BackgroundMany complex intervention trials fail to show an intervention effect. Although this may be due to genuine ineffectiveness, it may also be the result of sub-optimal intervention design, implementation failure or a combination of these. Given current financial constraints and the pressure to reduce waste and increase value in health services research, pre-trial strategies are needed to reduce the likelihood of design or implementation failure and to maximise the intervention’s potential for effectiveness. In this scoping review, we aimed to identify and synthesise the available evidence relating to the strategies and methods used to ‘optimise’ complex interventions at the pre-trial stage.MethodsWe searched MEDLINE, CINAHL, AMED, PsycINFO and ProQuest Nursing & Allied Health Source for papers published between January 2000 and March 2015. We included intervention development and optimisation studies that explored potential intervention weaknesses and limitations before moving to a definitive randomised controlled trial (RCT). Two reviewers independently applied selection criteria and systematically extracted information relating to the following: study characteristics; intervention under development; framework used to guide the development process; areas of focus of the optimisation process, methods used and outcomes of the optimisation process. Data were tabulated and summarised in a narrative format.ResultsWe screened 3968 titles and 231 abstracts for eligibility. Eighty-nine full-text papers were retrieved; 27 studies met our selection criteria. Optimisation strategies were used for a range of reasons: to explore the feasibility and acceptability of the intervention to patients and healthcare professionals; to estimate the effectiveness and cost-effectiveness of different combinations of intervention components; and to identify potential barriers to implementation. Methods varied widely across studies, from interviews and focus groups to economic modelling and probability analysis.ConclusionsThe review identifies a range of optimisation strategies currently used. Although a preliminary classification of these strategies can be proposed, a series of questions remain as to which methods to use for different interventions and how to determine when the intervention is ready or ‘optimised enough’ to be tested in a RCT. Future research should explore potential answers to the questions raised, to guide researchers in the development and evaluation of more effective interventions.

Current rehabilitation adherence measures are limited. Some possess promising validity and acceptability for certain parameters of adherence, situations, and populations and should be used in these situations. Rigorous evaluation of adherence measures in a broader range of populations is needed.

Although most of the UK and Italy RNs appear to be aware of the risks posed by their online exposure, their online activity indicates the blurring of their personal and professional lives; this is posing new ethical, legal and professional challenges to members of the nursing profession. Further research and debate is encouraged at national and international level.

Objectives: To describe the rationale, organization, and procedures of the Corona Immunitas Digital Follow-Up (CI-DFU) eCohort and to characterize participants at baseline.Methods: Participants of Corona Immunitas, a population-based nationwide SARS-CoV-2 seroprevalence study in Switzerland, were invited to join the CI-DFU eCohort in 11 study centres. Weekly online questonnaires cover health status changes, prevention measures adherence, and social impacts. Monthly questionnaires cover additional prevention adherence, contact tracing apps use, vaccination and vaccine hesitancy, and socio-economic changes.Results: We report data from the 5 centres that enrolled in the CI-DFU between June and October 2020 (covering Basel City/Land, Fribourg, Neuchâtel, Ticino, Zurich). As of February 2021, 4636 participants were enrolled and 85,693 weekly and 27,817 monthly questionnaires were collected. Design-based oversampling led to overrepresentation of individuals aged 65+ years. People with higher education and income were more likely to enroll and be retained.Conclusion: Broad enrolment and robust retention of participants enables scientifically sound monitoring of pandemic impacts, prevention, and vaccination progress. The CI-DFU eCohort demonstrates proof-of-principle for large-scale, federated eCohort study designs based on jointly agreed principles and transparent governance.

Objectives. Using longitudinal data from Southern Switzerland we assessed ten-month temporal trajectories of moderate-to-severe depression, anxiety and stress among adults after the first pandemic wave and explored differences between socio-demographic and health status groups. Study design. Population-based prospective cohort study. Methods. Participants were 732 (60% women) adults aged 20–64 who completed the Depression, Anxiety and Stress Scale on a monthly base since August 2020 until May 2021, as part of the Corona Immunitas Ticino study based on a probability sample of non-institutionalized residents in Ticino, Southern Switzerland. Results. Prevalence of moderate-to-severe depression increased from 7.5% in August 2020, to 12.5% in May 2021; anxiety increased from 4.8% to 8.1%; and stress increased from 5.5% to 8.8%. A steeper increase in poor mental health was observed between October 2020 and February 2021. Men had a lower risk for anxiety (OR = 0.58, 95%CI = 0.36–0.95) and stress (OR = 0.61, 95%CI = 0.44–0.95,) compared to women. Suffering from a chronic disease increased the risk for depression (OR = 1.82, 95%CI = 1.12–2.96), anxiety (OR = 2.38, 95%CI = 1.44–3.92) and stress (OR = 1.87, 95%CI = 1.14–3.08). The differences between these groups did not vary over time. Conclusions. In a representative Swiss adult sample, prevalence of moderate-to-severe depression, anxiety and stress almost doubled in the course of ten months following the end of the first pandemic wave in spring 2020. Women and participants with pre-existing chronic conditions were at higher risk of poor mental health.