Improving the quality of life (QOL) of the people we serve is the essence of gerontological nursing and other disciplines that treat older adults. The symptoms and functional impairments that accompany chronic illnesses common in older adulthood often diminish QOL. Further, measuring QOL outcomes in older adults is critically important to the development and testing of interventions.
The scientific knowledge base regarding QOL measurement has expanded and matured greatly during the past 4 decades. In this commentary, we provide insight into the state of the science and make recommendations regarding five QOL measurement issues in gerontology: (a) conceptualizing QOL outcomes, (b) making choices between standardized measures and selecting a mode of administration for assessing QOL, (c) understanding issues inherent in proxy and self-report measures, (d) confounding the measurement of care and outcomes, and (e) translating results into practice.
Conceptualization of Quality of Life Outcomes
Although there is strong agreement that evaluating QOL in older adults is vital for providing quality care, how best to do this remains a question. One reason for the controversy is the lack of a gold standard for measuring QOL. Because studies with substantially different approaches to conceptualization and measurement of QOL lead to conceptual confusion, conflicting results even in the same population, and difficulty interpreting and synthesizing findings, we urge the research community to use consistent conceptual definitions and to choose measurement instruments that are consistent with the stated QOL definition. We maintain that the definition provided by the World Health Organization Quality of Life Group is currently most suitable for gerontological health care research. This group defines QOL as:
An individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns. It is a broad ranging concept affected in a complex way by the person’s physical health, psychological state, level of independence, social relationships and their relationships to salient features of the environment.
This frequently cited definition emphasizes that the individual is the most suitable judge of his or her own QOL and acknowledges that QOL is a multidimensional concept that encompasses all aspects of an individual’s life.
As QOL science moves from descriptive, observational work to developing intervention studies designed to improve QOL, the need for conceptual clarity becomes increasingly important to facilitate translation into clinical practice. For example, findings from QOL instruments that evaluate the functional impact of an illness may or may not be consistent with results obtained from an instrument that measures QOL from a satisfaction perspective. Information obtained from QOL studies of older adults is used for numerous purposes, including comparing QOL outcomes in clinical trials that examine the effectiveness of a new treatment, detecting factors that predict QOL, and identifying QOL outcomes that predict survival. Findings from these types of QOL studies are particularly important for older adults with chronic disease(s).
Choosing an instrument or instruments to measure QOL in older adults can be challenging given the plethora of instruments available. The Patient-Reported Outcome and Quality of Life Instruments database (PROQOLID, http://www.proqolid.org) contains information regarding more than 700 QOL instruments, including their psychometric properties and available translations. QOL instruments generally fall into one of three categories: (a) global measures that result in one score to characterize overall QOL; (b) more detailed generic measures that are applicable to all populations and allow for comparisons across a wide variety of diverse groups; and (c) disease- or population-specific measures that emphasize QOL issues most pertinent to the disease, population, treatment, and/or potential side effects. Therefore, selection of a measure should consider whether QOL is to be assessed relative to other conditions or should instead capture QOL as targeted to the specific disease state or condition.
Standardized Measures and Modes of Administration for Assessing Quality of Life
Many current QOL measures emphasize physical function, which is not always a central concern, especially for active, healthy older adults (Hickey, Barker, McGee, & O’Boyle, 2005). We recommend measures that capture aspects of life that are important for the group in question. Also relevant to consider are the appropriate methods with which to assess QOL. A wide range of data collection methods have been used to measure QOL, some of which allow for an individualized assessment. Most standardized measures include forced-choice, Likert-type scales administered in interview formats or through self-administration (Ferrans, 2010; McCabe, Begley, Collier, & McCann, 2008; Verdugo, Schalock, Keith, & Stancliffe, 2005). Less common, but also used, are qualitative interviews, including focus groups, that subjectively capture QOL and aspects often not included in global measures (Schenk, Meyer, Behr, Kuhlmey, & Holzhausen, 2013). These methods, including respondent-generated QOL measures to capture individual values, address concerns regarding lack of specificity in a “standard needs” approach (Ferrans, 2010; Macduff, 2000). However, although they have advantages regarding increased validity, self-constructed QOL measures lack representativeness across patient groups and proxy ratings.
Because technology has advanced along with the need to collect QOL data across care settings, the range of data collection approaches has similarly expanded. For example, computer-generated responses via Internet interfaces (web-based assessment tools) and handheld devices, such as smartphones or tablets, may be used to capture QOL in community-based settings if respondents are able to use and have access to technology (Hacker, 2010). In addition, interactive automated telephone assessments are increasingly used to assess QOL, due to their low costs, feasibility, and consistency in response options (Skolarus et al., 2012). In hospital settings, patient-worn devices, such as wrist actigraphs with subjective event markers, have been used to both cue and capture QOL responses, even in older adult patients who are critically ill and/or fatigued (Hacker & Ferrans, 2007). Electronic devices have some advantages over traditional pen-and-paper administration approaches in that they allow for immediate entry of the data into databases for scoring, decrease errors in entry, and assure the data are more complete. Conversely, increased costs associated with using technology may be prohibitive for researchers and clinicians with limited funds. Another limitation is that unless access to the technology is provided, those older adults without access to the Internet or devices, or who have limited experience completing online surveys, may not be adequately represented in QOL assessments. Consequently, to reliably capture QOL data, different data collection approaches may be appropriate, depending on the functional capacity and computer literacy of the group being studied. That is, regardless of setting, older adults should be given the option to complete an interview, a pen-and-paper measure, or a computer-generated instrument. However, the psychometric properties of QOL instruments across different modes of administration need to be examined prior to offering this option within the same study. Furthermore, researchers need to be facile and open to matching the mode of measurement to the target population and be aware of potential sample selection and response set biases that can ensue.
The National Institutes of Health has dedicated substantial resources to advancing and promoting robust patient-reported measures of health, including QOL. The Patient Reported Outcomes Measurement Information System (PROMIS) initiative represents one of the most comprehensive efforts to develop a computer-adaptive testing system for the collection of patient-reported outcomes to be used in clinical research and practice (Cella et al., 2007; Reeve et al., 2007). This system has been used successfully in older adults (Weaver et al., 2012) and provides static short forms to capture QOL information for those who are unable to access the system through technology or are more comfortable with a traditional pen-and-paper approach.
Issues in Proxy, Self-Report, and Observation Measures
Proxy ratings of QOL are often obtained for individuals with communication barriers and cognitive impairment, such as those with dementia. The rationale for obtaining proxy ratings is that the person’s inability to understand and respond appropriately to item ratings may jeopardize the validity of the responses. However, proxy ratings (family members and health care professionals) have consistently been shown to have a poor relationship to the person’s ratings and generally underestimate the person’s QOL (Hounsome, Orrell, & Edwards, 2011; Sprangers & Aaronson, 1992; Woodend, Nair, & Tang, 1997; Yip, Wilber, Myrtle, & Grazman, 2001). Variables identified as contributing to discrepancies in ratings include lack of communication between proxy and patient, patient dependency, and a view that the patient is incompetent (von Essen, 2004). Further, evidence has shown that characteristics of the proxy himself or herself influence their ratings. For example, one study found that nursing assistants’ ratings of an individual’s QOL related to their own attitudes toward the person and perceived competence to address their care needs (Winzelberg, Williams, Preisser, Zimmerman, & Sloane, 2005).
Overall, studies have failed to identify consistent relationships among QOL and impaired cognitive functioning, even in people with progressive diseases such as dementia (Murrell, 1999). Although advanced disease does affect completion rates for QOL measures, even older adults with severe dementia have been found to provide valid and reliable self-ratings of quality of life (Hoe, Katona, Roch, & Livingston, 2005). In addition, insight and awareness have been shown to be intact in people with dementia even in the later disease stages (Clare, 2003; Clare et al., 2012). Overall, evidence supports the need to capture self-perceptions and insights regarding QOL in older adults and people with dementia, even into the later stages of memory loss.
Easy-to-administer assessment tools are available to evaluate older adults’ capacity for decision making, including their understanding of the information being evaluated, which reduces barriers to the use of self-report measures (Sahadevan, Chin, Yap, & Yeoh, 2003). For example, researchers and clinicians often assume that older adults with cognitive impairment, including progressive dementia, are not capable of thinking in the present or grasping the meaning of decisions, but this assumption has not been supported by systematic studies (Burgener & Dickerson-Putman, 1999; Horton-Deutsch, Twigg, & Evans, 2007). Once the older adult’s capacity and understanding are confirmed, tailored data collection approaches (e.g., use of large-print, easily understood scoring definitions) can be used to assure valid collection of self-report data. To help ensure the older adult’s rights are protected, questions to guide the choice of assessment method might include: “Are the right questions being asked in the right form?” or “Is the greatest ‘stakeholder’ making the decision?” Given the documented discrepancies between proxy and self-reported QOL outcomes, even in older adults with cognitive impairment, an optimal measure will include the person’s own assessment.
Observational measures of QOL have been used primarily in long-term care settings, but also in the home. Often, these measures are administered without the person’s knowledge and so have the potential to invade privacy while underestimating QOL. Well-documented stigmatized views of people with dementia may contribute to inaccurate (i.e., low) QOL ratings and lack of concern from health care providers regarding protecting the person’s privacy (Burgener & Berger, 2008; Harris & Sterin, 1999). Health care providers are often not aware of these stigmatized views; however, subjective assessments of adults with dementia as incompetent or as not having “a life worth living” often influence providers’ QOL assessments (Benbow & Reynolds, 2000; Cahill et al., 2008; Farsides & Dunlop, 2001). Such evidence increases concerns regarding the extent to which privacy is protected when using observational measures and their potential validity. When observational measures are used, it is essential that the individual’s privacy be fully protected and that the observations themselves not result in a deleterious outcome, such as increased anxiety from being observed during a stressful procedure.
Confounding the Measurement of Quality of Life and Quality of Care
Another consideration in the measurement of QOL in older adults is how closely it is related to the quality of care. In general, individuals’ own characteristics contribute most to their QOL. For example, individuals with more comorbid conditions or fewer social supports tend to have a poorer QOL than others. Once these characteristics are controlled, differences in structures and processes of care become more strongly related to QOL outcomes, which is consistent with the Donabedian (1978) model of health care quality. Structures of care include material resources (e.g., private rooms, a familiar homelike environment) and human resources (e.g., expertise of staffing, hours of care per day), whereas processes include services and systems of care, such as assistance with activities of daily living or provision of activity programs. Outcomes are those at the individual level that result from exposure to such structures and processes of care. It is easy to understand how, for example, residing in a familiar environment and receiving more care from more expert staff would benefit QOL.
Conceptually, it is possible to separate QOL outcomes from quality of care, but in practice, many measurement tools include items reflecting both. Maintaining a distinction between the two is important to inform quality improvement, where the intent is to understand care provision and its relation to QOL outcomes. As an example, a recent review of 11 measures used to assess the quality of dying and quality of care when dying in long-term care settings found that only four (36%) measured care or its evaluation, rather than components of both constructs (van Soest-Poortvliet et al., 2011). A discrete example illustrates the sometimes subtle differences between care and outcomes: The Quality of Dying in Long-Term Care instrument includes an item assessing “being kept clean” (a process of care, but one that is important to quality of life/dying), as well as one assessing “[being] prepared to die” (an outcome of care, but one that reflects earlier processes of care) (Munn et al., 2007).
Differences Between Statistical and Clinical Significance and Translating Results into Practice
Translating QOL research findings into clinical practice is critically important, given the emphasis on evidence-based practice in nursing and other disciplines. To effectively accomplish this translation, it is necessary to consider the extent to which outcomes are clinically relevant. For this reason, evaluating QOL outcomes should occur at two levels: determining statistical significance and clinical significance. This dual approach is an important advance in QOL research in the past decade.
Although the meaning of statistical significance is universally understood by researchers and most clinicians, determining when statistically significant findings result in clinically meaningful changes for older adults is less understood. For a QOL change to be clinically significant, it must be large or important enough to affect patient care or treatment (Wyrwich et al., 2005). There are numerous examples in which statistically significant findings result in so small a change in QOL that they are clinically meaningless and do not call for a change in practice. Of note, a series of manuscripts published by a consensus group of expert QOL researchers detailed the state of the science related to the clinical significance of QOL findings in oncology (Cella, Bullinger, Scott, & Barofsky, 2002; Frost, Bonomi, Ferrans, Wong, & Hays, 2002; Guyatt, Osoba, Wu, Wyrwich, & Norman, 2002; Sloan et al., 2002; Sprangers, Moinpour, Moynihan, Patrick, & Revicki, 2002; Symonds, Berzon, Marquis, & Rummans, 2002). This series catapulted the clinical significance of QOL findings to the forefront of QOL research. The relevance of this work is equally important for older adults regardless of the underlying diagnosis or health care problem.
Even when clinically significant results are detected in QOL research, translating these changes into practice is often complicated and dependent on the needs of the user. Multiple groups, such as patients, clinicians, and policy makers, use information from QOL studies to make determinations of clinical significance prior to incorporating changes into practice. Because the perceptions, standards, and values of each group are different, diverse thresholds for determining clinical significance may be used—meaning that a QOL change that is considered clinically significant to one group may or may not be to another. For example, an older adult receiving life-sustaining treatment may perceive a related 10-point decrease in QOL as a result of care to be clinically significant to the extent that he or she decides that treatment is not worthwhile. Clinicians, however, must consider the clinical significance of QOL findings on two levels: the individual and the group. Although a 10-point decrease in QOL for an individual may justify ending treatment for that particular person, at the group level, a 10-point decrease in QOL may not justify a modification in treatment strategies for an entire population of patients when that treatment has a high cure rate. From an overall health care perspective, the benefits in survival achieved from the treatment may outweigh the decrease in QOL, even if diminished QOL is long lasting or permanent. These potential differences in thresholds for clinical significance must be considered prior to translating research into practice.
Selecting QOL measures for older adults is complex and depends on a wide range of factors; thus, recommending a specific instrument or battery of instruments for use across all older adult populations is not feasible. The choice of measures should relate to the intent of the project. Ideally, QOL should be conceptualized in the manner most consistent with the perspectives of the older adult. If his or her condition is one that has a notable impact on QOL, then aspects of that condition should be incorporated into the assessment. Whenever possible, QOL ratings should be obtained directly from the individual, with proxy assessment used only when circumstances dictate this approach. Special consideration needs to be given to older adults with cognitive impairment and communication barriers. Further, questions should be asked, and results interpreted, in a metric that has meaning to inform practice and policy, including the clinical significance of outcomes.
Lastly, deciding how to best use information gained from QOL assessments is evolving and should be guided by the intent of the assessment. Evaluating the economic impact of health care, including changes in QOL, is becoming increasingly important due to ongoing limitations in health care resources. At first glance, using QOL outcomes as a mechanism to allocate health care resources might appear reasonable; however, controversy stems from deciding whose point of view to use when evaluating those outcomes—that of the individual, the group, or the policy makers. For this reason, making decisions related to allocation of health care resources based solely on subjective QOL outcomes should be considered a slippery slope, particularly if the individual’s evaluation is considerably different from society’s view. Furthermore, making decisions regarding allocation of health care resources becomes even more complicated if the overall goals of treatment are not clearly defined, as is frequently the case when caring for an older adult population with multiple comorbid conditions.
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