Condición Social y Familiar

To better understand the role that psychological factors play in CPAP adherence, researchers have begun to explore the utility of specific models in the prediction of CPAP use (Edinger et al., 1994; McFadyen, Espie, McArdle, Douglas, & Englemann, 2001; Stepnowsky, Bardwell, Moore, Ancoli-Isreal, & Dimsdale, 2002; Stepnowsky et al., 2002; Wild, Englemann, Douglas, & Espie, 2004; Aloia,

Arnedt, Stepnowsky, Hecht, & Borrelli, 2005). The Health Belief Model (HBM) is the most widely used theory in health related research that aims to understand health behaviour change (Glanz, Rimer, & Lewis, 2002).

First introduced by the U.S. Public Health Services in the 1950s as a means to understand why certain medical screening programs were unsuccessful, the

underlying premise of the HBM is that “health behaviour is determined by personal beliefs or perceptions about a disease and strategies available to decrease its

occurrence” (Hochbaum, 1958 cited in Glanz et al., 2002, p.31). There are four perceptions that contribute to the main construct of the HBM: perceived seriousness, perceived susceptibility, perceived benefits, and perceived barriers.

Perceived seriousness: an individual’s belief about the seriousness or severity of the disease, syndrome, or condition. This belief is generally derived from medical

information as well as the belief an individual develops pertaining to the difficulties a disease, syndrome, or condition is likely to create or the effects it would have on his or her life in general (McCormick-Brown, 1999). For example, most individuals would not think twice about contracting the common flu annually, as it may mean a day or two of bed rest. However, if you suffer from severe asthma or chronic fatigue, contracting the flu could lead to hospitalisation, hence the perceived seriousness of contracting the flu may be considerably higher.

Perceived susceptibility: personal risk is considered to be a great influence in motivating people to adopt healthier behaviours. Generally speaking, the higher the perceived risk, the greater the likelihood is of accepting behaviours to reduce the risk.

Literature shows that perceived susceptibility is an influential factor to motivating individuals to be vaccinated for influenza; to use sunscreen to prevent skin cancer; to brush their teeth to prevent gum disease and tooth loss; and use condoms in an effort to decrease susceptibility to HIV infection, contraction of sexually transmitted

infections and unplanned pregnancies (Belcher, Sternberg, Wolitski, Halkitis, & Hoff, 2005; de Wit, Vet, Schutten, & van Steenbergen, 2005; Chen, Fox, Cantrell,

Stockdale, & Kagawa-Singer, 2007).

Perceived benefits: the value or usefulness an individual places on a new behaviour to reduce the risk of developing a disease, syndrome, or condition. People are motivated to adopt healthier behaviours when there is a belief that the new behaviour will reduce the risk of developing a disease. It is suggested that this is why individuals strive to eat five servings of fruits and vegetables a day, quit smoking, and use sunscreen (Glanz et al., 2002).

Perceived barriers: an individual’s self-assessment of the perceived obstacles or barriers that may stand in the way of them adopting a new behaviour. This construct is likely to be the most significant in determining behaviour change (Janz & Becker, 1984). In order for a barrier to be overcome and the new behaviour to be adopted, an individual needs to believe that the benefits of the new behaviour outweigh the consequences of continuing the old behaviour (Centre for Disease Control and Prevention, 2004).

There is limited consistent literature regarding HBM and CPAP adherence, but it is likely that patients begin to develop perceptions, expectations, and beliefs

surrounding treatment before commencement of CPAP use at home (Smith, Lang, Sullivan, & Warren, 2004). Thus, CPAP adherence is likely to be greatly influenced by initial beliefs derived from patients’ subjective experiences, self-reported benefits and side-effects (Aloia et al., 2005). Those that develop negative perceptions,

expectations, and beliefs towards treatment may be less likely to try to accept CPAP as a form of OSA treatment in the first place.

Sage, Southcott, and Brown (2001) were one of the first to apply the HBM construct to OSA research regarding CPAP use via the development of a specific CPAP questionnaire based on the HBM. Forty patients with a mean age of 54 (10 women and 30 men) diagnosed with OSA (RDI = > 10) completed the specifically designed HBM-based questionnaire post-CPAP titration. When CPAP adherence was measured after one month, Sage et al. found a statistically significant moderate

correlation with the perceived benefits and perceived barriers of CPAP use. Predictive analysis showed that these two HBM constructs explained 23% of the variance in adherence over the traditional sleep-related variables. Sage and colleagues explained that such results are common findings in HBM and adherence literature regarding other medical conditions such as diabetes and in psychology in general. Their results imply that a patient’s belief in relation to perceived benefits and perceived barriers are probably linked to the short-term and long-term costs and benefits of CPAP use. Interestingly, the authors found no statistically significant association between perceived susceptibility and CPAP adherence, despite patients being informed about the long-term consequences of untreated OSA (i.e., cardiovascular disease). However, given the sample size, such results are best deemed exploratory in nature, but they

nonetheless successfully provided initial links between HBM and CPAP adherence and indicated the potential value of using the HBM for predicting CPAP adherence.

Closer to a decade later, Olsen, Smith, Oei, and Douglas (2008) employed the HBM to compare the role of psychological constructs with the commonly used sleep- related variables to determine CPAP adherence and acceptance. Their sample

comprised 77 CPAP naïve patients (30 women and 47 men) aged between 26 and 80 years old (M = 55.25) with a mean BMI of 35.11 and a mean RDI of 38.36 (placing on average most patients in the obese range with severe OSA). In addition to the demographic information collected, each patient completed the ESS, the Functional Outcomes of Sleep Questionnaire (FOSQ), the Self-Efficacy Measure for Sleep Apnoea (SEMSA), the Depression Anxiety Stress Scales (DASS) and a standard PSG. Following four months of CPAP use, patients were contacted to gather adherence data from their CPAP device meter and to discuss any ongoing difficulties with treatment. Olsen et al. found that patients on average used CPAP 4.57 hours per night. Results from their multiple regression analysis showed that the HBM constructs accounted for 22% of the variance in CPAP adherence compared to 32% when the sleep-related variables were included. Olsen et al. reported that their results suggested that patients had developed perceptions, expectations and beliefs about OSA and CPAP use before implementation. Olsen et al.’s findings provide valuable support for the HBM as a solid model for understanding individuals’ motivations to accept and adhere to CPAP use as a treatment.

While research in this domain is still somewhat limited, Tzischinsky, Shahrabani, and Peled (2011) investigated the possible factors that influenced the

decision to purchase a CPAP device at the pre-PSG stage. Their study initially consisted of 83 patients with a mean age of 55 years and a mean BMI of 30 who completed a demographic questionnaire, the Mini Sleep Questionnaire (MSQ), the Pittsburgh Sleep Quality Index (PSQI), the ESS, and their own developed HMB measure. Three month post-PSG, patients diagnosed with OSA were contacted via telephone to complete a short survey. Post-PSG out of the 66 patients diagnosed with OSA (MRDI = 21.58), 50 (23 women and 27 men) participated in the short survey. Applying the specifically developed questionnaire based on the HBM constructs, Tzischinsky et al. found that only perceived susceptibility significantly explained the decision to purchase a CPAP device post-PSG, which was contrary to their

expectations. While limited, these results do suggest that potential OSA sufferers who initially believed they were susceptible to having OSA were more likely to purchase a CPAP device, compared to individuals who did not feel at risk. The results from Tzischinsky et al.’s research did, however, provide statistically significant evidence to suggesting that sleep-related variables and patients’ demographic information as well as having a higher level of OSA knowledge and higher levels of health motivation positively affected the decision to purchase a CPAP device. These results were similar to the findings of Smith et al., which suggested that OSA patients who were more informed about OSA had greater positive attitudes towards CPAP treatment and use. Tzischinsky and colleagues concluded that their results contributed to the body of evidence suggesting that individuals who were aware of their own health conditions had a greater tendency to invest in their own treatment. While the research of Tzischinsky et al. is somewhat removed from of that of Olsen et al. and Sage et al. with regards to their focus on predicting CPAP adherence, it does contribute to the

limited literature in this domain and suggests that HBM constructs are likely to be an important overall factor.