analytical techniques in clinical research

JAMA Guide to Statistics and Methods

Explore this JAMA essay series that explains the basics of statistical techniques used in clinical research, to help clinicians interpret and critically appraise the medical literature.

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This JAMA Guide to Statistics and Methods article explains the test-negative study design, an observational study design routinely used to estimate vaccine effectiveness, and examines its use in a study that estimated the performance of messenger RNA boosters against the Omicron variant.

This JAMA Guide to Statistics and Methods article discusses accounting for competing risks in clinical research.

This JAMA Guide to Statistics and Methods article explains effect score analyses, an approach for evaluating the heterogeneity of treatment effects, and examines its use in a study of oxygen-saturation targets in critically ill patients.

This JAMA Guide to Statistics and Methods explains the use of historical controls—persons who had received a specific control treatment in a previous study—when randomizing participants to that control treatment in a subsequent trial may not be practical or ethical.

This JAMA Guide to Statistics and Methods discusses the early stopping of clinical trials for futility due to lack of evidence supporting the desired benefit, evidence of harm, or practical issues that make successful completion unlikely.

This JAMA Guide to Statistics and Methods explains sequential, multiple assignment, randomized trial (SMART) study designs, in which some or all participants are randomized at 2 or more decision points depending on the participant’s response to prior treatment.

This JAMA Guide to Statistics and Methods article examines conditional power, calculated while a trial is ongoing and based on both the currently observed data and an assumed treatment effect for future patients.

This Guide to Statistics and Methods describes the use of target trial emulation to design an observational study so it preserves the advantages of a randomized clinical trial, points out the limitations of the method, and provides an example of its use.

This Guide to Statistics and Methods provides an overview of the use of adjustment for baseline characteristics in the analysis of randomized clinical trials and emphasizes several important considerations.

This Guide to Statistics and Methods provides an overview of regression models for ordinal outcomes, including an explanation of why they are used and their limitations.

This Guide to Statistics and Methods provides an overview of patient-reported outcome measures for clinical research, emphasizes several important considerations when using them, and points out their limitations.

This JAMA Guide to Statistics and Methods discusses instrumental variable analysis, a method designed to reduce or eliminate unobserved confounding in observational studies, with the goal of achieving unbiased estimation of treatment effects.

This JAMA Guide to Statistics and Methods describes collider bias, illustrates examples in directed acyclic graphs, and explains how it can threaten the internal validity of a study and the accurate estimation of causal relationships in randomized clinical trials and observational studies.

This JAMA Guide to Statistics and Methods discusses the CONSERVE guidelines, which address how to report extenuating circumstances that lead to a modification in trial design, conduct, or analysis.

This JAMA Guide to Statistics and Methods discusses the basics of causal directed acyclic graphs, which are useful tools for communicating researchers’ understanding of the potential interplay among variables and are commonly used for mediation analysis.

This JAMA Guide to Statistics and Methods discusses cardinality matching, a method for finding the largest possible number of matched pairs in an observational data set, with the goal of balanced and representative samples of study participants between groups.

This Guide to Statistics and Methods discusses the various approaches to estimating variability in treatment effects, including heterogeneity of treatment effect, which was used to assess the association between surgery to close patent foramen ovale and risk of recurrent stroke in patients who presented with a stroke in a related JAMA article.

This Guide to Statistics and Methods describes how confidence intervals can be used to help in the interpretation of nonsignificant findings across all study designs.

This JAMA Guide to Statistics and Methods describes why interim analyses are performed during group sequential trials, provides examples of the limitations of interim analyses, and provides guidance on interpreting the results of interim analyses performed during group sequential trials.

This JAMA Guide to Statistics and Methods describes how ACC/AHA guidelines are formatted to rate class (denoting strength of a recommendation) and level (indicating the level of evidence on which a recommendation is based) and summarizes the strengths and benefits of this rating system in comparison with other commonly used ones.

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Stanford Online

Clinical trials: design, strategy, and analysis.

SOM-XCHE0030

Stanford School of Medicine , Stanford Center for Health Education

New medical innovations are constantly hitting the market and changing patient care and research - but what goes into creating these technologies? Researchers need to design, conduct, and report on clinical trials to create impactful technologies that can better our understanding of health and medicine. From learning frameworks and reporting methods to determining how to accurately collect, manage, and measure data, you’ll gain the knowledge necessary to understand the components that go into running a successful clinical trial. Whether you’re in a patient-facing role looking to implement research into your professional career or seeking how to better communicate and collaborate with colleagues working in the labs, the clinical trials course gives you the tools you need to find answers for your health and medicine-related inquiries.

Learning Objectives:

Understand and apply the principles of clinical trial design, such as using frameworks to create research questions and establish study objectives
Ensure diverse, representative study populations by studying participant selection criteria and recruitment and retention strategies
Minimize bias and ensure trial reliability and validity through blinding techniques and randomization methods
Use effective data collection and management techniques to ensure data integrity throughout trials
Effectively communicate trial findings through an understanding of reporting methods, ethical considerations, and regulatory bodies

Course Outline

Begin your studies of clinical trials with an introduction to the background and rationale, a review of protocol development and trial registration, and an outline of a trial’s phases and stages.	Compare the advantages and disadvantages of different trial types and review three frameworks for clinical trials.
Learn how to organize a research question using PICOT framework, be able to identify two types of statistical hypotheses, and explore Type I and II errors.	Understand methods for recruitment, retention, and tracking, and dive into the ethical principles and nuances of human subject research.
Discover the best practices for randomization and blinding for intervention and comparison.	Explore multiple testing methods, outline strategies for controlling Type I errors, and understand the importance of selecting and refining primary outcomes.
Define Adverse Events (AE) and the roles and responsibilities of the Data and Safety Monitoring Boards (DSMBs).	Dive into Regulatory Framework and the Medical Device Regulatory Strategy. Data Collection, Management, and Sharing Uncover the importance of reproducible research techniques and adequate data checking and cleaning.
Review CONSORT reporting guidelines and scientific manuscript tips for accurately conveyed information.

Accreditation In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

Credit Designation American Medical Association (AMA) The Stanford University School of Medicine designates this enduring material for a maximum of 12.00 AMA PRA Category 1 Credits ™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

View the full accreditation information HERE from Stanford Medicine

Core Competencies

Trial Phases
Study Design
Hypothesis Testing
Statistical Analysis

Small Groups and Team Programs

Special Pricing

Enroll as a group or team and learn together. We can advise you on the best group options to meet your organization’s training and development goals and provide you with the support needed to streamline the process. Participating together, your group will develop a shared knowledge, language, and mindset to tackle the challenges ahead.

Teaching Team

Regina Nuzzo

Gallaudet University

Kristin Sainani

Epidemiology and Population Health

Kristin Sainani (née Cobb) is an associate professor at Stanford University and also a health and science writer. After receiving an MS in statistics and a PhD in epidemiology from Stanford University, she studied science writing at the University of California, Santa Cruz. She has taught statistics and writing at Stanford for more than a decade and has received several Excellence in Teaching Awards from the graduate program in epidemiology. Dr. Sainani writes about science and health for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is also the statistical editor for the journal Physical Medicine & Rehabilitation; and she authors a statistics column, Statistically Speaking, for this journal.

Design and Conduct of Clinical Trials

Stanford School of Medicine

Thinking Critically: Interpreting Randomized Clinical Trials (CME)

SOM-YCME0019

Introduction to Clinical Data

SOM-XCHE0009

Stanford School of Medicine, Stanford Center for Health Education

Clinical Trial Design: From Initial Concept to Regulatory Approval

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General Chemistry
Analytical Chemistry

analytical techniques in clinical research

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A critical review and discussion of analytical methods in the L-arginine/nitric oxide area of basic and clinical research

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1 Institute of Clinical Pharmacology, Hannover Medical School, 30623 Hannover, Germany. [email protected]
PMID: 18510938
DOI: 10.1016/j.ab.2008.04.018

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Open access
Published: 25 June 2024

Understanding barriers and facilitators to palliative and end-of-life care research: a mixed method study of generalist and specialist health, social care, and research professionals

Catherine Walshe 1 na1 ,
Lesley Dunleavy 1 na1 ,
Nancy Preston 1 ,
Sheila Payne 1 ,
John Ellershaw 2 ,
Vanessa Taylor 3 ,
Stephen Mason 2 ,
Amara Callistus Nwosu 4 ,
Amy Gadoud 4 ,
Ruth Board 5 ,
Brooke Swash 6 ,
Seamus Coyle 7 ,
Andrew Dickman 8 ,
Andrea Partridge 5 ,
Jaime Halvorsen 9 &
Nick Hulbert-Williams 10

BMC Palliative Care volume 23 , Article number: 159 ( 2024 ) Cite this article

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Palliative care provision should be driven by high quality research evidence. However, there are barriers to conducting research. Most research attention focuses on potential patient barriers; staff and organisational issues that affect research involvement are underexplored. The aim of this research is to understand professional and organisational facilitators and barriers to conducting palliative care research.

A mixed methods study, using an open cross-sectional online survey, followed by working groups using nominal group techniques. Participants were professionals interested in palliative care research, working as generalist/specialist palliative care providers, or palliative care research staff across areas of North West England. Recruitment was via local health organisations, personal networks, and social media in 2022. Data were examined using descriptive statistics and content analysis.

Participants (survey n = 293, working groups n = 20) were mainly from clinical settings (71%) with 45% nurses and 45% working more than 10 years in palliative care. 75% were not active in research but 73% indicated a desire to increase research involvement. Key barriers included lack of organisational research culture and capacity (including prioritisation and available time); research knowledge (including skills/expertise and funding opportunities); research infrastructure (including collaborative opportunities across multiple organisations and governance challenges); and patient and public perceptions of research (including vulnerabilities and burdens). Key facilitators included dedicated research staff, and active research groups, collaborations, and networking opportunities.

Conclusions

Professionals working in palliative care are keen to be research active, but lack time, skills, and support to build research capabilities and collaborations. A shift in organisational culture is needed to enhance palliative care research capacity and collaborative opportunities across clinical and research settings.

Peer Review reports

Palliative care provision should be informed by high quality research, so that clinical practice is underpinned by a robust evidence base. Improving the evidence base in palliative care is a ‘moral imperative’, with arguments highlighting that it is ethically important to offer effective treatments supported by an evidence base, and equally that futile treatments are avoided [ 1 ]. A principal focus of much of the research conducted to understand why developing the evidence base is difficult has focused on the specific challenges of recruiting patient and carer participants to palliative care research studies. Gatekeeping can be an issue, with staff concerned about overburdening vulnerable patients and carers, and feeling ill prepared to discuss research with potential participants [ 2 , 3 , 4 ]. This is despite evidence suggesting patients and families are willing to engage in research at the end of life [ 5 , 6 , 7 ]. Despite this readiness, there can be many reasons why patients and carers may not feel able to engage in research such as illness severity, symptom burden, misconceptions about palliative care, lack of cure and perceived therapeutic benefit, and study burden [ 8 , 9 , 10 ]. This can mean that many studies experience recruitment difficulties [ 11 , 12 ]. Facilitators that may address some of these complex structural, cultural and personal barriers include dedicated research staff on site [ 3 , 13 ], training on how to recruit to palliative care studies [ 14 , 15 ], and improving communication with patients and their families to promote research participation, and within staff teams to address gatekeeping.

Researchers outside palliative care have chosen to explore the professional and organisational facilitators and barriers to conducting research [ 16 , 17 ]. Less is known about the personal, professional, organisational, and structural barriers and facilitators to conducting palliative care research. Palliative care requires a multi-professional approach, and patients are cared for in a variety of settings, including hospitals, hospices, nursing homes and primary care. Palliative care research is historically under-funded in comparison to research that focuses on the prevention or cure of cancer and other life-limiting illnesses [ 18 , 19 ]. There may also be challenges with access to staff with the relevant research expertise, and complicated or undeveloped governance arrangements particularly in settings outside statutory provision [ 20 , 21 , 22 , 23 ]. Research may not be a strategic priority, especially for standalone voluntary organisations who largely rely on charitable funding to fund patient care [ 23 ]. Palliative care research can be time consuming and staff may see it is an ‘add on’ to their role and not part of the routine care they provide to patients [ 24 ]. Staff may feel that they lack the necessary knowledge, skills and expertise to be involved in palliative care research [ 4 , 25 ] and may have limited opportunity to participate or learn more, especially when balancing clinical pressures that have increased during the COVID 19 pandemic [ 26 ]. An organisational research culture improves outcomes for all patients, and not just those involved in the research [ 27 ]. The aim of this study therefore is to further understand professional and organisational facilitators and barriers to conducting all types of palliative care research.

Research question

What are the barriers and facilitators to conducting palliative and end-of-life care research across North West Coast England ?

A mixed method study following a convergent design [ 28 ] , incorporating a cross-sectional online survey and working groups using a nominal group technique [ 29 ]. The survey is reported according to the CHERRIES guidelines for e-surveys [ 30 ].

Both the survey and working groups were conducted across the UK NIHR North West Coast region of England (incorporating South Cumbria, Lancashire, Cheshire, and Merseyside). Currently, palliative care research activity within this area is low. In the UK, palliative care is provided by generalists, the patient’s usual care team, in the hospital, community or care home setting. Specialist inpatient, hospital, home and home nursing palliative services are provided by professionals specifically trained in palliative care, and they largely rely on charitable funding [ 31 , 32 ].

All those who had interest in the provision of, or research into, generalist or specialist palliative care across the region including across acute and community NHS Trusts, GP practices, voluntary hospices, other community and private providers of care, clinical research networks, and academic settings including Universities were invited to participate. The survey was accessed via an online link that included a screening question incorporating the inclusion criteria (see Table 1 ).

Survey: The survey used a convenience sampling approach and was designed to collect largely descriptive data and yield rich information across a range of respondents. Without a viable sampling frame of potential participants, no anticipated sample size could be reliably estimated. Working groups : Those who indicated an interest in taking part via their survey response, or who responded to additional calls for participation, were invited to participate, and then purposively selected to maximise variability across professional background, expertise, and geography.

Recruitment

Survey: Potential participants were recruited via several routes that included dissemination via collaborators in local NHS Trusts and Hospices and the North West Coast Clinical Research network to ensure primary care organisations were reached. Information about the survey was openly and widely disseminated through a project website, personal networks, and social media (Twitter, Facebook, and LinkedIn). No incentives for survey completion were offered. Dissemination included a link to the online survey, with screening questions at the start of the survey confirming eligibility, with clicking through to progress to the survey indicating consent. Potential participants were reassured that taking part was voluntary and that survey results would be aggregated and anonymised. It was explained that their data would be inputted into a secure online survey platform, and these data would be then stored in a secure institutional filestore at Lancaster University. (see additional file 1).

Working groups

Individuals who expressed an interest in taking part in further research after completing the survey were sent working group invitation packs. Additionally, collaborators in local NHS Trusts, Hospices and the North West Coast Clinical Research network circulated packs to eligible participants. Social media (Twitter, Facebook, and Instagram) was also used to advertise the working groups. Participants could take part in the working groups even if they had not completed the survey. Participants contacted the research team if they were interested in taking part and electronic consent was obtained prior to the working group.

Data collection

Survey: The open online survey was built using Qualtrics XM [ 33 ], and the full survey is included in additional file 1. Both closed and free-text questions were used, together with skip options dependent on given answers; 19 possible questions (some with multiple components) were asked across 5 blocks. Participants could navigate through the survey using forward and back buttons. The survey identified current and desired levels of palliative care research involvement, current research barriers, suggestions for sustainable solutions and research training needs. The survey was developed from the IPOS survey (a survey of the research barriers and training needs within the International Psycho-Oncology Society) [ 34 ] and literature on barriers and facilitators to palliative care research [ 3 ]. Survey development followed an iterative approach, with members and colleagues of the project steering committee reviewing survey questions to ensure the survey was appropriate. Participants could only complete the survey once. There was not a completeness check for respondents. The survey was open from 02/03/2022 to 08/06/2022.

Four online (via Microsoft Teams) working groups took place. The groups lasted two hours and were facilitated by LD and another member of the research team (from CW, AG, BS, RB). Nominal group technique was used as it is a method that elicits the views and opinions of a group of experts through the ranking of priorities related to a particular topic of interest. It combines both qualitative and quantitative data collection and involves a number of stages that include; introductions, silent generation of ideas, listing of ideas, discussion of ideas, ranking of top ten ideas, voting on top ten ideas, discussion of voting and conclusions [ 29 ]. Mentimeter [ 35 ] was used to facilitate the voting process and the working groups were recorded.

Data analysis

Survey: Data were downloaded from Qualtrics™ as.csv and.sav files for Excel and SPSS, hosted on Lancaster University secure OneDrive, and checked for potential duplicate entries (using IP, email address or organisation name to ensure only one entry per respondent), and to remove incomplete entries. Entries were judged as complete when participants had provided sufficient descriptive personal information alongside survey responses, even if answers to all available questions had not been given. Pseudonymised data were used for analysis. Descriptive analysis included the use of frequency counts, percentages, and rankings, with some collapsing of categories.

For the analysis of free-text comments, data were extracted into Microsoft Excel. Comments tended to be brief, expanding on answers to closed questions [ 36 , 37 ]. After initial familiarisation, a coding framework was inductively developed by LD and CW and applied to the free text data using a conventional content analysis technique [ 38 ]. Coding and theme development were driven by the content of the free-text comments.

Working groups, using nominal group technique

Each working group was initially analysed separately by LD using the group’s Mentimeter rankings as an initial a priori framework [ 39 ]. The working group recordings and transcripts were read and listened to, and the key issues were summarised within the a priori frameworks. The findings were then compared across the working groups by LD, SM, BS, and AP with input from the study’s Patient and Public Involvement group and finally the study steering committee, to identify key themes.

Four overarching groupings were inductively generated after completion of the working groups. Survey free text and working group findings were compared as part of the four theme development. Mentimeter rankings were allocated to the four groups along with the survey statements where there was strongest agreement about the barriers to research across all survey respondents (see Table 5. ).

Approval was granted by the East of England—Cambridge South Research Ethics Committee (Ref: 22/EE/0049) on the 24/02/2022. Organisational approval was obtained via the Health Research Authority and each participating site.

Survey response

The online survey received 495 visitors, of whom 8 declared they did not meet the inclusion criteria, 36 provided no data, and 158 did not proceed beyond the screening questions. Valid responses were received from 293 participants (59% of visitors), with 171 of the 293 (58%) recording 100% survey progress, and a mean progress of 82% (range 100% to 25%).

Characteristics of survey respondents

Full descriptive data from these respondents are found in Table 2 . The highest proportion of respondents worked in hospice settings, were nurses, and had worked in palliative care for over 10 years. Unexpectedly, there was a high number of paramedics who completed the survey ( n = 17).

Characteristics of working group participants

Twenty palliative care providers/research staff participated in the working groups (see Table 4 for details).

Barriers and facilitators to participating in palliative care research (quantitative data)

Survey respondents were asked to indicate the strength of agreement with statements about facilitators or barriers to engagement and involvement with palliative care research. Working group participants inductively generated statements about barriers which were then ranked. In Table 5. below we present the survey statements where there was strongest agreement across all survey respondents, together with the ranking of inductively generated statements from each of the working groups. Full survey data are found in additional file 2.

The top research barriers were conceptualised across four main areas: organisational culture and capacity (including prioritisation and time given to research); research knowledge (including research skills, how to obtain funding); research infrastructure and collaborations (including collaborative opportunities and governance arrangements), and patient and public perceptions of palliative care research (including vulnerabilities and burdens). Data on facilitators and training needs were collected in the online survey and are presented in Tables 6 and 7 .

Barriers to participating in palliative care research (qualitative data)

Additional data on the four areas of organisational culture and capacity, research knowledge, research infrastructure and collaborations, and patient and public perceptions of research were generated in both the free text comments from the survey and working group analysis. A narrative exploring each of these is presented in turn, illustrated with verbatim data extracts from the working groups and survey.

Organisational culture and capacity

This was the top barrier identified in the survey and most working groups. The focus was about whether research is prioritised within the organisation, including if people are enabled to conduct research in terms of protected time. Across the working groups and survey, participants explained how staff have no time to be involved in research because of clinical pressures and commitments. Staffing shortages, patient complexity, and the impact of COVID 19 have made the situation even more challenging for clinicians:

‘It's really difficult because everyone is so stretched that everybody's so busy sort of, you know, the AHP's [allied health professionals], the doctors, the nurses, everyone's very busy, sort of fighting fires that nobody's got time to move away from that at the moment’ (Hospice Doctor, working group 2)

‘The main barrier from my experience is not having protected time to spend in research activities. My case load is vast and give me no time to participate in research. This is disheartening to me as we need to constantly develop and not stagnate. Also, with palliative care we get one opportunity to make that difference so we need to be equipped with the best we can do.’ (Survey study ID 163, Hospital Doctor)

Organisational culture and external requirements also mitigate against engagement in palliative care research, where priority is given to meeting key performance indicators, which rarely include research engagement:

‘The clinical demands and their key performance indicators required by our service specifications and our trust, demand that you spend the majority of your time 90% if not more, undertaking clinical aspects of the role and that there isn't necessarily buy in [to research] I don't feel from the senior management within the organisation to support us’ (Palliative care nurse specialist, working group 1)

Research not being part of an organisations culture and ethos and therefore not seen as a strategic priority was an important barrier.

‘Even if someone said here's some funding, what do you want? We reel off a million answers, but research would probably be at the bottom just because there's other things that we need or want that we feel is probably more important than research. Whether that's right or wrong, I think it's just not. Not a priority. It's no one’s first thought.’ (Hospice nurse, Working group 2)

Participants highlighted the need for a ‘research champion’ within an organisation who would be responsible for leading, prioritising and raising the profile of research therefore making research less daunting for staff.

‘I think you're somebody who's motivated to drive a research agenda forward, I think makes a big difference in the organisation that you're in, whether that's hospital based or community Hospice and based because I think if you haven't got anybody who's keen and enthusiastic, you're not going to go anywhere. So you've got to have someone who's willing to take that on.’ (Hospital Doctor, Working group 4)

Research knowledge

Health and social care staff can have a limited understanding of research processes, and therefore may not have the necessary skills to conduct research. Whilst some basic knowledge was covered at pre- and post-registration undergraduate or postgraduate level, continuing to develop skills and knowledge could be challenging:

‘We're encouraging our staff to undertake further education or sort of masters level qualifications, and at that level it does require for the qualification a piece of research and a number of research questions to be undertaken, but it's how do we move beyond that?’ (Hospice manager/admin Working group 1)

You do the research project within the course to get through the course and then you know you like, breathe sigh of relief and then you don't go near research again.’ (Palliative care nurse specialist, Working group 1)

Research can feel distant and overwhelming, academic and jargon filled, without relevant pathways to support professional development:

‘I think from a perspective of peoples understanding and knowledge of research and where to get support and there's a lot of people shy away from it because they don't know where to start. They don't know where to go to. They don't know how to find the literature and they just feel like they're in a minefield of information they don't know which avenue to take.’ (Hospice nurse, Working group 4)

The need for mentorship, support, and guidance from more experienced research staff and how to access this support was clearly identified. Engaging junior staff was seen as important and training sessions/e-learning needed to be accessible, including tailored resources for palliative care, and level of involvement in research.

‘If people haven't done a lot of research and they want to be involved and it's sort of supporting that group of people if they haven't got links to people already or groups within their organization or network that they can link into, and they're really interested in it, it's getting those people involved and how to direct them?’ (Hospice nurse, Working group 4)

‘Need the support of an experienced researcher and also someone to help plan and develop the research, mentor and guide throughout research project and assist with analysis of results-/stats and writing up the project.’ (Survey study ID 39 specialist palliative care clinical manager)

Participants explained how there tended to be a lack of research expertise (e.g. knowledge of research processes) within hospices and how it was important to have someone with the right skill set in the setting/small organisation.

‘Having somebody with the right skill set to take something through ethics committee and everything I suppose, and you need to have that one person in every Hospice or in every setting who can do all that. It's a skill all of its own.’ (Manager/admin, Working group 2)

Research infrastructure and collaborations

Palliative care research was felt to have a weak infrastructure, with few studies in the National Institute for Health Research (NIHR) portfolio, limiting opportunities to be involved in research and access to research nurse support. Hospices had few financial resources to support research activity, and seemed reluctant to divert funds from direct patient care:

‘So, there's huge financial implications in terms of them [charitably funded hospices] providing sort of and delivering research … it was a massive competing pressure on money because you don't want to be impacting on the organisations finances and within the charity sector to the detriment of immediate patient care.’ (Hospice Doctor, working group 1 )

‘Releasing people to take part in research is just impossible for a Hospice with our current funding arrangements. Research feels like a "nice to have" aspect of Hospice work. Even though I know it would be valuable to our sector long-term to be research active, the climate we find ourselves in means research is way down the list of priorities for a charity receiving 30% (and diminishing) funding [from the NHS] to run a 24/7 service.’ (Survey Study ID 85, Hospice CEO)

The lack of or limited research infrastructure outside the hospital setting, particularly within standalone hospices, was raised as a barrier. The necessary structures to support research activity, such as governance arrangements, training, and adequate staffing levels, could often be lacking.

‘I think when you're working with within small groups you could be quite isolated with only having one research nurse who then is on their own, and I think the link I think that's probably an issue in terms of I guess the funding for that person. It can be an issue but also attracting somebody to a post which is going to feel quite isolating.’ (Hospital Doctor, Working group 4)

‘But the thought of actually undertaking some research ourselves. We're a million miles away from that in our hospice you know. We are trying to be involved in other bigger trials, but where to actually put through an ethical approval ourselves. We're nowhere near that here.’ (Hospice Doctor, Working group 2)

The importance of engaging nursing and allied health professionals in research and giving them the opportunity to be involved was raised. The four pillars of professional practice of the clinical nurse specialist and advanced practitioner roles includes research alongside clinical, education and leadership components [ 40 ]. However, research is not always recognised or developed. It was noted that organisations support training in Independent and Supplementary Prescribing, diagnostics, and advanced communication skills, so it was questioned why not research. Some short-term research positions may not provide opportunities for all staff, as posts may be linked to certain roles (e.g. medical, nursing) or require professional registrations, thus limiting opportunities for staff without these qualifications (e.g. healthcare assistants). The importance of recognising the role and expertise of non-clinical staff in research and its potential impact on care and services needs to be promoted.

Currently, there was not a strong sense that people or organisations were working collaboratively locally or regionally to facilitate research:

‘We don't work collaboratively, and we have a really big list of research projects that we'd like to do. We'd like to get started on. We don't have the capacity to do it, but actually other hospices or other professionals in palliative care might be working on it. But we just don't know because we don't talk to each other. Perhaps we just need to talk more?’ (Manager/admin Working group 2)

‘I think we're all busy, aren't we? So, the opportunity to meet, collaborate, share ideas doesn't to me seem like it's there. I could be wrong, but I think lack of existing collaboration, just perhaps due to how busy we all are individually, and rather than what I didn't mean, was competitiveness between hospices, yeah.’ (Hospice nurse, Working group 3)

‘From a researcher perspective, the barriers I face are around making the necessary connections with relevant practitioners interested and available to work on research projects. This is partly to do with few opportunities to meet people in informal environments where research priorities or interests can be discussed….(Survey study ID 43 researcher)

The need for some form of alliance or collaborative infrastructure was highlighted to pool research ideas, share information, collaborate on policies and governance issues. This was felt to need buy in from multiple organisations, potentially with a funded post to lead on research across voluntary hospices:

‘it's almost like we need some sort of alliance, isn't it? And that may well be where all this is headed and in terms of, you know, somewhere in the region somebody's putting a bid in for this research and who wants to jump on board to recruit in their area to get some opportunity for the expertise.’ (Palliative care nurse specialist, Working group 1)

‘And so maybe having some kind of umbrella group or network that… then everything kind of filters through it and information comes back out the other way so that that information is shared and you kind of know where to go. Maybe if you've got an idea to check that no one else is already doing it and to be in touch with the right people at the right time, I don't know if something around the kind of coordination of the whole thing.’ (Hospice manager/admin, Working group 2)

There were concerns raised that the palliative care research community involved a select group of individuals and could be elitist. It could be difficult for those sitting outside the elite to know how to be involved and included in any research activity:

‘I did reflect on initially when I got interested in research it was sort of seen as this area of expertise in which a select group were involved, and it was sort of how do we get into that Network.’ (Hospice nurse, Working group 4)

Patient and public perceptions of palliative care research

Concerns were also raised that patient and public perceptions of palliative care research may be an issue either because there were assumptions that research was not happening, or only in large/cancer settings, that people did not want to take part, or that the end of life is an inappropriate time to request participation.

‘Sometimes staff feel oversensitive. Almost oversensitive to not wanting to upset patients and relatives to recruit them in, or to ask the relevant questions that we need them to ask.’ (Hospice educator, Working group 2)

However, counter arguments were also recognised:

‘Anecdotally, we've had people tell us when they've taken part in studies that we've done, that they've enjoyed taking part that it's been beneficial for them, not because the research will impact them, but because of the process of...I guess the therapeutic aspect that's a side line to them taking part that they've enjoyed taking part and sharing. Their views and being able to put something back and to help other people.’ (Researcher, Working group 3)

The aim of this research is to understand professional and organisational facilitators and barriers to conducting palliative care research. Palliative care research was recognised as important and valuable, with three-quarters of those involved in this study wanting to increase their involvement in research, despite most not being currently research active. Several key barriers to palliative care research were identified including lack of organisational research culture and capacity (including prioritisation and available time); research knowledge (including skills/expertise and funding opportunities); research infrastructure and collaboration (including lack of collaborative opportunities across multiple organisations and governance challenges); and patient and public perceptions of research (including vulnerabilities and burdens). Key facilitators included dedicated research staff, and active research groups, collaborations, and networking opportunities.

What this research adds

A key finding is the apparent lack of progress in facilitating palliative care research over time, and the challenge for the sector is why change has been so slow. Previous palliative care research identifies a suite of remarkably similar barriers [ 23 , 41 , 42 , 43 , 44 ], albeit not necessarily unique to this specialty [ 45 , 46 ]. There needs to be a concerted and sustained focus on collaboration and sharing best practice, developing a research culture and facilitating research within and between palliative care providers, enhancing staff capacity and expertise, and providing guidance on research processes and procedures [ 23 , 41 , 43 , 44 ]. Our research further highlights the importance of organisational barriers, pointing to the need to prioritise organisational solutions.

Organisations have a critical role in building research culture and capacity [ 46 , 47 , 48 ]. It is imperative that organisations recognise and value research and incorporate research into the core business of the organisation. This means that research should be visible throughout, from mission statements to policies, business plans, and job descriptions. They should protect research time and resources, recognise talent, and reward positive research related behaviours [ 48 ]. This may be a particular challenge for those palliative care organisations that are charitably funded due to the uncertainty and volatility of their funding [ 49 , 50 ], and business models that may not account for research activity [ 51 ]. The focus is also set nationally, with the recently launched Hospice UK 2024–29 strategy having no overt mention of research [ 52 ].

A key finding is that for many the organisational lack of support for research translates into research not being seen as a core part of people’s jobs. Again, this is not unique to palliative care, with capacity to be engaged in research limited in time or job plans [ 53 ]. As an example an audit of clinical nurse specialist job descriptions found that 80% had an expectation of research engagement [ 40 ], however, in detailed studies of how such roles are enacted, research is typically absent [ 54 , 55 ]. Where research is mentioned, it was in the context of it being the least important aspect of the role, or that others (such as medical consultants) should be leading research [ 56 ]. However, whilst there is little contemporary data, previously the median time palliative care consultant doctors spent on research was zero hours [ 57 ]. A recent survey of UK palliative medicine consultants found that while 78% ( n = 140/180) were interested in conducting research, 83% had no allocated time within their job plan [ 58 ]. Given the serious and significant workforce pressures and challenges currently facing many healthcare workers it is unlikely this position will change without both investment in, and prioritisation of, research time and roles. It may be that research time or engagement needs to explicitly form part of key performance indicators or other metrics to enable such prioritisation to occur.

Research should be important to palliative care provider organisations. It is known that a strong research culture and organisational research performance lowers mortality rates, increases patient and staff satisfaction, reduces staff turnover, and improves organisational efficiency [ 59 ]. Our research encompassed a variety of different organisations and settings, demonstrating that these barriers were remarkably similar wherever a person worked. Solutions may differ though depending on the size, funding, and specialism of the organisation. An independent voluntary funded hospice may have different solutions to a palliative care team working as part of a larger general hospital or community care provider.

The opportunity to collaborate between individuals and across organisations may be important, as in other specialities such as General Practice [ 60 ]. Evidence indicates that the creation of research cooperatives, collaborations and partnerships can be fruitful. There are palliative care examples from the UK [ 61 ], US [ 62 , 63 ], Australia [ 64 , 65 , 66 ], and Africa [ 67 ]. Some of these are large collaboratives, across multiple sites, facilitating multiple studies [ 68 ]. It is possible that such collaboratives mitigate the effect of the employing organisation for members, facilitating research in a way that sits above, and possibly either bypasses, negates, or gives the skills to overcome institutional and local organisational barriers. Joint approaches between universities and public and charitable providers of palliative care may help overcome structural issues such as indemnity, sponsorship and gaining research ethics committee approvals. However, funding to sustain some of these collaborations can be fragile or time limited. For example, in the UK, very welcome but time-limited funding to build palliative care research partnerships has been awarded, but it is too early to see the impact of this on the research landscape [ 69 ]. The benefits of such collaborations may also be on the wider research culture of the organisations that participate in such research. The initial impact of participating in a trial may be staff stress and workload, but this has found to be replaced by enthusiasm for the changes and benefits achieved [ 70 ].

Those who completed our survey had wide variability in levels of research experience and involvement. It is important to recognise when considering developing an organisational research culture that not all members of staff need the same level of skill and expertise, and not all organisations will be at the same level of engagement. Previous recommendations for hospices suggested a typology of engagement, through which hospices could progress if they wished, from research aware, to research engaged, to research leading [ 23 , 43 ]. Equally, individuals can have different levels of preparation, with recognition that generating and leading new research likely needs the higher levels of research preparation such as research focused PhDs, and that organisations that aspire to these levels need to invest in educating staff to these levels and supporting their continued research development.

Strengths and limitations of the research

A strength of this research was the breadth of response from across different sectors and professional backgrounds. There was a particularly strong response from nurses, and a reasonable proportion of those providing general palliative care. However, it was harder to recruit respondents who do not provide specialist palliative care (perhaps because they do not identify themselves as palliative care providers despite the high numbers of those with palliative care needs that they provide care for). Care home respondents were particularly poorly represented. We aimed to invite patients, family members and the public to a working group. Whilst we involved Patient and Public Involvement (PPI) study team members in planning this work and attempted to recruit the public to our working groups, challenges both in institutional permissions and recruitment meant that this planned aspect of the study did not go ahead. This work also represents the views of people from across a particular UK geography. Whilst this includes a large, diverse, population it may be that this does not represent wider views, although this is unlikely given the congruence with past and related research. This study also includes participants who were involved or would wish to be involved in palliative care research so the views of those who are not interested are not reflected in the findings.

Engagement in palliative care research appears stagnant, with this study revealing a range of barriers that appear unchanged from a decade or more ago. The challenge for palliative care is not to identify further the barriers and facilitators to research, but to invest time and funding to address the known barriers and enable the facilitators of research. It is likely that such investments will reap dividends in terms of staff satisfaction, organisational performance, and importantly the quality of care provided to patients and families.

Availability of data and materials

Data are stored in Lancaster University’s PURE repository, consent to share data was not given by participants.

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This project is funded by the NIHR Palliative and End of Life Care Research Partnerships Funding Committee [NIHR135334]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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Catherine Walshe and Lesley Dunleavy are joint senior authors.

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International Observatory On End-of-Life Care, Division of Health Research, Faculty of Health and Medicine, Lancaster University, Lancaster, UK

Catherine Walshe, Lesley Dunleavy, Nancy Preston & Sheila Payne

Liverpool University, Liverpool, UK

John Ellershaw & Stephen Mason

University of Huddersfield, Huddersfield, UK

Vanessa Taylor

Lancaster Medical School, Lancaster University, Lancaster, UK

Amara Callistus Nwosu & Amy Gadoud

Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK

Ruth Board & Andrea Partridge

Chester University, Chester, UK

Brooke Swash

The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK

Seamus Coyle

Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK

Andrew Dickman

NIHR Clinical Research Network North West Coast, Liverpool, UK

Jaime Halvorsen

Edge Hill University, Ormskirk, UK

Nick Hulbert-Williams

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Conceptualisation and funding acquisition: LD, NHW, NP, SP, JE, VT, SM, ACN, AG, RB, BS, SC, AD, AP, JH, CW; Investigation and analysis: LD, NHW, CW, AG, RB, BS; Writing – original draft – CW, LD; Writing – review and editing - LD, NHW, NP, SP, JE, VT, SM, ACN, AG, RB, BS, SC, AD, AP, JH, CW.

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Walshe, C., Dunleavy, L., Preston, N. et al. Understanding barriers and facilitators to palliative and end-of-life care research: a mixed method study of generalist and specialist health, social care, and research professionals. BMC Palliat Care 23 , 159 (2024). https://doi.org/10.1186/s12904-024-01488-2

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6DOF MAGNETIC TRACKING AND ITS APPLICATION TO HUMAN GAIT ANALYSIS

There is growing research in analyzing human gait in the context of various applications. This has been aided by the improvement in sensing technologies and computation power. A complex motor skill that it is, gait has found its use in medicine for diagnosing different neurological ailments and injuries. In sports, gait can be used to provide feedback to the player/athlete to improve his/her skill and to prevent injuries. In biometrics, gait can be used to identify and authenticate individuals. This can be easier to scale to perform biometrics of individuals in large crowds compared to conventional biometric methods. In the field of Human Computer Interaction (HCI), gait can be an additional input that could be provided to be used in applications such as video games. Gait analysis has also been used for Human Activity Recognition (HAR) for purposes such as personal fitness, elderly care and rehabilitation.

The current state-of-the-art methods for gait analysis involves non-wearable technology due to its superior performance. The sophistication afforded in non-wearable technologies, such as cameras, is better able to capture gait information as compared to wearables. However, non-wearable systems are expensive, not scalable and typically, inaccessible to the general public. These systems sometimes need to be set up in specialized clinical facilities by experts. On the other hand, wearables offer scalability and convenience but are not able to match the performance of non-wearables. So the current work is a step in the direction to bridge the gap between the performance of non-wearable systems and the convenience of wearables.

A magnetic tracking system is developed to be applied for gait analysis. The system performs position and orientation tracking, i.e. 6 degrees of freedom or 6DoF tracking. One or more tracker modules, called Rx modules, is tracked with respect to a module called the Tx module. The Tx module mainly consists of a magnetic field generating coil, Inertial Measurement Unit (IMU) and magnetometer. The Rx module mainly consists of a tri-axis sensing coil, IMU and magnetometer. The system is minimally intrusive, works with Non-Line-of-Sight (NLoS) condition, low power consuming, compact and light weight.

The magnetic tracking system has been applied to the task of Human Activity Recognition (HAR) in this work as a proof-of-concept. The tracking system was worn by participants, and 4 activities - walking, walking with weight, marching and jogging - were performed. The Tx module was worn on the waist and the Rx modules were placed on the feet. To compare magnetic tracking with the most commonly used wearable sensors - IMUs + magnetometer - the same system was used to provide IMU and magnetometer data for the same 4 activities. The gait data was processed by 2 commonly used deep learning models - Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The magnetic tracking system shows an overall accuracy of 92\% compared to 86.69\% of the IMU + magnetometer system. Moreover, an accuracy improvement of 8\% is seen with the magnetic tracking system in differentiating between the walking and walking with weight activities, which are very similar in nature. This goes to show the improvement in gait information that 6DoF tracking brings, that manifests as increased classification accuracy. This increase in gait information will have a profound impact in other applications of gait analysis as well.

Degree Type

Doctor of Philosophy
Electrical and Computer Engineering

Campus location

West Lafayette

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Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

Electronic sensors
Biomedical instrumentation
Electronic instrumentation

Open access
Published: 24 June 2024

Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis

Amani Ali Kappi 1 ,
Rania Rabie El-Etreby 2 ,
Ghada Gamal Badawy 3 ,
Gawhara Ebrahem 3 &
Warda El Shahat Hamed 2

BMC Psychology volume 12 , Article number: 364 ( 2024 ) Cite this article

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Metrics details

Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically. The purpose of this research was to examine the mediation effects of attention and child memory on the relationship between video games addiction and cognitive and learning abilities in Egyptian children.

A cross-sectional research design was used in the current study in two schools affiliated with Dakahlia District, Egypt. The study included 169 children aged 9 to 13 who met the inclusion criteria, and their mothers provided the questionnaire responses. The data collection methods were performed over approximately four months from February to May. Data were collected using different tools: Socio-demographic Interview, Game Addiction Scale for Children (GASC), Children’s Memory Questionnaire (CMQ), Clinical Attention Problems Scale, Learning, Executive, and Attention Functioning (LEAF) Scale.

There was a significant indirect effect of video game addiction on cognitive and learning skills through attention, but not child memory. Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills.

Conclusions

These results revealed the significant effect of video game addiction on cognitive and learning abilities in the presence of mediators. It also suggested that attention-focused therapies might play an important role in minimizing the harmful effects of video game addiction on cognitive and learning abilities.

Peer Review reports

Introduction

The use of video games has increased significantly in recent years. Historically, such games are used more often by children. Despite the positive impacts of video games on socialization and enjoyment, empirical and clinical research has consistently demonstrated that many children can become addicted due to excessive use. Among Arab children and adolescents, the prevalence of video game addiction is 62% of 393 adolescents in Saudi Arabia, 5% in Jordan, 6% in Syria, and 7.8% in Kuwait [ 1 , 2 ]. The varying incidence rates can be attributable to variations in the research population, cultural determinants, and evaluation or diagnostic standards.

In addition, video games, the internet, and other new technologies have become children’s top leisure pursuits. Today, they comprise a virtual environment in which thousands of gamers simultaneously participate worldwide; rather than being a personal or lonely leisure activity, they are often a group activity that establishes new social networks [ 3 ]. Although playing video games in moderation can have many positive effects, their exploitation may lead to addictions and societal issues [ 4 ]. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), identifies repetitive and persistent behavior related to online video games as the core element of addiction. This behavior should persist for at least 12 months and result in significant impairment. Additionally, addiction should be accompanied by psychological and social symptoms, as well as tolerance and withdrawal symptoms [ 5 ].

Different studies have examined the impact of video games on children’s cognitive abilities and school performance [ 6 , 7 ]. The recent literature has shown how video games affect the brain and alter its functioning while being played. It demonstrates how specific cortical and subcortical structures are involved [ 8 , 9 , 10 ]. Research indicates that excessive play of the same typees of games might negatively impact school-age children’s cognitive and academic skills as well as their capacity to maintain and enhance memories [ 7 ]. Possible consequences of video game addiction may include memory and attention-related difficulties [ 4 , 6 , 11 ]. For instance, children’s memory scores negatively correlated with greater levels of video game addiction in Lebanon [ 6 ]. Furthermore, studies show that action-game players are more likely to succeed at short-term concentration tests while they perform below average in long-term, less exciting activities. At the point of game addiction, difficulties with focus are likely to become much more apparent [ 12 ]. Studies show a substantial association between gaming addiction and inattention, even after controlling other variables such as personality factors, anxiety and depression symptoms, and attention deficit hyperactivity disorder [ 13 , 14 ].

Prior studies have illustrated the association between video game addiction and psychiatric disorders, social phobia, mental well-being, and risky health behaviors [ 15 , 16 , 17 ]. Another study shows an association between video game addiction and memory, attention, cognitive, and learning abilities among Lebanese children [ 18 ]. However, all of these studies explain the association without controlling for any history of mental or behavioral disorders such as ADHD, anxiety, or depression. However, to the best of our knowledge, a few studies have specifically investigated the effect of attention and child memory on the relationship between video game addiction and cognitive and learning abilities in Egyptian children. Therefore, this study aimed to explore the mediation effect of attention and child memory on the association between addiction to video game and cognitive and learning abilities among Egyptian children. Our hypotheses were: (1) child attention mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children; and (2) child memory mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children.

Literature review

Video games have transformed into complex experiences that embody principles recognized by psychologists, neuroscientists, and educators as crucial for behavior, learning, and cognitive functions. While video games offer social and entertainment benefits, extensive research indicates that their excessive use can lead to adverse psychological consequences and even addiction in a minority of players. Symptoms like impaired control over gaming and prioritizing games over daily responsibilities may signify gaming addiction [ 19 ].

The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) acknowledged video game addiction as an internet gaming disorder in its fifth edition, highlighting the need for further research [ 20 ]. Similarly, the 11th edition of the International Classification of Diseases (ICD-11) classified gaming disorder as a recurrent pattern of gaming behavior that encompasses both online and offline gaming [ 21 ]. Scientific evidence indicates that addictions can develop due to a combination of genetic susceptibility and repeated exposure to specific stimuli [ 22 ].

Growing public concerns have emerged regarding the potential negative impacts of video games, notably on children’s memory [ 23 ]. Individuals with various behavioral disorders and those with addictive tendencies often find their memory, crucial for comprehension and cognitive abilities like memory updating and working memory, compromised [ 24 ]. Although some research delves into video games’ effects on cognitive functions and academic achievement in children [ 25 , 26 ], the impact on memory remains a contentious topic.

Despite being a leisure activity, video gaming can pose issues for certain children, impacting their ability to focus. Meta-analysis and systematic reviews by Ho et al. and Carli et al. indicated a link between inattention and addiction to the internet and gaming [ 27 ]. Additionally, numerous studies corroborated this connection, demonstrating a robust correlation between the severity of inattention in ADHD and addiction to the internet or gaming. This correlation persisted even after controlling for factors such as depression and anxiety symptoms, as well as personality traits [ 27 ].

Study design and sample

This study has a cross-sectional descriptive design. It was conducted in two convienient selected preparatory schools, Emam Mohamed Abdo Preparatory and Omar Ibn Elkhatab Preparatory School. The two schools are affiliated with xxx. The participants were selected at random from the list of school principals. The research was open to all students between the ages of 9 and 13 with no history of physical, mental, or cognitive disorders. Each student’s parents provided the questionnaire responses. Using the G-power software 3.1.9.2, the study’s sample size was determined. Based on an average effect size of f = 0.15, a 2-sides test at alpha = 0.05, a statistical power (1-β) of 0.95, and eight predictors (age, gender, educational level of the child and mother, video game addiction, memory, attention, and learning abilities), power analysis was performed. A minimum of 166 participants were required based on these criteria.

Ethical consideration

The study approved by the Research Ethics Committee (REC) of Mansoura University’s Faculty of Nursing (IRB P0506/9/8/2023). The study’s purpose, methodology, duration, and benefits were also explained to the directors of the two selected institutions. Mothers’ consents obtained after explaining the study’s objective and the data kept confidential. The participants were informed that they had their right to withdraw from the study at any time.

Data Collection

The following tools were utilized in the study:

Socio-demographic questionnaire

Child and mother’s information was collected, such as age, sex, number of children, and level of education.

Video game addiction

We used the Game Addiction Scale for Children (GASC) to measure children’s video game addiction. The GASC developed by Yılmaz, Griffiths [ 28 ] according to DSM criteria to evaluate gaming addiction. It includes 21 self-reported items rated on five-point Likert scale (from 1 = never to 5 = very frequently), where higher score shows more hazardous online gaming usage. An individual’s total score can range from a minimum of 21 to a maximum of 105; a score above 90 may be a sign of a video game addiction. It is also emphasized that this is not a diagnostic tool, however, but merely an indicator that a child may have a gaming addiction. Such a diagnosis could only be made by a comprehensive clinical evaluation. Seven criteria for video game addiction are determined by the scale: salience, tolerance, mood modification, withdrawal, relapse, conflict, and issues. The scale shows an acceptable internal consistency reliability ( r = 0.89, p < 0.001) [ 19 ].

Children’s memory

We used the Children’s Memory questionnaire (CMQ) to assess children’s memory rated by their parents. The CMQ developed by Drysdale, Shores [ 29 ]. It included 34 items that rated on a five-point Likert scale ranging from 1 = never or almost never, to 5 = more than once a day. Higher scores indicate a more significant reduction in the cognitive domain. The scale is divided into three subscales: working memory and attention, visual memory, and episodic memory. The Cronbach alpha value for the episodic memory subscale was 0.88, the visual memory is 0.77, and the working memory is 0.84 [ 29 ].

Attention of children

The Clinical Attention Problems Scale was used to measure children’s attention level in the morning and afternoon. This scale was developed by Edelbrock and Rancurello [ 30 ] and includes 12 items. The possible responses are 0 = not true, 1 = somewhat or sometimes true, and 2 = very often or often true. The higher the scores, the more attention there is. The Cronbach alpha values for the clinical attention problem in the morning is 0.84 and for the afternoon is 0.83.

Cognitive and learning skills

We used the Learning, Executive, and Attention Functioning (LEAF) scale to measure children’s cognitive and learning skills. The LEAF scale is a self-reported 55 items scale developed by Castellanos, Kronenberger [ 31 ]. The scale assesses core cognitive abilities and related academic and learning abilities. The LEAF assesses cognitive skills such as attention, processing speed, working memory, sustained sequential processing to accomplish goals (such as planning and carrying out goal-directed tasks), and new problem-solving. Moreover, the LEAF approach takes into account academic functioning, declarative/factual memory, and understanding and concept formulation.

The LEAF includes 55 items, with 11 academic subscales that rate a person’s reading, writing, and mathematics proficiency. The LEAF is divided into subscales that measure comprehension and conceptual learning, factual memory, attention, processing speed, visual-spatial organization, sustained sequential processing, working memory, new problem-solving, mathematics, basic reading, and written expression skills. Each subscale has the same number of items. The responses were rated on a three-point scale ranging from 0 to 3. Higher scores indicate more significant issues with cognition. The five component items are added to provide the subscale score for each of the 11 subject areas. Three criterion-referenced ranges are established for the interpretation of LEAF subscale raw scores. Out of nine, a score of five to nine is classified as the “borderline problem range,” a score of less than five as the “no problem range,” and a score of nine or above as the “problem range.” The Cronbach alpha value for the LEAF scale is 0.96.

Validity and reliability

Study tools were translated into Arabic by the researchers. Five pediatric nursing and psychiatric and mental health nursing experts tested them for content validity. At first, the scales were translated into Arabic using a forward and backward translation method. The translated questionnaires were then adapted to fit Arabic cultural norms. Two highly proficient native Arabic speakers who are accomplished academics in the fields of psychiatry and mental health nursing, and hold the academic status of Full Professor translated the questionnaire from English to Arabic. An English-language expert who is fluent in Arabic back translated the Arabic version. Native Arabic speakers who were not involved in the translation process verified the final translation. The forward-to-back translation process was repeated until the comparative findings matched exactly. The questionnaires were then given to three Arabic psychiatric nursing professionals, who provided their opinions on its importance, relevance, and simplicity. The tools’ reliability was tested using Cronbach’s alpha test (tool I α = 0.86, tool II α = 0.81, tool III α = 0.95, and tool IV α = 0.95, respectively). Additionally, a confirmatory factor analysis were carried out to validate the content of the four scales after translation. The data collection methods were performed over approximately four months from February to May. Also, a pilot study was conducted to assess the study tools’ feasibility and determine the time required to complete the tools. 10% of the initial participants were randomly selected from the same schools. Minimal modifications were then made to the tools. Mothers of students who participated in the pilot study were excluded from the primary study. The data was collected for four months (February to May). An online Google form was created to collect data. The link was then shared with selected student parents through WhatsApp groups. The link outlined the study’s purpose and methods, and participants signed a consent form.

Data collection procedure

We obtained permission to translate the study scales into Arabic. We collected data from February to May using an online Google Form for four months. The Google Form included full details regarding the study’s aims and processes to ensure transparency and establish participants’ trust. An extensive description of the response process additionally supports the Attention Problems Scale. For instance, mothers are required to respond to the items and their relevance to their children in the morning and afternoon. We distributed the survey link to the selected students’ mothers through WhatsApp groups as it was convenient and widespread among the target demographic. Before proceeding to the survey questions, participants were required to read and sign this consent form to ensure that participants received information about the study and voluntarily consented.

Statistical analysis

We employed the Statistical Package for Social Science version 26 [ 23 ] to analyze the data. We analyzed the demographic data using descriptive statistics such as means, standard deviations, frequency, and percentages. In order to evaluate the mediator effects of memory and attention on the relationship between cognitive, academic, and learning skills and video gaming addiction, we ran the multiple regression PROCESS macro with 5,000 bootstraps in SPSS version 3.4 [ 24 ]. We also included confounding variables, such as the age of the child, gender, the age of the mother, education, and job status, as covariates in the mediation model.

Sample characteristics

There were 169 children their mothers responded to the study surveys. The children’s mean age was 13 (SD = 3.9), while the mothers’ mean age was 41 (SD = 7.1). According to mothers, the children were ranked third in their household. Most mothers (72%) said they lived in rural areas. About 61% of the families had at least three children. Half of the mothers had high school or less education, and more than half were unemployed. Most children were in middle school (72%), see Table 1 .

Study variables description

The mean scores for all scales are presented in Table 2 . The mean score of the video gaming addiction total scale was 61 ± 19.3, indicating a moderate level of addiction. The attention total scale mean was 9 ± 6.50, indicating moderate attention problems. The mean score on the total scale for child memory was 80 ± 31,4, indicating moderate memory issues. Eight subscales of the LEAF had mean scores of 5: factual memory, processing speed, visual-spatial organization, sustained sequential processing, working memory, novel problem-solving, mathematics skills, and written expression skills. These mean scores indicate that a borderline problem exists. However, the mean scores for the comprehension and conceptual learning subscale, attention subscale, and basic reading skills subscale were below five, indicating that there was no problem.

Mediating effect of memory, attention problem on the association between video gaming addiction and cognitive, learning, and academic skills

Video game addiction had a significant impact on attention problems (b = 0.34, p < 0.001; a1), and child memory (b = 0.18, p < 0.001; a2). In turn, both attention problems (b = 0.48, p < 0.001; b1) and child memory (b = 0.38, p < 0.001; b2) had significant impact on cognitive and learning skills. The results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention problems (b = 0.17, CI: 0.82, 0.25; c ’ 1). However, there was no significant indirect effect of video game addiction on cognitive and learning skills through child memory (b = 0.07, CI: -0.01, 0.16; c ’ 2). The analysis revealed that confounding variables had no significant effect on the direct or indirect pathways linking video game addiction to cognitive and learning skills. The direct effect of video game addiction on cognitive and learning skills in the presence of the mediators was also found to be significant (b = 0.11, CI: 0.008, 0.401; c ’ -c). Figure 1 displays the mediation analysis findings.

Mediation effect of attention problem and child memory on the association between video gaming addiction and cognitive and learning skills

Previous research has explored the relationship between video game addiction, attention, and memory. Some studies have focused on the relationship between video game addiction and cognitive and learning skills. Others have examined the association between video gaming addiction and all other variables (attention, memory, learning, and cognitive skills). However, no study has explicitly examined the direct and indirect effect of video gaming addiction on learning and cognitive skills through the mediation effect of attention and memory.

This study was done on a sample of Egyptian school children to evaluate the mediation effect of attention and memory on the relationship between video game addiction and cognitive and learning abilities in children. The present study reveals that a gaming addiction can significantly impact attention and memory. This result agrees with Farchakh, Haddad [ 6 ], who conducted a study on a group of Lebanese school children aged 9 to 13 to investigate the association between gaming addiction, attention, memory, cognitive, and learning skills. They found that a greater degree of addiction to video gaming was significantly associated with worse attention scores and worse memory scores. An earlier study suggests that the link between inattention and video game addiction could be described by game genres’ immediate response and reward system. Alrahili, Alreefi [ 2 ] suggest that this may alleviate the boredom typically reported by inattentive users while simultaneously introducing a lack of responsiveness to real-world rewards. Another study on Turkish schoolchildren aged 10 to 16 years old revealed that the total recall scores of the subject group (children who regularly play video games) are significantly lower than those of the control group (children who do not regularly play video games; [ 7 ]).

The current study demonstrates that attention and child memory significantly impacted cognitive and learning skills. This agrees with the opinion of, Gallen, Anguera [ 32 ], who argues that children and young people process information differently, affecting the performance of various cognitive tasks. Additionally, this result disagrees with the findings of Ellah, Achor, and Enemarie [ 26 ], who have stated that students’ working memory has no statistically significant correlation with learning and problem-solving skills. Moreover, their same study showed that different measures of working memory can be attributed to a small variation in low-ability students’ problem-solving skills.

The results revealed a significant indirect effect of video game addiction on cognitive and learning skills through attention. This could be related to the relationship between attention and learning skills. Attention is an essential factor in the learning process because it helps a person make efficient use of data by directing their learning to relevant components and relationships in the input material. If a student can pay attention, they may be able to better retain and understand this material; if not, a lack of attention may lead to difficulties in learning and academic performance. As video gaming addiction affects students’ attention, it may directly affect learning skills [ 33 ]. Another study agrees with the current result, revealing that video game addiction negatively affects adolescents’ learning skills and grade point average [ 34 ].

A child’s memory has an effect on their cognitive and learning skills. Encoding, consolidating, and retrieving experiences and information are the foundation for learning new skills and knowledge [ 35 ]. Video game addiction affects children’s memory. Hence, the expectation is that video game addiction directly affects cognitive and learning skills. However, the present study reveals no significant indirect effect of video game addiction on cognitive and learning skills through child memory. For example, perceptual attention to the exterior world and reflective attention to interior memories need modification of shared representational components in the occipitotemporal cortex. This is shown in episodic memory by recovering an experience from memory, which includes reactivating some of the same sensory areas used during encoding. Furthermore, the prefrontal cortex involves continuous and reflecting attention [ 36 ]. The prefrontal cortex controls memory recall by choosing target memories and filtering or suppressing competing memories [ 36 ].

Another aspect that may be responsible for the absence of a mediating effect of memory on the association between video game addiction and cognitive and learning skills is the presence of the many factors that affect learning and cognitive skills besides memory alone. Life circumstances can affect learning skills rather than memory itself, for example. Problem solving (one of the learning skills) requires a brain that works effectively. Therefore, it is critical to address needs such as physical health, which is influenced by self-care needs such as diet, sleep, and relaxation, as well as children’s social and emotional needs. Furthermore, learning experiences that use all the senses, rather than only hearing or seeing information, result in effective and straightforward information retrieval from memory during problem-solving processes. Such abilities are supposed to be acquired by active participation in learning activities by children [ 37 ]. Finally, long-term focus on online gaming may eventually lead to neglect in learning, leading to a deterioration in learning performance [ 38 ].

Limitations

Our study has some limitations. First, we administered the Clinical Attention Problems Scale only once per student rather than conducting repeated measurements in the morning and afternoon. This approach overlooks potential daytime variations in attention levels, limiting our understanding of each child’s attentional profile. This choice was driven by practical considerations such as reducing the testing burden and participant fatigue. Future research could address this limitation by implementing repeated assessments to comprehend better daytime patterns in children’s attention levels and their implications for learning and behavior. Causality analysis was not possible due to the use of a cross-sectional sample. In addition, some results may be attributable to the small sample size. To fully understand the complex interplay between video game addiction and cognitive outcomes, longitudinal studies and controlled experiments are necessary to provide more conclusive insights into the relationship. It was difficult to include both parents in the study, as most of the fathers said they were too busy to participate. Hence, mothers were the subjects of the study. Certain differences (or lack thereof) are probably artifacts of the sample size. As a result, our findings must be validated by analyzing larger samples. Despite these limitations, this work has the potential to provide insights and open new research avenues.

Implications

Healthcare professionals should be aware of how much children participate in these games and be willing to engage in in-depth conversations with parents about the impact these games may have on children’s health. Therefore, periodical workshops should be held by pediatric and community mental health nurses to enhance student awareness of the effects of video games on their memory, attention, and academic performance. In addition, teaching programs should be held at schools to improve students’ attention, memory, learning, and cognitive skills.

Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills. These results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention. However, video game addiction had no significant indirect effect on cognitive and learning skills through child memory. In the presence of the mediators, the direct impact of video game addiction on cognitive and learning skills was also significant.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors extend their heartfelt appreciation and gratitude to all parents who willingly participated in the study.

The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project Number: GSSRD-24.

Author information

Authors and affiliations.

Department of Nursing, College of Nursing, Jazan University, Jazan, Kingdom of Saudi Arabia

Amani Ali Kappi

Psychiatric and Mental Health Nursing Department, College of Nursing, Mansoura University, Mansoura, Egypt

Rania Rabie El-Etreby & Warda El Shahat Hamed

Pediatric Nursing Department, College of Nursing, Mansoura University, Mansoura, Egypt

Ghada Gamal Badawy & Gawhara Ebrahem

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Amany Ali Kappi contributed to the project by designing the methodology, performing formal analysis, analyzing the data, and writing both the original draft and the manuscript. Rania Rabie El-Etreby contributed to conceptualizing, methodology, conducting, drafting, reviewing, and editing the manuscript. Ghada Gamal Badawy, was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation. Gawhara Ebrahem was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation Warda El Shahat Hamed conceptualized and prepared the methodology and investigation and contributed to writing the original draft. She also reviewed and edited the document. All authors read and approved the final manuscript.

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Kappi, A.A., El-Etreby, R.R., Badawy, G.G. et al. Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis. BMC Psychol 12 , 364 (2024). https://doi.org/10.1186/s40359-024-01849-9

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v.11(2); 2019 Feb

Planning and Conducting Clinical Research: The Whole Process

Boon-how chew.

1 Family Medicine, Universiti Putra Malaysia, Serdang, MYS

The goal of this review was to present the essential steps in the entire process of clinical research. Research should begin with an educated idea arising from a clinical practice issue. A research topic rooted in a clinical problem provides the motivation for the completion of the research and relevancy for affecting medical practice changes and improvements. The research idea is further informed through a systematic literature review, clarified into a conceptual framework, and defined into an answerable research question. Engagement with clinical experts, experienced researchers, relevant stakeholders of the research topic, and even patients can enhance the research question’s relevance, feasibility, and efficiency. Clinical research can be completed in two major steps: study designing and study reporting. Three study designs should be planned in sequence and iterated until properly refined: theoretical design, data collection design, and statistical analysis design. The design of data collection could be further categorized into three facets: experimental or non-experimental, sampling or census, and time features of the variables to be studied. The ultimate aims of research reporting are to present findings succinctly and timely. Concise, explicit, and complete reporting are the guiding principles in clinical studies reporting.

Introduction and background

Medical and clinical research can be classified in many different ways. Probably, most people are familiar with basic (laboratory) research, clinical research, healthcare (services) research, health systems (policy) research, and educational research. Clinical research in this review refers to scientific research related to clinical practices. There are many ways a clinical research's findings can become invalid or less impactful including ignorance of previous similar studies, a paucity of similar studies, poor study design and implementation, low test agent efficacy, no predetermined statistical analysis, insufficient reporting, bias, and conflicts of interest [ 1 - 4 ]. Scientific, ethical, and moral decadence among researchers can be due to incognizant criteria in academic promotion and remuneration and too many forced studies by amateurs and students for the sake of research without adequate training or guidance [ 2 , 5 - 6 ]. This article will review the proper methods to conduct medical research from the planning stage to submission for publication (Table (Table1 1 ).

a Feasibility and efficiency are considered during the refinement of the research question and adhered to during data collection.

Concept	Research Idea	Research Question	Acquiring Data	Analysis	Publication	Practice
Actions	Relevant clinical problem or issue	Primary or secondary	Measuring	Prespecified	Writing skills	Guidelines
	Literature review	Quantitative or qualitative	Measuring tool	Predetermined	Guidelines	Protocol
	Conceptual framework	Causal or non-causal	Measurement	Exploratory allowed	Journal selection	Policy
	Collaboration with experts	Feasibility	Feasibility	Strength and direction of the effect estimate	Response to reviewers’ comments	Change
	Seek target population’s opinions on the research topic	Efficiency	Efficiency
		Theoretical Design	Data Collection Design	Statistical design
		Domain (external validity)	Experimental or non-experimental	Data cleaning
		Valid (confounding minimized)	Sampling or census	Outlier
		Precise (good sample size)	Time features	Missing data
		Pilot study		Descriptive
				Inferential
				Statistical assumptions
				Collaboration with statistician

Epidemiologic studies in clinical and medical fields focus on the effect of a determinant on an outcome [ 7 ]. Measurement errors that happen systematically give rise to biases leading to invalid study results, whereas random measurement errors will cause imprecise reporting of effects. Precision can usually be increased with an increased sample size provided biases are avoided or trivialized. Otherwise, the increased precision will aggravate the biases. Because epidemiologic, clinical research focuses on measurement, measurement errors are addressed throughout the research process. Obtaining the most accurate estimate of a treatment effect constitutes the whole business of epidemiologic research in clinical practice. This is greatly facilitated by clinical expertise and current scientific knowledge of the research topic. Current scientific knowledge is acquired through literature reviews or in collaboration with an expert clinician. Collaboration and consultation with an expert clinician should also include input from the target population to confirm the relevance of the research question. The novelty of a research topic is less important than the clinical applicability of the topic. Researchers need to acquire appropriate writing and reporting skills from the beginning of their careers, and these skills should improve with persistent use and regular reviewing of published journal articles. A published clinical research study stands on solid scientific ground to inform clinical practice given the article has passed through proper peer-reviews, revision, and content improvement.

Systematic literature reviews

Systematic literature reviews of published papers will inform authors of the existing clinical evidence on a research topic. This is an important step to reduce wasted efforts and evaluate the planned study [ 8 ]. Conducting a systematic literature review is a well-known important step before embarking on a new study [ 9 ]. A rigorously performed and cautiously interpreted systematic review that includes in-process trials can inform researchers of several factors [ 10 ]. Reviewing the literature will inform the choice of recruitment methods, outcome measures, questionnaires, intervention details, and statistical strategies – useful information to increase the study’s relevance, value, and power. A good review of previous studies will also provide evidence of the effects of an intervention that may or may not be worthwhile; this would suggest either no further studies are warranted or that further study of the intervention is needed. A review can also inform whether a larger and better study is preferable to an additional small study. Reviews of previously published work may yield few studies or low-quality evidence from small or poorly designed studies on certain intervention or observation; this may encourage or discourage further research or prompt consideration of a first clinical trial.

Conceptual framework

The result of a literature review should include identifying a working conceptual framework to clarify the nature of the research problem, questions, and designs, and even guide the latter discussion of the findings and development of possible solutions. Conceptual frameworks represent ways of thinking about a problem or how complex things work the way they do [ 11 ]. Different frameworks will emphasize different variables and outcomes, and their inter-relatedness. Each framework highlights or emphasizes different aspects of a problem or research question. Often, any single conceptual framework presents only a partial view of reality [ 11 ]. Furthermore, each framework magnifies certain elements of the problem. Therefore, a thorough literature search is warranted for authors to avoid repeating the same research endeavors or mistakes. It may also help them find relevant conceptual frameworks including those that are outside one’s specialty or system.

Conceptual frameworks can come from theories with well-organized principles and propositions that have been confirmed by observations or experiments. Conceptual frameworks can also come from models derived from theories, observations or sets of concepts or even evidence-based best practices derived from past studies [ 11 ].

Researchers convey their assumptions of the associations of the variables explicitly in the conceptual framework to connect the research to the literature. After selecting a single conceptual framework or a combination of a few frameworks, a clinical study can be completed in two fundamental steps: study design and study report. Three study designs should be planned in sequence and iterated until satisfaction: the theoretical design, data collection design, and statistical analysis design [ 7 ].

Study designs

Theoretical Design

Theoretical design is the next important step in the research process after a literature review and conceptual framework identification. While the theoretical design is a crucial step in research planning, it is often dealt with lightly because of the more alluring second step (data collection design). In the theoretical design phase, a research question is designed to address a clinical problem, which involves an informed understanding based on the literature review and effective collaboration with the right experts and clinicians. A well-developed research question will have an initial hypothesis of the possible relationship between the explanatory variable/exposure and the outcome. This will inform the nature of the study design, be it qualitative or quantitative, primary or secondary, and non-causal or causal (Figure (Figure1 1 ).

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A study is qualitative if the research question aims to explore, understand, describe, discover or generate reasons underlying certain phenomena. Qualitative studies usually focus on a process to determine how and why things happen [ 12 ]. Quantitative studies use deductive reasoning, and numerical statistical quantification of the association between groups on data often gathered during experiments [ 13 ]. A primary clinical study is an original study gathering a new set of patient-level data. Secondary research draws on the existing available data and pooling them into a larger database to generate a wider perspective or a more powerful conclusion. Non-causal or descriptive research aims to identify the determinants or associated factors for the outcome or health condition, without regard for causal relationships. Causal research is an exploration of the determinants of an outcome while mitigating confounding variables. Table Table2 2 shows examples of non-causal (e.g., diagnostic and prognostic) and causal (e.g., intervention and etiologic) clinical studies. Concordance between the research question, its aim, and the choice of theoretical design will provide a strong foundation and the right direction for the research process and path.

Research Category	Study Title
Diagnostic	Plasma Concentration of B-type Natriuretic Peptide (BNP) in the Diagnosis of Left Ventricular Dysfunction
Diagnostic	The Centor and McIsaac Scores and the Group A Streptococcal Pharyngitis
Prognostic	The Apgar Score and Infant Mortality
Prognostic	SCORE (Systematic COronary Risk Evaluation) for the Estimation of Ten-Year Risk of Fatal Cardiovascular Disease
Intervention	Dexamethasone in Very Low Birth Weight Infants
Intervention	Bariatric Surgery of Obesity in Type 2 Diabetes and Metabolic Syndrome
Etiologic	Thalidomide and Reduction Deformities of the Limbs
Etiologic	Work Stress and Risk of Cardiovascular Mortality

A problem in clinical epidemiology is phrased in a mathematical relationship below, where the outcome is a function of the determinant (D) conditional on the extraneous determinants (ED) or more commonly known as the confounding factors [ 7 ]:

For non-causal research, Outcome = f (D1, D2…Dn) For causal research, Outcome = f (D | ED)

A fine research question is composed of at least three components: 1) an outcome or a health condition, 2) determinant/s or associated factors to the outcome, and 3) the domain. The outcome and the determinants have to be clearly conceptualized and operationalized as measurable variables (Table (Table3; 3 ; PICOT [ 14 ] and FINER [ 15 ]). The study domain is the theoretical source population from which the study population will be sampled, similar to the wording on a drug package insert that reads, “use this medication (study results) in people with this disease” [ 7 ].

Acronym	Explanation
P =	Patient (or the domain)
I =	Intervention or treatment (or the determinants in non-experimental)
C =	Comparison (only in experimental)
O =	Outcome
T =	Time describes the duration of data collection
F =	Feasible with the current and/or potential available resources
I =	Important and interesting to current clinical practice and to you, respectively
N =	Novel and adding to the existing corpus of scientific knowledge
E =	Ethical research conducted without harm to participants and institutions
R =	Relevant to as many parties as possible, not only to your own practice

The interpretation of study results as they apply to wider populations is known as generalization, and generalization can either be statistical or made using scientific inferences [ 16 ]. Generalization supported by statistical inferences is seen in studies on disease prevalence where the sample population is representative of the source population. By contrast, generalizations made using scientific inferences are not bound by the representativeness of the sample in the study; rather, the generalization should be plausible from the underlying scientific mechanisms as long as the study design is valid and nonbiased. Scientific inferences and generalizations are usually the aims of causal studies.

Confounding: Confounding is a situation where true effects are obscured or confused [ 7 , 16 ]. Confounding variables or confounders affect the validity of a study’s outcomes and should be prevented or mitigated in the planning stages and further managed in the analytical stages. Confounders are also known as extraneous determinants in epidemiology due to their inherent and simultaneous relationships to both the determinant and outcome (Figure (Figure2), 2 ), which are usually one-determinant-to-one outcome in causal clinical studies. The known confounders are also called observed confounders. These can be minimized using randomization, restriction, or a matching strategy. Residual confounding has occurred in a causal relationship when identified confounders were not measured accurately. Unobserved confounding occurs when the confounding effect is present as a variable or factor not observed or yet defined and, thus, not measured in the study. Age and gender are almost universal confounders followed by ethnicity and socio-economic status.

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Confounders have three main characteristics. They are a potential risk factor for the disease, associated with the determinant of interest, and should not be an intermediate variable between the determinant and the outcome or a precursor to the determinant. For example, a sedentary lifestyle is a cause for acute coronary syndrome (ACS), and smoking could be a confounder but not cardiorespiratory unfitness (which is an intermediate factor between a sedentary lifestyle and ACS). For patients with ACS, not having a pair of sports shoes is not a confounder – it is a correlate for the sedentary lifestyle. Similarly, depression would be a precursor, not a confounder.

Sample size consideration: Sample size calculation provides the required number of participants to be recruited in a new study to detect true differences in the target population if they exist. Sample size calculation is based on three facets: an estimated difference in group sizes, the probability of α (Type I) and β (Type II) errors chosen based on the nature of the treatment or intervention, and the estimated variability (interval data) or proportion of the outcome (nominal data) [ 17 - 18 ]. The clinically important effect sizes are determined based on expert consensus or patients’ perception of benefit. Value and economic consideration have increasingly been included in sample size estimations. Sample size and the degree to which the sample represents the target population affect the accuracy and generalization of a study’s reported effects.

Pilot study: Pilot studies assess the feasibility of the proposed research procedures on small sample size. Pilot studies test the efficiency of participant recruitment with minimal practice or service interruptions. Pilot studies should not be conducted to obtain a projected effect size for a larger study population because, in a typical pilot study, the sample size is small, leading to a large standard error of that effect size. This leads to bias when projected for a large population. In the case of underestimation, this could lead to inappropriately terminating the full-scale study. As the small pilot study is equally prone to bias of overestimation of the effect size, this would lead to an underpowered study and a failed full-scale study [ 19 ].

The Design of Data Collection

The “perfect” study design in the theoretical phase now faces the practical and realistic challenges of feasibility. This is the step where different methods for data collection are considered, with one selected as the most appropriate based on the theoretical design along with feasibility and efficiency. The goal of this stage is to achieve the highest possible validity with the lowest risk of biases given available resources and existing constraints.

In causal research, data on the outcome and determinants are collected with utmost accuracy via a strict protocol to maximize validity and precision. The validity of an instrument is defined as the degree of fidelity of the instrument, measuring what it is intended to measure, that is, the results of the measurement correlate with the true state of an occurrence. Another widely used word for validity is accuracy. Internal validity refers to the degree of accuracy of a study’s results to its own study sample. Internal validity is influenced by the study designs, whereas the external validity refers to the applicability of a study’s result in other populations. External validity is also known as generalizability and expresses the validity of assuming the similarity and comparability between the study population and the other populations. Reliability of an instrument denotes the extent of agreeableness of the results of repeated measurements of an occurrence by that instrument at a different time, by different investigators or in a different setting. Other terms that are used for reliability include reproducibility and precision. Preventing confounders by identifying and including them in data collection will allow statistical adjustment in the later analyses. In descriptive research, outcomes must be confirmed with a referent standard, and the determinants should be as valid as those found in real clinical practice.

Common designs for data collection include cross-sectional, case-control, cohort, and randomized controlled trials (RCTs). Many other modern epidemiology study designs are based on these classical study designs such as nested case-control, case-crossover, case-control without control, and stepwise wedge clustered RCTs. A cross-sectional study is typically a snapshot of the study population, and an RCT is almost always a prospective study. Case-control and cohort studies can be retrospective or prospective in data collection. The nested case-control design differs from the traditional case-control design in that it is “nested” in a well-defined cohort from which information on the cohorts can be obtained. This design also satisfies the assumption that cases and controls represent random samples of the same study base. Table Table4 4 provides examples of these data collection designs.

Data Collection Designs	Study Title
Cross-sectional	The National Health and Morbidity Survey (NHMS)
	The National Health and Nutrition Examination Survey (NHANES)
Cohort	Framingham Heart Study
	The Malaysian Cohort (TMC) project
Case-control	A Case-Control Study of the Effectiveness of Bicycle Safety Helmets
	Open-Angle Glaucoma and Ocular Hypertension: the Long Island Glaucoma Case-Control Study
Nested case-control	Nurses' Health Study on Plasma Adipokines and Endometriosis Risk
	Physicians' Health Study Plasma Homocysteine and Risk of Myocardial Infarction
Randomized controlled trial	The Women’s Health Initiative
	U.K. Prospective Diabetes Study
Cross-over	Intranasal-agonist in Allergic Rhinitis Published in the Allergy in 2000
	Effect of Palm-based Tocotrienols and Tocopherol Mixture Supplementation on Platelet Aggregation in Subjects with Metabolic Syndrome

Additional aspects in data collection: No single design of data collection for any research question as stated in the theoretical design will be perfect in actual conduct. This is because of myriad issues facing the investigators such as the dynamic clinical practices, constraints of time and budget, the urgency for an answer to the research question, and the ethical integrity of the proposed experiment. Therefore, feasibility and efficiency without sacrificing validity and precision are important considerations in data collection design. Therefore, data collection design requires additional consideration in the following three aspects: experimental/non-experimental, sampling, and timing [ 7 ]:

Experimental or non-experimental: Non-experimental research (i.e., “observational”), in contrast to experimental, involves data collection of the study participants in their natural or real-world environments. Non-experimental researches are usually the diagnostic and prognostic studies with cross-sectional in data collection. The pinnacle of non-experimental research is the comparative effectiveness study, which is grouped with other non-experimental study designs such as cross-sectional, case-control, and cohort studies [ 20 ]. It is also known as the benchmarking-controlled trials because of the element of peer comparison (using comparable groups) in interpreting the outcome effects [ 20 ]. Experimental study designs are characterized by an intervention on a selected group of the study population in a controlled environment, and often in the presence of a similar group of the study population to act as a comparison group who receive no intervention (i.e., the control group). Thus, the widely known RCT is classified as an experimental design in data collection. An experimental study design without randomization is referred to as a quasi-experimental study. Experimental studies try to determine the efficacy of a new intervention on a specified population. Table Table5 5 presents the advantages and disadvantages of experimental and non-experimental studies [ 21 ].

a May be an issue in cross-sectional studies that require a long recall to the past such as dietary patterns, antenatal events, and life experiences during childhood.

Non-experimental	Experimental
Advantages
Quick results are possible	Comparable groups
Relatively less costly	Hawthorne and placebo effects mitigated
No recall bias	Straightforward, robust statistical analysis
No time effects	Convincing results as evidence
Real-life data
Disadvantages
Observed, unobserved, and residual confounding	Expensive
	Time-consuming
	Overly controlled environment
	Loss to follow-up
	Random allocation of potentially harmful treatment may not be ethically permissible

Once an intervention yields a proven effect in an experimental study, non-experimental and quasi-experimental studies can be used to determine the intervention’s effect in a wider population and within real-world settings and clinical practices. Pragmatic or comparative effectiveness are the usual designs used for data collection in these situations [ 22 ].

Sampling/census: Census is a data collection on the whole source population (i.e., the study population is the source population). This is possible when the defined population is restricted to a given geographical area. A cohort study uses the census method in data collection. An ecologic study is a cohort study that collects summary measures of the study population instead of individual patient data. However, many studies sample from the source population and infer the results of the study to the source population for feasibility and efficiency because adequate sampling provides similar results to the census of the whole population. Important aspects of sampling in research planning are sample size and representation of the population. Sample size calculation accounts for the number of participants needed to be in the study to discover the actual association between the determinant and outcome. Sample size calculation relies on the primary objective or outcome of interest and is informed by the estimated possible differences or effect size from previous similar studies. Therefore, the sample size is a scientific estimation for the design of the planned study.

A sampling of participants or cases in a study can represent the study population and the larger population of patients in that disease space, but only in prevalence, diagnostic, and prognostic studies. Etiologic and interventional studies do not share this same level of representation. A cross-sectional study design is common for determining disease prevalence in the population. Cross-sectional studies can also determine the referent ranges of variables in the population and measure change over time (e.g., repeated cross-sectional studies). Besides being cost- and time-efficient, cross-sectional studies have no loss to follow-up; recall bias; learning effect on the participant; or variability over time in equipment, measurement, and technician. A cross-sectional design for an etiologic study is possible when the determinants do not change with time (e.g., gender, ethnicity, genetic traits, and blood groups).

In etiologic research, comparability between the exposed and the non-exposed groups is more important than sample representation. Comparability between these two groups will provide an accurate estimate of the effect of the exposure (risk factor) on the outcome (disease) and enable valid inference of the causal relation to the domain (the theoretical population). In a case-control study, a sampling of the control group should be taken from the same study population (study base), have similar profiles to the cases (matching) but do not have the outcome seen in the cases. Matching important factors minimizes the confounding of the factors and increases statistical efficiency by ensuring similar numbers of cases and controls in confounders’ strata [ 23 - 24 ]. Nonetheless, perfect matching is neither necessary nor achievable in a case-control study because a partial match could achieve most of the benefits of the perfect match regarding a more precise estimate of odds ratio than statistical control of confounding in unmatched designs [ 25 - 26 ]. Moreover, perfect or full matching can lead to an underestimation of the point estimates [ 27 - 28 ].

Time feature: The timing of data collection for the determinant and outcome characterizes the types of studies. A cross-sectional study has the axis of time zero (T = 0) for both the determinant and the outcome, which separates it from all other types of research that have time for the outcome T > 0. Retrospective or prospective studies refer to the direction of data collection. In retrospective studies, information on the determinant and outcome have been collected or recorded before. In prospective studies, this information will be collected in the future. These terms should not be used to describe the relationship between the determinant and the outcome in etiologic studies. Time of exposure to the determinant, the time of induction, and the time at risk for the outcome are important aspects to understand. Time at risk is the period of time exposed to the determinant risk factors. Time of induction is the time from the sufficient exposure to the risk or causal factors to the occurrence of a disease. The latent period is when the occurrence of a disease without manifestation of the disease such as in “silence” diseases for example cancers, hypertension and type 2 diabetes mellitus which is detected from screening practices. Figure Figure3 3 illustrates the time features of a variable. Variable timing is important for accurate data capture.

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The Design of Statistical Analysis

Statistical analysis of epidemiologic data provides the estimate of effects after correcting for biases (e.g., confounding factors) measures the variability in the data from random errors or chance [ 7 , 16 , 29 ]. An effect estimate gives the size of an association between the studied variables or the level of effectiveness of an intervention. This quantitative result allows for comparison and assessment of the usefulness and significance of the association or the intervention between studies. This significance must be interpreted with a statistical model and an appropriate study design. Random errors could arise in the study resulting from unexplained personal choices by the participants. Random error is, therefore, when values or units of measurement between variables change in non-concerted or non-directional manner. Conversely, when these values or units of measurement between variables change in a concerted or directional manner, we note a significant relationship as shown by statistical significance.

Variability: Researchers almost always collect the needed data through a sampling of subjects/participants from a population instead of a census. The process of sampling or multiple sampling in different geographical regions or over different periods contributes to varied information due to the random inclusion of different participants and chance occurrence. This sampling variation becomes the focus of statistics when communicating the degree and intensity of variation in the sampled data and the level of inference in the population. Sampling variation can be influenced profoundly by the total number of participants and the width of differences of the measured variable (standard deviation). Hence, the characteristics of the participants, measurements and sample size are all important factors in planning a study.

Statistical strategy: Statistical strategy is usually determined based on the theoretical and data collection designs. Use of a prespecified statistical strategy (including the decision to dichotomize any continuous data at certain cut-points, sub-group analysis or sensitive analyses) is recommended in the study proposal (i.e., protocol) to prevent data dredging and data-driven reports that predispose to bias. The nature of the study hypothesis also dictates whether directional (one-tailed) or non-directional (two-tailed) significance tests are conducted. In most studies, two-sided tests are used except in specific instances when unidirectional hypotheses may be appropriate (e.g., in superiority or non-inferiority trials). While data exploration is discouraged, epidemiological research is, by nature of its objectives, statistical research. Hence, it is acceptable to report the presence of persistent associations between any variables with plausible underlying mechanisms during the exploration of the data. The statistical methods used to produce the results should be explicitly explained. Many different statistical tests are used to handle various kinds of data appropriately (e.g., interval vs discrete), and/or the various distribution of the data (e.g., normally distributed or skewed). For additional details on statistical explanations and underlying concepts of statistical tests, readers are recommended the references as cited in this sentence [ 30 - 31 ].

Steps in statistical analyses: Statistical analysis begins with checking for data entry errors. Duplicates are eliminated, and proper units should be confirmed. Extremely low, high or suspicious values are confirmed from the source data again. If this is not possible, this is better classified as a missing value. However, if the unverified suspicious data are not obviously wrong, they should be further examined as an outlier in the analysis. The data checking and cleaning enables the analyst to establish a connection with the raw data and to anticipate possible results from further analyses. This initial step involves descriptive statistics that analyze central tendency (i.e., mode, median, and mean) and dispersion (i.e., (minimum, maximum, range, quartiles, absolute deviation, variance, and standard deviation) of the data. Certain graphical plotting such as scatter plot, a box-whiskers plot, histogram or normal Q-Q plot are helpful at this stage to verify data normality in distribution. See Figure Figure4 4 for the statistical tests available for analyses of different types of data.

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Once data characteristics are ascertained, further statistical tests are selected. The analytical strategy sometimes involves the transformation of the data distribution for the selected tests (e.g., log, natural log, exponential, quadratic) or for checking the robustness of the association between the determinants and their outcomes. This step is also referred to as inferential statistics whereby the results are about hypothesis testing and generalization to the wider population that the study’s sampled participants represent. The last statistical step is checking whether the statistical analyses fulfill the assumptions of that particular statistical test and model to avoid violation and misleading results. These assumptions include evaluating normality, variance homogeneity, and residuals included in the final statistical model. Other statistical values such as Akaike information criterion, variance inflation factor/tolerance, and R2 are also considered when choosing the best-fitted models. Transforming raw data could be done, or a higher level of statistical analyses can be used (e.g., generalized linear models and mixed-effect modeling). Successful statistical analysis allows conclusions of the study to fit the data.

Bayesian and Frequentist statistical frameworks: Most of the current clinical research reporting is based on the frequentist approach and hypotheses testing p values and confidence intervals. The frequentist approach assumes the acquired data are random, attained by random sampling, through randomized experiments or influences, and with random errors. The distribution of the data (its point estimate and confident interval) infers a true parameter in the real population. The major conceptual difference between Bayesian statistics and frequentist statistics is that in Bayesian statistics, the parameter (i.e., the studied variable in the population) is random and the data acquired is real (true or fix). Therefore, the Bayesian approach provides a probability interval for the parameter. The studied parameter is random because it could vary and be affected by prior beliefs, experience or evidence of plausibility. In the Bayesian statistical approach, this prior belief or available knowledge is quantified into a probability distribution and incorporated into the acquired data to get the results (i.e., the posterior distribution). This uses mathematical theory of Bayes’ Theorem to “turn around” conditional probabilities.

The goal of research reporting is to present findings succinctly and timely via conference proceedings or journal publication. Concise and explicit language use, with all the necessary details to enable replication and judgment of the study applicability, are the guiding principles in clinical studies reporting.

Writing for Reporting

Medical writing is very much a technical chore that accommodates little artistic expression. Research reporting in medicine and health sciences emphasize clear and standardized reporting, eschewing adjectives and adverbs extensively used in popular literature. Regularly reviewing published journal articles can familiarize authors with proper reporting styles and help enhance writing skills. Authors should familiarize themselves with standard, concise, and appropriate rhetoric for the intended audience, which includes consideration for journal reviewers, editors, and referees. However, proper language can be somewhat subjective. While each publication may have varying requirements for submission, the technical requirements for formatting an article are usually available via author or submission guidelines provided by the target journal.

Research reports for publication often contain a title, abstract, introduction, methods, results, discussion, and conclusions section, and authors may want to write each section in sequence. However, best practices indicate the abstract and title should be written last. Authors may find that when writing one section of the report, ideas come to mind that pertains to other sections, so careful note taking is encouraged. One effective approach is to organize and write the result section first, followed by the discussion and conclusions sections. Once these are drafted, write the introduction, abstract, and the title of the report. Regardless of the sequence of writing, the author should begin with a clear and relevant research question to guide the statistical analyses, result interpretation, and discussion. The study findings can be a motivator to propel the author through the writing process, and the conclusions can help the author draft a focused introduction.

Writing for Publication

Specific recommendations on effective medical writing and table generation are available [ 32 ]. One such resource is Effective Medical Writing: The Write Way to Get Published, which is an updated collection of medical writing articles previously published in the Singapore Medical Journal [ 33 ]. The British Medical Journal’s Statistics Notes series also elucidates common and important statistical concepts and usages in clinical studies. Writing guides are also available from individual professional societies, journals, or publishers such as Chest (American College of Physicians) medical writing tips, PLoS Reporting guidelines collection, Springer’s Journal Author Academy, and SAGE’s Research methods [ 34 - 37 ]. Standardized research reporting guidelines often come in the form of checklists and flow diagrams. Table Table6 6 presents a list of reporting guidelines. A full compilation of these guidelines is available at the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network website [ 38 ] which aims to improve the reliability and value of medical literature by promoting transparent and accurate reporting of research studies. Publication of the trial protocol in a publicly available database is almost compulsory for publication of the full report in many potential journals.

No.	Reporting Guidelines and Checklists
	CONSORT - CONsolidated Standards Of Reporting Trials
	A 25-item checklist for reporting of randomized controlled trials. There are appropriate extensions to the CONSORT statement due to variations in the standard trial methodology such as different design aspects (e.g., cluster, pragmatic, non-inferiority and equivalence trials), interventions (e.g., herbals) and data (e.g., harms, including the extension for writing abstracts)
	SPIRIT - Standard Protocol Items: Recommendations for Interventional Trials
	A 33-item checklist for reporting protocols for randomized controlled trials
	COREQ - COnsolidated criteria for REporting Qualitative research
	A 32-item checklist for reporting qualitative research of interviews and focus groups
	STARD - STAndards for the Reporting of Diagnostic accuracy studies
	A 25-item checklist for reporting of diagnostic accuracy studies
	PRISMA - Preferred Reporting Items for Systematic reviews and Meta-Analyses
	A 27-item checklist for reporting of systematic reviews
	PRISMA-P - Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols
	A 17-item checklist for reporting of systematic review and meta-analysis protocols
	MOOSE - Meta-analysis Of Observational Studies in Epidemiology
	A 35-item checklist for reporting of meta-analyses of observational studies
	STROBE - STrengthening the Reporting of OBservational studies in Epidemiology
	For reporting of observational studies in epidemiology
		Checklist for cohort, case-control and cross-sectional studies (combined)
		Checklist for cohort studies
		Checklist for case-control studies
		Checklist for cross-sectional studies
	Extensions of the STROBE statement
	STROME-ID - STrengthening the Reporting Of Molecular Epidemiology for Infectious Diseases
	A 42-item checklist
	STREGA - STrengthening the REporting of Genetic Associations
	A 22-item checklist for reporting of gene-disease association studies
	CHEERS - Consolidated Health Economic Evaluation Reporting Standards
	A 24-item checklist for reporting of health economic evaluations

Graphics and Tables

Graphics and tables should emphasize salient features of the underlying data and should coherently summarize large quantities of information. Although graphics provide a break from dense prose, authors must not forget that these illustrations should be scientifically informative, not decorative. The titles for graphics and tables should be clear, informative, provide the sample size, and use minimal font weight and formatting only to distinguish headings, data entry or to highlight certain results. Provide a consistent number of decimal points for the numerical results, and with no more than four for the P value. Most journals prefer cell-delineated tables created using the table function in word processing or spreadsheet programs. Some journals require specific table formatting such as the absence or presence of intermediate horizontal lines between cells.

Decisions of authorship are both sensitive and important and should be made at an early stage by the study’s stakeholders. Guidelines and journals’ instructions to authors abound with authorship qualifications. The guideline on authorship by the International Committee of Medical Journal Editors is widely known and provides a standard used by many medical and clinical journals [ 39 ]. Generally, authors are those who have made major contributions to the design, conduct, and analysis of the study, and who provided critical readings of the manuscript (if not involved directly in manuscript writing).

Picking a target journal for submission

Once a report has been written and revised, the authors should select a relevant target journal for submission. Authors should avoid predatory journals—publications that do not aim to advance science and disseminate quality research. These journals focus on commercial gain in medical and clinical publishing. Two good resources for authors during journal selection are Think-Check-Submit and the defunct Beall's List of Predatory Publishers and Journals (now archived and maintained by an anonymous third-party) [ 40 , 41 ]. Alternatively, reputable journal indexes such as Thomson Reuters Journal Citation Reports, SCOPUS, MedLine, PubMed, EMBASE, EBSCO Publishing's Electronic Databases are available areas to start the search for an appropriate target journal. Authors should review the journals’ names, aims/scope, and recently published articles to determine the kind of research each journal accepts for publication. Open-access journals almost always charge article publication fees, while subscription-based journals tend to publish without author fees and instead rely on subscription or access fees for the full text of published articles.

Conclusions

Conducting a valid clinical research requires consideration of theoretical study design, data collection design, and statistical analysis design. Proper study design implementation and quality control during data collection ensures high-quality data analysis and can mitigate bias and confounders during statistical analysis and data interpretation. Clear, effective study reporting facilitates dissemination, appreciation, and adoption, and allows the researchers to affect real-world change in clinical practices and care models. Neutral or absence of findings in a clinical study are as important as positive or negative findings. Valid studies, even when they report an absence of expected results, still inform scientific communities of the nature of a certain treatment or intervention, and this contributes to future research, systematic reviews, and meta-analyses. Reporting a study adequately and comprehensively is important for accuracy, transparency, and reproducibility of the scientific work as well as informing readers.

Acknowledgments

The author would like to thank Universiti Putra Malaysia and the Ministry of Higher Education, Malaysia for their support in sponsoring the Ph.D. study and living allowances for Boon-How Chew.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

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