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National Research Council (US) Committee on National Statistics; National Research Council (US) Committee on Population. Improving the Measurement of Late-Life Disability in Population Surveys: Beyond ADLs and IADLs, Summary of a Workshop. Washington (DC): National Academies Press (US); 2009.

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Improving the Measurement of Late-Life Disability in Population Surveys: Beyond ADLs and IADLs, Summary of a Workshop.

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5Measuring Functioning and Disability in Context

This chapter focuses on a number of cutting-edge areas in conceptualizing factors external to individuals that are key either to identifying changes in functioning or to modifying experienced function, and the context in which people function, and the need to measure them in context, taking into account suitability for population surveys and relevance for monitoring trends. The presentations covered three issues:

  1. Environmental barriers and modifications
  2. Behavioral adaptations
  3. The utility of participation measures in population surveys


The objectives of Emily Agree’s (Johns Hopkins Bloomberg School of Public Health) presentation were to discuss how accommodations, like the use of assistive technology or the use of home modifications, relate to individuals’ functioning and to describe findings from a pilot study on assistive technology, fielded by Agree and colleagues in 2005, designed to develop survey questions on assistive technology and home environments.

The use of assistive technology among older adults has increased in recent decades, especially for mobility and bathing. On the basis of analysis of several national data sets, an estimated 14–20 percent of people use some kind of assistive technology, regardless of difficulty with tasks. Use of assistive technology may have contributed substantially to declines in dependence on personal care over time. Estimates show that in the 1990s nearly half of Americans aged 65 and older had home modifications or adaptations to reduce barriers. Results from the 2006 Health and Retirement Study (HRS) showed almost 70 percent of people aged 50 and older had a home modification. In general, both clinical and population-based studies suggest that the use of assistive technology and home modifications may improve functioning and quality of life, expand participation in activities, expand neighborhood mobility, and protect caregivers’ health.

Disability can be conceived as a gap between individuals’ capacities (physical, cognitive, and sensory ability) and their performance in daily activities and participation in social life. The ability of individuals to translate their intrinsic capacity into successful performance is affected by the context in which they perform each of these activities. What is required in order to do an activity depends on the specific task—getting out of bed, socializing with family and friends, or going to work. Each of these activity demands, in addition, has an activity-specific environmental context, which can incorporate barriers to accomplishing that activity as well as dimensions of the environment that actually facilitate conducting that activity.

The extent to which individuals can translate capacity into performance also depends on what they themselves can do to change the environment or to change the demands of the activity by adapting or accommodating, what has sometimes been termed compensation or using a compensatory strategy. Compensation includes the use of human help (both formal and informal care), changes in the way tasks are done (including the use of technology), and changes that are made to the home environment.

To expand on the concept a bit, if disability is an activity-specific gap between individual capacity and performance, technology expands the capacity of the individual and environmental modifications reduce barriers in the environment.

Current Survey Measurement of Assistive Technology Use

A review of six major national surveys—(1) HRS, (2) the National Health Interview Survey (NHIS), (3) the National Long-Term Care Survey (NLTCS), (4) the Medicare Current Beneficiary Survey, (5) the Medical Expenditure Panel Survey, and (6) the Survey of Income and Participation—shows that in the past few years there has been a proliferation in the number of surveys including questions about assistive technology. However, terminology varies across surveys (aids, special equipment, adaptive devices, medical devices or supplies, etc.), and so there has been little agreement in the data.

The level of detail on device use also varies across surveys. Questions may be asked globally about the use of devices for all activities or specific to each task. Similarly, detail on the devices themselves varies, whether a general question about overall use is asked about special equipment or whether individual devices are catalogued.

Another area of variation is the characterization of use. Particularly important has been the use of a reference time period in describing use. Basically, only two surveys have actually used any reference period at all: NHIS asks if people used devices now, and NLTCS asks about devices used in the past week.

Finally, these items most often are still embedded in ADL or sensory limitation questions, most often restricted to those reporting any difficulty. This approach uses ADL questions as a screener for questions about health and technology use. Consequently, if people have successfully resolved their difficulties with a task by using some adaptation, they would not be screened into the assistive technology questions at all; in national surveys, these people are being missed.

With regard to the environment, in general there are very few questions about the home environment in national surveys of health and aging, and surveys generally do not distinguish between the existence, addition, and use of adaptations or devices. Therefore, modifications are often only asked in terms of use because they are asked in the same way as other assistive technology questions, whereas dwelling features are asked without reference to disability.

There are a series of instruments used in the rehabilitation profession to do home assessments, which can involve a long checklist of classifications of potential barriers in the home. Their suitability for national population surveys is questionable because they are very long and use subjective terminology. It is hard to figure out how to objectively use those checklists at a population level (rather than at a clinical level) to assess the features of home environments and their potential to impose barriers.

Agree noted some of the limitations of current measurement approaches. Surveys of health and aging often conflate the use of assistive technology with disability by embedding questions on device use in disability questions and restricting device questions to those who report difficulties. Assistive technology tends to be task specific by design, and so survey questions on assistive technology should be task specific; however, they need to be separate from questions about task difficulty. Such an approach would help people understand how effective assistive technology may be in reducing difficulty with tasks.

Another limitation of existing approaches is that the current assistive technology measures are quite basic. They are often broad dichotomies that do not capture patterns of use, such as “Do you use a cane?” The response, yes or no, does not provide information about the frequency with which a device is used, the location, and other aspects of use that give a more nuanced view about whether people have tradeoffs between devices, use them in different places, or use them in different ways.

Finally, the home environment is even more neglected in surveys than assistive technology. There are very few measures of features of the home environment, and they do not distinguish whether the respondent added a feature, whether that person uses it, or whether someone else in the household uses it. No attempt has been made to translate some of the clinical assessment tools to national survey questions and test them. Of particular interest is to better understand how and when environmental modifications are made, relative to the progression of a disability, the nature of the use of modifications, and whether they address the barriers in the home environment.

Pilot Study on Assistive Technology

In 2005, with support from the Office of the Assistant Secretary for Planning and Evaluation in the U.S. Department of Health and Human Services, Agree and her colleagues conducted a nationwide pilot study to develop and test survey instruments on assistive technology use and the home environment for national surveys on health and aging. The goals of the pilot study were to design questions that are useful for people of all abilities, use positive language, and assess device use across environments and activities.

The study included 360 persons aged 50 and older of all levels of ability. The sample was racially and geographically diverse and oversampled persons in assisted living facilities. The sample was not representative and there was no conversion for nonresponse. However, the sample for the pilot was weighted to match the 2005 NHIS to provide somewhat representative estimates.

Roughly equal numbers of people in age groups 50–64, 65–79, and 80 and older were included in the sample in order to be able to test items across a wide age spectrum. The need for and the use of assistive technology vary widely between the younger end of the age range (those who are still working, in good health, and familiar with available technologies) and the oldest old (who tend to be retired, experiencing more dynamic health changes, and less familiar with available technologies).

Disentangling assistive technology use from ADL difficulty meant setting up two types of questions: first, establishing task-specific device use without regard to difficulty, and, second, establishing “difficulty” in activities when device use is taken into account. Agree and her colleagues referred to this measure as “independent functioning.”

First, data on device use were collected using a specific reference period and asking about frequency by task and location. For example, for mobility devices, the person was asked: “In the last 30 days, have you used a cane, [walker, wheelchair, and scooter], yes or no?” Respondents who said yes were then asked a series of questions about how often they used their device for relevant activities, such as getting around inside their home or building, transferring between home and outside, and getting around outside their home or building. They were then asked about the frequency of use: “In the last thirty days, when you got out of a bed or chair, how often did you use your cane to help? Would you say every time, most times, sometimes, rarely or never?”

The approach for the home environment questions was to inquire about the existence, acquisition, and use of adaptations or devices. The existence of home modifications is important for aging in place and the potential adaptability of home environments. Acquisition is important to ascertain for home modifications (not for portable assistive technology) because the item may not have been added by (or for) the respondent, even if it is clearly an adaptation for disability. The most obvious example is grab bars, which may be a part of general bathroom renovations but may also include any accessibility modifications that are often included in new construction to meet requirements of the Americans with Disabilities Act. Or a feature may have been installed for another current or previous household member. It is also important to collect information on the frequency of use for these items and to limit use to a specific period of time, say the last 30 days, because some items may be installed for safety or used only at certain times. For example, it is very common to use a portable commode at night only.

The second part of disentangling difficulty from device use was to assess “independent functioning,” a measure of task performance that incorporates use of devices without human help. Agree said she and her colleagues suggest refining ADL measures to ask about activities in a way that represents independent functioning. These items differ from those commonly found in national surveys in two ways. First, they focus on the level of difficulty with activities when using assistive devices and without help from another person. Second, the items are tailored to mention a specific list of devices and features that are reported by each respondent in separate questions about device use and use of home modifications.

Items were designed to determine how well the person can do a task using the particular devices without help, and so some modification was needed to allow respondents to volunteer that they never do the activities without human help. Respondents who said that they never do the activity without help were then asked “using your [device(s)] could you do this task by yourself?” Almost all respondents who answered that that they “never do the activity without help” responded to the follow-up question that they “could do” the activity. The follow-up question was therefore eliminated and respondents recoded as having severe difficulty with the task.

There were concerns about whether respondents would understand the concept of “difficulty with assistive technology and without help” and be able to answer such questions. To examine that issue, traditional functional limitation questions also were asked in the pilot, and Westat conducted behavior coding on a substantial portion of interviews. The responses indicated that, particularly for the ADL questions, they performed quite well. They required fewer clarifications and less probing than standard Nagi functional limitation questions, and they had a very small percentage of respondents (0.05 to 1.0 percent) who gave inappropriate answers (such as “don’t know” or “refused to answer”). They also scaled very well.

Finally, the items can be used in ways that help on a policy level to target potential groups who may need home modifications or help. About 15 percent of adults age 50 and older in the sample who had severe lower body limitations also had at least one unmodified barrier in their homes that was either in the entry to the home, inside the home, or in the bath area. The percentage who could benefit from an environmental modification varies by location in the home. For example, 9.1 percent of respondents had a severe lower body limitation, must use at least one step to leave home, and have no railings or ramp at the entrance; 7.0 percent had a severe limitation and no separate shower, grab bar, or seat in the tub. However, only a small percentage of adults aged 50 and older (2.8 percent) had severe body limitations, living space on multiple floors, and no stair glide. About one in five older adults (20 percent) had a severe lower body limitation but no safety features (grab bars or raised toilet seat) for the toilet area. Overall, nearly one in four adults aged 50 and older (23 percent) could be candidates for environmental modifications in their homes. Broadening the criteria to include anyone with a lower body limitation (irrespective of severity) results in a much larger group—up to 43 percent of adults aged 50 and older. These data show that there is a substantial group of people who have functional limitations, as they were measured in the pilot study, and who face barriers in their homes. Thus, these data can be used to determine the potential need for targeting effective interventions.


Carlos Weiss (Johns Hopkins University School of Medicine) reported on some of the work in the area of behavioral adaptation as a way of trying to improve population health. He offered some thoughts about how future studies might be able to design a set of meaningful measures for population surveys.

One of the main, and important, reasons that behavioral adaptations matter is prevention of more advanced disability and the identification of people who could be targeted for effective intervention. Participation occurs on a continuum, from having difficulty doing tasks to restricted participation to dependence, and behavioral adaptations occur at many different levels along that continuum. However, it may make sense to first focus on understanding behavioral adaptation by studying it in the absence of dependence, and even in the absence of difficulty, to understand some of its salient features. Although behavioral adaptation certainly occurs in the presence of difficulty and even dependence, it may play a more salient role in the absence of these, as an early change that precedes difficulty. It follows, however, from this preventive focus, that behavioral adaptations in the absence of difficulty may be less tightly linked to such outcomes as institutionalization and death than more advanced manifestations of dependence. That is a way of saying that if research focuses on behavioral adaptations alone, one is looking at a group of people who may be at lower risk for some of the important disability outcomes.

Data compiled by Thomas Gill and colleagues (1998) on the relation between different stages on the continuum and important endpoints, such as admission to a nursing facility or dying within 3 years, illustrate the fact that focusing on behavioral adaptations in the absence of dependence, and even difficulty, means that one is looking at a group with lower risk of the more downstream outcomes that are necessary anchors for disability research. However, there is strong public health imperative to understand this part of the continuum. Studies of the distribution of the Medicare population have shown that people with dependence are a small but important minority (only about 4.0 percent), and people who are independent but with difficulty are also a minority (about 4.5 percent). People without difficulty, many of whom are using behavioral adaptations, are a majority (about 50–55 percent) (Shumway-Cook et al., 2005).

In addition, disability fluctuates. Sometimes it lurches catastrophically toward a bad outcome; more generally, over long periods, it tends to be gradual and to progressively diminish participation. This phenomenon occurs in older adults and also for some younger adults who have significant chronic disease. These facts are justification to use behavioral adaptations to attempt prevention, but they also mean that by this very framework some adaptations will no longer be needed, and they are bound to have less predictive accuracy. The main question, then, is whether one can identify a modifiable preclinical phase at which early intervention is more effective than waiting for more overt presentation. The emerging answer to this question is quite probably yes.

What are behavioral adaptations? Weiss noted that it is useful to think of behavioral adaptations within a compensatory strategy framework, taking a slightly different perspective than a focus on dependence and difficulty. When human help replaces the person actually doing the task, perhaps that is no longer a compensatory strategy; rather it is something different. However, there are different degrees of human help, and so behavioral adaptations often occur with, and without, the concomitant use of other strategies. A useful way to narrow the otherwise innumerable differences in the ways people participate in important social roles is to focus on behavioral adaptations that are responses to mild to moderate impairment, to ensure that the behavioral adaptations are occurring despite normal conditions and to ask whether they involve performing tasks or roles in a way that is not usual for that person. Consider walking as a prime example: one may sometimes walk more slowly. That, by itself, is not a compensatory strategy, unless the person recently injured a tendon in the foot. If the slow walking is under normal conditions, rather than on a rocky trail or on a slippery surface, it may be more of a compensatory strategy in the form of behavioral adaptation.

The literature on behavioral adaptation has already shown that pre-clinical disability, defined as task modification in the absence of difficulty, is a strong risk marker for the development of difficulty (Fried et al., 2000). Preclinical disability measurement is reliable and has both construct validity and predictive validity (Weiss et al., 2007). Weiss presented the results of some recent analyses, involving subsequent questions and with outcomes going further out in time. The data come from the Women’s Health and Aging Study II, which comprised 436 women aged 70–79, selected from among the two-thirds most high-functioning women living in the community. Framing may be important in this work, because the questions on task modification—that is, preclinical disability—came after the standard questions about difficulty for a health or physical reason. Interestingly, by far the most common behavioral adaptation for walking among this highly functional group was to slow down. This notion is supported by a large nonsurvey literature made up of physiological and experimental studies with small numbers of healthy older adults or older adults with specific conditions, such as diabetic neuropathy.

Using data from the Women’s Health and Aging Study II, Weiss and his colleagues examined 3-year outcomes among women who were initially walking at least eight blocks outdoors a week. They modeled the probability that the women would cease to walk as much according to baseline level of self-selected walking speed, whether the women had a physical impairment in strength or balance, and whether they started to adapt the way they walked at some point after the baseline measurement. Self-report of starting-to-adapt behavior (after baseline) was the variable that spread the risk the most, much more so than baseline impairment. The self-reported behavioral adaptation data complemented the data on walking speed. There were follow-up questions in this study that asked the women why they had started to adapt—why they changed the way they walk. The respondents were shown about 20 symptoms related to health conditions as response options. For the tasks of walking half a mile and climbing 10 steps, pain and low energy emerged as significant reported causes; for climbing 10 steps, safety was also an important issue.

In summary, these results show that compensatory strategies are the result of a complex interaction between demand and capability. They are a manifestation of diminished reserve relative to demand. A compensatory strategy can take at least two forms in such a scheme: offloading demand or increasing capability through augmenting leverage. In other words, a compensatory strategy can mean doing less or staying active despite an impairment. Compensatory strategies may also be revealing about additional factors, such as the environment, but one of the things they reveal is about health status and physical capacity, suggesting that there is a lot of qualitative heterogeneity among different compensatory strategies. For example, among women who reported that they changed the way they walk, there might be one type of compensation that essentially involves doing the same amount of walking—a leveraging strategy. These women are staying as active as they were before, in different ways. They could walk slower and spend more time walking. They could do other things that involve interacting with the environment and other human beings and using devices. They could seek good light and concentrate on walking, or they might make sure to have a walking cane and walk with someone who is not too talkative and does not interrupt concentration on walking. At different times, a compensatory strategy could instead involve cutting back on the demand of that task, to things that involve walking slowly. These different types of compensatory strategies would have different outcomes in terms of the amount of activity performed or time spent doing the activity.

Weiss pointed out that the study of behavioral adaptation is already taking place under many different names in population surveys, such as subclinical disability, time use, avoidance, and life space. Some of the lessons learned include that it is possible to identify early adaptations that appear to have meaning for the purpose of prevention, and that self-reports complement objectively measured performance. In particular, the heterogeneity among adaptations—staying active versus doing less—correlates with time-use ways of looking at behavioral adaptations. One of the exciting things about the time-use data is that they involve an external scale, in contrast to the internal scale used for adaptation.

Behavioral adaptations are an area worthy of further exploration. Research questions that that should be addressed in the near future include (a) whether one should allow the high specificity and low sensitivity of a longer, more ambiguous time frame for some of these adaptations; (b) a more refined understanding of the reasons for changes, that is, why people change how they perform these activities, including personal and environmental factors; and (c) which of these behavioral adaptations are modifiable and therefore high-priority targets for intervention.


Gale Whiteneck’s (Craig Hospital, Englewood, Colorado) presentation focused on the relevance of measured participation from his perspective as a disability and rehabilitation researcher. His presentation addressed five issues: what is participation, why is it important, how is it measured, why is it hard to measure, and how should it be used in population surveys.

What Is Participation?

The World Health Organization’s International Classification of Function, Disability and Health (ICF) model focuses on participation and defines participation as involvement in life situations. However, this is not a good definition, because it does not adequately differentiate activity from participation. Participation is performance at the societal level; it is fulfilling the social roles of being a worker, a volunteer, a homemaker, a spouse, a friend, a grandparent, a citizen, a neighbor. It is being an active, productive member of society who is well integrated into family and community life.

Why Is Participation Important?

Participation is a major construct in all of the disability models. Full participation in society is the goal of the Americans with Disabilities Act. It is the ultimate goal of rehabilitation. It is what people with disabilities and their families are most interested in—they are interested in functioning in life. They are more interested in fulfilling social roles than whether they can dress or ambulate well. Although the role may change, it is no less important in late life—it is what makes life worth living.

How Is Participation Measured?

In comparison with activity limitation (ADL limitations) measures, participation restriction measurement or participation measurement is more recent in the field, it is measured less frequently, and it is less well developed. There is no gold standard or agreed measure of participation. Participation is often measured with time-use methods, as described in an earlier session of the workshop.

Domains of participation are evaluated with common examples asking self-reported frequency or counts using stylized questions. If the interest is in the domain of productivity, the hours per typical week spent working, homemaking, in school, or volunteering are often asked. For the social domain, information would be obtained on counts of friends, frequency of contacts with family, involvement in a romantic relationship, and whether married and living with a spouse. For the community domain, questions may be asked about the days per week outside the house, times per month shopping, eating out, going to church, or going to the movies, or the number of times involved with community organizations.

ICF provides this taxonomy or classification of participation in the chapters on activity and participation in a list of ways people participate in interpersonal relationships, major life areas, and community, social, and civic life. However, ICF does not provide a measurement of participation, only a classification.

The Craig Handicap Assessment and Reporting Technique (CHART) is an early participation measure developed at Craig Hospital (Whiteneck et al., 1992). It sums across relatively objective items in domains weighted by importance of the item as perceived by the general population, and it computes a participation score within domains for people with disability in comparison to the norms of people without disability.

Why Is It Hard to Measure?

There are individual preferences about how to participate, and choices in communities, but there is no list of participation items that applies to all. For example, not everyone has to work to participate in society; not everyone has to be a full-time homemaker or be married to participate in society.

People with disabilities do not want to be judged on the basis of norms for people without disabilities: CHART and similar tools have been criticized on those grounds. Therefore, there has been a recent focus on more subjective aspects of participation. The Participation Objective, Participation Subjective measure, which is part of the measure known as Living Life After Traumatic Brain Injury (Brown et al., 2004), is a good example of that type of measure. The objective section measures frequency or counts of involvement in various elements of participation. The subjective section asks how important each item is to the individual and how satisfied the individual is in each area or item. For example, a person is asked how many hours the person works, how important is working to the person, and if the person is satisfied with the amount of time spent working. The results are performance and satisfaction scores weighted by importance.

Other metrics are being tried. The Participation Measure for Post Acute Care focuses on the difficulty of participation more than the frequency or quantity, although some of those latter items are also included. The community participation indicators (CPI) include enfranchisement items measuring the extent to which people feel engaged, accepted, and valued in their communities.

There are many reasons why it is hard to measure participation. The choice among ways of participating means there really is no hierarchy of participation. Ways of participation cannot be arrayed on either a difficulty or a value continuum, just a frequency continuum. There certainly is no expectation that one has to do each piece or that there is a hierarchy. For example, it is not comparable to the situation in which a person stands before walking and then running, so that anybody who can run can be assumed to be able to walk and stand. If one is talking about work, homemaking, and being a student, which is the most difficult? Assuming that being a student is easiest and homemaking is most difficult, one cannot assume that a person who is a full-time homemaker is also a full-time worker and also a full-time student.

The above illustrates that there is no hierarchy within the elements of participation. Without hierarchy, the assumptions of item response theory, which is the current state of the art in developing measurement tools, at least in the participation arena, are not met and may not be appropriate.

How Should Participation Measures Be Used in Population Surveys?

Whiteneck argued that participation measures should not be used to expand the definition of who is disabled. Measures of activity limitations should be used to distinguish people with and without disability, as well as among types and severity levels of disability. Once the disability measures have been selected, participation measures can be used to compare the extent of participation among groups of people with and without disabilities or among people with disabilities of various types and severities.

Population surveys can be used to assess the integration or equalization of opportunity for a population of concern—people with disability in this case—compared to the general population. Participation measures can be used in population surveys to assess the participation gap between people with and without disabilities, trends over time, and the effects of environmental interventions. For example, participation measures developed in rehabilitation, such as CHART and CPI, have been used in Colorado, piggybacking on the Behavioral Risk Factors Surveillance System of the Centers for Disease Control and Prevention. By using these measures in general population surveys, people with and without disabilities can be compared in terms of their responses.

Another example of using participation measures in population surveys is the Canadian Participation and Activity Limitation Survey. That is a quinquennial postcensal survey, targeting people with positive responses to two disability screeners in the census. In 2006, the sample was 47,500 persons. It is a lengthy branching survey primarily focusing on activity limitations, but it also includes participation and quality of life.

To conclude, participation measures should be used to determine the extent to which people with disabilities are fully participating in society.


Participants asked several questions for clarification or elaboration mostly focused on three topics: whether there is an assumption of decline in measuring behavior modifications, participation measures, and assistive devices.

Whether There Is an Assumption of Decline in Measuring Behavior Modifications

A comment was made that people not only change their behavior, say in walking, but also take on other activities. There is a substitution of behavior, and the choice element needs to be included. Carlos Weiss responded that decline should not be assumed. There may be some sort of a general tendency for decline among people who have chronic diseases, but it should not be assumed. He referred to the work done in the 1980s by Lois Verbrugge on diaries of changes in health status and change in function, realizing that there are some people who are remarkably stable over time, and some who are able to change a little, maybe through the use of different strategies, but then maintain their activity levels over a long period of time. It was noted that activities such as mobility are essential activities that are the stepping stones to other more volitional and complex activities. People do have to adapt for the set of basic activities to function on a daily basis.

Participation Measures

Participants raised two questions: Is it appropriate to be looking for participation measures that work across different population groups, such as clinical population surveys of the general older population? Can universal measures of participation be developed for the older people who may have progressively declining function?

The concept of participation is life-long: participation as children, participation as adults, and participation as late-life adults. Age-appropriate norms based on different items for participation with enough options are required, said one participant. An obstacle for older adults is the real dearth of opportunities for meaningful roles. What is needed is a clear measure of participation and a clear measure of environments in order to know how the two interact. If enough opportunities for meaningful roles do not exist, that is a problem of the environment and not of the person. So interventions to offer meaningful roles in late life can be introduced to be able to measure increased participation. However, a lot of variability exists, both among individuals and in terms of environmental demands. Just extending the list of participation measures as a way to measure and understand disability may not hold either across age groups or across different culture groups and subgroups in the population.

The difference between these participation measures and the more usual measures of activity limitations has to do with the fact that people value what they participate in differently. It is going to hinge on asking people how important each of these areas is to them so that each person has his or her own valuation scale. So it is not what you are doing or how often you are doing it, but whether it relates to what you value. Perhaps across cultures and across age groups, people value different things.

Simply drawing or producing correlations between disability states and levels of participation in late life does not give a good specification of what is going on. People have lifelong patterns of participation, and what people do affects other people and has health consequences, including disabilities. Longitudinal data are needed to make sense of such relationships.

Assistive Devices

In response to a question, it was noted that training is needed to use an assistive device, and the process of adopting a device may be complicated. People do not land on the right device the first time; they try the device, adjust it, or substitute it for another device. There is the problem of abandonment of a device; is it replaced, are new devices added, or are they used differently? These possible adaptations are hard to measure on a national survey instrument, and they were not addressed in the pilot study described by Agree. Nevertheless, many participants said it is an extremely important area to develop.

Other issues were raised. There is the problem of the risk of injury in using a device. This is particularly true in wheelchair use, and it also affects the people pushing wheelchairs. Training is important to ensure that people use a device appropriately. The intersection between the device and the home environment needs to be addressed. Older people function in many environments, but researchers tend to concentrate on the home environment because of a focus on the most impaired subgroup of the older population, people with the greatest problems in the most basic activities. However, one needs to understand the environment in terms of the areas outside the home, the neighborhood environment, and the environments that are specific to those activities that are of value to people, including how they get to those activities—the transportation and communication environments. This area also was not addressed in the pilot study. It may be far too simplistic to say that environmental barriers impede participation and environmental facilitators enhance participation.

Copyright © 2009, National Academy of Sciences.
Bookshelf ID: NBK28476


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