Digital health tools for the passive monitoring of depression: a systematic review of methods

The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.

Developing Reporting Guidelines for Studies of HIV Drug Resistance Prevalence: Protocol of a Mixed-methods Study (Preprint)

BACKGROUND HIV drug resistance is a global health problem which limits the effectiveness of antiretroviral therapy (ART). Adequate surveillance of HIV drug resistance is challenged by heterogenous and inadequate data reporting, which compromises the accuracy, interpretation, and usability of prevalence estimates. Previous research has found that the quality of reporting in studies of HIV drug resistance prevalence is low, and thus better guidance is needed to ensure complete and uniform reporting. OBJECTIVE This paper aims to develop reporting guidelines for studies of HIV drug resistance by achieving consensus among experts on what items should be reported in these studies. METHODS We will conduct a sequential explanatory mixed methods study among authors and users of studies of HIV drug resistance. The two-phase design will include a cross-sectional electronic survey (quantitative phase) followed by a focus group discussion (qualitative phase). Survey participants will rate the essentiality of various reporting items, which will be analyzed in a validity ratio to determine the items that will be retained for further evaluation. Retained items will form a list of potential reporting items that will be reviewed in a focus group discussion informed by grounded theory to produce a finalized set of reporting items. RESULTS This study received ethics approval from the Hamilton Integrated Research Ethics Board (project number #11558) on November 11, 2020. As of March 2021, 46 participants provided informed consent and completed the electronic survey. In October 2021 nine of these participants participated in virtual focus group discussions. CONCLUSIONS This study will provide a reporting checklist for studies of HIV drug resistance by achieving consensus among experts on what items should be reported in these studies. The results of this work will be refined and elaborated on by a writing committee of HIV drug resistance experts and external reviewers to develop finalized reporting guidelines.

A data quality assessment of the first four years of malaria reporting in the Senegal DHIS2, 2014–2017

Background As the global burden of malaria decreases, routine health information systems (RHIS) have become invaluable for monitoring progress towards elimination. The District Health Information System, version 2 (DHIS2) has been widely adopted across countries and is expected to increase the quality of reporting of RHIS. In this study, we evaluated the quality of reporting of key indicators of childhood malaria from January 2014 through December 2017, the first 4 years of DHIS2 implementation in Senegal. Methods Monthly data on the number of confirmed and suspected malaria cases as well as tests done were extracted from the Senegal DHIS2. Reporting completeness was measured as the number of monthly reports received divided by the expected number of reports in a given year. Completeness of indicator data was measured as the percentage of non-missing indicator values. We used a quasi-Poisson model with natural cubic spline terms of month of reporting to impute values missing at the facility level. We used the imputed values to take into account the percentage of malaria cases that were missed due to lack of reporting. Consistency was measured as the absence of moderate and extreme outliers, internal consistency between related indicators, and consistency of indicators over time. Results In contrast to public facilities of which 92.7% reported data in the DHIS2 system during the study period, only 15.3% of the private facilities used the reporting system. At the national level, completeness of facility reporting increased from 84.5% in 2014 to 97.5% in 2017. The percentage of expected malaria cases reported increased from 76.5% in 2014 to 94.7% in 2017. Over the study period, the percentage of malaria cases reported across all districts was on average 7.5% higher (P < 0.01) during the rainy season relative to the dry season. Reporting completeness rates were lower among hospitals compared to health centers and health posts. The incidence of moderate and extreme outlier values was 5.2 and 2.3%, respectively. The number of confirmed malaria cases increased by 15% whereas the numbers of suspected cases and tests conducted more than doubled from 2014 to 2017 likely due to a policy shift towards universal testing of pediatric febrile cases. Conclusions The quality of reporting for malaria indicators in the Senegal DHIS2 has improved over time and the data are suitable for use to monitor progress in malaria programs, with an understanding of their limitations. Senegalese health authorities should maintain the focus on broader adoption of DHIS2 reporting by private facilities, the sustainability of district-level data quality reviews, facility-level supervision and feedback mechanisms at all levels of the health system.

Systematic Review as a Research Method in Library and Information Science

The aim of this chapter is to assess the current state of application of systematic reviews (SRs) in library and information science (LIS) field and determine how information scientists can advance the SRs as a methodology. The literature shows that there is an increasing number of SRs in LIS although there are still knowledge gaps about the use of SRs as a methodology. The quality of reporting in primary studies in LIS is still poor, and hence, it becomes difficult to appraise the value of the study undertaken. In order to advance the use of SRs in LIS domain, it is important to introduce SRs in LIS education curricular, integrate SRs as part of the continuing scientist development programmes (CPD), use automated SR software to minimize workload, introduce SRs a formal role and service in the libraries, collaborate with research teams as co-authors to conduct SRs not only in the topics defined by research teams, but also in LIS topics, and create SR databases and tools in LIS.

Quality Reporting of Mixed Methods Research in Tourism-Related Studies

The aim of this study is to gain scientific insight into internationally-accepted criteria for quality reporting of mixed methods research (MMR). Articles published post-2012 in a particular journal, which referred to “mixed methods” and “tourism”, and reported that qualitative and quantitative data were collected, were drawn from Google Scholar and Scopus. The reporting quality of these studies was analysed according to the GRAMMS framework (Good Reporting of a Mixed Methods Study). Most of the articles in the data set did not report on all the elements embedded in GRAMMS. It must not be seen as a reflection of the quality of the MMR design itself, nor is the study flawed. It indicates gaps in the reporting of important MMR elements that could be addressed in future research. Exemplars were identified that could serve as case studies for researchers in terms of the quality of reporting on MMR. Editorial boards should adopt guidelines on how MMR could be presented in articles submitted to their journals. These guidelines could assist authors in preparing their articles to conform to international standards on the reporting of MMR studies. Peer reviewers should use the guidelines to judge the quality of reporting on MMR methodology in articles under review. This study could also serve as a future reference for researchers, postgraduate students and supervisors who aim to incorporate MMR in their research.

Social Ties impacting Audit Quality Exploring Government Entities

Purpose: The purpose of the study is to analyze the first hand data regarding intimacies between government entities and government audit institution of Pakistan, to see its impact on the quality. Design/Methodology/Approach: Qualitative research design is used to explore the concept base on theoretical saturation technique. Findings: The results revealed by default presence of social ties among interactive agents. It further explores indirect relationship between audit quality and social interactions in the presence of petty corruption due to familiarity, unwarranted mutual trust and favoritism however this relationship is shifted toward direct relationship in the presence of material corruption due to fear of losing good reputation, loss of job, fear of departmental inquiry, threat of floating your weaknesses before your rival clique. Implications/Originality/Value: The results presented in this paper should therefore be of great interest to government, regulators and standard-setters charged with developing accounting standards to improve the audit quality of reporting information related to existing government auditing setup.

Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review

PurposeMachine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.Materials and MethodsFour databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy included both keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, as well as related terms. The review was conducted in stepwise fashion with abstract screening, full text screening, and data extraction. Quality of reporting was assessed using TRIPOD criteria.ResultsA total of 11,727 candidate articles were identified, of which 12 articles were included in the final analysis. Studies investigated the differentiation of normal from abnormal images in datasets which include gliomas (7 articles) and the differentiation of glioma images from non-glioma or normal images (5 articles). Single institution datasets were most common (5 articles) followed by BRATS (3 articles). The median sample size was 280 patients. Algorithm testing strategies consisted of five-fold cross validation (5 articles), and the use of exclusive sets of images within the same dataset for training and for testing (7 articles). Neural networks were the most common type of algorithm (10 articles). The accuracy of algorithms ranged from 0.75 to 1.00 (median 0.96, 10 articles). Quality of reporting assessment utilizing TRIPOD criteria yielded a mean individual TRIPOD ratio of 0.50 (standard deviation 0.14, range 0.37 to 0.85).ConclusionSystematic review investigating the identification of gliomas in datasets which include non-glioma images demonstrated multiple limitations hindering the application of these algorithms to clinical practice. These included limited datasets, a lack of generalizable algorithm training and testing strategies, and poor quality of reporting. The development of more robust and heterogeneous datasets is needed for algorithm development. Future studies would benefit from using external datasets for algorithm testing as well as placing increased attention on quality of reporting standards.Systematic Review Registrationwww.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020209938, International Prospective Register of Systematic Reviews (PROSPERO 2020 CRD42020209938).

Opportunities to improve reporting of rapid response in health technology assessment

Introduction Mini health technology assessment (HTA) reports have been used to support policy makers and health systems by providing a timely summary of scientific evidence. The objective of this meta-epidemiologic study was to evaluate the quality of reporting of mini-HTA reports published in Brazil. Methods An electronic search for all mini-HTA reports published between 2014 and March 2019 was conducted in the SISREBRATS and CONITEC databases. The study selection and data extraction were performed by two independent assessors. The following data were extracted: bibliographic data; research question; characteristics of the population, health technologies and outcomes assessed; eligibility criteria; information about searches and study selection; risk of bias assessment; quality of evidence assessment; synthesis of results; and recommendation about the technology evaluated. A descriptive analysis was used to summarize the information retrieved from all the included mini-HTA reports. Results We included 103 mini-HTA reports, the great majority of which (92.3 percent) focused on the coverage of the technologies in the healthcare system, with more than 60 percent being about drugs. Only five mini-HTA reports (4.8 percent) gave reasons for the choice of outcomes, and fifteen (14.5 percent) discriminated between primary and secondary outcomes. All mini-HTAs reported the databases searched and 99 percent of them reported using Medline. Sixty percent of the mini-HTA reported assessing the risk of bias, and 52 percent reported assessing the quality of evidence. Conclusion The quality of reporting of the mini-HTA reports performed in Brazil is insufficient and needs to be improved to guarantee transparency and replicability.

Intravitreal antivascular endothelial growth factor in diabetic macular oedema: scoping review of clinical practice guidelines recommendations

BackgroundDiabetic macular oedema (DME) is a worldwide major cause of low vision and blindness. Intravitreal antivascular endothelial growth factor (anti-VEGF) constitutes an effective treatment. Clinical practice guidelines (CPGs) are synthesis documents that seek to improve patient care.ObjectivesTo identify CPGs that make anti-VEGF recommendations for DME and to assess their reporting quality and their considerations when making recommendations.Eligibility criteriaCPGs published between December 2009 and December 2019 that make explicit anti-VEGF recommendations in DME.Sources of evidenceSensitive search strategy in Embase, Google Scholar and hand-searching on 165 websites.MethodsWe extracted information from each CPG with a previously piloted sheet. Two independent authors applied theAppraisal of Guidelines, Research and Evaluation tool (AGREE-II) assessment for each CPG.ResultsThe 21 included CPGs recommend anti-VEGF for DME, but there is a wide variation among the clinical aspects included, such as location of DME, visual acuity required, therapeutical alternatives or discontinuation. Most have a poor quality of reporting based on the AGREE-II tool assessment, especially those developed by ophthalmological societies, those that have an exclusive content about DME, and those where most of their authors disclose conflict of interest (COI) with pharmaceutical industry or where their authors did not report COIs. Pharmaceutical-sponsored CPGs did not use systematic reviews (SRs) to support their recommendations. Very few recommendations consider patient values and preferences, equity, acceptability and feasibility of the intervention.ConclusionsMost of the CPGs that made recommendations of anti-VEGF for DME have poor quality of reporting, do not use SRs and do not consider patients’ values and preferences.

Protocol of investigation into reporting quality of RCT abstracts on COVID-19 pursuant to CONSORT (CoCo study)—a review

Background In 2020, the COVID-19 pandemic developed into a global crisis, the enormity and urgency of which accelerated research activities in the field. At the same time, manuscripts describing these research projects underwent fast-track peer review procedures and were published in freely accessible formats. Although full texts about COVID-19 are currently available for free, abstracts continue to play a key role since they provide essential information and possibly a decision basis for therapies. Abstracts are particularly important in case the full texts are not free, not all reports have been published in English and in emergency situations when there is less time for comprehensive analysis of all full texts. It is therefore necessary to ensure that abstracts—as publications in miniature format—contain comprehensive and transparent information. The CONSORT statement for abstracts (CONSORT-A) offers guidelines to authors how to include all necessary information in an abstract. Prior to the COVID-19 pandemic, the quality of reporting in medical research had already been the object of debate and criticism. The current crisis makes comprehensive documentation all the more important. Abstracts of COVID-19 RCTs should therefore report the criteria listed in the CONSORT-A statement fully and verifiably. The objective of this study is to check the completeness of abstracts of all COVID-19 RTCs published to date. Methods Based on a literature search in PubMed, Embase and the Cochrane Library, all publications up to 29 October 2020 are identified and examined in terms of the subject matter (reported results from COVID-19 studies) and their study design (RTC). Subsequently, suitable publications are examined for completeness and quality of abstracts. The CONSORT checklist for RTC abstracts serves as a basis in this procedure. The primary endpoint of the study is the percentage of correctly implemented items of the CONSORT statement for abstracts. The frequency of correct reporting of each individual item is checked in a second step. Discussion The study is expected to contribute to evaluating the reporting quality on COVID-19 studies, and specifically the completeness of abstracts of RTCs. It may thus support the assessment of current research into COVID-19. Trial registration Registration was not required as the study investigated existing literature.