The extent to which a good person-environment (PE) interest fit between student and study program leads to better study results in higher education is an ongoing debate wherein the role of the study program environment has remained inadequately studied. Unanswered questions include: how diverse study programs are in the interests of their student populations, and how this program interest diversity influences study results, in comparison to individual PE fit? The present study addressed these questions in students  enrolled in open-access university education. In such an open access system, students are allowed to make study choices without prior limitations based on previous achievement or high stakes testing. Starting from the homogeneity assumption applied to this open access setting, we propose several hypotheses regarding program interest diversity, motivation, student-program interest fit, and study results. Furthermore, we applied a method of measuring interest diversity based on an existing measure of correlational person-environment fit. Results indicated that interest diversity in an open access study environment was low across study programs. Results also showed the variance present in program interest diversity was linked to autonomous and controlled motivation in the programs’ student populations. Finally, program interest diversity better explained study results than individual student fit with their program of choice. Indeed, program interest diversity explained up to 44% of the variance in the average program’s study results while individual student-program fit hardly predicted study success at all. Educational policy makers should therefore be aware of the importance of both interest fit and interest diversity during the process of study orientation. Literature has shown that students who choose a study program that fits their vocational interests, arguably have better study results and have a better chance of finishing higher education in a timely fashion . Such stVocational interest is typically defined as the liking or disliking of certain activities or environments, usually represented by a concise number of dimensions . VocatioFirst, vocational interest has predictive power towards study program choice . Up to 7Next, student interests always have an object or an environment . As an eLiterature also reports that vocational interests are stable constructs . This opFinally, interests are also linked to motivation . PerformRealistic, Investigative, Artistic, Social, Enterprising, and Conventional. By comparing the profiles of a student and a study program, PE fit indicates how well a (future) student’s interests match a specific study program. The current iteration of the RIASEC model still instigates a lot of research and applications specifically targeting (higher) education. As an example, the current study builds on the SIMON-I RIASEC questionnaire that was recently introduced as a study orientation tool, tailored for the transition towards higher education [Geneticist? (I) or Starting up an enterprise? (E) are two of a total of 153 very short items to which the (future) student had to answer with yes or no. The scores on all dimensions can be recalibrated to a score on a 0–100 scale for each dimension, effectively rendering a student RIASEC profile, e.g. R: 80, I: 70, A:60, S:50, E:55, C:59.The Holland RIASEC model has a long standing tradition as one of the most influential models of vocational interest ,2,17. Thducation . SIMON-Iincumbents of a study program, this incumbent method can empirically generate environment profiles using the profiles of said students [Beside these person profiles, there are a number of ways to construct RIASEC study program environment profiles. One of these methods is built on a common principle that the environment is determined through the people that are in it –20. As vstudents ,10. Due students . The relPearson’s product-moment correlation coefficient. As such, correlational fit indicates how well the student’s interests fit with his or her study program of choice. For instance, fitting a study program profile (M) R:80, I:70, A:60, S:50, E:60, C:70 to a student profile (S) R:80, I:60, A:60, S:50, E:50, C:60 results in a correlation of .87 (or vice versa). A high correlation means a better PE fit. Literature has shown that correlational fit also has predictive value towards study results, especially for first year students [Correlational fit is a pattern based similarity measure, that correlates commensurate student and study program RIASEC dimension profile patterns by using the students ,9.Although the predictive validity of PE fit between students and their study programs on study results has repeatedly been established, the positive influence of this PE fit on study results remains somewhat limited, varying from a very small to medium effect at best –25. IndeHowever, when starting a new study related to concepts like PE fit, researchers are usually confronted with a selection bias problem. Access to study programs, especially in the United States, is often already restricted and linked to performance criteria like passing a specific exam or satisfying grade point average (GPA) requirements. As such, these access restrictions yield a pre-selected sample, possibly not only biased in terms of intellectual competence, but potentially also biased towards PE fit between students and programs. It is therefore a possibility that the variation in reported PE fit effects reflects–at least partly–the variation in educational access policies. The present study provides a unique opportunity to enhance our knowledge on the range of PE fit effects by using a large student data set from an open access higher education environment. It will be very interesting to observe whether the positive effect of PE fit on study results generalizes to, or is even stronger under conditions of free study choice, while also examining the influence of program interest diversity on average program results.The age old homogeneity assumption states that people who display similarity in characteristics such as vocational interests tend to lean towards similar environments, with literature spanning more than half a century ,28,20. ATo start, study programs should display a low interest diversity in their student population. Indeed, individual students who have a high PE fit with a study program are more like to enroll for this specific study program than students who lack a high PE fit. This mechanism will be even more explicit if there are no further requirements (like GPA or exams) to enter a program as is the case in our present study with open access. As a consequence, the average PE fit in our open access study program population should be quite high when compared to programs with a more restricted access. Due to this high individual PE fit across students, each study program should display high internal similarity, or a low diversity, regarding the vocational interest of its student population. In order to be able and test our predictions of open access versus restricted access, the present study also features data from a small control group (same university) that had to pass an entry exam to enter the Medicine or Dentistry programs.Next, a high interest in a specific program is just one, autonomous motivation why students enroll for that program. Students can also opt to act on exterior, more controlled motives to make their choice, like pleasing their parents. As these students are less interested in the specific program, they will have a lower PE fit with the chosen program. Some study programs might be more prone to such externally controlled study choice than others; these programs will attract more students with a lower PE fit and thus show a higher interest diversity in their student population. For the present study, we therefore predict that program interest diversity will vary over programs. We also predict this variance will be linked to autonomous and controlled motivation.Finally, the internal similarity of the environment will exert an influence on the behavior of the population, with different programs rewarding different interest patterns . In caseThe debate regarding the best fitting (dichotomous) measure for studying homogeneity or internal similarity of environments is still undecided. However, Bradley-Geist and Landis provide evidence that first and foremost, the measures used should depend on the study’s major hypotheses . For theIn the present prospective study, we have derived and investigated a number of research questions from the theoretical predictions made in the introduction regarding program interest diversity in an open access environment. For our first question, we have investigated how diverse study programs actually are in the vocational interest of their student population. Students with similar interest patterns should be attracted to similar programs, especially in an open access environment. Consequentially, the fit between students and their program of choice should be high. As such, we hypothesize program interest diversity will be low. We also expect that this general low interest diversity will still show variance over the range of programs, linked to the motivation of their student populations. Indeed, some students will choose the program autonomously, because they are interested in the program itself, while others will take into account exterior motives like parental approval and are less interested in the program itself. We expect some programs could be more prone to such exterior motives of student choice. Therefore, the student population of these programs should show more diverse interest patterns. In sum, we hypothesize that a student population with high autonomous motivation is related to a low program interest diversity, while a population with high controlled motivation is linked to a higher program interest diversity.For our second question, we have explored if and how program interest diversity has an effect on (average) program study results. On an individual level, literature already showed us that a higher PE fit will lead to better study results . One couFor our final research question, we have compared the found effects of program interest diversity on study results to the effect of individual PE fit. As we are still unsure about the nature of the program interest diversity effect, we can only make predictions regarding individual PE fit. From theory discussed in the introduction, we hypothesize that the individual effects of PE fit on study results will be small, as program environments with low interest diversity (high similarity) are expected to limit the effect of individual PE fit.N0 = 6,772, 55% female) starting an academic bachelor at a large Western European university  across eleven faculties and 41 study programs, with an open access policy (anyone who completed secondary education) were invited to participate in a long term assessment to enhance study choice and study results. Though the programs have an open access structure, they are also strictly stratified. In other words, within one program, everyone has to take the same set of study courses during the first year. The study programs are listed in N = 4,827, 57% female). Participating students immediately filled out online interest and motivation questionnaires . All students were also subject to periodic evaluation systems (once for each course) split up into two sessions (January and May/June 2017) and a retry session (August/September 2017) if they failed the test on the first attempt. At the end of the academic year (September 2017), the results from the SIMON-I test were cross-referenced to the exam results. A total of 4,422 students (92% of N) at least participated in some form of evaluation. The remaining 8% dropped out, prior to any form of evaluation. As literature shows that PE fit also has an influence on perseverance, we took a conservative approach and included the dropouts in our study [n = 192, response rate 86%) already had to pass an entry exam to be allowed to start, in contrast to the students who picked any of the other 39 study programs. As such a high stakes access mechanism (possibly) influences the homogeneity inside a such a program, we have decided to again act conservatively and exclude both programs from our study. However, we have pooled the students from both programs (program RIASEC profiles correlate 0.99 and have about 50% common courses) as a control group for our first question regarding program interest diversity. The final pool of student participants (SP) was N = 4,635, spread out across 39 study programs.All students SIMON@ugent.be.The provided personal information can be linked to study results in higher education. 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(for the student) I agree to these terms: yes noStandardized Root Mean square Residual = 0.051; Normed Fit Index = 0.97; Comparative Fit Index = 0.97; Goodness of Fit Index = 0.99), all pointing towards a good to excellent circular fit. For an overview on interpretation of these indices, we refer to the exhaustive listings provided by Kenny [randomization test of hypothesized order relations (RTOR) using the RANDALL package [correspondence index of .92, while a circular fit of the data also reached significance, p = .02. For a full discussion on the RANDALL function and RTOR analyses, we refer to [The SIMON-I questionnaire  for each student, each student’s RIASEC profile was correlated with his or her study program RIASEC profile based on the profiles of successful and persistent students. As this correlational fit measure also represents a deviance, the measure  was then rescaled to an easy-to-interpret interest deviance between 0 and 1, with D COR = 1 -  / 2. As an example, a student with a correlational fit measurement of 0.76 would rescale to D COR = 0.12, indicating the student’s RIASEC profile deviates 12% in regard to the profile of his or her study program.A program interest diversity measure should indicate how deviant students’ interests are in a given study program of choice. By averaging out D COR across students in a program, we can obtain a measure of interest diversity AD COR for each study program, that represent how much students diverge on average from the program profile. As an example, a study program with an AD COR of 0.23 indicates that the RIASEC profiles of students within the program deviate (on average) 23% in regard to the program profile.M = 0.70, ranging from 0.50 to 0.83) but not on the controlled factor. In contrast, items from the controlled subscale showed high loadings on the controlled factor .The Self-Regulation Questionnaire was presented to all students to measure their autonomous (8 items) and controlled (8 items) motivation . For theGPA is a widely known and used measure of study success . HoweverDespite these precautions, we still deem it possible  that a student’s GPA could be biased due to specific program choice and subsequent deviant grading. As such, we have taken a conservative approach by obtaining an estimate of the maximal hypothetical bias for GPA by calculating the intra class correlation coefficient through a multilevel model. For our purposes, this coefficient indicates how much of the variance in individual GPA can be attributed to the program level. Practically, the coefficient provides us with an estimate of the maximum bias due to possible different grading standards in different programs. However, stronger students could also systematically choose more difficult programs. This confound cannot be disentangled within the scope of the present study. As a results, we have included the intra class correlation coefficient as an absolute maximum of hypothetical bias in a student’s GPA due to the program of his/her choice.To avoid unwanted bias of  skewed distributions of the correlational data on outcomes and subsequent conclusions, we performed all statistical testing on standardized scores by taking the z—scores of the D COR measures (by subtracting the grand mean and dividing by the grand standard deviation of the full data set) as the base measure. Averaging out these z—scores over programs renders the standardized equivalent of the AD COR measures.To address our first research question, we inspected the variance of interest diversity over programs. The interest diversity of open access programs was tested against our control group that had to pass an entry exam. We also regress interest diversity (AD COR) on autonomous and controlled motivation to test whether both types of motivation in the student population are indeed linked to the variance in program interest diversity. To address our second research question regarding the influence of interest diversity on study results, we have regressed the average study program GPA and PASS on program interest diversity. As we are pitting three hypotheses against each other, we have considered both linear and curvilinear relations. To address our final research question regarding the comparison between the individual PE fit effect and the environmental program interest diversity effect, we have constructed two hierarchical models, a linear one for GPA and a logistic one for PASS In these models, we have investigated the effect of individual PE fit on study results, the effect of program interest diversity on study results and the cross-level interaction between individual PE fit and study program diversity. All analyses were conducted using R(Studio), SPSS and HLM software.Geology (36) with 0% PASS rate. Because this find could prove problematic, we considered the average GPA (370.29) of the program, which was very low (compared to the other programs). The correlation between GPA and PASS did not change by excluding Geology (36). There does not seem to be a huge discrepancy between the GPA and PASS results for Geology (36). Because 0% PASS rate is still a huge outlier, we decided to act conservatively and pay special attention to the Geology (36) result in specific PASS analyses.Important to note, the intra class correlation coefficient for GPA amounted to 6%. This means that only 6% of the variance in GPA can be attributed to the program level. In other words, the individual GPA is determined for 94% based on personal achievement. As such, the maximum possible bias of GPA due to non-equivalent grading can be estimated at 6%.M = .15 ; SD = .05). This interest diversity also corresponds to a rather impressive average PE fit correlation of .70 between the RIASEC profiles of students and their program of choice. We thus clearly observe a concentration of programs at the lower end of the diversity continuum (due to the high average PE fit), ranging from .05 (African Studies) to .26 (Mathematics). This concentration at the lower end also means that 74% of the interest diversity continuum at the higher end remains unused. To formally test if an open access environment indeed results in more interest diversity regarding study programs, we compared the interest diversity of 39 study programs with open access to the control group with restricted access. A two-sided one sample t-test indeed revealed a significant difference, t(38) = -4.27, p < .001, with a somewhat large effect size of Cohen’s d = 0.69. We therefore conclude that the evidence shown confirms our hypothesis. Study programs in an open access environment indeed display a low interest diversity regarding their student population.F = 6.86, p = .003, with an explained variance of 28%. Standardized regression coefficients  indicate a negative relation between program interest diversity and average autonomous motivation in the program student population, and a positive relation between interest diversity and average controlled motivation in the program student population. Closer graphical inspection  = 0.63, p = .43 and F = 3.01, p = .09 for GPA and PASS respectively. The curvilinear regressions of study results on interest diversity however, were significant with high levels of explained variance, F = 7.19, p = .002, R2 = .29 and F = 13.84, p < .001, R2 = .44 for GPA and PASS respectively. Adding the Geology (36) results to the PASS regression rendered similar results. These curvilinear findings provide evidence that rising program interest diversity has a mixed effect on the average study results of the program’s student population. Different programs do seem to reward different interest patterns . More specifically, a very low diversity in a small number of programs is associated with very high average study results. However, for the majority of the programs that had a higher diversity to begin with, we observe that study results tend to improve as program interest diversity rises.To examine the possible direct effect of interest diversity in study programs on average program study results, we conducted two (curvi-) linear regressions of both average program GPA and PASS rates on interest diversity (AD COR). Figs Educational Sciences, Speech Language and Hearing Sciences, East European Languages and Cultures and African Studies. We decided to compare the six dimension RIASEC profiles of these four programs to each other and to the other programs. As we were curious about the nature of these curvilinear effects, we ran further post hoc analyses. Closer inspection of Figs F = 6.13, p = .02, R2 = .16, while the curvilinear one no longer did, F = 3.10, p = .06.When considering the other programs of the present study, we repeated the regression of study results (GPA and PASS) on interest diversity by excluding the results from these four programs. F = 2.84, p = .10, interest diversity still showed a strong linear trend towards an effect by explaining 8% of the variance in program study results. The curvilinear regression did not reach significance, F = 1.72, p = .20. The addition of Geology (36) to the regression rendered similar results, although the explained linear variance only amounted to 5%.In sum, these results confirm our hypothesis that interest diversity has a direct, but mixed effect on average study program results in an open access environment. Different programs seem to reward different interest patterns. In general, a higher interest diversity of the student population has a positive effect on average program study results. However, this effect seems to reverse for very specific programs displaying a very high social dimension and a very low realistic dimension. These programs reach high average study results if the student population shows very low levels of interest diversity.R2.To compare the effect of program interest diversity on study results to the effect of individual PE fit, we have performed multilevel analyses of student (level 1) and program (level 2) effects on study results. As GPA is a linear variable and PASS is a dichotomous variable, we constructed a linear and a logistic multilevel model. Effect sizes for the different effects at the individual level, program level or both levels combined (full model) were calculated by combining the variance components (GPA model) and model deviance statistics (PASS model) into a pseudo As hypothesized regarding our third question, our GPA multilevel model thus indicates the influence of the individual’s PE fit on individual study results can indeed be considered low. However, at the same time, these GPA model results also reveal that interest diversity over programs explains much more variance in average study results in that program compared to the individual level.χ2 (38) = 45.26, p = 0.20. This result indicates the individual PE fit does not interact with the program interest diversity. In other words, there is no different individual effect of PE fit on study results depending on the study program environment.We also tested the addition of a cross-level interaction (through a random slope for PE fit) between interest diversity (AD COR) at the program level and PE-fit (D COR) at the individual level. The chi-squared test returned a non-significant result, R2 (around 0.1%), for both individual PE fit and program interest diversity when considering full model deviance . PE fit only reached a pseudo R2 of about 0.1% at the individual level, while the pseudo R2 for interest diversity did reach 37% at the program level.As hypothesized regarding our third question, our PASS multilevel model thus indicates the influence of the individual’s PE fit on individual study results can indeed be considered low. In contrast to the GPA model, both individual PE fit and program interest diversity displayed very low levels of explained variance when considering full model deviance. Analogous to the GPA model, PASS model results also reveal that interest diversity in the program explains much more variance in typical passing rates for that program, across students.χ2 (38) = 35.04, p > .50. Hence, we do not find any evidence for a cross-level interaction between individual student PE fit and program interest diversity on study results.The addition of a random slope for the PE predictor again resulted in a non-significant result, In sum, these findings again confirm our hypothesis that the individual effect of PE fit on study results is small to almost non-existent in an open access environment, while interest diversity has a profound effect on results at the program level. For the full models, we have found mixed evidence that program interest diversity is more explanative towards study results than individual PE fit. Furthermore, we did not find any evidence for an interaction effect between group level program interest diversity on individual study results.Vocational interest refers to the liking or disliking of certain activities or environments, represented by a number of base dimensions and characterized by the properties of prediction, contextualization, stability and motivation ,5,13,6. The present prospective study set out to remedy these voids in literature. As such, the present study was conducted in an open access environment, exploring the interactions between individual interest PE fit and environment program diversity. Although we derived our hypotheses from homogeneity theory, we do not consider interest diversity as a mere synonym for homogeneity ,28,20. IDuring the present study, special care was given to the validity and reliability of predictors and study result measurement. As indicated by Graham and Harris, reliability and validity are function of both sample and measure ,44. The For our first research question, we investigated how diverse study programs actually are in the vocational interest of their student population. We hypothesized program interest diversity would be low, as predicted by the homogeneity assumption. We also expected that this general low interest diversity would still show variance over the range of programs, linked to the motivation of their student populations. Indeed, some programs are chosen through autonomous motivation by students who are highly interested in their program of choice. As students are highly interested in their program of choice, such programs should display a low interest diversity. Other programs could be more attractive to students who have ulterior motives like pleasing their parents and should display a higher interest diversity. As students are less intrinsically interested in their program of choice, these programs display more variance in student vocational interest, resulting in a higher interest diversity. Results for our first question indeed showed that program diversity was low across all study programs, leaving 74% of the higher end diversity continuum unused. On an individual level this find indicates that in general, the PE fit between students and their programs is quite high: RIASEC profiles between student and program correlate .70, on average. Indeed, students predominantly seem to choose a higher education study program that fits their interests quite well when given the opportunity, as is the case in an open access environment. Results also showed that the variance in program interest diversity is related to motivation. Study programs with low interest diversity were linked to students with relatively higher autonomous motivation, while programs with a higher interest diversity were linked to students with a higher controlled motivation. This relation between student motivation and interest diversity indicates that some programs do attract more students with a higher controlled motivation.For our second research question, we explored the direct effect of the program interest diversity on average study results. As different programs could reward different interest patterns, we took a conservative approach and pitted three hypotheses against each other on how program interest diversity would influence average program study results. The curvilinear relation between program interest diversity and average study results provided evidence for our mixed effects hypothesis. Different programs indeed rewarded different interest patterns . To provFor our final research question we compared the effect of program interest diversity on study results to the effect of individual PE fit. We hypothesized that due to the low interest program diversity  the effect of PE fit would be low. We also tested the cross-level interaction of individual program fit and environmental program diversity. Analyses indicated that the effects at the individual level on study results were very modest at best: student PE fit only explained up to 0.6% at the individual level. Hence, in an open access higher education environment, the variance in PE fit between students and their program barely has a meaningful impact on individual study results. In opposition to these results at the individual level, program interest diversity explained up to 37% of the variance at program level, which is a huge contrast to the observed explained variance at the individual level. Moreover, program interest diversity of different study programs did not only influence average study results, we also obtained partial evidence that this diversity explained more study result variance in the total multilevel models than the individual indicators of PE fit. As only a small part of the total variance in study results (up to 6%) was situated at the program level to begin with, this is no small feat indeed. These findings are analogous to those found in our second question and provide additional evidence that higher education institutions should indeed consider program interest diversity when making policy decisions towards student orientation and admission. As a possible explanation, most students in this open system showed a high PE fit with their program of choice. In systems were choice is restricted (on the basis of exams or GPA requirements), students may have to choose for programs that match their interests less well, and then this (larger variety in) PE fit has a bigger impact on individual study results. Indeed, earlier research that examined PE fit effects on study results in constrained access systems typically observed more explained variance ,24,9,25.This discussion on the consequences of open access policy illustrates the importance of studying PE fit effects in a variety of study contexts. As Nauta already indicated, (study) environments remain understudied . Entry eTo close the discussion on our third question, the variance in program interest diversity and student PE fit was limited to the extent the cross level interaction between individual and environment was not significant. In other words, program interest diversity did not influence the student PE fit-study results relation: effects of individual PE fit remained low, regardless of the interest diversity of study programs. These findings in our open access study environment are at odds with results from internal similarity research from Tracey and colleagues . They shThe present study is unique in its assessment of PE fit effects in an open access system. It would be interesting in the future to directly compare study environments with more constrained entry restrictions on the exact same measurements, using the exact same analyses. We speculate that such an approach would show enlarged PE fit effects in more restricted study programs, while the influence of interest diversity will diminish. The access restrictions could thus be a crucial factor in explaining the mixed findings in literature regarding PE fit, while elaborating literature with interest diversity research.Our conceptualization of interest diversity and its motivational connection could also be used in organizational and occupational research. We predict that not all work environments will show the same amount of interest diversity. As access to the work environment works quite differently in comparison to access to higher education, we can also expect different effects. For instance, open access to certain jobs (low degree requirements) could result in higher interest diversity. Indeed, a student in an open access environment picks a certain program because that program is of particular interest to him or because his parents wants him to study that specific program. An employee could have other motives to pick a job. Employees who have no (or a low) degree can decide to work out of financial motives exclusively. Though highly speculative, we think that the different motivation in work and study contexts will lead to different patterns of interest diversity for both contexts and could ultimately end up explaining why the strength of the PE fit–study results relation is so underwhelming in comparison to the theoretical predictions.In the present study, we have assessed program interest diversity of student populations in study programs. In an open access environment, interest diversity of student populations in study programs is low. RIASEC profiles of students and programs correlate .70 on average. Interestingly, study programs with low interest diversity attract students with relatively higher autonomous motivation, while programs with a higher interest diversity show higher controlled motivation. Despite overall low diversity, the interest diversity of the program environment still had a profound effect on the program’s typical study results, while the influence of individual PE fit seems to be nonexistent or very limited at best. In order to enhance student study success, the present study has shown policy makers and educational institutions should focus on a sufficiently high PE fit amongst their student populations, while still allowing for some study program interest diversity.S1 Table(DOCX)Click here for additional data file.


However, getting rich interview data on eHealth working mechanisms can be surprisingly challenging, as several of the authors have experienced. Moreover, when encountering difficulties as we did, there are few places to turn to, there are currently no guidelines for conducting such interview studies in a way that ensure their quality. In this paper, we build on our experience as well as the qualitative literature to address this need, by describing 5 challenges that may arise in such interviews and presenting methodological tools to counteract each challenge. We hope the ideas we offer will spark methodological reflections and provide some options for researchers interested in using qualitative interview studies to explore eHealth’s working mechanisms.Future development of electronic health  programs  will be furthered if program design can be based on the knowledge of eHealth’s working mechanisms. A promising and pragmatic method for exploring potential working mechanisms is qualitative interview studies, in which eHealth working mechanisms can be explored through the perspective of the program user. Qualitative interview studies are promising as they are suited for exploring what is yet unknown, building new knowledge, and constructing theory. They are also pragmatic, as the development of eHealth programs often entails user interviews for applied purposes . By capitalizing on these existing (applied) user interviews to also pursue (basic) research questions of how such programs work, the knowledge base of eHealth’s working mechanisms can grow quickly. To be useful, such interview studies need to be of sufficient quality, which entails that the interviews should generate enough data of sufficient quality relevant to the research question (ie,  The outcomes of individual eHealth programs are well established; a vast majority of the research published between 1996 and 2013 concerned outcome (74%). However, much fewer publications focused on processes (26%) [Building the next generation of automated electronic health  programs will require a shift of attention from the performance of individual programs to a joint effort of understanding eHealth’s working mechanisms ,2. The tpurposes , or eHeaoutcome 7%. Howevees (26%) -7. This not static; many programs include different degrees of interactivity and tailoring [Instead, the development of eHealth programs often relies on rather static traditional behavior change theories  or modelailoring ,5, makinailoring ,2. On thailoring ,2. This eHealth’s working mechanisms can be studied using various methods, but a promising and pragmatic venue of investigation is the qualitative interview, that is, “professional conversations (...) where knowledge is constructed in the inter-action between the interviewer and the interviewee (...) about a theme of mutual interest” . The quaThe qualitative interview is also a pragmatic research method, as many researchers already conduct interviews with program users as part of an applied research goal (developing or implementing an intervention). In the process of conducting interviews with program users, a researcher may become intrigued by a more basic research question and may perhaps consider the pragmatic solution of pursuing both the applied and the basic research goal in the same interviews by simply adding questions to the existing interview guide. We believe that such studies mixing basic and applied research goals have the potential of becoming an important asset to the field, by accumulating knowledge on more general issues that may help us understand how eHealth therapy works.thick descriptions or rich data [not rich—that lack in breadth or depth—might threaten the study’s quality or the potential reach of its conclusions. Thus, getting rich data that inform the research question is an important aspect of a qualitative study. In the case of qualitative interviews, producing rich data means conducting interviews in a way that makes the participants spend a lot of time talking about the aspects that are central to the investigation, including both breadth and depth in their descriptions. This may seem straightforward, but it can be surprisingly difficult in practice.However, to become such an asset, the interviews conducted in these studies should provide what in qualitative methodology is known as ich data ,21. Richich data ,23. DataThe reflections that are presented in this viewpoint paper arose from some of the authors’ experiences with a specific interview study   and the interactional processes (the how of the interaction). For example, psychotherapy’s working mechanisms can be described as the therapy sessions’ influence on the client’s internal change processes. The therapy sessions, in turn, can be described as comprising 2 main elements: their content  and the interactional processes, when and how often interaction is initiated, how the interaction unfolds, how the next interaction is initiated, and so on.We suggest that a person’s interaction with any health intervention can be visualized in terms of a triangle, which includes the individual help seeker, the intervention, and the behavior change processes . However, the researchers struggled with superficial answers that did not seem to reveal anything about how the participants related to the program (such as I thought the program was fine) until 1 participant called the program a secret friend. The researchers were puzzled. Was this person’s program experience unique? Why did other participants not talk about the program in this way at all? However, apart from a few statements similar to this one, the interviews were not generating data to answer the research question of how the participants related to the program.When a researcher sets out to explore a potential eHealth working mechanism in an interview, it may be difficult to achieve a joint understanding of the interview topic together with the participant. For example, as mentioned previously, in the study that was the starting point for this paper, the researchers were interested in understanding how the participants related to the program . The intFailure to get rich data on a research question may indicate a marginal phenomenon—or that the interviewer is failing to communicate the focus of enquiry in a way that facilitates joint understanding with the participant. We believe it is a truism that experience is multifaceted and that an experience can be described from many perspectives. For example, a client may describe a therapy session from a factual perspective of when and where it took place, from an experiential perspective of his or her emotions before, during, and after the session, from a historical perspective of the session as a stage in his or her spiritual development, and so on. If the interviewer’s questions are mostly descriptive, there may be a scarcity of cues concerning which perspective to assume, leaving the decision up to the participant—and the participant’s choice may not be the researcher’s choice. This may be especially challenging in studies on eHealth working mechanisms, as the interactional processes may not be part of the participant’s conscious experience. Therefore, descriptive questions asking for the participant’s program experiences will perhaps not cause him or her to talk about the (invisible) person-program interaction but rather about the program as a thing with a content. On the other side of the conversation, the interviewer may fear that more direct questions onto the focus of enquiry will put words in the participant’s mouth and disqualify any subsequent answer.An interviewer can use several methodological tools to foster a joint understanding of the interview topic with the participant. One such tool is vignettes: vivid, exemplifying prose stories that guide the conversation toward a particular aspect of the participant’s experience -38. The Another and more direct way of fostering a joint understanding of the interview topic is to involve the participants as coresearchers, or using epistemic interviewing ,14,39,40A final tool to clarify and exhaust the interview topic is to ensure the possibility of conducting follow-up interviews . A follobeginners mind: remaining curious and receptive, open to all possibilities [Coresearcher design and vignettes foster clearer communication, but they may also threaten the study’s validity if the researcher holds on to his or her initial assumptions about the studied process, failing to acknowledge unexpected perspectives. To ensure that these tools strengthen and not hamper the quality of the study, the researcher should adopt what in psychotherapy is known as the bilities -44. Furtbilities ,44. Reflbilities ,43,44. FTo allow time for joint exploration of the person-program interaction, it is necessary to limit the interview time spent on matters that are not at the core of the research question. Returning to If the participants continuously stray off the focus of enquiry by spending time on contextual aspects, it can threaten the data richness. Aspects that are contextual to the researcher may be aspects the participant wants to share or aspects she or he believes to be important to the investigation. The interviewer may try to lead the conversation back onto the focus of enquiry, but the participant may return to the contextual aspects, turning the interview into a battle over topic. Apart from being unpleasant for both, the result may be scant data on the focus of enquiry. When the focus of enquiry is potential eHealth working mechanisms, the invisible interaction may add to the challenge of straying off the topic. As the participant may be largely unaware of the interactional processes, she or he will instead talk about the aspects of which she or he is aware: the change processes (in isolation of the program) or the program (in isolation of the change processes). Information about the behavior change and about the program is certainly relevant contextual information, but talking about these aspects in isolation should not dominate the interview.How long have you been worried about your gambling? Or Have you tried restricting how much you gamble before?), together with any other questions that might serve as relevant analytic background . The interviewer may fill out the questionnaire together with the participant at a suiting point during the interview. Using a piece of paper to fill out the answers will help keep the answers short, by providing limited space and communicating a wish for answers that the interviewer can write down. Short contextual answers will in turn leave more time for the focus of enquiry.The interview conversation can be kept from straying off the research topic by using in-interview questionnaires to keep contextual answers short. The questionnaire can include questions addressing contextual issues  [Recalling specific program experiences may be challenging as although participants may be active program users at the time of the interview, they are not engaging with the program at that particular moment (unless you are combining the interview with a d below) . That med below) ; rather,d below)  to trigglive access to the person-program interaction through the think-aloud procedure [think aloud, reporting all thoughts without censoring them. The interviewer should not interrupt the participant’s flow of thoughts, and follow-up questions should be saved for after the think-aloud procedure is completed [There are, however, methodological tools to amend the problem with recall in the interview situation: 1 such tool is to get rocedure . In the ompleted . HoweverAnother tool for aiding recall is asking memory-facilitating interview questions. If program experiences have not been encoded as specific episodic memories, the interviewer’s phrasing of questions becomes increasingly important, as the words she or he uses will influence the participant’s memory-retrieval process by serving as memory cues . The intAs a final note on program recall, it may not be necessary for the participant to remember any particular program session at all; the researcher must consider what level of detail is necessary to answer the research questions meaningfully. For some research questions, the sum of program experiences may be more important than any particular experience. If so, using the interview to discuss the participant’s overall experience with the program can be more meaningful than facilitating recall of specific sessions .typical woman, valuing emotions  over facts (the program content). This subconscious fear had caused her to rush through the questions (which she during the interview had found awkward), partly answering some of them on behalf of the participant and ending the interview early.All interviews are also social situations, and aspects of the social situation will influence the data . In 1 ofinterviewer as the interacting agent, pushing the experience of the program as an interacting agent to the background and adding to the interaction’s invisibility. In sum, the social interview situation may cause the participant to talk differently about his or her program experiences than she or she would have otherwise. The consequence of this may be less rich data, or data that do not correctly represent the participant’s experience.Gender stereotypes are not the only potential social disturbances in an interview—other social roles may be prominent, and within eHealth research, the interviewer may be particularly prone to be perceived as an interviewer or clinician or interviewer or developer. Perceiving the interviewer as also a clinician may cause the participant to think of him or her as a therapeutic interactional partner and to be less attentive to the therapeutic agency of the eHealth program. Similarly, perceiving the interviewer as also a program developer may highlight the program as a thing made by someone else, making it more difficult to see the program’s role as a therapeutic agent—or cause the participant to self-censor negative experiences, as 1 of the authors experienced in 2 different studies ,31. In bThe potentially negative influence of the social interview situation can be counteracted with methodological tools. The researcher acknowledging the potential negative influence of roles and stereotypes, both before and after the interviews, can minimize their negative effect. Before an interview, researchers should reflect on potentially salient social aspects and whether something should be done about them . If circemotional woman was considered as empirical material highlighting a possibly relevant aspect of how people relate to a program, namely, that relating to a program may go against social norms and produce feelings of embarrassment .Finally, it is important to acknowledge that although the social interview situation may sometimes be a negative influence on the data, it can also be an asset. Through the interviewer’s reflexivity, the social situation may generate insights that would otherwise be missed. The interview in which the interviewer had rushed through the questions as she feared being labeled an related to the program as a potential eHealth working mechanism). The interview guide started with questions addressing possible sources for program improvement . Toward the end of the interview guide, questions on how the participants related to the program gradually increased in number (Has the program ever made you happy? Have you ever been upset by the program?). However, most participants answered interview questions on how they related to the program briefly and superficially, resulting in scant data.It was mentioned in the introduction that qualitative interviews are pragmatic for exploring potential eHealth working mechanisms as the development or implementation of eHealth programs often entail user interviews anyway. Therefore, researchers who are interested in exploring potential eHealth working mechanisms may do so through existing interviews with applied purposes. However, when applied and basic research goals are mixed like this in the same interview study, it may create an additional challenge in getting rich data on the basic research question. In the study that inspired this paper , early iapplied interviewing mode to a basic interviewing mode will involve changing the point of his or her focal attention, that is, what to listen for and which follow-up questions to ask. For the participant, changing interviewing modes will involve changing how she or he is expected to answer, from talking more superficially about the breadth of his or her program experiences (applied mode) to talking in depth about a few aspects (basic mode). If the transition between the different modes is not explicit to the participant, she or he may answer interview questions with the wrong mindset —basic interview questions as if they were applied questions or applied interview questions as if they were basic. Unclear transitions may also cause the interviewer to miss important leads in the participant’s answers because of the need to split his or her attention between the 2 research questions. The invisible interaction may exacerbate this challenge: in applied research, the program is treated as a thing, whereas in the search for basic working mechanisms, the program can be considered an interacting agent. An unclear transition between applied and basic research goals may make it more difficult for the participant to take the perspective of the program as an agent influencing the interaction.Mixing applied and basic research aims can be problematic as different aims may require different interviewing modes. For the interviewer, changing from an Interviews with both applied and basic research aims may serve both aims through topical blocks and clear introductions. The transition can be facilitated by structuring the interview in topical blocks : one covinvisible interaction: that eHealth programs affect the program users’ change processes through their content and how they influence the person-program interaction, but that their influence on the interaction is largely invisible to the user. We have described 5 interview challenges and suggested tools from qualitative methodology to counteract each challenge. These tools may serve as a step toward a set of guidelines for conducting interview studies on eHealth working mechanisms, with the goal of generating rich data that will improve the quality and reach of the findings. Findings from high-quality interview studies can in turn be used to build more general, theoretical knowledge about the working mechanisms of automated eHealth programs. Through theorizing the general working mechanisms of eHealth interventions, we believe that the next generation of eHealth programs can be developed to fully take advantage of this medium’s potential.Conducting qualitative interviews is a promising and pragmatic approach for identifying the working mechanisms of automated eHealth programs. Existing user interviews for applied purposes can be used to also pursue basic research questions on eHealth working mechanisms. Researchers planning to conduct user interviews for applied purposes would be wise to ensure the possibility to pursue research questions concerning potential eHealth working mechanisms by including this purpose in the study information provided to ethics boards and prospective participants. However, getting rich data on eHealth working mechanisms through qualitative interviews may be challenging. In this paper, we suggest that challenges may arise partly due to what we have described as the


An early molecular response is spectacularly predictive of outcome in chronic myeloid leukemia (CML) and early response landmarks may identify the high-risk patients likely to be benefit from an early therapy switch. In this study, we evaluated the most relevant cutoffs for early molecular response markers  in 476 first-line imatinib-treated Chinese patients with chronic phase CML. All outcomes were significantly superior for the 324 patients with 3-month BCR-ABL1 ≤10%, so did for the 270 patients with BCR-ABL1 >0.61 log reduction. BCR-ABL1 halving time ≤22 days was identified for patients with the most favorable outcome. Moreover, the prognosis was significantly poorest for patients with both halving time >44 days and BCR-ABL1 >10%. Importantly, multivariate regression analysis demonstrated that a BCR-ABL1 log reduction calculated at 3 months of 0.61 was the only variable that significantly predicted for OS. Our results highlight the importance of rapid initial decline of BCR-ABL1 in predicting satisfactory outcome. Our data support the evidence that monitoring BCR-ABL1 values at an early time point could contribute to accurately assess response and ultimately guide clinical decisions regarding the timing of therapeutic intervention. Early molecular response (EMR) to TKI treatment has a strong predictive value in CML patients and early response landmarks may identify patients at higher risk for transformation and poor outcome, who would benefit from an early switch to second-line therapy4. Assessment of BCR-ABL1 at 3 months has been demonstrated to be the only indicator for predicting prognosis5. Indeed, BCR-ABL1 transcript level ≤10% on the international reporting scale (IS) at 3 months is consistently associated with significantly superior overall survival (OS), progression-free survival (PFS), event-free survival (EFS), failure-free survival (FFS), as well as cytogenetic and molecular responses7. Therefore, this molecular milestone value was incorporated into the National Comprehensive Cancer Network (NCCN) and European Leukemia Net (ELN) recommendations for the management of CML8.Prognosis of patients with chronic myeloid leukemia (CML) has been dramatically improved with the introduction of imatinib as the first tyrosine kinase inhibitor (TKI)9. Specifically, BCR-ABL1 transcript values at diagnosis are markedly different among chronic phase (CP) CML patients11. Since imatinib-treated newly diagnosed CML patients often require transient discontinuation of treatment during the early phase due to adverse events, treatment responses and outcomes may be underestimated by BCR-ABL1 transcript levels at 3 months3. A more precise prognosis evaluation method is urgently needed, which reflects individual clinical entities in patients with CML-CP.Although EMR evaluation criteria seem promising, various factors such as baseline biological characteristics, treatment intensity and tolerance may have an impact on the treatment response and survival of CML patients13, the effect of imatinib in the Chinese subset was relatively favorable while the incidence of non-hematological toxicities seemed to be more frequent, in part because Chinese patients have higher plasma concentrations of imatinib15. Recent studies proposed that the rate of BCR-ABL1 decline is a critical prognostic predictor in CML treated with TKI and patients at risk of disease progression can be identified precisely by the lack of a half-log reduction of BCR-ABL1 transcripts at 3 months11. However, there is no evident information predicting treatment response and prognosis especially in the Chinese population, with the distinct clinical profile of TKI therapy.Compared to the effect on the worldwide population In this multicenter study, we investigated the prognostic significance of patient baseline characteristics and EMR markers, as well as evaluated the ability of BCR-ABL1 kinetics to predict outcome in our cohort of 476 first-line imatinib-treated CML patients. Additionally, we explored a better early discriminator of the highest-risk/lowest-risk patients at 3 months, which would help to refine recommendations for treatment decisions at early time points.n = 197), Tianjin Institute of Hematology and Blood Diseases Hospital (n = 96), Fujian Medical University Union Hospital (n = 96), The First Affiliated Hospital of Soochow University (n = 80), The First Affiliated Hospital of Zhejiang University (n = 73), Qilu Hospital of Shandong University (n = 70), West China Hospital of Sichuan University (n = 44), Tongji Hospital of Huazhong University of Science and Technology (n = 23), and Union Hospital of Huazhong University of Science and Technology (n = 14). Only CML-CP patients who were ≥14 years old and expressing typical BCR-ABL1 transcripts  were included. Besides, patients with imatinib onset before baseline sample collection as well as patients pretreated with hydroxyurea of more than 6 months were excluded from the analysis. A total of 476 imatinib-treated patients were investigated. BCR-ABL1 transcript levels at diagnosis and 3 months were available in 412 patients for review and were thereafter included in this study for final analysis. Written informed consent was obtained from each patient. This study was approved by the ethics committee at each participating institution and was conducted in accordance with the Declaration of Helsinki.From April 2000 to May 2016, a total of 693 patients with newly diagnosed CML from nine Chinese hospitals were enrolled in our clinical trials, data entry was closed on 23 November 2016. These trials included a subset of patients from Peking University People’s Hospital , at 3, 6, and 12 months, and every 3–6 months thereafter.The expression levels of BCR-ABL1 transcripts were determined by quantitative real-time PCR as described previously8. Cytogenetic assessment was performed at baseline, 3, 6, and 12 months, and every 12 months after the achievement of complete cytogenetic remission (CCyR). If the optimal response has not been achieved, cytogenetic monitoring should be conducted every 3–6 months. The achievement of a major molecular response (MMR ≤0.10%IS) and molecular response 4.5 (MR4.5 ≤0.0032%IS) required confirmation at two consecutive measurements. OS was defined by absence of death from any reason, PFS by absence of accelerated phase (AP), blast crisis (BC) and death from any reason. EFS was measured from the start of treatment to the date of any of the following events while on therapy: loss of complete hematologic remission (CHR), loss of major cytogenetic remission (MCyR), progression to AP/BC or death from any cause17. Because of the limitations of this definition, we also measured FFS that accounts for other events such as lack of milestone responses at 3, 6, and 12 months, loss of cytogenetic or molecular response, acquisition of BCR-ABL1 mutations, clonal chromosomal abnormalities in Ph+ cells, intolerance, or treatment discontinuation for any reason11.Cytogenetic and molecular responses were defined according to European Leukemia Net criterian = 41 in our study). To enable assessment of the discriminatory power of halving time, the halving times of those patients were imputed to the longest positive halving time of 8000 days, which was calculated for the patient with the smallest reduction11.The rate of BCR-ABL1 decline from baseline was assessed by estimating the log reduction and halving time of BCR-ABL1 values. The log reduction in BCR-ABL1 transcript level was defined as log (transcript level at diagnosis/transcript level at 3 months). The halving time defines the number of days over which the BCR-ABL1 transcripts achieve one-half of the baseline value, and was calculated as ln2 × c/[ln(a) − ln(b)] where (a) is the transcript value at diagnosis, (b) the transcript value of the 3-month follow-up and (c) the number of days between both measurements. Where there was no BCR-ABL1 decline from baseline at 3 months, halving times were negative value (Receiver operating characteristic (ROC) curves were generated and area under the curve (AUC) was used to compare the correlations between factors and prognosis. The optimal thresholds along the ROC curves were determined using the Youden index. Survival probabilities were performed using the Kaplan–Meier method and compared by the log-rank test. Hazard ratios (HR) and 95% confidential interval (CI) were derived using the Cox proportional hazard model. Cumulative incidence probability curves were calculated to analyze treatment response considering all permanent discontinuations of TKI for any reason as competing risks. Relative risks (RR) were calculated from Fine–Gray regression models, and significance was determined with the Wald test. Level of significance was 0.05. All calculations were performed with the SPSS software Version 13.0.Patient demographics and disease characteristics at the time of imatinib therapy are presented in Table With regard to prognostic significance, survival analysis showed that age and gender had no detectable effect on clinical outcome and achievement of molecular response Table . The mean = 281) and those who did not (n = 131). The BCR-ABL1 median ratio at diagnosis was 42.80% in the first group and 44.77% in the second, and no significant difference was found between them. Besides, no prognostic cutoff could be identified for BCR-ABL1 transcript levels at diagnosis (Table BCR-ABL1 transcript levels at diagnosis varied in a wide range (0.0114–598.64%) with a median of 43.20%. In order to determine whether the transcript level at diagnosis could be used as a prognostic indicator for disease progression, we compared patients who achieved ≤10% BCR-ABL1 at 3 months  with BCR-ABL1 values ≤10% at 3 months had significantly better outcome than those with >10% . The outcomes comparing ≤10% vs >10% BCR-ABL1 were as follows: 3-year OS, 98.5% vs 94.0%, P = 0.006; 3-year PFS, 97.8% vs 89.9%, P < 0.001; 3-year EFS, 95.7% vs 87.2%, P = 0.001; 3-year FFS, 81.5% vs 44.3%, P < 0.001; 1-year CCyR, 81.5% vs 45.0%, P < 0.001; 1.5-year MMR, 67.0% vs 24.8%, P < 0.001; and 3-year MR4.5, 34.6% vs 4.7%, P < 0.001 . It was recently demonstrated that the persistence of BCR-ABL1 transcript levels >10% at 3 months identified a group of high-risk patients that would benefit from treatment optimizationNotwithstanding the usefulness of 3-month 10% BCR-ABL1 values for outcome prediction, there was a considerable number of patients with BCR-ABL1 >10% who did not fail to therapy and some subsequently achieve satisfactory outcomes despite being initially categorized as a poor responder at 3 months . Of the highly relevant outcomes OS and PFS, we identified the optimal value of 0.61 log reduction at 3 months as the best predictive cutoff for outcome evaluation.We therefore estimated the log reduction of 3-month BCR-ABL1 values from the individual baseline values and found that lower log reduction indicated a slow or no decline of BCR-ABL1, suggesting unsatisfactory outcomes. Of 412 evaluable patients, the median reduction was a 10.23-time decrease, corresponding to a 1.01 log reduction. Using ROC analysis, the optimal log reduction thresholds for discriminating between outcomes were as follows: OS, 0.61 ; PFS, 0.61 ; EFS, 0.71 ; FFS, 0.91 ; CCyR, 0.68 ; MMR, 0.91 ; and MRP = 0.003; 3-year PFS, 97.8% vs 88.7%, P < 0.001; 3-year EFS, 95.6% vs 85.2%, P < 0.001; 3-year FFS, 82.6% vs 40.8%, P < 0.001; 1-year CCyR, 79.3% vs 52.1%, P < 0.001; 1.5-year MMR, 66.3% vs 28.2%, P < 0.001; and 3-year MR4.5, 34.8% vs 8.5%, P < 0.001, Fig. Those patients with BCR-ABL1 >0.61 log reduction  had significantly superior outcomes compared with the 142 of 412 patients (34%) where the log reduction was ≤0.61 , PFS , EFS , FFS , CCyR , MMR  and MR4.5  in favor of Group I. Comparing Groups I and II, Group II showed inferior FFS , but no difference in OS , PFS  and EFS . However, when subgroup analyses were restricted to Groups I and III, there were no differences in OS , PFS , EFS  and FFS  between those two groups. Together, the 110 patients (26.7%) with BCR-ABL1 >10% and ≤0.61 log reduction at 3 months were identified to have the poorest survival.Of note, we also evaluated the discriminatory power for outcome prediction of the 0.61 log reduction combined with 10% cutoff at 3 months. Four groups were defined as follows: group I included those who achieved BCR-ABL1 transcript levels ≤10% and >0.61 log reduction both at 3 months. Groups II included those who achieved BCR-ABL1 transcript levels ≤10% and ≤0.61 log reduction both at 3 months. Groups III included those who failed to achieve an EMR (BCR-ABL1 >10%) and >0.61 log reduction both at 3 months. Groups IV included those who failed to achieve an EMR and ≤0.61 log reduction both at 3 months. When outcomes were compared among these four groups, statistical differences were noted in OS, PFS, EFS, and FFS at 3 years as well as CCyR at 1 year, MMR at 1.5 years and MRP = 0.012).Importantly, multivariate regression analysis demonstrated that a BCR-ABL1 log reduction calculated at 3 months of 0.61 was the only variable that significantly predicted for OS . Since this predictive marker could contribute to assess early response to first-line TKI and relieve the potentially psychological burden of patients, we selected the optimal MR4.5 halving time of 22 days for further outcome prediction in favor of sensitivity.We investigated whether the patient with the most favorable outcome could be identified at an earlier time point. n = 246; 3-year OS, 98.8% vs 95.1%, P = 0.041; 3-year PFS, 98.2% vs 92.3%, P = 0.008; 3-year EFS, 97.0% vs 88.6%, P = 0.002; 3-year FFS, 84.9% vs 56.9%, P < 0.001; and 1-year CCyR, 86.1% vs 64.2%, P = 0.017; 1.5-year MMR, 78.3% vs 36.2%, P < 0.001; 3-year MR4.5, 45.2% vs 12.6%, P = 0.003, Fig. Of the 166 patients 40.3%) with BCR-ABL1 halving time ≤22 days, only one patients did not achieved ≤10% BCR-ABL1 at 3 months, whereas was still survival without progression during 5-year follow-up. Patients with halving time ≤22 days had significantly superior outcomes compared with patients whose BCR-ABL1 values did not halve by 22 days . More importantly, among the 31.8% of evaluable patients in our cohort with BCR-ABL1 >10% at 3 months, BCR-ABL1 halving time >44 days was statistically associated with the worst outcomes  with halving time >44 days  that are predictors for OS using ROC curves5. Hanfstein et al. reported the predictive role of BCR-ABL1 transcript levels of 10% at 3 months for PFS and OS6. Consistently, our analysis verified that 3-month EMR was also significantly associated with higher cytogenetic and molecular response rates. The estimation of 3-month EMR predicted higher survivals with a decreased risk of progression.In the current era of various TKIs, the early selection of patients requiring alternative treatment is critical. Previous studies demonstrated the prognostic value of 3-month BCR-ABL1 transcript levels22. In our study, the molecular 0.61-log reduction landmark  yields a relatively small P value and high Hazard ratio when compared with 3-month 10% BCR-ABL1 values, indicating a more precise prediction. Hanfstein et al. found that the lack of a 0.46-log reduction of BCR-ABL1 transcripts at 3 months was a discriminator of patients at risk for disease progression10. Branford et al. emphasized that the rate of BCR-ABL1 decline as assessed by BCR-ABL1 halving time (76 days) was a critical predictor for very poor outcomes among patients with BCR-ABL1 >10% at 3 months11. Thus, the velocity of early transcript elimination in the Chinese population accordingly appeared to be earlier than those in the west, possibly due to the relatively higher imatinib plasma levels in the Chinese patients15. Previous reports showed that imatinib trough level on day 29 were significantly associated with an achievement of 3-month EMR23.It is no surprising that the prediction based on the rate of reduction seems to be more precise than the use of isolated 3-month BCR-ABL1 values24, we evaluated this optimal time point as being 22 days after the start of treatment in our cohort. Patients with a more rapid decline  had a higher chance of ultimately achieving MMR and therefore a better prognosis. Therefore, BCR-ABL1 halving time ≤22 days was identified as a better early cutoff for patients with the most favorable outcome.Since prognostic information available even as early as 3 months may be too late for effective intervention in patients who experience early transformationAdditionally, our study highlights the importance of performing molecular analysis periodically to assess the BCR-ABL1 decline over the critical first 3 months, which may provide a cost-effective process for the better identification of patients for whom the risks and potential additional drug costs of therapy change are justified. Therefore, in a near future the survival prediction of each patient to TKI therapy will not only use the raw transcript levels but also different time-dependent variables assessing the BCR-ABL1 kinetics which are predictive of future molecular response and survival. Standardization of halving time or velocity of reduction will likely help establish more stringent recommendation and modify current clinical practices.In conclusion, our study demonstrated that patients with CML-CP treated with imatinib can be stratified according to the BCR-ABL1 kinetics and that this stratification might contribute to the timing and necessity of therapeutic intervention. Because TKI therapy might have regional effects and might reflect distinct patient clinical profiles, we hope to validate these findings by studying larger cohorts in each region in the near future.Supplementary Figure 1 LegendSupplementary Figure 1Supplementary Table 1Supplementary Table 2


Chronic myelogenous leukemia (CML) is commonly treated with tyrosine kinase inhibitors (TKIs) that inhibit the pro‐leukemic activity of the BCR‐ABL1 oncoprotein. Despite the therapeutic progress mediated by TKI use, off‐target effects, treatment‐induced drug resistance, and the limited effect of these drugs on CML stem cells (SCs) are major drawbacks frequently resulting in insufficient or unsustainable treatment. Therefore, intense research efforts have focused on development of improved TKIs and alternative treatment strategies to eradicate CML SCs. Alongside efforts to design superior protein inhibitors, the need to overcome the poor therapeutic effect of TKIs on CML SCs has led to a renaissance of antisense strategies, as they are reported as effective in more primitive cell types. Despite the greater drug design flexibility offered by antisense sequence variability and remarkable chemical improvements, antisense drugs exhibit unacceptable levels of off‐target effects, precluding them from large‐scale clinical testing. Recent advances in antisense drug design have led to a pioneering mRNA recognition concept that may offer a helping hand in eliminating off‐target effects, and has potential to bridge the gap between research and clinical practice. Since its identification, the BCR‐ABL1 oncoprotein, a hallmark of CML cells, has become the primary molecular target for the development of selective therapeutics for patients with CML.BCR‐ABL1 mRNA rapidly became a potential treatment alternative for patients with CML. Compared to the limited, structure‐based design of protein inhibitors, antisense strategies take advantage of far greater target sequence variability for the design of specific antisense drugs. The benefits of sequence‐mediated specificity, allowing selective therapeutic intervention in CML cells, are so great that antisense strategies may be considered one of the most promising future pharmacological prospects for CML treatment since the approval of TKIs.A completely different treatment approach is offered by antisense strategies that enable control of disease‐associated genes through interaction with mRNA.Interestingly, despite the evident therapeutic potential of antisense strategies, only a minimal number of antileukemic drugs exploiting mRNA interference have reached clinical trials.BCR‐ABL1 suppression developed to date, 2 of which have even been tested in clinical trials,BCR‐ABL1 gene fusion junction.BCR‐ABL1, in the vast majority of studies, binding to off‐target mRNAs resulted in nonselective effects also in BCR‐ABL1‐negative cells. Interference with nontargeted homologous mRNAs, and consequent suppression of proteins outside of therapeutic intent, is a source of clinically relevant side effects and adverse events. Hence, addressing the issue of insufficient selectivity could significantly accelerate the entry of antisense strategies into clinical practice, and innovative solutions for elimination of this phenomenon are in high demand. A promising target recognition concept was recently reported with the intention of addressing this fundamental problem,BCR‐ABL1 mRNA recognition, and hence selective therapeutic intervention, exclusively in CML cells.Antisense systems for BCR and ABL1 mRNAs flanking the gene fusion junction can occur only when these sequences are separated from one another by a specific distance, which is defined by the linker. This simple but strict requirement significantly reduces the probability of stable interference with off‐target mRNAs, and thereby may provide dramatically enhanced selectivity towards CML cells.The solution addresses the basic design of the antisense construct, which is engineered to recognize the target mRNA via 2 antisense oligonucleotides connected by a size‐specific linker Figure .44 SimulBCR and ABL1 gene sequences, or with homologous sequences of off‐target mRNAs, will be thermodynamically unstable and energetically unfavorable because of the increased degree of conformational freedom and the absence of cooperative hydrogen bonds, respectively (Figure BCR‐ABL1 mRNA (Figure BCR‐ABL1 mRNA in CML SCs. The hope for a permanent cure for CML has been further advanced by recent reports of the phenotype of CML SCs.BCR‐ABL1‐independent signaling pathways which can contribute to escape from drug‐induced apoptosis.FOXO, BCL6, and CXCR4, or proteins involved in the WNT, Hedgehog and JAK signaling pathways have already been identified as relevant in this respect.This selective antisense strategy is based on the assumption that partial interaction of the antisense construct with wild‐type The vision of CML SCs eradication got even more realistic contours after the cell surface marker CD26 has been identifiedConsidering the history and evolution of treatment strategies for CML, and irrespective of the tortuous path toward treating CML without side effects, the therapeutic potential of the novel mRNA targeting approach described above is clearly worthy of thorough investigation. The progressive nature of this antisense concept is underlined by its theoretical applicability to any disease where the presence of a particular protein has causal role in pathophysiology.status quo in oligonucleotide‐based therapeutics will emerge over time. The fascinating possibility of leukemia treatment using a specific agent that can completely spare healthy cells remains a potent stimulus to the continued exploration of this approach and would facilitate its implementation into clinical practice.The extent to which this novel strategy will be able to alter the The authors declare no potential conflicts of interest.