The perception of self-efficacy (SE) denotes a process that influences the performance of behavior (Bandura, 1989). The expectation of SE is one's personal estimation of being capable of carrying out a specific behavior (Bandura, 1977, 2004). The concept was initially used to refer to expected self-efficacy (Bandura, 1977, 1992, 2004), which focuses on feeling confident in one's capability to manage life stressors, but a more in-depth meaning of SE embraces a sense of mastery of the demands of life overall. As people face life events, specifically managing health-related matters, which involve the maintenance of a healthier lifestyle (Gebhardt, van der doef, & Paul, 2001; Smith, Wallston, & Smith, 1995), SE is a key component of health promotion programs. Individuals with high-perceived SE are considered more likely to initiate preventive health care and seek earlier treatment (Grembowski et al., 1993; Olivari & Urra, 2007).
Subjective perception of memory and other cognitive failures expressed by individuals are frequently called cognitive complaints. Subjective cognitive complaints (SCC) have been related to objective cognitive performance and perceived health status and affective state (Hill et al., 2016). Assessment of SCC is currently gaining attention, as it has been related to perceived health and objective cognitive status (Montejo et al., 2013). Emerging diagnostic categories in the field of cognitive health include SCC as a key diagnostic criterion (Jessen et al., 2014). There is a growing recognition of the importance of SE in regulating cognitive health and applying successful cognitive strategies (West, Bagwell, & Dark-Freudeman, 2008). A close association has been established between SCC and affective factors, including subclinical symptoms of depression, anxiety, and personality traits (Amarigilio et al., 2012; Balash et al., 2013); however, the role of dementia in cognitive performance and SCC remains to be fully explained (Garcia-Ptacek et al., 2016). According to the relationship between self-perception and SCC in perceived health and the potential link between the subjective nature of memory complaints and the perception of SE, the objectives of the current study were: (a) to study whether SE is a relevant subjective variable in predicting SCC in middle-aged and older adults living in the community; and (b) to explore the role of SE and SCC in predicting health care use in this population.
A cross-sectional observational and correlational study was conducted with data collected between January and May 2018. Questionnaires were administered in the homes of each study participant. The current study was approved by the Ethical Committee of the Galician Health System.
Questionnaire and Variables
The questionnaire included SE, cognitive (i.e., objective and subjective), and sociodemographic variables, as well as items asking about the use of health care resources. To study SE, the assessment protocol included a coping SE scale of health problems applied to health (SEH), which contains 10 items referring to health issues with a 4-point Likert response format: totally disagree, disagree, agree, and totally agree (Gandoy-Crego et al., 2016). To assess cognitive status, the Montreal Cognitive Assessment (MoCA) was used. The MoCA is a time-effective test comprising 22 items widely used to screen patients with suspected mild cognitive impairment (MCI) (Nasreddine et al., 2005). Regional normative scores considering age and educational level (Pereiro et al., 2017) were followed to construct three cognitive status groups: normal, possible MCI, and possible dementia. The Memory Failures of Everyday (MFE) test (Sunderland, Harris, & Gleave, 1984) was used to assess memory and cognitive failures in daily life. Different studies have used the MFE to compare SCC across different age groups and cognitive status (Montejo Carrasco, Montenegro-Peña, & Sueiro-Abad, 2012a,b). The authors used the validated Spanish version of the questionnaire, which includes 28 items and a three-point scoring system (0 = never/rarely, 1 = sometimes/not often, 2 = often/frequently). Questions about health care use included the number of times in the past 2 months the person visited: (a) a general practitioner; (b) a medical specialist; (c) a rehabilitation service; and (d) had a medical diagnostic or analytical test. A dichotomous variable was created as follows: (a) “without health use,” for participants who did not use any of these health services in the past 2 months, and (b) “with health use,” for participants who used at least one of these health services in the past 2 months.
Sample and Data Collection
The current study was conducted in 438 community-dwelling middle-aged adults (247 women, 56.4%). Recruitment of participants was undertaken by investigators (M.G.C., R.R.G.) from the Nursing School of Santiago de Compostela (Spain). Participants were informed of the aims of the study prior to signing an informed consent form. There were only two inclusion criteria, which were age 55 to 69 years and living in the community. This age range is supported by the International Association of Geriatrics and Gerontology (IAGG) recommendations for cognitive impairment screening (Morley et al., 2015). Exclusion criteria were: (a) diagnosis of dementia; (b) diagnosis of major mental impairment including toxic consumption; and (c) injuries, brain damage, or other health circumstances that prevent evaluation.
Sociodemographic data of the sample are indicated in Table 1. According to cut-off scores in the MoCA, participants living in the community were divided into three groups: (a) 308 (70.3%) participants were cognitively un-impaired (CU); (b) 49 (11.2%) participants were at risk of MCI; and (c) 81 (18.5%) participants were at risk of dementia.
Participant Sociodemographic Characteristics (N = 438)
A specific database was created with the statistical program SPSS version 20. For group comparisons, the nonparametric Kruskal-Wallis and Mann-Whitney U tests were used due to differences in group sizes. The predictive value of SE, age, and cognitive status in SCC was calculated through linear regression. The predictive value of age, cognitive status, SE, and SCC in non-use or use of health services was calculated through logistic regression, using age and cognitive status as categorical variables (i.e., age 55 to 59, 60 to 64, 65 to 69; and CU, MCI, and dementia, respectively) and SE and SCC as continuous variables.
Table 2 presents data about SE; objective cognitive performance measured with the MoCA; SCC measured with the MFE; and health care use in the groups with CU, at risk of MCI, and dementia, as well as in the total sample. As expected, group differences were found in cognitive performance (χ2 = 254.08, p < 0.01). Significant differences were also found in SCC (χ2 = 11.63, p < 0.01), but not in the SE test (χ2 = 0.55, p = 0.76). Although the percentage of participants without health care use in the past 2 months tended to decrease in groups with lower cognitive performance, differences did not reach statistical significance (χ2 = 4.61, p = 0.1).
Self-Efficacy, Cognitive Status, Subjective Cognitive Complaints, and Health Care Use
Role of Self-Efficacy in Predicting Subjective Cognitive Complaints
The linear regression model including SCC scores as dependent variables and SE, age, and cognitive performance as predictors was significant (F = 14.45, p < 0.01), although variance explained was low (corrected R2 = 0.09). All predictors were significant (SE [t = −3.30, p < 0.01]; age [t = 2.85, p < 0.01]; cognitive performance [t = −3.48, p < 0.01]).
Role of Self-Efficacy and Subjective Cognitive Complaints in Predicting Health Care Use
The logistic regression model including non-use/use of health services as a dichotomous variable predicted by age, cognitive status, SE, and SCC was significant (χ2 = 42.41, p < 0.01). Cox and Snell's R2 was 0.09 and Nagelkerke's R2 was 0.13. In univariate analysis, ages 55 to 59 years (6.83, p < 0.05), ages 60 to 64 years (6.69, p < 0.05), cognitive status MCI (3.81, p < 0.05), SE (23.08, p < 0.01), and SCC (5.34, p < 0.05) were statistically significant. Multivariate analysis (Table 3) showed an increased probability of use of health services in the 60 to 64 age group compared to the 55 to 59 age group, in the MCI group compared to the CU group, and an increase of 0.88 for each point decreased in the SE test. SCC scores did not remain significant predictors in the multivariate analysis.
Multivariate Logistic Regression Model
The current study has displayed the role of SE and SCC as predictors of health care use. SE and SCC were significant predictors in the univariate analysis; however, only SE remained significant in the multivariate analysis. Therefore, the influence of SCC in health care use seems to be mediated by subjective estimations such as those measured by SE. Results are in line with previous interpretations about SE effects in health care use (Gandoy-Crego et al., 2016), such as increased affective symptoms, reduced engagement in a range of activities, and more functional problems when compared to individuals without SCC (Hill et al., 2017). People with poor expectations tend to have low self-esteem and negative feelings regarding their abilities.
The perception of SE facilitates cognition concerning one's own abilities, with thoughts acting as motivators of action. In this context, people who feel efficacious choose more challenging tasks, set higher goals, and are more persistent. If people used adequate coping strategies to overcome health problems, they would be less reliant on health services. Regarding cognitive health, an increase in SE would contribute to a decrease in SCC, which would lead to reduction of the burden of care, with considerable savings in overstretched health care resources (Morley et al., 2015). Accordingly, the role of SE as a significant predictor of SCC supports the well-established link between affective factor and subjective, self-reported perceptions about cognitive function (Amariglio et al., 2012; Balash et al., 2013; Garcia-Ptacek et al., 2016). In addition, development and implementation of well-designed health education programs can help improve SE and health behaviors.
Cognitive health programs should emphasize not only the importance of assessing current and baseline cognition according to cognitive assessment tools appropriately normalized and validated (Perry et al., 2018), but also the dynamic link between affective and cognitive factors throughout older adults' lifespans. Although regression models are significant, the percentage of variance explained is relatively low—9% in the linear regression model and 9% to 13% in the logistic regression model. Future studies might include more exhaustive measures of affective state (Hill et al., 2016), subjective perception of health, loneliness, or social support. Specifying the type and number of SCC (Amariglio, Townsend, Grodstein, Sperling, & Rentz, 2011), as well as the potential role of educational level and other cognitive reserve proxies (Garcia-Ptacek et al., 2016), could help better understand this relationship.
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Participant Sociodemographic Characteristics (N = 438)
|Age range (years)|
| 55 to 59||188 (42.9)|
| 60 to 64||117 (26.7)|
| 65 to 69||133 (30.4)|
| Primary education||151 (34.5)|
| Secondary education||169 (38.6)|
| Higher education||118 (26.9)|
| Married||328 (74.9)|
| Divorced||30 (6.8)|
| Single||28 (6.4)|
| Widow/widower||4 (0.9)|
| Other (e.g., living as a couple, other situations)||48 (11)|
| In their own house||408 (93.2)|
| In their family's house||30 (6.8)|
Self-Efficacy, Cognitive Status, Subjective Cognitive Complaints, and Health Care Use
|Item||Mean (SD) (Range)|
|CU Group||MCI Group||Dementia Group||Total|
|SEHa||28.58 (3.92) (17 to 40)||28.24 (4.29) (19 to 37)||28.21 (4.56) (17 to 40)||28.47 (4.08) (17 to 40)|
|MoCAb||26.72 (1.99) (23 to 31)||23.10 (0.87) (22 to 24)||20.09 (2.38) (12 to 23)||25.08 (3.30) (12 to 31)|
|MFEc||13.44 (7.81) (0 to 44)||16.33 (7.43) (3 to 31)||16.26 (9.42) (2 to 48)||14.28 (8.17) (0 to 48)|
|Without health use||210 (68.2)||31 (63.3)||45 (55.6)||286 (65.3)|
|With health use||98 (31.8)||18 (36.7)||36 (44.4)||152 (34.7)|
Multivariate Logistic Regression Model
|Item||B||Self-Efficacy||Waldχ2||p Value||OR||95% CI|
| 55 to 59||8.71||0.01|
| 60 to 64||−0.72||0.27||7.27||0.01||0.49||[0.29, 0.82]|
| 65 to 69||−0.17||0.30||0.32||0.57||0.85||[0.47, 1.51]|
| Cognitively unimpaired||8.97||0.01|
| Mild cognitive impairment||0.83||0.28||8.82||0.03||2.30||[1.33, 3.99]|
| Dementia||0.52||0.40||1.72||0.19||1.68||[0.77, 3.66]|
|Self-efficacy||−0.13||0.003||21.76||< 0.001||0.88||[0.83, 0.92]|
|Subjective cognitive complaints||0.02||0.01||2.62||0.11||1.02||[0.00, 1.05]|