The unrecognized role of fidelity in effectiveness-implementation hybrid trials: simulation study and guidance for implementation researchers

Effectiveness-implementation hybrid designs are a relatively new approach to evaluate efficacious interventions in real-world settings while concurrently gathering information on the implementation. Intervention fidelity can significantly influence the effectiveness of an intervention during implementation. However little guidance exists for applied researchers conducting effectiveness-implementation hybrid trials regarding the impact of fidelity on intervention effects and power.; We conducted a simulation study based on parameters from a clinical example study. For the simulation, we explored parallel and stepped-wedge cluster randomized trials (CRTs) and hypothetical patterns of fidelity increase during implementation: slow, linear, and fast. Based on fixed design parameters, i.e., the number of clusters (C = 6), time points (T = 7), and patients per cluster (n = 10) we used linear mixed models to estimate the intervention effect and calculated the power for different fidelity patterns. Further, we conducted a sensitivity analysis to compare outcomes based on different assumptions for the intracluster-correlation coefficient and the cluster size.; Ensuring high fidelity from the beginning is central to achieve accurate intervention effect estimates in stepped-wedge and parallel CRTs. The importance of high fidelity in the earlier stages is more emphasized in stepped-wedge designs than in parallel CRTs. In contrast, if the increase of fidelity is too slow despite relatively high starting levels, the study will likely be underpowered and the intervention effect estimates will also be biased. This effect is more accentuated in parallel CRTs, here reaching 100% fidelity within the next measurement points is crucial.; This study discusses the importance of intervention fidelity for the study`s power and highlights different recommendations to deal with low fidelity in parallel and stepped-wedge CRTs from a design perspective. Applied researchers should consider the detrimental effect of low fidelity in their evaluation design. Overall, there are fewer options to adjust the trial design after the fact in parallel CRT as compared to stepped-wedge CRTs. Particular emphasis should be placed on the selection of contextually relevant implementation strategies

Joint analysis of dependent features within compound spectra can improve detection of differential features

Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student’s t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects

Methodological approaches in analysing observational data: A practical example on how to address clustering and selection bias

Because not every scientific question on effectiveness can be answered with randomised controlled trials, research methods that minimise bias in observational studies are required. Two major concerns influence the internal validity of effect estimates: selection bias and clustering. Hence, to reduce the bias of the effect estimates, more sophisticated statistical methods are needed.; To introduce statistical approaches such as propensity score matching and mixed models into representative real-world analysis and to conduct the implementation in statistical software R to reproduce the results. Additionally, the implementation in R is presented to allow the results to be reproduced.; We perform a two-level analytic strategy to address the problems of bias and clustering: (i) generalised models with different abilities to adjust for dependencies are used to analyse binary data and (ii) the genetic matching and covariate adjustment methods are used to adjust for selection bias. Hence, we analyse the data from two population samples, the sample produced by the matching method and the full sample.; The different analysis methods in this article present different results but still point in the same direction. In our example, the estimate of the probability of receiving a case conference is higher in the treatment group than in the control group. Both strategies, genetic matching and covariate adjustment, have their limitations but complement each other to provide the whole picture.; The statistical approaches were feasible for reducing bias but were nevertheless limited by the sample used. For each study and obtained sample, the pros and cons of the different methods have to be weighted

How external and agency characteristics are related to coordination in homecare - findings of the national multicenter, cross-sectional SPOTnat study.

BACKGROUND Homecare client services are often distributed across several interdependent healthcare providers, making proper care coordination essential. However, as studies exploring care coordination in the homecare setting are scarce, serious knowledge gaps exist regarding how various factors influence coordination in this care sector. To fill such gaps, this study's central aim was to explore how external factors (i.e., financial and regulatory mechanisms) and homecare agency characteristics (i.e., work environment, workforce, and client characteristics) are related to care coordination in homecare. METHODS This analysis was part of a national multicentre, cross-sectional study in the Swiss homecare setting that included a stratified random sample of 88 Swiss homecare agencies. Data were collected between January and September 2021 through agency and employee questionnaires. Using our newly developed care coordination framework, COORA, we modelled our variables to assess the relevant components of care coordination on the structural, process, and outcome levels. We conducted both descriptive and multilevel regression analyses-with the latter adjusting for dependencies within agencies-to explore which key factors are associated with coordination. RESULTS The final sample size consisted of 1450 employees of 71 homecare agencies. We found that one explicit coordination mechanism ("communication and information exchange" (beta = 0.10, p <.001)) and four implicit coordination mechanisms-"knowledge of the health system" (beta = -0.07, p <.01), "role clarity" (beta = 0.07, p <.001), "mutual respect and trust" (beta = 0.07, p <.001), and "accountability, predictability, common perspective" (beta = 0.19, p <.001)-were significantly positively associated with employee-perceived coordination. We also found that the effects of agency characteristics and external factors were mediated through coordination processes. CONCLUSION Implicit coordination mechanisms, which enable and enhance team communication, require closer examination. While developing strategies to strengthen implicit mechanisms, the involvement of the entire care team is vital to create structures (i.e., explicit mechanisms) that enable communication and information exchange. Appropriate coordination processes seem to mitigate the association between staffing and coordination. This suggests that they support coordination even when workload and overtime are higher

Nurses' burden caused by sleep disturbances of nursing home residents with dementia: multicenter cross-sectional study

Background Sleep disturbances are common in people with dementia. In nursing homes, this is frequently associated with residents' challenging behavior and potentially with nurses' burden. This study examined nurses' burden associated with nursing home residents' sleep disturbances. Methods A multicenter cross-sectional study was conducted. Nurses' burden associated with residents' sleep disturbances was assessed using the Sleep Disorder Inventory (SDI). Additionally, the proportion of nurses' total burden associated with sleep disturbances of residents with dementia was assessed. A linear mixed regression model was used to investigate the association with nurses', residents' and institutional characteristics. Results One hundred eleven nurses from 38 nursing homes were included. 78.4% stated to be regularly confronted with residents' sleep disturbances during nightshifts, causing distress. The mean proportion of nurses' total burden caused by residents' sleep disturbances was 23.1 % (SD 18.1). None of the investigated characteristics were significantly associated with nurses' total burden. Conclusions Nurses report burden associated with sleep disturbances as common problem. There is a need to develop effective interventions for sleep problems and to train nurses how to deal with residents' sleep disturbances

Plant-to-Plant Variability in Root Metabolite Profiles of 19 Arabidopsis thaliana Accessions Is Substance-Class-Dependent

Natural variation of secondary metabolism between different accessions of Arabidopsis thaliana (A. thaliana) has been studied extensively. In this study, we extended the natural variation approach by including biological variability (plant-to-plant variability) and analysed root metabolic patterns as well as their variability between plants and naturally occurring accessions. To screen 19 accessions of A. thaliana, comprehensive non-targeted metabolite profiling of single plant root extracts was performed using ultra performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS). Linear mixed models were applied to dissect the total observed variance. All metabolic profiles pointed towards a larger plant-to-plant variability than natural variation between accessions and variance of experimental batches. Ratios of plant-to-plant to total variability were high and distinct for certain secondary metabolites. None of the investigated accessions displayed a specifically high or low biological variability for these substance classes. This study provides recommendations for future natural variation analyses of glucosinolates, flavonoids, and phenylpropanoids and also reference data for additional substance classes

Feasibility and effectiveness of a telephone-based social support intervention for informal caregivers of people with dementia: Study protocol of the TALKING TIME project

Abstract Background Caring for people with dementia at home requires a significant amount of time, organization, and commitment. Therefore, informal caregivers, mainly relatives, of people with dementia often feel a high burden. Although on-site support groups are known to have positive effects on the subjective well-being (SWB) and perceived social support of informal caregivers, there are cases in which relatives have either no time or no opportunity to leave the person alone or in which there are no support groups nearby. The TALKING TIME project aims to close this supply gap by providing structured telephone-based support groups in Germany for the first time. International studies have shown benefits for informal caregivers. Methods The TALKING TIME study is a randomized controlled trial. The effects of the 3-month TALKING TIME intervention will be compared with those of a control group without intervention at two time points (baseline = T0, after 3 months = T1). The control group will receive the TALKING TIME intervention after T1. With a planned sample size of 88 participants, the study is powered to detect an estimated effect size of 0.70 for psychological quality of life, considering an α of 0.05 (two-sided), a power of 80%. Caregivers are informal caregivers who are eligible if they are 18 years of age or older and have cared for a person with diagnosed dementia for at least four hours, four days per week, in the past six months. The exclusion criteria are psychiatric disorders of the informal caregiver. The primary outcome is the mental component summary of the SF-12 rated by informal caregivers. The secondary outcomes for informal caregivers are the physical component summary of the SF-12, the Perceived Social Support Caregiver Scale (SSCS) score, and the Caregiver Reaction Scale (CRS) score. The secondary outcome for care recipients is the Neuropsychiatric Inventory (NPI-Q). For the process evaluation, different quantitative and qualitative data sources will be collected to address reach, fidelity, dosage and context. Discussion The results will provide further information on the effectiveness and optimization of telephone-based support groups for informal caregivers of people with dementia, which can help guide the further development of effective telephone-based social support group interventions. Trial registration Clinical Trials: NCT02806583 , June 9, 201

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the <i>Arabidopsis thaliana</i> Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the <i>Arabidopsis thaliana</i> Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes