The value of χ for the studied systems.
\r\n\t
",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. 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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:"22882",title:"Therapeutic Potential of Polyphenols in Parkinson’s Disease",doi:"10.5772/20055",slug:"therapeutic-potential-of-polyphenols-in-parkinson-s-disease",body:'Phase diagrams of multicomponent systems provide full information on the thermodynamic compatibility of components in wide concentration and temperature ranges. One of the first phase diagrams of polymer–solvent systems was published by Papkov et al., (1937), Rogovin et al. (1937), Kargin et al. (1939). In 1941, the works of Tager & Kargin (1941) devoted to the thermodynamics of polymer solutions were published. From the end of the 1940s, systematic research into the thermodynamic properties and construction of phase diagrams of polymer solutions have been performed at the Laboratory of Colloid Chemistry (at the Polymer Chair with 1958), Ural State University. Over the course of sixty years, phase diagrams have been constructed for hundreds of polymer systems with amorphous and crystalline phase separations. Many of these data were included in textbooks, monographs, and reviews: Tager (2007), Papkov (1981), Nesterov & Lipatov (1987), Vshivkov (1991), Chalykh et al. (1998), Malkin & Kulichikhin (1996), Vshivkov et al. (1998), Vshivkov & Rusinova (1998, 2001), Rusinova & Vshivkov (1997), Klenin (1995). The phase liquid crystalline transitions of the cellulose derivatives solutions are studied at the polymer chair of Ural State University last ten years.
Academician Kargin was the first who described the ability of polymers to produce mesophases. In 1941, he wrote “interactions between big molecules is rather strong even when the interaction between individual units is weak. As a result, this can lead to the orientation of big molecules in one common direction”. In 1956 Robinson (1956, 1958) has discovered, that poly(γ-benzyl-L-glutamate) (PBG) can form the liquid crystals in concentrated solutions in chloroform, methylene chloride, trichlorethane, dioxane, m-cresol. Flory (1956) has suggested the phase diagram for a polymer – solvent system with the liquid crystalline transition. At a later date such diagrams were built for the systems: PBG – DMF (Wee & Miller, 1971), polycarbobenzoxyline – DMF (Miller et al., 1974, 1978), poly-p-benzamide – DMA (Papkov et al., 1974), poly-p-benzamide – DMA, LiCl (Iovleva et al., 1976), poly(p-phenyleneterephthalamide) – H2SO4 (Papkov & Kulichikhin, 1977, Andreeva et al., 1981), PBG – ethylene chloride, PBG – benzyl alcohol (Sasaki et al., 1983), poly(p- phenyleneterephthalamide) – H2SO4 – water (Nakajima et al., 1978), PBG – m-cresol (Kiss & Porter, 1977), copolymer on the bases of p-phenylenediamine + terephthalic acid and 4,4”-diphenyldicarboxylic acid) – H2SO4\n\t\t\t\tLukashova et al., 1978), PBG – DMF and PBG – dicloracetic acid (Konevets et al., 1985), poly(p-benzamide) – DMA – LiCl (Salaris et al., 1976), polyhexylisocyanate and poly(50 % butyl + 50 % p-anisole-3-propyl)isocyanat in tetrachlorethane (Aharoni & Walsh, 1979), copolymer on the bases of p-phenylenterephyhalaamide + benzimidazole) – H2SO4 (Iovleva & Banduryan, 2010).
The influence of a magnetic field on the liquid crystal structure was studied by Meuer (1968), de Gennes (1968). The authors have considered such a field distortion of the cholesteric structure and find that the transition to complete nematic order occurs at a critical field strength given by
Where p0 is the zero field pitch, Δχm, the diamagnetic anisotropy of the liquid crystal, K22, the twist elastic constant. As the field increases the pitch is predicted to oncrease slowly at first and then diverge logarithmically as the critical field is approached. The theory has been verified for lyotropic liquid crystals of PBG in a number of different solvents (Chandrasekhar, 1977, Iizuka, 1973, DuPre & Duke, 1974, 1975, DuPre et al., 1976, 1977, Patel & DuPre, 1979). Molecules of liquid crystals orient themselves in the magnetic field so that their long chains are oriented parallel to the magnetic field lines (Miller, 1978). This orientation is associated with the molecular anisotropy of macromolecules rather than the existence of permanent magnetic moments.
The LC state in solutions and melts of cellulose derivatives was characterized later on 1970–1980s (Kulichikhin & Golova, 1985, Meeten & Navard, 1982, Bhadani & Gray, 1983, Navard & Haudin, 1981, Yunusov et al., 1982, Iovleva, 1989, Vshivkov et al., 2006, 2007, Vshivkov & Rusinova, 2007, 2008). Molecules of cellulose and cellulose derivatives are characterized by a rigid helical conformation and, hence, they are capable of ordering and formation of cholesteric liquid crystals in concentrated solutions. Investigation of the LC state in polymer solutions is of evident practical importance because, owing to the ability to orient under the action of external fields, such solutions are used for the preparation of high-modulus fibers. To control the above processes, the knowledge of phase diagrams for the systems under processing is crucial. However, data on the phase diagrams of such systems in the applied magnetic and mechanical fields are not numerous (Vshivkov & Rusinova, 2007, 2008). The goal of this work is to study phase liquid crystalline transitions of cellulose derivative solutions in magnetic and in a shear stress fields for the following systems: CEC – DMA, CEC – DMF, CEC – mixture of trifluoroacetic acid with methylene chloride, HPC – ethanol, HPC – acetic acid, HPC – DMA, HPC – DMF, HPC –water and poly(γ-benzyl-L-glutamate) (PBG) – DMF.
Cyanoethyl cellulose (CEC) sample with a degree of substitution of 2.6 and Mw= 1.9x105 and hydroxypropyl cellulose (HPC) samples with a degree of substitution of 3.4 and Mw=9.5x104 (HCP-1), 1.4x105 (HPC-2), and 1.15x106 (HPC-3) were used. According to X-ray studies, degree of crystallinity of HPC samples did not exceed 15%. The degree of crystallinity for the CEC samples was 35%. All X-ray measurements were performed on a DRON-13 diffractometer (Cu Kα-irradiation). The CEC and PBG sample with Mη = 2.4× 105 were synthesized at the laboratory of the Institute of Macromolecular Compounds, Russian Academy of Sciences.
Dimethylformamide (DMF), dimethylacetamide (DMA), acetic acid (reagent grade), twice-distilled water, ethanol and a 1 : 1 (by mass) mixture of trifluoroacetic acid with methylene chloride were used as solvents. The polymer solutions were prepared in sealed ampoules for several weeks at 298 (water), 340 (in ethanol), 350 K (DMF, DMAA, and TFAA–methylene chloride mixed solvent).
Phase-transition temperatures Tph were estimated by the cloud-point method. The solution temperatures were varied at a rate of 12 K/min. The structure of solutions was examined with the help of an “Olympus BX-51” polarization microscope. A polarization photoelectric setup was used to determine the type of phase transition in solutions: a sealed ampoule containing the transparent polymer solution was placed in the gap between the crossed polaroids and the temperature of the ampoule was decreased. The polarized light of an LGN-015 He–Ne laser was transmitted through the polaroids in the direction normal to the ampoule containing the solution. When the solution was transparent (isotropic) the intensity of the transmitted light was zero. As the system became turbid upon variation in temperature or increase in the concentration of solution, the transmitted light intensity increased. This indicated formation of the anisotropic phase, that is, the LC phase transition. Experiments in the magnetic field were performed using a setup generating a constant magnetic field with an intensity of up to 15 000 Oe (Fig. 1).
Schematic presentation of the magnetic facility: ( 1 ) electromagnet tips, ( 2 ) the thermostating jacket and ( 3 ) the ampoule with the test solution. H is the magnetic field vector.
The sealed ampoule containing a transparent polymer solution was placed between the magnet poles. The magnetic field vector was directed normal to the solution layer (~5 mm thick) in the sealed ampoule. The temperature of solution was varied with the thermostating jacket, and the onset temperature of opalescence development was measured. This temperature was related to the appearance of the LC state. The coefficients of magnetic susceptibility χ were determined by means of a vibration magnetometer. The values of χ are presented in the table 1. The energy of the magnetic field E stored by the solution volume unit was calculated via equation E=χH2, where H is the magnetic intensity.
System | -χ·107 |
HPC-1 | 18,1 |
CEC | 5,3 |
HPC-1 – ДМА (ω2=0.5) | 6,9 |
CEC - ДМА (ω2=0.5) | 6,7 |
HPC-1 – Water (ω2=0.5) | 3,4 |
HPC-1 – СН3СООН (ω2=0.3) | 71,3 |
PBG | 78,8 |
PBG – DMF (ω2=0.4) | 2,3 |
The value of χ for the studied systems.
The phase transition temperature under dynamic conditions was measured using two methods: (1) a polymer solution that occurred in the isotropic state at elevated temperatures was placed in a gap between a glass rotor and a stator of the plastoviscometer. The shear rate was set constant, and the working unit was cooled at a rate of 12 K/h. A temperature corresponding to the onset of solution opalescence was taken as the phase transition temperature. (2) a polymer solution was placed in a metallic working unit of the rheometer HAAKE MARS. The shear stress was measured as a function of temperature, and viscosity η was calculated. The temperature corresponding to a sharp change in the run of the η – T curve was taken as the phase transition temperature Tph.
The boundary curves delimiting transparent isotropic and opalescent anisotropic solutions for HPC-1–DMA, HPC-2–DMAA, HPC-1–ethanol, HPC – acetic acid, HPC-2–ethanol, CEC–DMA, CEC–DMF, CEC–(methylene chloride/TFAA) and PBG – DMF systems are determined. Under conventional light, the concentrated solutions of HPC and CEC are opalescent. This is suggests formation of cholesteric liquid crystals.
Figures 2 a – c show boundary curves delimiting transparent isotropic and opalescent anisotropic solutions for solutions of the polymers with the different molecular weights.
It is seen, that as the molecular mass of the polymer increases, the boundary curve corresponding to the development of anisotropic LC phase in solutions is shifted to lower concentrations. This behavior agrees with the existing theoretical concepts (Flory, 1956). According to Flory, the critical concentration of a polymer, φ2*, above which the LC order arises, is related to the asymmetry of macromolecules x (the length-to-diameter aspect ratio) through the following relationship:
Boundary curves
Figures 2 d – 2 g show the boundary curves for the polymer solutions in the different solvents. It is seen from these figures and from the table 2, that as the solvent polarity is increased (solvent dipole moment µ is increased), the LC phase appears at higher concentrations and lower temperatures since with an increase in polarity, a solvent ruptures bonds between macromolecules to a higher extent. It is seen, that DMA and DMF are the best solvents of the cellulose derivatives. The cellulose and cellulose derivative molecules may form the hydrogen bonds between the chains. So the good solvent has to be also an electron donor. This ability is determined by the ionization potential φ. As φ is decreased, ω2* is increased, that is such solvent is better.
Solvent | µ dipole moment, D [61] | φ-ionization potential, eV [62] | ω2* (mass fraction) Т=298 К | |||
HPC-1 | HPC-2 | HPC-3 | CEC | |||
DMA | 3,86 | ≤9,65 | 0,45 | 0,43 | 0,42 | |
DMF | 3,81 | ≤10,16 | 0,42 | |||
ethanol | 1,69 | 10,25 | 0,44 | 0,38 | <0,36 | |
acetic acid | 1,74 | 10,35 | 0,3 | 0,305 | ||
water | 1,84 | 12,59 | 0,25 | 0,25 | 0,195 |
Physical parameters of the solvents and critical polymer concentration ω2*, above which the LC order arises. T = 298 K.
Figures 3 a, b, c show the temperature dependences of viscosity for the solutions under study. The above dependences are described by curves with well-pronounced sharp maxima. This behavior is typical of the solutions with LC transitions (Kulichikhin & Golova, 1985, Vshivkov & Rusinova, 2008, Gray, 1962). According to Gray (1962), this profile of the temperature dependences of viscosity corresponds to the (isotropic liquid)–(nematic liquid crystal) phase transition. Therefore, upon cooling of HPC, CEC and PBG solutions under deformation conditions, no cholesteric crystals are formed: in other words, under dynamic conditions, a liquid crystal changes its type from cholesteric to nematic. The results obtained are in good agreement with the data of other authors (Volkova et al., 1986), who showed that the shear deformation of CEC solutions (c= 30%) in trifluoroacetic acid and a 2 : 1 TFAA–methylene chloride mixture results in the formation of similar textures that indicated the formation of an XRD-detectable nematic liquid crystal. Thus, the deformation of CES solutions leads to the change of an LC type from cholesteric to nematic. When the deformed solutions were studied by the method of polarization microscopy, the development of striped textures was observed (fig. 4). This fact is indicative of the formation of the domain supramolecular structure (Papkov & Kulichikhin, 1977, Aharoni & Walsh, 1979). Since, compared to cholesteric liquid crystals, nematic liquid crystals exist at higher temperatures, the temperature–concentration region corresponding to the existence of anisotropic solutions under the mechanical field should change.
Temperature dependences
Mocrograph of the CEC solution in DMA с=51.2 % after deformation. γ=60 s-1. х120.
The phase transitions in the CEC–DMF and CEC–DMAA systems under static conditions and in a shear field are studied. The mechanical field leads to an extension of the temperature – concentration region of the existence of the LC phase (fig. 5), a phenomenon that is due to the change of orientation of CEC macromolecules in solutions.
Boundary curves
The dependence of ΔT (ΔT is the difference of phase transition temperatures under dynamic and static conditions) on the shear rate is described by a curve with a maximum (fig. 6). The same behavior was reported for some polymer–solvent and polymer–polymer systems with crystalline phase separation (Vshivkov et al., 1998, Vshivkov & Rusinova, 2001).
∆T vs. shear rate
This pattern of the curve was associated with two opposite processes in the system, namely, the orientation of macromolecules along the flow direction, which is favorable for phase transition, and the destruction of nuclei of the new phase by a mechanical field, a process that retards the formation of the LC phase. In the examined range of shear rates, the orientation processes dominate, thereby resulting in the elevation of the formation temperature (relative to static conditions) of the LC phase. as manifested in the elevation of the LC phase transition temperature. For comparison, Fig. 6 a shows the data for the PE – p-xylene system with crystalline phase separation (Vshivkov et al., 1998). As follows from fig. 6 a, the orientational processes (increase in ∆T) during the LC transition are observed at lower shear rate (by approximately an order of magnitude).
Application of the magnetic field raises the temperature of LC phase formation Tph in HPC, CEC and PBG solutions; that is, it widens the temperature–concentration region of the existence of anisotropic solutions. Molecules of liquid crystals orient themselves in the magnetic field so that their long chains are oriented parallel to the magnetic field lines [46]. According to published data (Meuer (1968), de Gennes (1968), Chandrasekhar, 1977, DuPre & Duke, 1974, 1975, DuPre et al., 1976, 1977, Patel & DuPre, 1979), the cholesteric liquid crystal–nematic liquid crystal phase transition occurs in magnetic field. From a certain critical intensity, magnetic field causes untwisting of the cholesteric helix. Eventually, nematic liquid crystals are formed which occur at higher temperatures than cholesteric liquid crystals.
Polarization microscopy studies revealed a striped texture of HPC and CEC solutions treated in magnetic field (fig. 7), thus suggesting formation of large supramolecular structures—domains. A similar phenomenon was reported for other polymer–solvent systems (Papkov & Kulichikhin, 1977).
Micrograph of the HPC-1 solution in DMA. с=52.0 %. х120. Н=9 kOe.
It was discovered that after the magnetic field was switched off, an increased Tph was preserved in solutions for many hours. This is clearly seen from fig. 8, which demonstrates the time dependence of the time dependences of ΔT (ΔT is the difference in LC phase transition temperatures in the presence and absence of magnetic field) are determined. This fact provides evidence that structures induced by the magnetic field are preserved in solutions. Thus, the systems under study possess memory.
Time dependence
On the basis of the above data, the times of relaxation τ were calculated for the nematic liquid crystal – cholesteric liquid crystal reverse transition in solutions after switching off the magnetic field. Calculations were performed according to the common exponential equation. The values of τ were found to be 18 h (HPC-3–DMAA); τ1= 11 h at 298 K and τ2= 8 h at 370 K (CEC–DMAA). The calculation results made it possible to estimate the order of the enthalpy of activation ΔH* for the nematic liquid crystal–cholesteric liquid crystal transition in solutions after switching off the magnetic field. The value of ΔH* is estimated via the equation ln(τ1/τ2) = (ΔH*/R)(1/T1– 1/T2) as ~ 4 kJ/mol, in qualitative agreement with rather low enthalpies of LC phase transitions (Chandrasekhar, 1977, Plate, 1988).
Figures 9 a – 9 c show the boundary curves measured for the HPC-3-DMAA, HPC-1 – ethanol and CEC–DMAA systems at various magnetic field intensities H.
Boundary curves
∆T vs magnetic field intensity
As is seen, with an increase in H, the temperature–concentration region of LC solutions widens. The higher the value of H, the more pronounced the shift of the boundary curves. A similar behavior was observed for solutions of CEC in DMF and PBG in DMF.
Figures 10 a – 10 e show the concentration dependences of ∆T for the cellulose ester – solvent systems (ΔT is the difference of phase transition temperatures in magnetic field and in its absence). It is seen, that as the magnetic field intensity H is increased, the ∆T value increases. It testifies about the macromolecule orientation increase.
Aqueous solutions of HPC belong to systems with strong electron-donor (hydrogen) bonds (Belousov & Panov, 1983). Because of the presence of two mobile protons and two unshared electron pairs at the oxygen atom, a water molecule may function both as an electron donor and an electron acceptor and form four hydrogen bonds with an energy of 20 kJ/mol. Therefore, a loose structure with a large free volume is formed in water. In the case of water, the fraction of nonspecific interaction is as low as 7%. Intermolecular interactions of HPC with water may be determined by both the hydrophilic hydration giving rise to hydrogen bonding between a polymer and a solvent and the hydrophobic hydration of water, which consists in densification of water structure around nonpolar methyl and methylene groups of HPC molecules during formation of solutions. Many studies were devoted to phase equilibrium in the HPC–water system, and the LCST values were reported in a number of papers (Vshivkov et al., 2007, Fischer et al., 1995, Kagemoto et al., 1970, Nystrom & Bergman, 1978, Werbowyi & Gray, 1976, 1979, 1980, Nishio et al., 2002, Fortin & Charlet, 1989, Ryotarou & Yoshiyuki, 2003, Guido, 1995, Furusawa & Tagawa, 1985, Suto et al., 1989, Lu & Schwartz, 2002, Bergman & Sundelof, 1977). Figure 11 a displays the phase diagram measured for the HPC-1–water system. This diagram is largely consistent with the phase diagrams described for this system. Four regions can be distinguished in the diagram: (I) the region of isotropic transparent solutions; (II) the region of anisotropic transparent solutions; (III) the region of heat-induced phase separation giving rise to formation of a white anisotropic precipitate; and (IV) the region of anisotropic solutions opalescent over the entire volume (the colorless solutions are observed, which is typical of cholesterol LC solutions. (1* refers to colorless solutions and 2* refers to blue solutions, which is typical of cholesterol LC solutions (Kapustin, 1978). According to Fisher et al. (1995), the crystal solvates are formed in solutions at an HPC concentration of ~ 80 % or above. The boundary curve 1 that characterized the heated induced phase transition has a binodal shape. It appears that the breakdown of hydrophilic and hydrophobic hydration of HPC initially leads to the amorphous phase separation of solutions and formation of two coexistent dilute and concentrated phases (the LCST is 298 K). Simultaneously, anisotropic crystal solvates precipitate in the concentrated phase.
Boundary curves
Figures 11 b and 12 show the phase diagrams for the HPC-2–water and HPC-3–water systems measured under static conditions and in the shear field. The phase diagram of the HPC-2 – water system virtually coincides with that of the HPC-1–water system, since a difference in the molecular masses of these samples is insignificant; the LCST is 298 K. The diagram comprises the regions similar to those characteristic of the HPC-1–water system. The distinctive feature is a change in the color of solutions with the weight fraction of the polymer ω2 > 0.45. The observed color transition from red to violet through green may be related to a reduction in the cholesteric helix pitch with an increase in the polymer concentration in solutions. Also note that, in contrast to HPC-1 solutions, in the case HPC-2 solutions, the curve delimiting regions II and IV shifts toward lower polymer concentrations at low temperatures. The molecular mass of the HPC-3 sample is almost an order of magnitude higher than that of the HPC-1 and HPC-2 samples, therefore we failed to prepare HPC-3 solutions in a wide concentration range because of its poor solubility. Three regions may be distinguished in the phase diagram: (I) the region of isotropic solutions; (II) the region of transparent anisotropic solutions; and (III) the region, where heating causes phase separation accompanied by formation of the while anisotropic precipitate. A comparison of the phase diagrams indicates that an increase in the molecular mass of the polymer leads to a shift in the boundary curve reflecting the formation of anisotropic LC solutions to lower concentrations. This fact is consistent with the current theoretical concepts [20].
Boundary curves for the HPC-3–water system at γ = (1) 0 and (2) 12 s–1. Comments are given in text.
The deformation of these systems increases the temperatures of heat-induced separation and decreases the temperatures of transition from region II to region III. As the shear rate is increased, the absolute value of ΔT increases for both transitions and achieves 7 K (ΔT is a difference between phase transition temperatures under dynamic and static conditions). This phenomenon may be explained by the breakdown of nuclei of a new phase under the action of the mechanic field, as was observed for a number of polymer – solvent systems characterized by amorphous and crystalline phase separation (Vshivkov et al., 1998, Vshivkov & Rusinova, 2001).
Application of magnetic field causes an increase in the phase transition temperature under heating, which is likely associated with a change in the orientation of macromolecules in solution (fig. 13).
Boundary curves for the HPC-1–water system. H = (1) 0, (2) 5, and (3) 9 kOe.
Time dependence of ∆T for HPC-1 solutions in water. c = (1) 53.5 and (2) 49.6%. H= 5 Oe.
Like the HPC and CEC solutions in organic solvents, the HPC – water system possesses memory: an increased Tph is preserved for many hours after the magnetic field is switched off (fig. 14). The calculated time of relaxation necessary to achieve the initial orientation of macromolecules is τ = 260 h (c = 53.5%) and 103 h (c = 49.6%). With an increase in the concentration of the polymer, the time of relaxation grows, since the viscosity of the system increases.
Phase diagrams have been constructed for the cellulose ester – water systems under static conditions, in the shear stress and magnetic fields. As the molecular mass of the polymer is increased, the curves delimiting isotropic and anisotropic solutions shift to lower concentrations. The deformation causes the formation of domain structure, the changes in the type of the liquid crystal, and in the phase transition temperatures of solutions both upon heating and cooling. As the molecular mass of the polymer increases, the ability of macromolecules to orient under the shear stress field decreases. The concentration and dependence ΔT is described by the curve with maxima. The formation of domains in solutions was observed under the shear stress field.
The magnetic field also widens the temperature–concentration region of the existence of the LC phase. This effect is related to the cholesteric liquid crystal – nematic liquid crystal phase transition and the orientation of macromolecules in the direction parallel to the magnetic field lines. In this case, large supramolecular structures (domains) develop in solutions. The effect of magnetic field on the variation in LC transitions with the polymer concentration in solution shows an extremal pattern. Figures 15 and 16 demonstrate the concentration dependences of ∆T for HPC1– DMAA, HPC-3–DMAA, and HPC-1–water systems measured at various magnetic field intensities.
Concentration dependence of ∆T for solutions of (1, 2) HPC-3 and (3, 4) HPC-1 in DMAA. H =(1, 3) 5 and (2, 4) 9 kOe.
Concentration dependence of ∆T for HPC-1 solutions in water. H = (1) 5, (2) 9, and (3) 13 kOe.
In analyzing the effect of the polymer concentration on magnetic field-induced changes in phase transition temperatures, two factors should be taken into account. First, as concentration increases, the number of macromolecules capable of orientation in the magnetic field grows; as a consequence, Tph should increase. Second, a rise in the polymer concentration in solution facilitates densification of the fluctuation network of entanglements. This impedes the occurrence of orientation processes and weakens the effect of the magnetic field. On the whole, the concentration dependence of ΔT is apparently described by a curve with a maximum.
It should be noted that for solutions of an HPC-1 sample with a lower molecular mass, the value of ΔT is much higher. This observation indicates a more distinct orientation of smaller molecules in the magnetic field, in agreement with the data from (Kol”tsov et al., 1995).
Figures 17 and 18 plot ∆T as a function of lnE for HPC-1–DMAA, HPC-3–DMAA, and CEC – DMAA systems. It is seen that these dependences are described by straight lines. The analogous dependence is determined for the PBG – DMF system. With an increase in the magnetic field energy stored by solutions, the value of ΔT increases. The effect of field on the phase transitions shows a threshold character: a change in Tph begins from a certain critical intensity of the field Hcr. These values are 2.3 and 2.0 kOe for the HPC – DMAA and CEC –DMAA systems, respectively, and 2.3 kOe for the PBG – DMF system. In order of magnitude, these values are consistent with Hcr necessary for the nematic liquid crystal - cholesteric liquid crystal phase transition (Papkov & Kulichikhin, 1977, Chandrasekhar, 1980). In this case, ∆T = Kln(E/E0) or Tph (H > Hcr) = Tph(H = 0) + Kln(E/E0). Coefficient K depends on the molecular mass of the polymer and its concentration in solution.
Plot of ∆T vs. lnE for solutions of (1–4) HPC-3 and (5, 6) HPC-1 in DMAA c = (1) 46.1, (2) 48.3, (3) 49.6, (4) 51.3, (5) 52.0, and (6) 49.0%.
Plot of ∆T vs. lnE for CEC solutions in DMAA. c = (1) 46.0 and (2) 48.8%.
Thus it is revealed a perfect analogy in influence magnetic and mechanical fields in their influence on phase liquid crystal transitions in solutions of rigid chain polymers.
PBG – poly(γ-benzyl-L-glutamate, DMF – dimethylformamide, DMA – dimethylacetamide, Hc – a critical field strength,
p0 – zero field pitch, Δχm – diamagnetic anisotropy of the liquid crystal, K22 – the twist elastic constant, LC – liquid – crystalline, CEC – cyanoethyl cellulose, HPC – hydroxypropyl cellulose, Tph – phase-transition temperature, Χ – coefficients of magnetic susceptibility, H – magnetic intensity, E – energy of the magnetic field stored by the solution volume unit, η – viscosity, φ2* – critical concentration of a polymer,, above which the LC order arises,
x – asymmetry of macromolecules, φ – ionization potential, ΔT – the difference of phase transition temperatures under dynamic and static conditions, γ – shear rate, ΔT – the difference in LC phase transition temperatures in the presence and absence of magnetic field, τ – times of relaxation, ΔH* – enthalpy of activation for the nematic liquid crystal–cholesteric liquid crystal transition, ω2 – mass portion of polymer,
This work was supported by the Ministry of Education and Science of the Russian Federation (project no. AVTsP 2.1.1/1535 “Development of the Scientific Potential of the Higher School”) and by Federal Agency of Russian Federation (project no. NK-43 P(4), Federal Program “Scientific and Scientific – Pedagogic Personnel of Russia).
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
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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