Ethical Data Collection for Medical Image Analysis: a Structured Approach

Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression of healthcare research. Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Online Health Communities: an alternative feasible data registry tool for developing countries

Given the many challenges facing healthcare access in many developing countries and the added limitations observed in emergencies like COVID-19 pandemic, the authors here discuss an alternative and feasible approach to overcome all these limitations.

A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States

BACKGROUND

Suicide is currently the second leading cause of death among adolescents ages 10-14, and third leading cause of death among adolescents ages 15-19 in the United States (U.S). Although we have numerous U.S. based surveillance systems and survey data sources, the coverage offered by these data with regard to the complexity of youth suicide had yet to be examined. The recent release of a comprehensive systems map for adolescent suicide provides an opportunity to contrast the content of surveillance systems and surveys with the mechanisms listed in the map.

OBJECTIVE

To inform existing data collection efforts and advance future research on the risk and protective factors relevant to adolescent suicide.

METHODS

We examined data from U.S. based surveillance systems and nationally-representative surveys that included (1) observations for an adolescent population and (2) questions or indicators in the data that identified suicidal ideation or suicide attempt. Using thematic analysis, we evaluated the codebooks and data dictionaries for each source to match questions or indicators to suicide-related risk and protective factors identified through a recently published suicide systems map. We used descriptive analysis to summarize where data were available or missing and categorized data gaps by social-ecological level.

RESULTS

Approximately 1-of-5 of the suicide-related risk and protective factors identified in the systems map had no supporting data, in any of the considered data sources. All sources cover less than half the factors, except the Adolescent Brain Cognitive Development Study (ABCD), which covers nearly 70% of factors.

CONCLUSIONS

Examining gaps in suicide research can help focus future data collection efforts in suicide prevention. Our analysis precisely identified where data is missing and also revealed that missing data affects some aspects of suicide research (e.g., distal factors at the community and societal level) more than others (e.g., proximal factors about individual characteristics). In sum, our analysis highlights limitations in current suicide-related data availability and provides new opportunities to identify and expand current data collection efforts.

Qualitative Research and Cancer Nursing: A Guide for Novice Researchers

OBJECTIVE

To introduce the cancer nurse to qualitative research.

DATA SOURCES

A search of published literature including articles and books was conducted to inform the article using University libraries (University of Galway and University of Glasgow) and CINAHL, Medline, and Google Scholar databases using broad terms, including qualitative research, qualitative methods, paradigm, qualitative, and cancer nursing.

CONCLUSION

It is important for cancer nurses wishing to read, critically appraise, or undertake qualitative research to understand the origins and different methods employed in qualitative research.

IMPLICATIONS FOR NURSING PRACTICE

The article is of relevance for cancer nurses globally who wish to read, critique, or undertake qualitative research.

Identification of high-risk patients with a seven-biomarker prognostic signature for adjuvant treatment trial recruitment in American Joint Committee on Cancer v8 stage I-IIA cutaneous melanoma

PURPOSE

Many patients with resected American Joint Committee on Cancer (AJCC) early-stage cutaneous melanoma nonetheless die of melanoma; additional risk stratification approaches are needed.

PATIENTS AND METHODS

Using prospectively-collected whole-tissue sections, we assessed in consecutive stage I-IIA patients (N = 439), a previously-validated, immunohistochemistry-based, 7-biomarker signature to prognosticate disease-free survival (DFS), melanoma-specific survival (MSS; primary end-point) and overall survival (OS), independent of AJCC classification.

RESULTS

Seven-marker signature testing designated 25.1% of patients (110/439) as high-risk (stage IA, 13.3% [43/323], IB, 53.2% [42/79], and IIA, 67.6% [25/37]). A Kaplan-Meier analysis demonstrated high-risk patients to have significantly worse DFS, MSS and OS versus low-risk counterparts (P < 0.001). In multivariable Cox regression modelling also including key clinicopathological/demographic factors, 7-marker signature data independently prognosticated the studied end-points. Models with the 7-marker signature risk category plus clinicopathological/demographic covariates substantially outperformed models with clinicopathological/demographic variables alone in predicting all studied outcomes (areas under the receiver operator characteristic curve 74.1% versus 68.4% for DFS, 81.5% versus 71.2% for MSS, 80.9% versus 73.0% for OS; absolute differences 5.7%, 10.3% and 7.9%, respectively, favouring 7-marker signature risk category-containing models).

CONCLUSION

In patients with AJCC early-stage disease, the 7-marker signature reliably prognosticates melanoma-related outcomes, independent of AJCC classification, and provides a valuable complement to clinicopathological/demographic factors.

Outcomes of Patients Treated in the UK Proton Overseas Programme: Non-central Nervous System Group

AIMS

The UK Proton Overseas Programme (POP) was launched in 2008. The Proton Clinical Outcomes Unit (PCOU) warehouses a centralised registry for collection, curation and analysis of all outcomes data for all National Health Service-funded UK patients referred and treated abroad with proton beam therapy (PBT) via the POP. Outcomes are reported and analysed here for patients diagnosed with non-central nervous system tumours treated from 2008 to September 2020 via the POP.

MATERIALS AND METHODS

All non-central nervous system tumour files for treatments as of 30 September 2020 were interrogated for follow-up information, and type (following CTCAE v4) and time of onset of any late (>90 days post-PBT completion) grade 3-5 toxicities.

RESULTS

Four hundred and ninety-five patients were analysed. The median follow-up was 2.1 years (0-9.3 years). The median age was 11 years (0-69 years). 70.3% of patients were paediatric (<16 years). Rhabdomyosarcoma (RMS) and Ewing sarcoma were the most common diagnoses (42.6% and 34.1%). 51.3% of treated patients were for head and neck (H&N) tumours. At last known follow-up, 86.1% of all patients were alive, with a 2-year survival rate of 88.3% and 2-year local control of 90.3%. Mortality and local control were worse for adults (≥25 years) than for the younger groups. The grade 3 toxicity rate was 12.6%, with a median onset of 2.3 years. Most were in the H&N region in paediatric patients with RMS. Cataracts (30.5%) were the most common, then musculoskeletal deformity (10.1%) and premature menopause (10.1%). Three paediatric patients (1-3 years at treatment) experienced secondary malignancy. Seven grade 4 toxicities occurred (1.6%), all in the H&N region and most in paediatric patients with RMS. Six related to eyes (cataracts, retinopathy, scleral disorder) or ears (hearing impairment).

CONCLUSIONS

This study is the largest to date for RMS and Ewing sarcoma, undergoing multimodality therapy including PBT. It demonstrates good local control, survival and acceptable toxicity rates.

Investigating the effect of providing monetary incentives to participants on completion rates of referred co-respondents: An embedded randomized controlled trial. Study within a trial (SWAT) protocol

Background

Parent-report questionnaires are a common method of generating data on child outcomes in mental health studies. A second report from another person who knows the child (co-respondent) is implemented to reduce bias and increase objectivity. The success of this approach is dependent on the engagement of co-respondents, which can be difficult. Financial incentives are used to increase data return in clinical trials, and to promote referral rates in online marketing. This protocol describes the use of an embedded randomised controlled trial (RCT) to investigate the effect of financial incentives on rates of co-respondent data completion. In the host RCT (of an online intervention designed to reduce the impact of a parent's anxiety on their child) index participants (i.e. parents) are asked to invite a co-respondent to complete measures on the index child. This study will test the hypothesis that providing monetary incentives to index participants will increase the outcome measure completion rate of co-respondents.

Methods

Embedded RCT of two parallel groups. Participants in the intervention arm will be sent a £10 voucher if their chosen co-respondent completes online baseline measures. Participants in the control arm will not be offered payment regardless of their chosen co-respondent's behaviour. 1754 participants will take part. Analysis will compare co-respondent outcome measure completion rates between the two arms at baseline and follow-up.

Conclusion

Findings from this study will provide evidence on the impact of offering payment to index participants on return rates of co-respondent data. This will inform resource allocation within future clinical trials.

The power of feedback: Implementing a comprehensive hand hygiene observer program

BACKGROUND

Hand hygiene (HH) is a fundamental component of infection prevention within all healthcare settings. We implemented a hospital-wide program built on overt HH observation, real-time feedback, and thematic analysis of HH misses.

METHODS

A robust observer training program was established to include foundational training in the WHO's My Five Moments of HH. Observational data from 2011 to 2019 were analyzed by unit, provider type, and thematic analyses of misses.

RESULTS

During the study period, we conducted 160,917 hospital-wide observations on 29 units (monthly average of 1,490 observations). Institutional compliance remained above 95% from 2013 to 2019. Thematic analysis revealed "touching self" and "touching phone" as common, institution-wide reasons for HH misses.

DISCUSSION

Overt observations facilitated communication between HH program and healthcare staff to better understand workflow and educate staff on HH opportunities. This program is an integral part of the Infection Prevention team and has been deployed to collect supplemental data during clusters and outbreaks investigations.

CONCLUSIONS

In addition to having rich HH data, successes of this program, include increased awareness of IPC practices, enhanced communication about patient safety, enriched dialog and feedback around HH misses, and relationship building among program observers, unit staff and leaders.

Monitoring socioeconomic inequalities in health in Hong Kong: insights and lessons from the UK and Australia

In many developed countries such as the UK and Australia, addressing socioeconomic inequalities in health is a priority in their policy agenda, with well-established practices and authorities to collect and link selected health and social indicators for long-term monitoring. Nonetheless, the monitoring of socioeconomic inequalities in health in Hong Kong remains in a piecemeal manner. Also, the common international practice to monitor inequalities at area level appears to be unsuitable in Hong Kong due to its small, compact, and highly interconnected built environment that limits the variation of neighbourhood deprivation level. To enhance inequality monitoring in Hong Kong, we aim to draw reference and lesson from the UK and Australia to explore the feasible steps forward regarding collection of health indicators and contextually appropriate equity stratifiers with strong implication on policy actions, and discuss potential strategies to promote the public awareness and motivations for a more comprehensive inequality monitoring system.

Pandemic and precocious puberty - a Google trends study

BACKGROUND

Recent publications from several countries have reported that more young people (mainly girls) are experiencing precocious puberty (PP)/menarche during the coronavirus disease 2019 pandemic compared to the past. This variation is attributed to the stress of confinement, lack of exercise, obesity and disturbed sleep patterns. A common feature of the relevant papers, however, is the small number of reported cases of PP. Studies have shown that searches for diseases on the internet also reflect to some extent the epidemiology of these diseases.

AIM

To estimate, through internet searches for PP, any changes in the epidemiology of PP.

METHODS

We assessed in Google Trends searches for 21 PP-related terms in English internationally (which practically dwarf searches in other languages), in the years 2017-2021. Additionally, we assessed local searches for selected terms, in English and local languages, in countries where a rise in PP has been reported. Searches were collected in Relative Search Volumes format and analyzed using Kendall’s Tau test, with a statistical significance threshold of P < 0.05.

RESULTS

Internationally, searches for three PP-related terms showed no noticeable change over the study period, while searches for eight terms showed a decrease. An increase was found over time in searches for nine PP-related terms. Of the 17 searches in English and local languages, in countries where a rise in PP has been reported, 5 showed a significant increase over time.

CONCLUSION

Over the study period, more than half of the search terms showed little change or declined. The discrepancy between internet searches for PP and the reported increase in the literature is striking. It would be expected that a true increase in the incidence of PP would also be aptly reflected in Google trends. If our findings are valid, the literature may have been biased. The known secular trend of decreasing age of puberty may also have played a role.

Therapists acting as data collectors in a post stroke research project - a door to development

METHODS

Participants in the present study were eight clinically experienced occupational therapists and physiotherapists who collected data in an observational longitudinal study of the rehabilitation process after stroke. Semi-structured interviews were conducted, and transcripts of the interviews were analyzed using content analysis. The transcripts revealed the informants' perspectives and their experiences of follow-ups in the patient's home.

RESULTS

There was one main category, a door to development, and three subcategories: the entrance, discovery in place, and the exit. All informants expressed that they had gained new knowledge of the situation of people who have had a stroke and that taking part in research uncovered a wider perspective of the patients' situations and the importance of follow-ups in general.

CONCLUSION

New insights into the patients' situation with clinical implications for interprofessional care can be gained by collecting data in a research project that is related to, but different from, everyday clinical practice. Such an assignment can be experienced as professionally rewarding, and we propose that offering such a role change/transition may open the door to development for rehabilitation team members.

Best Practices to Promote Data Utility and Reuse by the Non-Traditional Model Organism Community

The dramatic increase in accessibility to sequencing technologies has opened new avenues into studying different processes, cells, and animal models. In the amphibian models used for regeneration research, these new datasets have uncovered a variety of information about what genes define the regenerating limb as well as how genes and cells change over the course of regeneration. The accumulation of data from these studies undoubtedly increases our understanding of regeneration. Throughout these studies, it is important to consider how data can be made most useful not only for the primary study but also for reuse within the scientific community. This chapter will focus on best practices for data collection and handling as well as principles to promote access and reuse of big datasets. However, the deposition and thorough description of data of all sizes generated for a publication (e.g., images, fcs files, etc.) can also be done following this generic workflow. The aim is to lower hurdles for reuse, access, and re-evaluation of data which will in turn increase the utility of these datasets and accelerate scientific progress.

A good use of time? Providing evidence for how effort is invested in primary and secondary outcome data collection in trials

BACKGROUND

Data collection is a substantial part of trial workload for participants and staff alike. How these hours of work are spent is important because stakeholders are more interested in some outcomes than others. The ORINOCO study compared the time spent collecting primary outcome data to the time spent collecting secondary outcome data in a cohort of trials.

METHODS

We searched PubMed for phase III trials indexed between 2015 and 2019. From these, we randomly selected 120 trials evaluating a therapeutic intervention plus an additional random selection of 20 trials evaluating a public health intervention. We also added eligible trials from a cohort of 189 trials in rheumatology that had used the same core outcome set. We then obtained the time taken to collect primary and secondary outcomes in each trial. We used a hierarchy of methods that included data in trial reports, contacting the trial team and approaching individuals with experience of using the identified outcome measures. We calculated the primary to secondary data collection time ratio and notional data collection cost for each included trial.

RESULTS

We included 161 trials (120 phase III; 21 core outcome set; 20 public health), which together collected 230 primary and 688 secondary outcomes. Full primary and secondary timing data were obtained for 134 trials (100 phase III; 17 core outcome set; 17 public health). The median time spent on primaries was 56.1 h (range: 0.0-10,746.7, IQR: 226.89) and the median time spent on secondaries was 190.7 hours (range: 0.0-1,356,832.9, IQR: 617.6). The median primary to secondary data collection time ratio was 1.0:3.0 (i.e. for every minute spent on primary outcomes, 3.0 were spent on secondaries). The ratio varied by trial type: phase III trials were 1.0:3.1, core outcome set 1.0:3.4 and public health trials 1.0:2.2. The median notional overall data collection cost was £8015.73 (range: £52.90-£31,899,140.70, IQR: £20,096.64).

CONCLUSIONS

Depending on trial type, between two and three times as much time is spent collecting secondary outcome data than collecting primary outcome data. Trial teams should explicitly consider how long it will take to collect the data for an outcome and decide whether that time is worth it given importance of the outcome to the trial.

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations

Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.

Toward an Understanding of Data Collection Integrity

Data collection is an integral part of the practice of behavior analysis because behavior analysts rely on data to inform their clinical decisions. Data collection integrity (DCI) is the degree to which data are collected as planned, and issues with DCI can lead to misinformed clinical decisions. The current study aims to add to the limited research on DCI by evaluating risk factors and interventions that target DCI. An online survey, conducted through Qualtrics^TM, was completed by a combined total of 232 Board-Certified Behavior Analysts (BCBAs) and Board-Certified Behavior Analysts-Doctoral (BCBA-Ds). Participants answered questions about their demographics, their data collectors, their concerns about data collection, the systems they use to collect data, the training they provide data collectors, and the strategies they use to address data-collection issues. Results indicated that many risk factors related to DCI issues might be prevalent in behavior analytic practice. Recommendations on how to address DCI issues are provided.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40617-022-00684-x.

Enhanced recovery after surgery (ERAS) in Latin America: The story so far

Despite modern perioperative care, postoperative complications continue to play a significant role in patient's recovery. Implementation of enhanced recovery pathways has consistently demonstrated better outcomes, reduced complications, and improved length of stay across the globe. However, the literature is scarce with regard to the peaks and valleys encountered during the implementation of these programs in Latin America. The purpose of this review is to shed light on the development and establishment of enhanced recovery pathways in the region. Moreover, it discusses current challenges and future perspectives on perioperative optimization.

Establishment survey participation during the COVID-19 pandemic

Establishment surveys around the globe have measured the impact of the COVID-19 pandemic on establishments' conditions and business practices. At the same time, the consequences of the pandemic, such as closures, hygiene standards, or remote work arrangements, may have also altered patterns of survey participation and introduced nonresponse bias, threatening the quality of establishment survey data. To investigate these issues, this article examines fieldwork outcomes, nonresponse bias, and predictors of survey participation in the IAB-Job Vacancy Survey. As comparisons with previous survey years show, it became more difficult to successfully interview establishments during the COVID-19 pandemic. Using linked administrative data, we show that nonresponse bias was higher in 2020 compared to previous years, even after applying the standard weighting adjustment. However, general patterns of survey participation in 2020 were similar to previous years and COVID-19 related measures were not strong predictors of survey participation in 2020. Further, we provide evidence that nonresponse bias during the pandemic can be reduced by incorporating additional administrative variables into the weighting procedure relative to the standard weighting variables. We conclude this article with a discussion of the findings and implications for survey practitioners.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12651-022-00321-8.

Development of a REDCap-based workflow for high-volume relational data analysis on real-time data in a medical department using open source software

BACKGROUND/AIM

The current availability of large volumes of clinical data has provided medical departments with the opportunity for large-scale analyses, but it has also brought forth the need for an effective strategy of data-storage and data-analysis that is both technically feasible and economically sustainable in the context of limited resources and manpower. Therefore, the aim of this study was to develop a widely-usable data-collection and data-analysis workflow that could be applied in medical departments to perform high-volume relational data analysis on real-time data.

METHODS

A sample project, based on a research database on prostate-specific-membrane-antigen/positron-emission-tomography scans performed in prostate cancer patients at our department, was used to develop a new workflow for data-collection and data-analysis. A checklist of requirements for a successful data-collection/analysis strategy, based on shared clinical research experience, was used as reference standard. Software libraries were selected based on widespread availability, reliability, cost, and technical expertise of the research team (REDCap-v11.0.0 for collaborative data-collection, Python-v3.8.5 for data retrieval and SQLite-v3.31.1 for data storage). The primary objective of this study was to develop and implement a workflow to: a) easily store large volumes of structured data into a relational database, b) perform scripted analyses on relational data retrieved in real-time from the database. The secondary objective was to enhance the strategy cost-effectiveness by using open-source/cost-free software libraries.

RESULTS

A fully working data strategy was developed and successfully applied to a sample research project. The REDCap platform provided a remote and secure method to collaboratively collect large volumes of standardized relational data, with low technical difficulty and role-based access-control. A Python software was coded to retrieve live data through the REDCap-API and persist them to an SQLite database, preserving data-relationships. The SQL-language enabled complex datasets retrieval, while Python allowed for scripted data computation and analysis. Only cost-free software libraries were used and the sample code was made available through a GitHub repository.

CONCLUSIONS

A REDCap-based data-collection and data-analysis workflow, suitable for high-volume relational data-analysis on live data, was developed and successfully implemented using open-source software.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Quality principles of retrospective data collected through a life history calendar

To assert the quality of retrospective data, most studies using tools such as life history calendars rely on comparisons with external sources. Our research aimed to integrate quality principles into a life history calendar and test their capacity to evaluate the data quality. The purpose was to avoid reliance on external data sources because of their possible unavailability. The first quality principle was the relationship between the dating accuracy of verifiable events and the data quality of the life domains of the calendar. The second was the certainty, as self-assessed by participants through color coding, that an event took place at the quarter indicated. We designed an experiment using a paper-and-pencil life history calendar that was completed by 104 university students. Our research highlighted the relevance to use the self-assessment of certainty to assert the data quality. However, we could not establish a relationship between the dating accuracy of verifiable events and the data quality of the life domains. In addition, we present a set of qualitative findings from 20 interviews conducted with study participants explaining the approaches used to complete a life calendar and the difficulties encountered.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11135-022-01563-x.

Khedr A, Salim A, Al Ali AI, El-Sawy AA. A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.