THE CONCEPT OF participation represents an advance in

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1 S38 SPECIAL COMMUNICATION Applications of Response Shift Theory and Methods to Participation Measurement: A Brief History of a Young Field Carolyn E. Schwartz, ScD ABSTRACT. Schwartz CE. Applications of response shift theory and methods to participation measurement: a brief history of a young field. Arch Phys Med Rehabil 2010;91 (9 Suppl 1):S Measurement of participation in people with disability can pose psychometric and conceptual challenges. Ambiguous or paradoxical findings can occur because of differences among people or changes within people regarding internal standards, values, or conceptualization of participation. These response shifts can affect standard psychometric indices, such as reliability and validity. We focus herein on the interpretation of patient-reported outcomes and, in particular, on the cognitive appraisal processes known as response shift. We present theoretical and conceptual distinctions building on response shift theory and other current developments in health-related quality of life research to inform participation measurement research. We discuss how response shifts can influence the interpretation of reliability, validity, and responsiveness of participation measures. We then discuss the evidence for the clinical significance of response shift phenomena and describe current design, statistical, and individualized approaches for detecting response shift phenomena. Key Words: Patient outcome assessment; Patient participation; Rehabilitation by the American Congress of Rehabilitation Medicine THE CONCEPT OF participation represents an advance in disability measurement because it attempts to characterize ways in which people with disabilities can still engage in meaningful and pleasurable activities despite their physical limitations. Albeit a new construct, there is a growing interest in its assessment. Current approaches to its assessment include objective and subjective parameters, with its objective assessment related to the frequency or duration of engagement in activities in real-life environments, and its subjective assessment more focused on the person s satisfaction with and evaluation of the importance of a particular set of activities. 1 Although standard psychometric approaches can be used in developing tools aimed at measuring participation, it is important to be aware of and accommodate person factors that may mitigate the putative reliability and validity of new measures of From the DeltaQuest Foundation Inc, Concord, and Departments of Medicine and Orthopedic Surgery, Tufts University School of Medicine, Boston, MA. Presented to the American Congress of Rehabilitation Medicine, Symposium on Measurement in Participation, October 14 15, 2008, Toronto, ON, Canada. Supported by an honorarium by the Rehabilitation Institute of Chicago using funds received from the National Institute on Disability and Rehabilitation Research (grant no. H133B040032). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated. Reprint requests to Carolyn E. Schwartz, ScD, DeltaQuest Foundation, Inc, 31 Mitchell Rd, Concord, MA 01742, carolyn.schwartz@deltaquest.org /10/9109S-00602$36.00/0 doi: /j.apmr participation. One such important factor is response shift phenomena, 2,3 a construct that focuses on the interpretation of patient-reported outcomes and, in particular, on cognitive appraisal processes. RESPONSE SHIFT PHENOMENA AND PARTICIPATION When individuals experience a health state change, they may change their internal standards (ie, recalibration), values (ie, reprioritization), or meaning (ie, reconceptualization) of the target construct one is asking them to self-report, such as participation. For example, people with a substantial physical disability may experience a severity of fatigue that they did not know prior to the development of the disability. Consequently, they would recalibrate what severe fatigue means to them, making it difficult to compare their predisability and postdisability ratings of fatigue as it relates to participation. They may also reprioritize life domains such that sense of community and interpersonal intimacy become more important to their sense of participation than career success or material gains. Finally, they may reconceptualize participation to focus on those domains where they continue to have control and be effective when rating their participation. These subtle and not-so-subtle response shifts are to be expected with evaluative constructs, which are assessed by idiosyncratic rather than objective criteria. 4 Evaluative ratings of participation are products of an appraisal process in which individuals must consider what participation means to them, what experiences they have had that are relevant to participation, how experiences compare with desired circumstances or outcomes, and the relative importance of different experiences. 5 Although clinicians and philosophers have long noted response shift phenomena, with early references linked to Aristotle 6 and Socrates, 7 the challenge for researchers has been to operationalize the construct in ways that are measurable and robust. WHAT DO PSYCHOMETRIC CHARACTERISTICS MEAN IN THE CONTEXT OF RESPONSE SHIFT? Indeed, one of the most challenging aspects of response shift research is that it calls into question fundamental assumptions of questionnaires (eg, measurement invariance) and psychometric criteria such as reliability, validity, and responsiveness. Schwartz and Rapkin 4 noted that every quantitative index of reliability, validity, and responsiveness may be distorted by reasonable and expected adaptation-related changes over time. For example, high internal consistency (reliability) and crossmeasure correlations (validity) provide little psychometric in- ES QOL SEIQOL List of Abbreviations effect size quality of life Schedule for the Evaluation of Individual Quality of Life

2 RESPONSE SHIFT AND PARTICIPATION, Schwartz S39 formation about what a measure is measuring but support the idea that people are answering a set of items in a similar way and that these items reflect a narrow bandwidth of a given construct. Similarly, for interobserver agreement to be high (another aspect of reliability), observers must share a frame of reference, sample the same experiences, apply the same standards, and give experiences equal priority. It is likely, however, that observers may differ in many aspects of QOL appraisal, particularly if their health trajectory has been quite different. Indeed, the meaning of QOL will likely be different among people with different health trajectories, and the constructvalidity correlations among QOL domain measures may be different among people with distinct disease trajectories because of appraisal differences. 4 The same phenomenon is likely to occur with subjective aspects of participation measurement. Even test-retest reliability may not have a unique meaning. Low test-retest reliability among people with a disabling chronic disease has been found to reflect a meaningful behavioral pattern related to existential angst rather than measurement error. 8 Responsiveness, another key psychometric index that is an extension of validity, 9 may also not reflect what is assumed. A measure that is not responsive that does not change in step with objective indices of health may be reflecting a provisionally stable set-point to which an individual returns despite a constant level of stress. 4,10,11 This unresponsiveness or stability may be a result of habituation 12 or active coping 13 and may follow a pattern described by engineers and economists as hysteresis. 14 That is, stress may be added without inducing apparent change in a system (or a person) up to a certain level of tolerance, beyond which the system may undergo permanent and profound change that makes it impossible to return to earlier tolerances. The impact of these subtle and not-so-subtle response shifts on psychometric characteristics such as reliability, validity, and responsiveness are not only conceptually important but also operationally important because they influence the interpretation of clinical research findings. Considering or adjusting for response shifts can change the magnitude of the detected treatment effect (ie, from medium to large 15 ) and can alter the domain to which the changes can be attributed (ie, from physical to psychologic 15 ). Research that has drilled down to an in-depth study of appraisal processes has revealed that individuals cognitive criteria for the appraisal of psychologic well being and distress are quite fluid, and putative improvements in well-being may be effectively reinterpreted in light of a person s selective emphasis on positive events. 16 Both classic and modern test construction methods are implicated and influenced by response shift phenomena, and thus a theory-driven approach to testing response shift hypotheses would be critical to advancing the field of participation measurement. A BRIEF HISTORY OF RESPONSE SHIFT THEORY The study of response shift began formally in the fields of management science and educational research and was exported to the field of health-related QOL research in the mid- 1990s. In 1999, a theoretical model was described by Sprangers and Schwartz 2 that provided a framework for thinking about the meaning of response shift in clinical research. It was proposed that after a change in health state (the catalyst), stable personality characteristics (ie, antecedents) interact with cognitive, affective, or behavioral processes that individuals use to deal with life changes (ie, mechanisms) and promote response shifts. In turn, these shifts result in a level of perceived QOL that may be higher or lower than expected based on objective criteria. 2 The response shift process is hypothesized to be iterative, happening continuously or repeatedly over time as part of a positive or negative adaptation process. Although this model was useful for generating hypotheses for new response shift research, it had limited testability because it was circular: operationalizations of response shift and mechanisms often looked identical. For example, reprioritizing goals could be a measure of reprioritization response shift as well as a coping measure (ie, a cognitive, affective or behavioral process or mechanism in the Sprangers and Schwartz 2 nomenclature). This circularity made it impossible to distinguish the 2 and to test response-shift-specific hypotheses. Rapkin and Schwartz 5 thus expanded this model by adding appraisal processes to the model and moving response shift from the status of a measurable latent variable to an epiphenomenon that was inferred when changes in appraisal explained the discrepancy between expected and observed outcome. Figure 1 shows how the Rapkin and Schwartz 5 model would apply to participation research. 17 This model has the advantage of being more testable because the circularity problem has been resolved. Additionally, the Appraisal Profile measure developed by Rapkin and Schwartz 5 seems to be an effective way to capture these cognitive processes. 16 The measure is useful because it can be applied to any construct and because it generates considerable detailed descriptive data about the appraisal process. The challenge in working with the measure is that even after substantial variable reduction, the researcher is left with a large number of cognitive measures that can generate complex cognitive pathways to clinical outcomes. 16 The evolution of response shift theory continues, with recent efforts addressing implications of response shift for measurement of constructs relevant to people with disability, 17 and current efforts considering trait/state distinctions in response shift theory 18 as well as formal definitions for and empirical examples of response shift measurement bias and response shift explanation bias. 19,20 These theoretical developments may lead to different measurement or quantitative analytic methods for detecting response shifts. Fig 1. The Sprangers and Schwartz 2 response shift model of QOL. This model postulates that a change in health state (the catalyst) leads to an interactive and dynamic response shift process. Stable personality characteristics (ie, antecedents) interact with mechanisms (ie, cognitive, affective, or behavioral processes that individuals use to deal with life changes) to promote response shifts. In turn, these shifts result in a level of perceived quality of life that may be higher or lower than expected based on objective criteria. Adapted from Schwartz et al. 17 Reprinted with permission.

3 S40 RESPONSE SHIFT AND PARTICIPATION, Schwartz DOES IT MATTER? THE CLINICAL SIGNIFICANCE OF RESPONSE SHIFT PHENOMENA Recent efforts in the field of QOL research have focused on the interpretation of changes in patient-reported outcome scores. The recognition that statistical significance does not necessarily mean clinical importance has led to the development of a host of indices of clinical importance (eg, the minimal clinically important difference; see reference 21 for overview of this subfield). Investigators are also increasingly required by professional journals to express their research findings in terms of ESs 22,23 so that a standardized estimate of the magnitude of the findings is accessible and transparent. One tool that has become increasingly used for summarizing effect sizes within a specific research question is meta-analysis, whereby findings from multiple studies addressing a similar question are summarized using a weighted average that considers the quality of the scientific implementation, robustness of the findings (eg, sample size), and magnitude of the effect. In an effort to clarify how important response shift effects are for interpreting QOL outcomes, Schwartz et al 24 implemented a meta-analysis of extant response shift studies. Extensive literature searches and multiple contacts with researchers circa 2005 resulted in the collection of 494 articles united by a shared keyword of response shift. Only published longitudinal studies that measured response shift were retained, resulting in 28, of which 19 reported the requisite data for computing an ES. The ES for each study was calculated and compared with regard to potential moderator variables: the QOL domains measured, disease group investigated, sample size used, and response shift method used. Studies were rated for quality to allow ES weighting. Figure 2 presents a forest plot showing effect sizes across the studies included in the meta-analysis for global QOL. When ES absolute values were examined, ES magnitude was small, with the largest ES detected for fatigue, followed by global QOL (see fig 2), physical role limitation, psychologic well being, and pain (mean ES weighted.32,.30,.24,.12, and.08, respectively). 24 ES varied considerably in direction across therapeutic areas, suggesting that response shift effects differ according to disease trajectory (see reference 25 ). For example, in populations in which total cure is likely, response shift is much less prevalent than in populations where residual disability 25 or disease progression 26,27 is expected. They concluded that the evidence is mixed about whether response shift is a clinically significant phenomenon, and it most likely is a small effect that can influence interpretation. This meta-analysis led to several important steps in response shift research. First, it called into question the sensitivity and utility of one of the most commonly used response shift methods because almost all of the articles included in the metaanalysis used this same method. The then-test method for detecting response shift includes a retrospective pretest in which respondents are asked to provide at the posttest a judgment of their baseline of functioning on a particular questionnaire. 28 This response shift approach has the advantage of being easy to administer but the disadvantages of being difficult to interpret 29 and having questionable psychometric comparability. 30 The then-test method is also increasingly acknowledged as a noisy measure that is substantially diluted by recall bias. 26,31 Thus, the conclusions of the meta-analysis applied only to the then-test approach for assessing response shift and not to the general clinical significance of response shift. Because other methods for assessing response shift did not provide adequate data in the published articles for inclusion in the analysis, the meta-analysis also led to recommendations for future response shift research so that a subsequent meta-analysis will be able to include and compare various response shift detection methods. These standards would include (1) providing standardized response mean, mean, and SD for all outcomes measured and considered in response shift effect size computation; (2) stating the direction of the scores derived from the outcome tools used or, preferably, rescaling scores to conform to consistent standards across tools (eg, higher scores reflect better QOL); (3) ensuring that the published article Oort et al, 2005 (n=170) Global Quality of Life Visser et al, 2005 (n=169) Lepore et al, 2000 (n=166) Ahmed et al, 2004 (n=144) Rapkin et al, 2000 (n=140) study Bernhard et al, 1999 (Adjuvant grp) (n=135) Bernhard et al, 1999 (Surgery grp) (n=129) Joore et al, 2002 (n=97) Timmerman et al, 2003 (n=77) Schwartz et al, 2002 (n=61) Jansen et al, 2000 (n=46) Adang et al, 1998 (n=22) Schwartz et al, 1999 (n=21) Mean ES =.30 Mean directional ES=.02 Total Fig 2. This forest plot shows the global QOL effect sizes reported in the studies included in the Schwartz et al 24 response shift meta-analysis. The solid vertical line represents the mean effect size across all studies, and the individual stem-and-leaf plots show the mean and 95% confidence intervals for each study. The mean effect sizes for the studies were.30 when absolute value was used and.02 when direction of the effects was retained. Given the diverse patient populations used, the absolute value was considered a better indicator of the true effect size. This forest plot suggests that response shift effects for global QOL are small to moderate. Abbreviation: Grp, group. Adapted from Schwartz et al. 24 Reprinted with permission.

4 RESPONSE SHIFT AND PARTICIPATION, Schwartz S41 contains all of the information used as criteria for quality rating (ie, large enough sample size, adequate response rate, a control or comparison group, randomized study design, an objective clinical criterion variable to distinguish patient subgroups, well-established psychometric tools, hypothesis-driven comparisons, type I error rate.10); and (4) providing more discussion of the meaning of the individual study s findings in terms of recalibration, reprioritization, and reconceptualization response shifts along with clear interpretation guidelines for the approach and findings. Finally, the meta-analysis made it clear that new methods needed to be developed and used. In the few years since the meta-analytic work was done, several important developments have been made in response shift detection methodology, which are described briefly. Additionally, current response shift research seems to be less likely to use the then-test approach. Perhaps the meta-analysis, in its documentation of the small effect size and variability of results, served to pave the way for more sophisticated and interpretable response shift approaches. METHODOLOGIC ADVANCES IN RESPONSE SHIFT DETECTION Current methods for detecting response shifts are evolving from a predominant focus on the then-test approach to an emphasis on statistical and individualized methods. As noted, the then-test approach has the advantage of being easy to administer and analyze but the disadvantages of random error and/or confounding with recall bias as well as difficulty of interpretation. For these reasons, we briefly describe promising statistical and individualized methods that have evolved in the past few years. There are 3 statistical methods and 1 mixed method (ie, qualitative and quantitative) that have been applied to response shift detection and that hold promise: structural equation modeling, latent trajectory analysis with subject-centered residuals, classification and regression tree analysis, and the SEIQOL individualized method. The 3 statistical methods require substantial sample sizes, on the order of 10 subjects a variable and a minimum of 200. The 4 methods vary in terms of how much they focus on aggregate analyses versus individual patientfocused analysis, and thus how sensitive they are to individual response shifts. Originally evolving from factor analytic methods, structural equation modeling is a technique that combines factor analysis and regression analysis to solve multivariate research questions at a group level. 32,33 By analyzing covariance matrices, these models test measurement and structural models to first test the assumption of measurement invariance and then to examine whether relationships among variables are similar over time (ie, the structural model). Recent advances of this method were made by Oort et al 34 to clarify how distinct changes detectable with structural equation modeling reflect different aspects of response shift. This work extended earlier work done by Schmidt 35 and yielded more sensitive algorithms for detecting response shifts. To date, the Oort 34 method has been applied in patients with cancer, stroke, musculoskeletal diseases, and chronic obstructive pulmonary disease. 15,30,36,37 Although this method has the advantage of allowing secondary analysis of existing data to test response shift hypotheses, it has the disadvantage of being sensitive to response shifts only when most of the sample is likely to make response shifts. 36 Because preliminary estimates of the prevalence of response shift suggest that about one half to one third of respondents exhibit response shifts that are detectable by these methods, 25,38 one would have to oversample people prone to response shifts to be able to detect such change using structural equation modeling. Oversampling will be feasible when we are better able to predict who experiences response shifts. Latent trajectory analysis with subject-centered residuals is a method developed by Mayo et al 38 that focuses on the individual and seeks to develop a predictive growth curve model to examine patterns in discrepancies between expected and observed scores. 39 By obtaining and scaling model residuals, Mayo 38 creates subject-centered residuals to categorize respondents as (1) exhibiting no response shift that is, the person s residuals were consistent over time, but there was some change in their perceived QOL at time t (QOL t ); (2) exhibiting a positive response shift that is, the person s evaluation started low and then shifted or was reassessed upward; and (3) exhibiting a negative response shift that is, the person s evaluation started higher than expected and then was reassessed down over time. To date, this method has been applied to patients with stroke. 38 This method is of interest because it classifies response shift at the individual rather than group level, and because it distinguishes groups based on the timing as well as the direction of the response shift. It is useful for stratified analyses with existing data and thus does not impose additional demands on the respondent. Its primary weakness is that it cannot distinguish random error from response shift. Like other statistically sophisticated methods, it requires a substantial sample size measured over multiple time points to create a predictive model. Classification and regression tree analysis 16,40,41 is a method applied by Li and Rapkin 16,42 and Li et al 43 that combines qualitative and quantitative methods to yield a rich analysis of complex data. These investigators used the Appraisal Profile developed by Rapkin and Schwartz, 5 which yields qualitative text data in response to open-ended questions as well as quantitative data in response to multiple choice questions. The tool measures 4 distinct parameters of the appraisal process: 5 (1) framing that is, what QOL means to the individual; (2) sampling that is, what relevant experiences the person has; (3) evaluating that is, how experiences compare with relevant standards; and (4) salience that is, what the relative importance is of different experiences. These data are then content-analyzed to yield categories 5 amenable to quantitative analysis, and trees are generated. The final product of this analysis is homogenous groupings of respondents who share patterns of appraisal. In this example, Li and Rapkin 16 evaluated appraisal processes in 644 patients with acquired immunodeficiency syndrome 6 months after enrollment in a study evaluating how appraisal patterns were related to reported general health. The method revealed substantial differences in level of reported general health as a function of distinct combinations of appraisal preferences. 16,42,43 Individualized methods are another novel approach to evaluating response shifts. One of the most familiar is the SEIQOL, 44 which has been used to examine stability in domains and internal metrics (ie, cues and anchors) over time and across groups. The 3 aspects of response shift (ie, recalibration, reconceptualization, reprioritization) are thus explicitly included in the SEIQOL method, making it an appealing and interpretable approach to getting highly individualized metrics of response shift. This method involves a semistructured interview that elicits from the person the domains or areas the person thinks about when considering QOL (cues), the response options that make the most sense to the person (anchors), and the person s level of functioning within each of these domains (levels). The person s overall score is generated using a regression model approach. At each subsequent interview, domains, cues, and levels are elicited from the person,

5 S42 RESPONSE SHIFT AND PARTICIPATION, Schwartz Strength Table 1: Summary of Strengths of Response Shift Detection Methods Then-Test Structural Equation Modeling Latent CART SEIQOL Easy to use Easy to analyze Meaningful interpretation If expert involved TBD If expert involved Individual-level interpretation Low participant burden RSP adjustment possible RSP stratification possible Does not need large samples Abbreviations: CART, classification and regression tree analysis; RSP, response shift phenomenon; TBD, to be determined. and an overall score is generated. The result is an idiographic (ie, individual-focused) QOL score that allows the person s domains, cues, and levels to vary as they do naturally. This individualized QOL tool has been found to be useful in understanding changes in individuals over time as they cope with a progressive and deteriorating disease, and could be useful for elucidating how this process is similar or different among people with a substantial albeit stable disabling condition. The SEIQOL approach has been successfully applied in a variety of patient populations, including cancer, 45 hip replacement, 46 and dental treatments. 47 The advantage of the SEIQOL approach is that it compares global QOL scores over time while explicitly allowing the cues, levels, and weights to vary within and across individuals. It reduces the ambiguity of QOL scores by making this variation explicit and measurable while retaining the option of comparing a global score over time. Its disadvantages include the cost of implementing the semistructured interview as well as its explicit comparison of overall scores that are composed of different domains, cues, and anchors. Current QOL measures such as the Medical Outcomes Study 36-Item Short-Form Health Survey 48 commonly compare overall scores, but they do not query or contain information about the disparate domains, cues, or anchors being considered and combined in these overall scores. All of the methods described have strengths and weaknesses, as summarized in table 1. Regardless of the response shift detection approach, however, the investigator interested in investigating response shifts in the context of participation research should adhere to the following guidelines: (1) always have a comparison/control group to enable theory-driven hypothesis testing for example, nondisabled or differently disabled respondents; (2) have clearly stated hypotheses about when the response shift will occur (what is the catalyst for response shift and change?); (3) use a combination of approaches to provide information about convergence among methods in detecting response shift in participation; and (4) include an objective clinical criterion measure so that it is possible to distinguish between expected and observed change in QOL over time for example, triangulate perception-based, performance-based, and evaluation-based measures of participation in work and social activities. CONCLUSIONS Response shift phenomena are cognitive appraisal processes that focus on the interpretation of patient-reported outcomes and, as such, are salient in participation measurement. QOL response shift has been evaluated in a number of populations characterized by disability (eg, multiple sclerosis, stroke, cancer, musculoskeletal diseases), but the focus on participation response shift has yet to occur. It is our hope that this overview will facilitate research into participation response shift. Response shift is likely to be prevalent in participation measurement because effective coping with disability whether stable or progressive would require a regular reappraisal of one s meaning of participation, relevant experiences to sample, relevant standards to apply, and the relative importance one assigns to the various life domains related to participation. Consideration of the parameters of appraisal that relate to response shift phenomena will enhance the conceptual clarity about relevant psychometric models and will facilitate participation measurement. Participation measurement would benefit from methods that move away from the then-test methodology to more sophisticated qualitative and quantitative methods that focus attention on appraisal processes over time. For example, the Appraisal Profile 5 could be given after a standard participation measure (see other articles in this special supplement for examples) and tailored to refer to participation rather than QOL. The resulting data collected would be qualitative text that elucidates what respondents are thinking about when answering these participation measures, and how each of the 4 parameters of appraisal differ across respondents. 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