We analyzed in-depth, home-based interviews comparing two samples of urban-living older adults: one with moderate to good health literacy and the other with low income and low health literacy. We found important differences between these groups in health promotion priorities and expectations for health and successful aging. In addition to personal resources frequently noted in the literature such as self-monitoring and self-regulation, we identified the following as important: (a) perceiving providers and medications as just one self-management tool, (b) caring for and supporting family members, (c) having physical and social activity as a priority, and (d) holding expectations for health and successful aging. The capacity to know when and where to look for information and how to interpret it seemed a particularly key skill for both illness treatment and health maintenance. Individuals with this skill seemed more knowledgeable about their illness and health and more proactive and less anxious about the illness. Information access was more limited in the vulnerable sample, as one might expect.
Models of effective longitudinal care for chronic conditions all recommend that primary care practices provide self-management education and support. One of the critical steps to effective self-management support is getting to know the patient and his or her priorities and expectations (Bodenheimer, 2007
). If an older adult has low expectations for health, then education and resources for self-management without discussion of expectations and priorities may have little impact. Having low expectations and few health-related priorities may be particularly problematic for self-management among socioeconomically vulnerable older adults, because the stories relayed by our participants suggest that these perceptions develop over a lifetime of experiences. One observation from our interview data is that stories of life experiences with illness, accidents, and death were more common among the vulnerable interviewees, and examples of disability-free aging among this group were few. The nonvulnerable sample had more stories of healthy aging and of persons managing the impact of their illnesses. We can not say from our data whether this was due to actual differences in experience, differences in perspective, or both. Regardless, there were differences in perceptions, and these may translate to expectations and priorities. Expectations and priorities are likely to have significant implications for chronic illness care and health disparities research. As noted earlier, Sarkisian and colleagues (2005)
have developed a survey measure of expectations that taps the extent to which people feel that aging is accompanied by undesirable and inevitable social, psychological, and physical consequences. So far, this measure has been associated with physical activity, and we suspect that it will be associated with other self-management behaviors as well.
In our samples of interviewees, there was a clear tendency for the nonvulnerable group to be more engaged in mental and physical activity and for that activity to be considered a key to successful aging and life meaning. These health-maintenance-oriented older adults appeared to gain meaning from being physically and mentally active. If one considers activity engagement to be a key ingredient to life meaning, then health maintenance may become a priority role. Vulnerable interviewees were less engaged in life activities outside of their immediate life space and were more likely to have roles that involved caring for others in their home on a daily basis. In fact, the amount of care that the older vulnerable interviewees were providing was in cases very substantial. In many cases this was a source of significant stress, but there were cases where it gave a sense of mattering and interdependence with others. In any case, the roles of vulnerable older adults seemed to be viewed as sustainable by maintaining comfort, freedom from pain, and rest.
This single qualitative study has clear limitations. We sampled from two distinct clinic populations from one city. Our data collection was exploratory in nature and not intended to provide tests of causation. However, as hypothesized, we discovered differences in aging expectations and self-management priorities between vulnerable and nonvulnerable older adults. If further research confirms such differences by educational attainment, socioeconomic status, and/or health literacy, this may be an important target for health disparities research. If priorities flow in large part from social roles that provide one a sense of importance and mattering, it is important to better understand these social roles and sources of identity. Social cognitive theory interventions attempt to improve priority for a target behavior in part by building positive expectations for the outcomes of that behavior. Tailoring messages so the outcomes for which outcome expectations are to be built fall within an individual’s life priorities may be particularly important for self-management programs in vulnerable populations. Tailoring has achieved efficiency in application to large populations through the use of Internet data collection and algorithms. How to scale tailored messages for a population that has limited information access in general and Internet access in particular is a significant challenge. Whether carefully designed social marketing campaigns with messages targeted to socioeconomically vulnerable older adults could be effective in this regard is not known.
Getting to know a population is critical to the success of any social marketing effort. Evaluating and applying existing measures of self-management self-efficacy (e.g., Patient Activation Measure), resources (e.g., Chronic Illness Resources Survey), and expectations (e.g., expectations regarding aging), and new measures of self-management knowledge and awareness, life priorities, and social network demands from and support to samples of vulnerable older adults, could prove valuable in improving researchers’ knowledge of factors that influence self-management in this population. With this improved knowledge, experts could develop an efficient tool that measures important elements for guiding self-management support. Applied to individuals, such a tool could screen and flag patients who have low self-management capacity and could help “jump-start” providers in getting to know the patient in ways very relevant to self-management support. Such a tool would have to go through multiple cycles of testing and revision to achieve brevity and validity, and even then significant effort would be needed to implement such a tool. However, without significant effort and progress in knowledge of self-management, chronic illness care will fall well short of desired outcomes, particularly among vulnerable populations.