERO CRD42018110795. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 23. See the NIHR Journals Library website for further project information.

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type 2 diabetes mellitus (T2DM) based on: i) the use of a system comprising mobile health (mHealth) technology integrated with electronic health records to send tailored text messages (SMSs) promoting lifestyle changes in people at risk of T2DM, and, ii) the provision of online education to primary healthcare workers about prediabetes management. Design: The PREDIABETEXT project is a controlled, parallel, three-arm, cluster randomized trial and involve five phases. Methods: In phases 1-4 we will develop and pilot-test the different components of the intervention (In phase 1 we will develop the brief text messages targeted to patients; In phase 2 we will adapt our existing technology system to deliver the messages; In phase 3 we will develop an educational intervention targeted to Primary Care workers, and; In phase 4 we will pilot-test and optimise both interventions). In phase 5 we will conduct a phase II, six-month, three-arm, cluster randomised, clinical trial with 42 primary care workers and 420 patients at risk of T2DM (HbA1c from 6% to 6.4% or fasting plasma glucose 110-125mg/dl, or both). Patients will be allocated to a control (usual care) group, intervention A (patient messaging intervention), or intervention B (patient messaging intervention plus online education to their primary healthcare workers). The primary outcome will be HbA1c. Secondary outcomes will include additional clinical, physiological, behavioural and psychological outcomes. Discussion: Recent trials suggest that digital health interventions can effectively prevent T2DM and reduce important T2DM risk factors such as overweight or hypertension. In Spain this type of interventions is understudied.