Characteristics of the sample.
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",isbn:"978-1-83969-561-2",printIsbn:"978-1-83969-560-5",pdfIsbn:"978-1-83969-562-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"65f2a1fef9c804c29b18ef3ac4a35066",bookSignature:"Dr. Luis Loures",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10756.jpg",keywords:"Urban Processes, Urban Patterns, Redevelopment Strategies, Landscape, Land Transformation, Urban Models, Urban Evolution, Urban Organisation, Legislation, Sustainable Development, Green Infrastructure, Regional Planning",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2021",dateEndSecondStepPublish:"March 22nd 2021",dateEndThirdStepPublish:"May 21st 2021",dateEndFourthStepPublish:"August 9th 2021",dateEndFifthStepPublish:"October 8th 2021",remainingDaysToSecondStep:"14 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Loures has worked on pioneering research on circular planning applied to post-industrial landscape redevelopment. Since he graduated he has published several peer-reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA) and at the University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"108118",title:"Dr.",name:"Luis",middleName:null,surname:"Loures",slug:"luis-loures",fullName:"Luis Loures",profilePictureURL:"https://mts.intechopen.com/storage/users/108118/images/system/108118.png",biography:"Luís Loures is a Landscape Architect and Agronomic Engineer, Vice-President of the Polytechnic Institute of Portalegre, who holds a Ph.D. in Planning and a Post-Doc in Agronomy. Since he graduated, he has published several peer reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA), and at University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).\nDuring his academic career he had taught in several courses in different Universities around the world, mainly regarding the fields of landscape architecture, urban and environmental planning and sustainability. Currently, he is a researcher both at VALORIZA - Research Centre for Endogenous Resource Valorization – Polytechnic Institute of Portalegre, and the CinTurs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve where he is a researcher on several financed research projects focusing several different investigation domains such as urban planning, landscape reclamation and urban redevelopment, and the use of urban planning as a tool for achieving sustainable development.",institutionString:"Polytechnic Institute of Portalegre",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Polytechnic Institute of Portalegre",institutionURL:null,country:{name:"Portugal"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"205697",firstName:"Kristina",lastName:"Kardum Cvitan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/205697/images/5186_n.jpg",email:"kristina.k@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"62578",title:"DNA Polymorphisms: DNA-Based Molecular Markers and Their Application in Medicine",doi:"10.5772/intechopen.79517",slug:"dna-polymorphisms-dna-based-molecular-markers-and-their-application-in-medicine",body:'Genetic polymorphism is the existence of at least two variants with respect to gene sequences, chromosome structure, or a phenotype (gene sequences and chromosomal variants are seen at the frequency of 1% or higher), typical of a polymorphism, rather than the focus being on rare variants [1].
The human genome comprises 6 billion nucleotides of DNA packaged into two sets of 23 chromosomes, one set inherited from each parent. The probability of polymorphic DNA in humans is great due to the relatively large size of human genome. Genomic variability includes a wide range of variations from single base pair change, many base pairs, and repeated sequences [2].
Single nucleotide polymorphisms are the most common type of genetic variations in humans [3], due to their abundance across the human genome; single nucleotide polymorphisms (SNPs) have become important genetic markers for mapping human diseases, population genetics, and evolutionary studies. SNPs have become very important since technologies for DNA sequencing have become feasible and widely available. Advance continues at a rapid rate [4].
A major step forward in genome identification is the discovery of about 30–90% of the genome which is constituted by regions of repetitive DNA which are highly polymorphic in nature [5]. Polymorphic tandem repeated sequences have emerged as important genetic markers and initially, variable number tandem repeats (VNTRs) were used in DNA fingerprinting. In recent years, evidence has been accumulated for the involvement of VNTR repeats in a wide spectrum of pathological states [6].
Throughout the past years, scientists have believed that genes strictly came in two copies in a genome. However, with the recent advancement in molecular technology, discoveries have revealed substantial segments of DNA, ranging in size from thousands to millions of DNA bases that could vary in copy number. Such copy number variations (or CNVs) encompass gene copies, newly discovered CNVs are important sources of genomic diversity [7, 8].
The development and use of DNA-based molecular markers is one of the most significant developments in the field of molecular genetics that facilitate the study of genetic variations in health and diseases [5].
This chapter reviews the DNA-based genetic markers and their application in medicine, with a particular emphasis on common DNA-based genetic markers, including single nucleotide polymorphisms and short tandem repeats (STRs).
Genomic variability at DNA level can be present in many forms including: single nucleotide polymorphisms, variable number of tandem repeats (e.g., mini- and microsatellites), transposable elements (e.g., Alu repeats), structural alterations, and copy number variations. It can occur in the nucleus or mitochondria. Two major sources: (1) mutations that may result as chance processes or have been induced by external agents such as radiation and (2) recombination. Once formed, it can be inherited, allowing its inheritance to be tracked from parent to child [3].
The genomes of humans may be divided into different parts based on known functional properties; the coding and noncoding regions mostly do not code for protein [2, 9]. The coding regions contain DNA sequences which determine primarily the amino acid sequences of the proteins for which they code. Noncoding DNA generally containing DNA sequences with no function has not yet been discovered or possibly no function exists [10]; such sequences may be either single copy or exist as multiple copies called repetitive DNA [10]. Indeed, regions of DNA that do not code for proteins tend to have more polymorphisms. Recently, there has been substantial progress in understanding genome content which centered on discovered protein-coding genes which considered a functional DNA sequence moving away for discoveries of many repeat families, and various copy number variations encompass gene copies leading to dosage imbalance that plays an important role in genome structure, evolution, and diversity [11, 12]. “The Human Genome Project has revealed that humans have only 20,000–30,000 structural genes (protein-coding genes) (International Human Genome Sequencing Consortium, 2004)” [13].
Single base change is “high-density natural sequence variations in human genome” [14]. SNPs are mostly formed when errors occur (substitution, insertion and deletion). SNPs are prominent sources of variation in human genome and serve as excellent genetic markers. Some regions of the genome are richer in SNPs than others. SNPs may occur within gene sequences or in intergenic sequences. SNPs mostly are located in noncoding regions of the genome and have mostly no direct known impact on the phenotype of an individual but their role till now remains elusive, and depending on where SNPs occurs, it might have different consequences at the phenotypic level [3].
It is a type of DNA variation in which a specific nucleotide sequence of various lengths ranging from one to several 100 base pairs is inserted or deleted. Indels are widely spread across the genome. Some authors consider one base pair as SNPs or repeat insertion/deletion as indels.
DNA repeats can be classified as interspersed repeats or tandem repeats. This can comprise over two-thirds of the human genome [15]. Interspersed repeats are dispersed across the genome within gene sequences or intergenic and include retro (pseudo) genes and transposons. Tandem repeats or variable number tandem repeats (≥2 bp in length) that are adjacent to each [16] can involve as few as two copies or many thousands of copies. Centromeres and telomeres largely comprise tandem repeats. Despite increasing evidence on the functionality of DNA repeats, their biologic role is still elusive and under frequent debate [11]. Tandem repeats are organized in a head-to-tail orientation; based on the size of each repeat unit, satellite repeats can be further divided into macrosatellites, minisatellites, and microsatellites [17]. Some of these repeats are described as follows: macrosatellites, with sequence repeats longer than 100 bp, are the largest of the tandem DNA repeats, located on one or multiple chromosomes [11], minisatellites, stretches of DNA, are characterized by moderate length patterns, 10–100 bp usually less than 50 bp [9, 18], and microsatellites also known as short tandem repeats (STRs) repeat units of less than 10 bp, [3].
Structural and copy number variations (CNVs) are another frequent source of genome variability [6, 19, 20]. The term CNVs therefore encompasses previously introduced terms such as large-scale copy number variants (LCVs) [19], copy number polymorphisms (CNPs) [20], and intermediate-sized variants (ISVs) [21]. Some currently used terms are structural variations; a genomic alteration (e.g., an inversion) that involves segments of DNA > 1 kb, copy number polymorphisms; a duplication or deletion event involving >1 kb of DNA [22], intermediate-sized structural variant; and a structural variant that is ∼8–40 kb in size, this can refer to a CNVs or a balanced structural rearrangement (e.g., an inversion) [21].
The development and use of molecular methods for the detection of DNA molecular markers is one of the most significant progresses in the field of molecular genetics. Mapping the human genome requires a set of genetic markers to which we can relate the position of genes. Some of these markers are genes, others SNPs and VNTRs. Molecular markers can be used to mark in genomes for various purposes such as mapping human diseases, pharmacogenetics, and human identification.
Single base pair change leads to single nucleotide variant, probably accounting for many genetic conditions caused by single gene or multiple genes. SNPs represent the major source of human genomic variability. Due to the lack of knowledge on exact SNP number, it is difficult to give a direct estimate of the number of the SNPs in the human genome but in different public and private data bases, more than 5 million have been recorded and about 4 million validated [23]. “The data from the Human Genome project revealed that that human nucleotide sequence differs every 1000-1500 bases from one individual to another” [24]. “The SNP Map working group observed that two haploid genomes differ at 1 nucleotide per 1331 bp”. Over 60,000 however are within genes and some of them associated with diseases [2].
Single nucleotide polymorphisms within protein-coding regions either synonymous polymorphisms; those that do not have any effect on the organism and are said to be selectively silent as the substitution causes no amino acid change in the protein produced (silent mutation) or nonsynonymous substitution results in change in encoded amino acids either missense mutation; change the protein through codon alteration or nonsense mutation results in a chain termination codon [3].
Single nucleotide polymorphisms within a coding sequence cause genetic diseases including sickle cell anemia. SNPs responsible for a disease can also occur in any genetic region that can eventually affect the expression activity of genes, for example, in promoter regions. SNPs in the noncoding region of the gene, though their effect is still debatable, most of the genome mostly consists of regulatory elements that control gene expression, but these regions have remained largely unexplored in clinical diagnostics due to the high cost of whole genome sequencing and interpretive challenges. Clinical diagnostic sequencing currently focuses on identifying causal mutations in the exome, where most disease-causing mutations are known to occur.
Another important group of SNPs is the one that alters the primary structure of a protein involved in drug metabolism; these SNPs are targets for pharmacogenetics studies.
However, some SNPs are not causative, some SNPs are in close association with, and therefore segregate with, a disease-causing sequence so, the presence of SNP correlates with the presence or an increased risk of developing the disease; these SNPs are useful in diagnostics, disease prediction, and other applications [3].
Single nucleotide polymorphisms can be used as genetic markers for constructing high genetic maps and to carry out association studies related to diseases because of their abundance and the availability of high throughput analysis technologies. SNPs have become an important application in the development and research of genetic markers [14].
There are numerous strategies that can be implemented to new single nucleotide variant (SNVs) discoveries; the most common and well-known method is by direct sequencing and in comparison to a puplic or other sequence date base [25, 26] or locus specific amplification of target genomic region followed by sequence comparison [27, 28]; prescreening prior to sequence determination is needed. SNV detection encompasses two broad areas: (1) scanning DNA sequences for previously unknown polymorphisms and (2) screening (genotyping) individuals for known polymorphisms. Scanning for new SNVs can be further classified to two different types of approaches, the first one being the global (or random approach) and the other one being the regional (targeted approach) [14]. There are certain methods which have been developed for using SNVs randomly in the genome; “such as representation shotgun sequencing [14, 29], primer-ligation-mediated PCR [14, 30] and degenerate oligonucleotide–primed PCR” [14, 31].
Haplotypes are groups of SNPs that are generally inherited together. Haplotypes can have stronger correlations with diseases or other phenotypic effects compared with individual SNPs and may therefore provide increased diagnostic accuracy in some cases [32].
Microsatellites are short tandem repeats (STRs), repeat units, or motifs of less than 10 bp; because of high variability, microsatellite loci are often used in forensics, population genetics, and genetic genealogy. Significant associations were demonstrated between microsatellite variants and many diseases [15].
Depending on the search algorithm, there are approximately 700,000–1,000,000 microsatellite loci which are 2–6 bp long in the human reference genome [33, 34]. Di- and tetra-nucleotides constitute about 75% of microsatellites, with the remaining loci containing tri-, penta, and hexanucleotide. Within genes, STRs are nonrandomly distributed across protein-coding sequences, untranslated regions (UTRs), and introns. STRs containing dinucleotide repeat units that are much more abundant in the regulatory or UTR regions than in other genomic regions. In the coding regions of the genes, repeats mostly have either trimeric or hexameric repeat unit, likely as a result of selection against frameshift mutations [34, 35]. “The mutation rates of STRs often lie between 103 and 106 per cell generation which is 10- to 105-fold higher than the average mutation rates observed in nonrepeated regions of the genome”[36, 37].
“Polymorphism of tandem repeats within protein-coding regions reveals that tandem repeat variation is an important source of variation in many proteins, many of this variation is of significant impact on protein function. Tandem repeats has been associated with a number of diseases and phenotypic conditions, changes in the protein products of genes, leading to diseases, other tandem repeat polymorphisms in noncoding regions are known to modify function through their impact on gene regulation”. “These polymorphisms can arise from events such as unequal crossover, replication slippage or double-strand break repair” [38].
Variations in the STR length play a significant role in modulating gene expression and STRs are likely to be general regulatory elements; regulatory STRs manifest significant polymorphism because of their high intrinsic mutation rate [15].
There are examples for distinctive phenotypic changes and diseases that are directly associated with the increases or decreases of microsatellite repeat arrays; for example, considering Huntington disease gene, triplet nucleotide mutations, the mutation that causes the disease, is an expansion of CAG repeats from the normal range of 11–14 copies to abnormal range of at least 38 copies. The extra CAG repeats that causes extra glutamine is produced [9] and there are more than 40 neurological diseases in humans, such as spinocerebellar ataxia with polyglutamine tracts, which are caused by microsatellite motif length changes in trinucleotide arrays [39].
Testing candidate genes for polymorphisms in exons, promoters, splice sites, or other regulatory regions will have to be done using SNP testing, because it is the most common polymorphisms and more likely responsible for phenotypic variations. For complex phenotypic traits and candidate loci, single-loci SNP analyses present less information due to the bi-allelic nature of the markers, as compared to the multi-allelic microsatellites. However, performing haplotype frequency may improve the accuracy [40]. Recently, polymorphic tandem repeated sequences and coy number variations have emerged as important sources of genomic diversity that facilitate the study of genetic variations in health and diseases.
Different forms of DNA-based molecular markers can be tracked using a variety of techniques. Some of these techniques include RFLPs with Southern blots and polymerase chain reactions (PCRs). Recently great advances in methodology for DNA polymorphisms detection using real time PCR, hybridization techniques using DNA microarray chips, genome sequencing each technique has its own advantage and disadvantage.
DNA digestion with restriction enzyme endonuclease cuts DNA at a specific sequence pattern known as a restriction endonuclease recognition site. Thus, the alleles differ in length and can be distinguished by gel electrophoresis, which can arise from a number of genetic events including point mutation in restriction sites, mutation that creates a new restriction site, insertion, deletion, and repeated sequences. The first polymorphic RFLP was described in 1980. RFLPs were the original DNA targets used for human identification, parentage testing, and gene mapping.
The method of hybridization of DNA with probes is called Southern blotting, after the name of the inventor, Southern [41]. RFLP requires relatively large amounts of DNA. Hence, it cannot be performed with the samples degraded by environmental factors and also takes longer time to get the results [42, 43]. PCR-RFLP is now replaced to avoid using Southern blot.
In-vitro amplification of particular DNA sequences with the help of specifically chosen primers and DNA polymerase enzyme is done. The amplified fragments are separated electrophonically and detected by different staining methods. Real-time PCR useful modification of PCR can detect polymorphisms by various methodologies using real-time PCR chemistries, for example, TaqMan assay or molecular beacons.
Genomic array technology is a type of hybridization analysis allowing simultaneous study of large numbers of targets or samples. In 1987, macroarray evolved into the microarray. Tens of thousands of targets can be screened simultaneously in a very small area. Automated depositing systems (arrayers) can place thousands of spots on glass substrate of the size of a microscope slide (chip) with spotting representative sequences of each gene in triplicate, simultaneous screening of the entire human genome on a single chip. This technique facilitates the process of identifying specific homozygous and heterozygous alleles, by comparing the disparity of hybridization of the target DNA with each redundant probe. Microarray is also used to characterize genetic diversity and drug responses, to identify new drug targets, and to assess the toxicological properties of chemicals and pharmaceuticals [44].
Since technologies for rapid DNA sequencing have become available they are now widely used. There is a great progression for the detection of single nucleotide variants (SNVs) by direct sequencing, but intermediate-sized (from 50 bp to 50 kb) structural variants (SVs) remain a challenge. Such variants are too small to detect with cytogenetic methods but too large to reliably discover with short-read DNA sequencing. Recent high-quality genome assemblies using long-read sequencing have revealed that each human genome has approximately 20,000 structural variants, spanning 10 million base pairs, more than twice the number of bases affected by SNVs. New long-read sequencing approaches are needed to meet this challenge, as short-read sequencing technologies only detect 20% of the SVs present in the human genome [45, 46, 47, 48].
DNA-based molecular markers are such powerful tools for mapping human diseases and discover many multifactorial diseases and disorders.
Genetic mapping and linkage: The mapping of the human genome has made possible to develop a haplotype map in order to better define human SNV variability. The haplotype map or HapMap will be a tool for the detection of human genetic variation that can affect health and diseases [23]. The HapMap project is far more useful because it will reduce the number of SNVs required to examine the entire genome for association with a phenotype or diseases from the 10 million SNPs that are expected to exist to approximately tag 500,000 SNPs [38]. The first large-scale effort to produce a human genetic map was performed mainly using RFLP; other several projects are underway to identify more markers in humans and to make this data publicly available to scientists worldwide. Many groups that are involved in these massive efforts through DNA polymorphisms discovery resource include the SNP consortium (TSC)
“The HapMap Project (
The identification of genes affecting complex trait is a very difficult task. For many complex traits, the observable variation is quantitative, and loci affecting such traits are generally termed quantitative trait loci (QTL). (SNVs) can be used as genetic markers for constructing high-density genetic maps and to carry out association studies related to complex traits and diseases [14].
Individual response to a drug is governed by many factors such as genetics, age, sex, environment, and disease. The influence of genetic factors on the response of a drug is a known fact. Polymorphic STRs, together with SNPs and CNVs, can explain variability in response to pharmacotherapy because of their prevalence in the human genome and their functional role as regulators of gene expression and its applications. Pharmacogenetics is the study of the influence of genetics factors on drug response and metabolism. The science of pharmacogenetics when applied can be used to evade adverse drug reactions, predict toxicity and therapeutic failure, and refine therapeutic efficiency and improve clinical outcomes [53].
Establishing an individual’s identity is one of the uses of DNA sequence information that highlights uniqueness of a particular sample [5], also known as genetic fingerprinting; DNA typing and DNA profiling are molecular genetic methods that enable the identification of individuals using hair, blood, semen, or other biological samples, based on unique patterns in their DNA. This uniqueness in each individual is the basis of human identification at the DNA level, forensic identification, determination of genetic variation, determination of family relationship, and one important instance is identifying good genetic matches for organ or marrow donation. When first described in 1984 by British scientist Alec Jeffreys, the technique used was minisatellites; these sequences are unique to each individual, with the exception of identical twins. Different DNA fingerprinting methods exist, using either restriction fragment length polymorphism (RFLP) or PCR or both. More than 200 RFLP loci have been described in human DNA. Initially, forensic medicine used minisatellite testing; however, this method requires a large amount of material and yield low-quality results especially when only little amount of materials are available. Nowadays, in most forensic samples, the study of DNA is usually performed by microsatellite analysis. The most useful microsatellite for human identification is those with a greater number of alleles, smaller size, higher frequency of heterozygotes (higher than 90%), and low frequency of mutations [43]. Among others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis [40]. Each person has some STRs that were inherited from the father and some from mother, useful in paternity testing but however no person has STRs that are identical to those of either parent. The uniqueness of an individual’s STR provides the scientific marker of identity and hence is helpful in forensic identification [54]. Genomic and mitochondrial are two types of DNA which are used in forensic sciences. The genomic DNA is found in the nucleus of each cell in the human body and represents a DNA source for most forensic applications. Mitochondrial DNA (mt DNA) is another source of material that can be used; various biological samples such as hair, bones, and teeth that lack nucleate cellular materials can be analyzed with mt DNA [43, 55].
“Majority of the length of the human Y chromosome is inherited as a single block in linkage from father to male offspring as a haploid entity. DNA genetic markers on the human Y chromosome are valuable tools for understanding human evolution, migration and for tracing relationships among males” [43, 56]. “Chromosome X specific STRs is used in the identification and the genomic studies of different ethnic groups worldwide, because the small size of X-chromosome STR alleles; about 100–350 nucleotides, it is relatively easy to be amplified and detected with high sensitivity” [43].
DNA typing becomes the method of choice for engraftment monitoring, donor cells are examined by following donor polymorphisms in the recipient blood and bone marrow. Although RFLP can efficiently differentiate donor and recipient cells, the detection of RFLP requires the use of southern blot methods, which is too labor intensive and has limited sensitivity for this application, in comparison with small minisatellites or microsatellites that are easily detected by PCR amplification, because of increased rapidity and the 0.5–1% sensitivity achievable with PCR. Sensitivity can be raised to 0.01% using Y-STR, but this approach is limited to that transplant from sex mismatched donor recipient pairs preferably from a female donor to a male recipient [2].
Nowadays, DNA fingerprinting is used as a tool for designing “personalized” medical treatments for cancer patients.
Single nucleotide polymorphisms (SNPs) have become an important application in the development and research of genetic diseases or other phenotypic traits. Haplotypes are groups of SNPs that are generally inherited together. Haplotypes can have stronger correlations with diseases or other phenotypic effects compared with individual SNPs and may therefore provide increased diagnostic accuracy in some cases.
Polymorphic tandem repeated sequences have emerged as important genetic markers and initially, variable number tandem repeats (VNTRs) were used in DNA fingerprinting; in recent years, evidence has been accumulated for the involvement of VNTR repeats in a wide spectrum of pathological states.
The new global CNV map will transform medical research in four main areas: detection for genes underlying common diseases, study of familial genetic conditions, exclude variation found in unaffected individuals, helping researchers to target the region that might be involved and the data generated will also contribute to a more accurate and complete human genome reference sequence used by all biomedical scientists. Currently, approximately 2000 CNVs have been described; there could be thousands more CNVs in the human population. About 100 CNVs were detected in each genome tested with the average size being 250,000 bases (an average gene is 60,000 bases). With advanced molecular technologies more CNVs will be discovered and more DNA samples from worldwide populations are examined.
Recently, there has been substantial progress in understanding genome content which centered on protein-coding genes which considered a functional DNA sequence moving away for many discoveries, many repeat families, and various copy number variations that play an important role in genome structure, evolution, and diversity. Additional efforts are being placed to develop strategies that would overcome the obstacles in alignment next-generation sequencing data. “Future precision medicine efforts will direct to connect genotypes to phenotypes and distinguish common, from rare or potentially disease linked variants. New long-read sequencing approaches are needed to meet this challenge.”
Other important applications of genetic polymorphism knowledge are improving health care through gene therapy, discovery of new drugs and drug targets, and upgradation of the discovery processes with advanced technologies.
Advances in molecular technologies, DNA sequencing technology, and microarray, coupled with novel, efficient computational analysis tools, have made it possible to analyze sequence-based experimental data, more discoveries, and development at a rapid rate.
The author declares that there is no conflict of interest.
The abysmal rate of accrual to clinical trials, particularly among members of minority and underserved populations, has impeded medical and scientific progress [1]. Ironically, when members of marginalized populations do not participate in numbers that allow the medical community to draw conclusions about the efficacy of new treatments for members of these communities, health disparities are deepened further [2]. This makes the participation of members of marginalized communities in clinical trials and research studies increasingly urgent.
There is growing evidence that the communication behaviors exhibited by medical and nonmedical professionals tasked with approaching and consenting patients impacts eventual enrollment [3, 4, 5, 6]. Most research on clinical trial communication has focused on general guidelines for communication practice. These guidelines include making sure that the type and amount of information are appropriate for the patient [7], using plain language to explain a trial [5, 8, 9] and being open to answering potential participants’ questions [3, 8, 10]. Additionally, recruiters are exhorted to be “warm” and respectful with patients [8, 11].
It is important, however, to examine the specific communication behaviors that lead to more effective recruitment, consent, and retention. A study of 63 medical professionals in two diverse U.S. cities indicates that both verbal and nonverbal communication practices support effective recruitment and consent processes [12, 13, 14, 15]. Specific verbal communication behaviors that are associated with effective patient recruitment and consent include translating and simplifying information through the use of lay language and examples; reframing information through the use of metaphors, analogies, and storytelling; balancing discussions of risks with benefits; and encouraging potential participants to ask questions [12].
Nonverbal communication behaviors may be even more important, given the central role of nonverbal communication in the process of meaning generation [16]. However, this topic has received little attention by researchers studying factors that impact clinical trial accrual. In the recruitment and consent process, nonverbal communication behaviors that appear to be particularly important include the ability to “read” patients’ state of mind before approaching them to participate in a study; the willingness to adapt to a patients’ mood and communication preferences; mirroring patients’ body posture, tone, and rate of speech; using eye contact, touch, and smiling in situationally and culturally appropriate ways; and being conscious of the impact of physical appearance [13]. Importantly, both verbal and nonverbal communication function to create a sense of relational connection which, in turn, creates both trust and the motivation required for patients to process often-complicated study information [14].
It should be noted that while these verbal and nonverbal communication behaviors are necessary (but not sufficient) for increasing enrollment in clinical trials, the goal of clinical trial communication interventions should not simply focus on accrual but rather improve informed decision making by potential participants. Thus, whether patients consent or do not consent is beside the point. All patients, we believe, should be (1) offered the opportunity to contribute to medical knowledge through study participation and (2) provided study information in language (and a format) that they understand so they can make an informed decision about whether to participate.
Contrary to popular belief, good communication skills come naturally to very few people. Just as public speaking abilities can be developed through professional training, the specific interpersonal verbal and nonverbal practices that foster positive interactions with patients in a clinical trial recruitment context can be taught [17]. The content of clinical trial communication training programs varies considerably (as do outcomes), but most programs appear to be successful in improving the confidence of those who recruit for studies [18].
While clinical trial communication training programs are not yet widely available, there are some laudable examples that warrant discussion. Fallowfield and colleagues [19, 20, 21] have been among the first to develop communication training programs specifically focused on clinical trial recruitment and consent issues. Their training programs provided information on common communication issues and ethical concerns and were primarily directed toward physicians and nurses with clinical trial management roles. The main outcomes from these trainings were improved knowledge of clinical trials and increased confidence in their ability to recruit and consent patients. Similarly, Wells and colleagues [22] developed a training program to improve professionals’ communication abilities but focused largely on developing increased cultural competency by focusing on barriers and beliefs of African Americans and Latinos. The program focused on outcomes related to knowledge and attitudes of minority patients’ cultural needs. Another communication training program, developed and piloted at the University of Miami, focused on educating research coordinators on specific verbal and nonverbal communication skills to improve clinical trial recruitment and informed consent discussions. This communication training program consisted of five modules and adopted several educational strategies including a didactic presentation, in-group discussions, live demos, and role play activities [17].
One issue that has troubled virtually all existing clinical trial communication programs is the actual assessment of training outcomes. This may be a symptom of a larger problem in that there seems to be little consensus about what the goal should be for communication trainings. We assert that there should be two central goals: (1) increasing the willingness and ability of recruiters to use “best practices” in communication about clinical trial participation, with the ultimate goal of (2) increasing informed decision making among potential participants. Whether patients and other potential participants provide informed consent to enroll in a study or make an informed decision to decline the opportunity to participate, we believe that all patients should be presented with the choice to advance knowledge relevant to their health conditions wherever such opportunities exist. The burden is on us to communicate well in order to maximize the patients’ comprehension of all factors that are relevant to their decisions.
Current assessments of the quality of communication practice as an outcome of clinical trial communication training has focused on several tools: (1) surveys of training participants’ knowledge, attitudes, and perceived self-efficacy; (2) role-plays to practice skills; (3) videotaping participants to provide individualized feedback, and (4) the use of check lists to assess recruiters’ behaviors when interacting with potential participants [18]. While all of these assessment strategies are valuable, none of these approaches has been validated, including the self-report survey, which is the most commonly used tool [18]. The development and evaluation of more effective training programs depend heavily on the use of validated and, preferably, triangulated outcome measures.
Toward this end, we have developed a self-report questionnaire that focuses on communication behaviors that are critical for effective clinical trial recruitment and consent. The measure is grounded in the empirical literature on clinical trial communication, particularly the work of Morgan and colleagues, who identified verbal and nonverbal communication behaviors that recruiters themselves associate with effective recruitment and consent processes [12, 13, 14, 15]. We created an initial pool of 138* items which corresponded to a wide variety of communication behaviors including eye contact; conversational style; protection of patient privacy; tone of voice; ability to “read” patients; ability to adapt to patient communication preferences; mirroring patient communication behaviors; smiling and friendliness; body positioning; the use of touch; physical appearance; simplifying/“translating” medical and technical information into lay language; reframing or using metaphors and analogies to explain difficult concepts; encouraging question asking; balancing the presentation of risks and benefits of study participation; describing the benefits to self and society of study participation; and other communication behaviors that ensure that potential participants comprehend information that is relevant to the decision to participate in a research study or clinical trial.
All survey items were entered into online formats including REDCap and Qualtrics for dissemination. Following institutional review board (IRB) approval, the survey was distributed to research professionals at three academic medical centers: University of Miami, University of Florida, and University of Texas Health Science Center. Because of a technical error, data from the University of Texas Health Science Center (n = 16 surveys) could not be retained for the study.
The eligibility criteria for participation were broad: Any employee whose job duties regularly involved recruiting and/or consenting patients for clinical trials or research studies could participate in the study. The survey was distributed via email link by managers within each academic medical center. No compensation for participation was offered. A total of 71 people who completed the survey were included in the analyses. Respondents had an average of 6 years of experience (M = 5.93, SD = 4.20). The demographic and professional characteristics of our sample appear in Table 1.
Variable | n | (%) |
---|---|---|
Gender | ||
Male | 14 | (19.7) |
Female | 54 | (76.1) |
Not reported | 3 | (4.2) |
Race | ||
American Indian | 0 | (0) |
Asian | 4 | (5.6) |
Pacific Islander | 0 | (0) |
Black or African American | 4 | (5.6) |
Middle Eastern | 0 | (0) |
White or Caucasian | 60 | (84.5) |
Not reported | 3 | (4.2) |
Ethnicity | ||
Hispanic | 32 | (45.1) |
Education | ||
High school–less than bachelors | 8 | (11.3) |
Bachelor | 23 | (32.4) |
Master | 23 | (32.4) |
PhD | 6 | (8.5) |
MD | 11 | (15.5) |
Institution | ||
University of Miami | 40 | (57.1) |
University of Florida | 28 | (40) |
Both UM and UF | 1 | (1.4) |
Other | 1 | (1.4) |
Not reported | 1 | (1.4) |
Type of trial* | ||
Drug | 44 | (62) |
Device | 11 | (15.5) |
Behavioral/social | 30 | (42.3) |
Medical intervention/procedure | 16 | (22.5) |
Characteristics of the sample.
Some individuals reported recruiting for more than one type of trial.
In addition to the items assessing communication behaviors in clinical trial contexts, demographic questions, the nature of their work, and their level of experience, we asked research professionals about how they feel about their jobs, their motivation levels, and their self-assessment of their competence in recruiting for clinical trials and research studies. These items were used to explore the relationship between responses to these questions and self-reported communication behaviors as a way to test the capacity of the clinical trial communication inventory (CTCI) to discriminate different audience characteristics.
Because of the high ratio of survey items to number of participants, an exploratory factor analysis that included all survey items did not yield meaningful results. Breaking the survey down into smaller groups of conceptually linked items proved to be a more useful strategy. All reported exploratory factor analyses used an oblimin rotation because items representing, for example, different dimensions of nonverbal communication necessarily have a strong relationship with each other. An item was considered to be an indicator of a factor if it had a loading of .5 and a loading of no more than .4 on any other factor. The results of the exploratory factor analyses for four sets of items appear in Table 2 (nonverbal communication), Table 3 (translation, simplification, and lay language), Table 4 (reframing medical information), and Table 5 (fostering understanding of medical research). Appendix A contains the items retained for each scale.
Item | 1 | 2 | 3 |
---|---|---|---|
I usually mirror a patient\'s body posture when I\'m discussing a study with them. | .85 | −.14 | .25 |
I try to adjust my facial expressions to reflect the current situation they are in. | .75 | .12 | .13 |
When I am discussing study participation, if a patient appears relaxed, I relax my body too. | .74 | −.23 | .06 |
I often mimic a patient\'s mannerisms when I talk about a study. | .74 | −.07 | .18 |
Based on my first impressions of a patient, I adapt how I talk about a study. | .69 | .00 | .07 |
Whether a person talks loud and fast or softly and slowly, I adjust the way I speak about a study to how they talk. | .69 | −.05 | .18 |
I slip into the same style and manner of speech as the person I am talking to about a study. | .68 | −.13 | .20 |
I think it\'s more important to be warm and friendly with patients than to maintain professional distance | .67 | .02 | −.45 |
When I walk into the exam room (or waiting area) with patients, I try to figure out what kind of mood they are in. | .46 | .36 | −.54 |
I am very good at ‘reading’ a patient before I start talking about study details. | .42 | .38 | −.6 |
I always maintain a highly professional tone and demeanor when I talk to a patient. | .06 | .77 | .32 |
I act the same way with every patient regardless of their mood. | −.03 | .67 | .40 |
Nonverbal communication (reading, adapting, mirroring) factor loadings for exploratory factor analysis with oblimin rotation.
Item | 1 | 2 | 3 | 4 |
---|---|---|---|---|
I ‘translate’ information about a study to help patients | .69 | .00 | .38 | −.18 |
I find ways of using lay language | .67 | −.08 | .31 | −.21 |
I believe that members of some minority/ethnic populations have specific preferences for words or research-related terminology | .68 | .10 | −.45 | .18 |
I try to avoid certain words or medical terms when talking with members of certain cultural groups | .73 | .30 | −.44 | .06 |
I try to use language that I think would be received well by members of the cultural group to which they belong | .78 | .01 | −.42 | −.02 |
When going through a consent form with a patient, I often say something like, ‘so this means…’ followed by a lay explanation | .70 | .03 | −.02 | −.38 |
Based on what I know about the educational level of the patient, I adapt my explanation of a study | .75 | −.16 | −.04 | −.24 |
I break down the study protocol into smaller steps to make the prospect of participating in the study less intimidating | .59 | −.15 | .41 | −.22 |
I simplify the language of the consent form | .58 | −.25 | .28 | .56 |
I substitute simple words for complicated medical terminology | .54 | −.38 | .11 | .58 |
I make sure that all of my explanations of a study can be found directly on the consent form | .15 | .71 | .50 | .18 |
Because the consent form must be approved by the IRB, I keep to the language that is specified in the consent form | .08 | .84 | .12 | .04 |
I do not diverge from the information and explanations offered in the consent form even when I understand a study well | .16 | .82 | −.11 | .11 |
Translation, simplification, and lay language use item factor loadings for exploratory factor analysis with oblimin rotation.
Item | 1 | 2 | 3 |
---|---|---|---|
I frame unfamiliar or potentially scary concepts in terms that are more familiar or acceptable to patients | .84 | −.04 | −.02 |
I frequently use examples as a way to explain technical information about a study | .78 | .30 | .19 |
I often use metaphors and analogies to explain randomization or other study concepts | .76 | −.29 | −.24 |
I use analogies to explain potentially scary tests or concepts | .76 | −.41 | −.31 |
If it\'s a complex study, I often reframe information in medical terms that are more familiar to them | .71 | −.32 | .30 |
I often give specific examples of what will happen to a patient if they join a study | .69 | .09 | .37 |
I find that I often use analogies (that aren\'t part of the consent form) when explaining a study | .66 | .14 | −.21 |
Patients like to hear stories about other patient’s experiences with research participation | .51 | .51 | −.10 |
I make sure that patients know that the consent form is not a contract | .42 | .42 | −.30 |
I often use predetermined and rehearsed stories to clarify difficult concepts | .44 | .32 | .60 |
I find it difficult to explain how randomization works in the context of the trial being offered | .31 | −.57 | .54 |
Reframing medical information factor loadings for exploratory factor analysis with oblimin rotation.
Item | 1 | 2 |
---|---|---|
I always begin a discussion with a patient by explaining the purpose of our conversation | .51 | −.24 |
Before getting a patient\'s signature on a consent form, I always check their understanding of the study information | .69 | −.44 |
I ask patients to ‘teach back’ (or summarize for me) the key points of a study to me before they consent to being in a study | .68 | −.26 |
I offer patients the option of delaying their decision about study participation | .59 | .19 |
I explain to patients that the research study is being conducted to improve scientific knowledge about a particular disease, condition, or treatment | .75 | −.15 |
I explain the general rationale for a randomized clinical trial (when appropriate) | .60 | −.50 |
When offering patients the opportunity to participate in a research study, I explain the researchers\' motivations for conducting the study | .70 | −.38 |
When offering patients the opportunity to participate in a research study, I tell them that all trials have to receive approval from ethics committees | .75 | −.08 |
When offering patients the opportunity to participate in a research study, I acknowledge the uncertainty of treatment benefits | .73 | .16 |
I explain the concept of equipoise (trials are conducted only when there is collective uncertainty that the benefit of an experimental treatment is better than the current best practice standard treatment) | .62 | .49 |
I explain the concept of beneficence (trials are conducted to determine whether there is a significant additional benefit from the experimental treatment) | .58 | .60 |
I explain the concept of non-maleficence (there is evidence to suggest that being involved in a clinical trial will in no way worsen the patient\'s chances) | .68 | .71 |
Fostering understanding of medical research factor loadings for exploratory factor analysis with oblimin rotation.
The results of the factor analyses (where viable results were obtained) were used to construct final versions of the scales. Descriptive statistics for each of the final subscales and Cronbach’s alpha appear as Table 6. Pearson correlations between all of the CTCI subscales appear in Table 7.
Mean | SD | Cronbach’s alpha | |
---|---|---|---|
Eye contact (3 items) | 4.10 | .55 | .69 |
Maintaining patient privacy (4 items) | 3.34 | .72 | .76 |
Translation of medical and technical information (7 items) | 3.55 | .60 | .86 |
Reframing medical and technical information (7 items) | 3.50 | .71 | .86 |
Fostering understanding of research (9 items) | 4.29 | .59 | .86 |
Explaining specific research concepts (3 items) | 3.96 | 1.13 | .88 |
Nonverbal communication (reading, adapting, mirroring) (8 items) | 3.12 | .57 | .90 |
Question answering (3 items) | 3.25 | .54 | .83 |
Means, standard deviations, and reliabilities of Clinical Trial Communication Inventory subscales.
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
---|---|---|---|---|---|---|---|
1. Eye contact | – | ||||||
2. Privacy | .53*** (70) | – | |||||
3. Translation | .51*** (64) | .60*** (64) | – | ||||
4. Reframing | .53*** (59) | .41** (59) | .70*** (58) | – | |||
5. Understanding | .11 (50) | .13 (57) | .23 (56) | .23 (54) | – | ||
6. Explaining | .19 (60) | .01 (60) | .14 (58) | .26 (55) | .46*** (56) | – | |
7. Nonverbal | .59*** (61) | .56*** (61) | .56*** (59) | .41** (54) | .04 (52) | .13 (54) | – |
8. Questions | .37** (62) | .41** (62) | .53*** (61) | .50*** (.59) | .36** (56) | .08 (58) | .17 (57) |
Pearson correlations of Clinical Trial Communication Inventory subscales.
*p = .05.
**p = .01.
***p < .001.
The relationships between the final CTCI subscales and other variables in the survey were examined. Specifically, we sought to look for possible difference in responses by gender, race/ethnicity, and type of trial recruited for. We also looked for correlations between responses to the CTCI subscales and job satisfaction and years of experience. None of these analyses produced a significant pattern of results except for years of experience. The number of years of experience as a research professional correlated significantly with use of eye contact (r(62) = .45, p < .001); efforts to preserve patient privacy (r(61) = .47, p < .001); translation of medical and research terminology into lay language (r(56) = .55, p < .001); the use of reframing to explain research (r(51) = .52, p < .001); fostering understanding of research concepts (r(49) = .43, p = .002); and attitudes toward answering patient questions (r(54) = .67, p < .001). The correlation between years of experience and fostering understanding of medical research was nearly significant, r(52) = .27, p = .06. However, correlations between years of experience and the measure of mirroring and adapting to patients’ nonverbal communication was nonsignificant, r(54) = −.05, p = n.s.
This chapter presents the development and analysis of an instrument designed to evaluate the communication behaviors of professionals who recruit for clinical trials and research studies. Of the original 133 items, 44 items were retained in 8 subscales. These subscales include maintaining patient privacy; translation of medical and technical information; reframing medical and technical information; fostering understanding of research; explaining specific research concepts; question answering; nonverbal communication, including reading patients, adapting to patients’ communication, their state of mind, and preferences, mirroring behaviors; and eye contact.
The results of supplemental analyses demonstrate that there are statistically significant relationships between all but one of the subscales of the instrument (including all of the verbal communication measures) and years of experience. This may indicate that as research professionals gain experience, they acquire knowledge about effective strategies to communicate about complex medical and scientific concepts. In fact, the fact that the measure of nonverbal communication (behaviors which are often described as something akin to “instinctual” or innate in the published studies of Morgan and colleagues) has a correlation of nearly zero may indicate that many individuals who are drawn to this type of research position may naturally be “people-people” who may nonetheless benefit from training programs with an emphasis on verbal communication techniques when recruiting and consenting potential research participants. Tentative validity testing of several items and subscales of the instrument described here was performed in early 2017. The results of this early pilot testing demonstrated that items contained in the Clinical Trial Communication Inventory can be used to assess the pre- to post-test impact of a clinical trial communication training (see Ref. [17] for full results of the evaluation).
While the CTCI is likely to prove useful to evaluate efforts in clinical trial communication training, it should be noted that with a relatively small sample, the validity of factor analytic strategies used to construct some of the initial scales may be limited, although the scales we created based on these results showed strong reliability. Future research should further develop this instrument by testing its robustness with a larger sample of research coordinators and validate it with other types of medical professionals who recruit for clinical trials, including physicians and study nurses. Additionally, it is vitally important for this instrument to be evaluated through convergent validity testing. The question remains whether the Clinical Trial Communication Inventory reflects real-world communication practice and indeed, whether these communication behaviors predict increased informed decision making or improved rates of clinical trial accrual. Convergent validity can be established through a variety of strategies, including checklists of exhibited communication behaviors during role plays and video recordings of actual recruitment and consent behaviors with patients. Predictive validity could be established by demonstrating that communication training results in changed scores on the CTCI from pre- to post-test, and more importantly, that scores post-training reflect improvements to informed consent with patients, which can be evaluated through patient “teach-backs” and an increased number of accurate responses to a set of study-related knowledge questions.
Improvement of low accrual to clinical trials and research studies is urgently needed, particularly for members of minority populations. Research has demonstrated that communication behaviors play an important role in the recruitment and consent processes. While communication behaviors can (and should) be developed through professional seminars and workshops, there are few available instruments to conduct evaluations of the outcomes of those trainings. In this chapter, we outline the development and testing of a measure of communication in clinical trial contexts: the Clinical Trial Communication Inventory. While additional testing needs to be conducted to more thoroughly establish convergent and predictive validity with multiple professional groups, we believe that this instrument will help advance the development of clinical trial communication training programs.
The authors wish to thank the participants who completed the survey. A special thank goes to Patricia Avissar and Robert Kolb for their help with the recruitment process.
Use of eye contact
I use eye contact to try to figure out whether a patient understands a study through eye contact.
I use eye contact to assess a patient’s state of mind while I talk with them about a study.
I find that most patients do not want to make eye contact when discussing study participation.
Maintaining patient privacy
If the patient is comfortable discussing a study in an area where privacy cannot be secured, I will still consent the patient.
Most patients don’t care about being consented in a private location.
It is not practical to always consent patients in a private location.
If a private location in unavailable, I talk in a quiet voice to enhance a sense of privacy when discussing a study.
Translation of medical and technical information
I ‘translate’ information about a study to help patients.
I find ways of using lay language.
I believe that members of some minority/ethnic populations have specific preferences for words or research-related terminology.
I try to avoid certain words or medical terms when talking with members of certain cultural groups.
I try to use language that I think would be received well by members of the cultural group to which they belong.
When going through a consent form with a patient, I often say something like, ‘so this means…’ followed by a lay explanation.
Based on what I know about the educational level of the patient, I adapt my explanation of a study.
Reframing medical and technical information
If it\'s a complex study, I often reframe information in medical terms that are more familiar to them.
I find that I often use analogies (that aren\'t part of the consent form) when explaining a study.
I frequently use examples as a way to explain technical information about a study.
I often give specific examples of what will happen to a patient if they join a study.
I frame unfamiliar or potentially scary concepts in terms that are more familiar or acceptable to patients.
I often use metaphors and analogies to explain randomization or other study concepts.
I use analogies to explain potentially scary tests or concepts.
Fostering understanding of research
I always begin a discussion with a patient by explaining the purpose of our conversation.
Before getting a patient\'s signature on a consent form, I always check their understanding of the study information.
I ask patients to ‘teach back’ (or summarize for me) the key points of a study to me before they consent to being in a study.
I offer patients the option of delaying their decision about study participation.
I explain to patients that the research study is being conducted to improve scientific knowledge about a particular disease, condition, or treatment.
I explain the general rationale for a randomized clinical trial (when appropriate).
When offering patients the opportunity to participate in a research study, I explain the researchers’ motivations for conducting the study.
When offering patients the opportunity to participate in a research study, I tell them that all trials have to receive approval from ethics committees.
When offering patients the opportunity to participate in a research study, I acknowledge the uncertainty of treatment benefits.
Explaining specific research concepts
I explain the concept of equipoise (trials are conducted only when there is collective uncertainty that the benefit of an experimental treatment is better than the current best practice standard treatment).
I explain the concept of beneficence (trials are conducted to determine whether there is a significant additional benefit from the experimental treatment).
I explain the concept of non-maleficence (there is evidence to suggest that being involved in a clinical trial will in no way worsen the patient\'s chances).
Nonverbal communication (reading, adapting, mirroring)
I think it is more important to be warm and friendly with patients than to maintain a professional distance.
I slip into the same style and manner of speech as the person I am talking to about a study.
Whether a person talks loud and fast or softly and slowly, I adjust the way I speak about a study to how they talk.
I usually mirror a patient’s body posture when I’s discussing a study with them.
When I am discussing a study participation, if a patient appears relaxed, I relax my body, too.
I often mimic a patient’s mannerisms when I talk about a study.
Based on my first impressions of a patient, I adapt how I talk about a study.
I try to adjust my facial expressions to reflect the current situation they are in.
Question answering
I enjoy answering a patient’s questions about a study.
I always invite patients to ask questions about a study.
I make sure to give a patient the names of who to contact if they have additional questions about the trial
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