We investigated the psychometric properties of the PAM in a population of multimorbid older adults. The data generally supported the construct validity for the PAM in this population, as PAM score was significantly associated with some health-related behaviors, with functional status, and with health care quality. Further, as expected, patient activation was not related to the number of comorbid illnesses. The latent class analysis offered partial support for the multistage theory of patient activation proposed by Hibbard et al. (2004)
Our findings that PAM scores are associated with health-related behaviors and functional status are in accord with similar research on other populations. In developing the PAM, Hibbard and colleagues conducted a telephone survey of 1,515 people, 45 years and older using a national probability sample. Respondents with high PAM scores were significantly more likely to exercise regularly, follow a low-fat diet, eat more fruits and vegetables, and not smoke. They also reported significantly better self-perceived health-related functioning as measured by the SF-8, a shorter version of the SF-36 (Hibbard et al. 2004
Similarly, in a survey of 4,108 Kaiser Permanente members (mean age=61.9 years) in seven regions of the country, Mosen et al. (2007)
found that participants with Stage IV PAM scores were nearly three times more likely to report high medication adherence than those with Stage I PAM scores, and five times more likely to report high quality of life. Finally, Skolasky and colleagues found that individuals in higher PAM classes were more likely to adhere to physical therapy recommendations after lumbar spine surgery. Those in lower PAM classes were more likely to report low self-efficacy for physical therapy, low hope, and external locus of control compared with those with high activation (Skolasky et al. 2008
As in the population of older adults studied here, weak associations were observed between patient activation and the presence of comorbid disease (Skolasky et al. 2009
). We expected this to be the case because the PAM aims to assess psychological factors such as self-efficacy and personal competencies such as disease-specific knowledge, characteristics that should not be influenced by the number of a patient's comorbid illnesses.
Unexpectedly, we did not find evidence of positive associations between PAM stages and other health-related behaviors. These findings may reflect both sample response characteristics and/or a lack of measurement fidelity, rather than a lack of relationship between patient activation and these types of behaviors. The lack of association may be due to the lack of variability in certain responses. For example, members of the study cohort had excellent medication adherence overall (74 percent of those in the lowest tertile of activation reported missing no medication doses in the past 7 days) and a small percentage in each group smoked. In addition, the lack of association between alcohol use and PAM stage may be due to measurement artifact. The question on alcohol use was dichotomous and thus does not differentiate appropriate from potentially harmful alcohol use.
Hibbard and colleagues proposed a multistage, developmental model of patient activation, in which individuals move from one stage to the next as they acquire specific beliefs and skills. Our latent class analysis provided partial evidence in support of this multistage model (Hibbard et al. 2004
). For example, respondents in Class I were likely to agree with items 1–3, 5–8, 10 and 13. This response pattern suggests that they believe to some extent that it is important to take an active role in their own health, and they are starting to develop the confidence and knowledge to take action, but they are not as activated as respondents in Class II, who agreed with all items in the PAM, or respondents in Class III, who agreed strongly with multiple items. Respondents in Class I were also likely to disagree with items 9, 11, and 12, indicating that they may not be able to follow through with effective self-care behaviors over time, or when new problems arise.
By contrast, the response profile of the participants in Class III—agreeing strongly with multiple items—indicates a higher degree of confidence in their ability to take an active role in self-management of their chronic disease, follow through on medical recommendations, and maintain lifestyle changes over time. Compared with respondents in Class III, those in Class I are still more likely to believe in an external locus of health control—that is, “the doctor will ‘fix’ them” (Hibbard and Mahoney 2005
None of the respondents agreed strongly with the items indicating the highest level of activation, items 12 and 13 (“I am confident I can figure out solutions when new situations or problems arise with my health condition,” and “I am confident that I can maintain lifestyle changes like diet and exercise even during times of stress”). This finding is interesting in light of the fact that Mosen et al. (2007)
observed the lowest PAM scores among older adults (results not published). It is unclear whether these findings together reflect a reduced confidence in the ability to maintain health behaviors due to some factor related to old age.
Our study is the first to explore the association between the PAM and two measures of health care quality, the PACIC and PCAS. Our finding that patients in higher PAM stages reported better quality of care may indicate that delivery system characteristics, including patient–doctor communication, play a role in activating patients. This observation is also particularly important because it suggests that activated patients may seek—and obtain—better care. They may engage their physicians, participate in the development of their treatment plans, and through such behaviors, help bring about higher-quality health care for themselves.
Our study is the first to investigate the psychometric properties of the PAM in a large sample of older adults with multiple chronic conditions. Our respondents were, on average, 15 years older than participants in previous studies of the PAM (Mosen et al. 2007
; Skolasky et al. 2008
;). As the U.S. population ages, it will become increasingly important to use limited resources wisely to improve health care for this population.
Previous research has suggested that increased patient participation and engagement in health care leads to improved outcomes (Clark and Janz 2000
; Lorig et al. 2001
; Chodosh et al. 2005
;). Patients that receive health coaching tailored to their level of patient activation demonstrate improvements in efficiency of disease management programs and in health outcomes (Hibbard, Greene, and Tusler 2009
). Therefore, it may be possible for health care providers to use the PAM to identify individual patients who lack the knowledge or self-efficacy to participate actively in the management of their health conditions. Interventions could then be directed at patients and families most at risk for poor participation. Further, it may be possible to tailor the type of intervention—education, action planning, and behavioral contracting—to specific stages of activation. Having a valid tool for measuring activation among older patients is an important step in this process.
Our study has several potential limitations. First, it is not possible to determine causality using a cross-sectional design, although there are other studies that speak to this issue. Skolasky et al. (2009)
found that higher levels of activation at baseline were related to higher rates of subsequent participation in postoperative physical therapy. Hibbard et al. (2007)
reported that positive change in patient activation was related to positive change in a number of health-related behaviors; however, the results did not show that the intervention used in the study, a chronic disease self-management class for patients with illnesses including diabetes, hypertension, arthritis, and chronic lung disease, was effective in increasing activation. Additional data are needed to establish a clear link between changes in a patient's levels of activation and subsequent engagement in health behaviors.
One potential limitation is that approximately 38 percent of eligible participants chose to participate in the current study, which may limit the potential generalizability of the findings. Similar difficulties in recruiting high-risk older adults have been noted in other studies. However, in a study of recruitment methods among community-dwelling older Medicare beneficiaries, Boult et al. (1998)
demonstrated using recruitment methods similar to those used in the Guided Care project, that those who consented to participate were no different than those who did not consent to participate with respect to mean hospital admissions in the previous year, probability of repeated admission, and racial composition (Boult et al. 2008
). The study did, however, find that participants were more likely to be male and to be younger.
A related potential participation bias is that older adults who were willing to participate in the Guided Care study may be more activated than those who refused to take part. For this reason, it is possible that our results are not generalizable to the general population of elderly people with multiple chronic health conditions. Finally, we relied on self-report of health behaviors such as exercise, dietary, and medication adherence. This may have made it less likely that we would detect a difference between respondents who were more activated and those who were less activated, especially if those with lower activation scores tended to overestimate the extent to which they engaged in recommended activities.