Mobile health and neurocognitive domains evaluation through smartphones: A meta-analysis

A narrative review of the development and performance characteristics of electronic delirium-screening tools

BACKGROUND

Electronic delirium-screening tools are an emergent area of research.

OBJECTIVE

The objective of this study was to summarise the development and performance characteristics of electronic screening tools in delirium.

METHODS

Searches were conducted in Pubmed, Embase, and CINAHL Complete databases to identify electronic delirium-screening tools.

RESULTS

Five electronic delirium-screening tools were identified and reviewed. Two were designed for and tested within a medical setting, and three were applied to intensive care. Adaptive design features, such as skip function to reduce test burden, were variably integrated into instrument design. All tools were shown to have acceptable psychometric properties, but validation studies were largely incomplete.

CONCLUSIONS

Electronic delirium-screening tools are an exciting area of development and may offer hope for improved uptake of delirium screening.

Effect of eHealth interventions on perinatal depression: A meta-analysis

BACKGROUND

Perinatal depression (PND) is a common mental health problem, and eHealth interventions may provide a strategy for alleviating PND.

AIM

This meta-analysis aimed to determine the effect of eHealth interventions on PND.

METHODS

Six databases were searched to retrieve published randomized controlled trials (RCTs) on the effect of eHealth interventions on PND. A meta-analysis was performed on the data of these studies using a random effects model.

RESULTS

A total of 21 RCTs were included in the meta-analysis, which revealed that eHealth interventions significantly reduced antenatal depression (WMD = -1.64, 95 % CI [-2.92, -0.35], P = .013), postpartum depression (SMD = -0.41, 95 % CI [-0.52, -0.29], P < .001), anxiety (SMD = -0.39, 95 % CI [-0.51, -0.28], P < .001), stress (WMD = -2.93, 95 % CI [-4.58, -1.27], P = .001), and improved self-efficacy (SMD = 0.42, 95 % CI [0.21, 0.63], P < .001) compared with the control group. However, eHealth interventions did not significantly improve social support (SMD = 0.27, 95 % CI [-0.01, 0.56], P = .058). For antenatal depression, significant subgroup differences were observed in the digital platform and material presentation format. In addition, for postpartum depression, significant subgroup differences were found in the type of therapy.

CONCLUSIONS

The meta-analysis results suggest that eHealth interventions can relieve depression, anxiety, and stress symptoms and improve self-efficacy in perinatal women. However, these interventions did not improve social support. Additional high-quality studies on eHealth interventions in PND are needed to validate these results.

Decision making in clinical trials: Interim analyses, innovative design, and biomarkers

The efficient and accurate execution of clinical trials testing novel treatments for Alzheimer's disease (AD) is a critical component of the field's collective efforts to develop effective disease-modifying treatments for AD. The lengthy and heterogeneous nature of clinical progression in AD contributes to the challenges inherent in demonstrating a clinically meaningful benefit of any potential new AD therapy. The failure of many large and expensive clinical trials to date has prompted a focus on optimizing all aspects of decision making, to not only expedite the development of new treatments, but also maximize the value of the information that each clinical trial yields, so that all future clinical trials (including those that are negative) will contribute toward advancing the field. To address this important topic the Alzheimer's Association Research Roundtable convened December 1-2, 2020. The goals focused around identifying new directions and actionable steps to enhance clinical trial decision making in planned future studies.

An automated process for supporting decisions in clustering-based data analysis

BACKGROUND AND OBJECTIVE

Metrics are commonly used by biomedical researchers and practitioners to measure and evaluate properties of individuals, instruments, models, methods, or datasets. Due to the lack of a standardized validation procedure for a metric, it is assumed that if a metric is appropriate for analyzing a dataset in a certain domain, then it will be appropriate for other datasets in the same domain. However, such generalizability cannot be taken for granted, since the behavior of a metric can vary in different scenarios. The study of such behavior of a metric is the objective of this paper, since it would allow for assessing its reliability before drawing any conclusion about biomedical datasets.

METHODS

We present a method to support in evaluating the behavior of quantitative metrics on datasets. Our approach assesses a metric by using clustering-based data analysis, and enhancing the decision-making process in the optimal classification. Our method assesses the metrics by applying two important criteria of the unsupervised classification validation that are calculated on the clusterings generated by the metric, namely stability and goodness of the clusters. The application of our method is facilitated to biomedical researchers by our evaluomeR tool.

RESULTS

The analytical power of our methods is shown in the results of the application of our method to analyze (1) the behavior of the impact factor metric for a series of journal categories; (2) which structural metrics provide a better partitioning of the content of a repository of biomedical ontologies, and (3) the heterogeneity sources in effect size metrics of biomedical primary studies.

CONCLUSIONS

The use of statistical properties such as stability and goodness of classifications allows for a useful analysis of the behavior of quantitative metrics, which can be used for supporting decisions about which metrics to apply on a certain dataset.