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Amy L. Ai, Christopher Peterson, Steven F. Bolling, Harold Koenig, Private Prayer and Optimism in Middle-Aged and Older Patients Awaiting Cardiac Surgery, The Gerontologist, Volume 42, Issue 1, 1 February 2002, Pages 70–81, https://doi.org/10.1093/geront/42.1.70
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Abstract
Purpose: This study investigated the use of private prayer among middle-aged and older patients as a way of coping with cardiac surgery and prayer's relationship to optimism. Design and Methods: The measure of prayer included three aspects: (a) belief in the importance of private prayer, (b) faith in the efficacy of prayer on the basis of previous experiences, and (c) intention to use prayer to cope with the distress associated with surgery. The sample was 246 patients awaiting cardiac surgery. The first in-person interview was administered 2 weeks before surgery and optimism was measured the day before surgery by telephone. Results: Private prayer predicted optimism, along with older age, better socioeconomic resources, and healthier affect. Neither measures of general religiosity nor any type of prayers used by patients were associated with optimism. Implications: Suggestions were made for clinicians to improve spiritual assessment and care, and for researchers to address spiritual coping in clinical situations.
Decision Editor: Laurence G. Branch, PhD
Coronary heart disease (CHD) is the major health threat and the leading cause of premature, permanent disability in middle and later life in the United States (American Heart Association 2001; National Academy on an Aging Society 2000). Given the importance of the heart in one's life and that abrupt heart attack in CHD patients could cause death, the cardiac procedure could be considered a life-or-death event during middle age and later life and assumed to be associated with a high level of stress.
The use of private prayer is a common spiritual coping strategy among patients with heart diseases but has been rarely examined with a large clinical sample. One exception was a retrospective study of middle-aged and older patients and their use of private prayer for coping with difficulties related to coronary artery bypass graft surgery (CABG), a common medical procedure in the United States (Ai, Dunkle, Peterson, and Bolling 1998). The results demonstrated that private prayer might serve to facilitate psychosocial adjustment in this population. Some methodological limitations of that earlier study included that (a) it used a yes or no choice for measuring prayer, and (b) it did not ask how prayer was used prior to the cardiac surgery. The waiting period for cardiac surgery is highly stressful (Eriksson 1988). The majority of CABG patients experience psychosocial effects before the surgery, from psychiatric complications and sexual difficulties to impaired social functioning. The prevalence of suicidal ideation can be as high as 16% among men awaiting CABG and that of depression can be as high as 39%. Presurgical psychological stress could account for considerable variance in short-term recovery (Snyder 1985). Thus, information about preoperative spiritual coping may have implications for the postoperative course. Accordingly, the present study investigated the use of private prayer for coping with distress related to cardiac surgery and its capacity to predict optimism immediately prior to the event.
Optimism and Its Role in Health, Well-Being, and Cardiac Care
Why is the assessment of optimism important in the treatment of older, sick populations? At the dawn of the new millennium, clinical psychology began to shift its century-old focus on a negative aspect of human nature—pathology—to a more appreciative perspective in a new frontier—positive psychology (Kogan 2001; Sheldon and King 2001), in which human potential, motives, and capacities are emphasized. Prior to this growing interest in positive attitudes, emotions, and beliefs, optimism was defined by Scheier and Carver as a generalized expectancy for positive outcomes (Scheier and Carver 1985, Scheier and Carver 1987, Scheier and Carver 1992). Regarded as an explanatory style, optimism is conceptualized as a stable cognitive set reflecting general rather than specific outcome expectancies, a general trait or outlook that includes a person's overall attitude and approach toward self and world (Myers 1992; Seligman 1991). In general, optimism predicts positive health and mental health outcomes (Buchanan 1995; Carver et al. 1993; Mineka, Pury, and Luten 1995; Peterson and De Avila 1995; Robins and Hayes 1995; Scheier and Carver 1987).
To date, several dozen studies have attempted to establish a correlation between optimism and physical well-being. Taken together, these studies converge impressively (see reviews by Carver, Spencer, and Scheier 1998; Michaels, Michaels, and Peterson 1997; Scheier and Bridges 1995; Scheier and Carver 1985, Scheier and Carver 1992). For instance, research has found that optimism is positively associated with better health outcomes (Scheier and Carver 1985, Scheier and Carver 1987, Scheier and Carver 1992). It predicts longevity and good health operationalized in a number of ways: self-report, physician ratings of general well-being, visits to doctors, survival time following a heart attack, immunological efficiency, and successful completion of rehabilitation programs. Research participants have included male and female adults, some initially healthy and others quite ill (Peterson and Bossio 2001). Some longitudinal studies statistically controlled for initial levels of health and potential confounding factors such as the tendency to complain. Optimism affects a number of aspects of health: making the onset of illness less likely, minimizing the severity of illness, speeding recovery, and making relapse less likely.
A few studies have addressed the role of optimism in aging in recent years. Optimism was associated with better life satisfaction in older adults with anxiety disorders (Bourland et al. 2000). In a cohort of 659 healthy middle-aged and older men, Achat, Kawachi, Spiro, DeMolles, and Sparrow 2000 found that optimism predicted higher levels of perceived general health, vitality, and mental health and lower levels of bodily pain over time. A recent study showed that optimism predicted the well-being of 188 elderly widows and widowers (Fry 2001). How does the process of aging affect optimism? Little is known about this potential effect. Because positive attitudes have been linked with health and longevity, further investigations are warranted to address how optimism affects outcome measures of older patients and how the generational difference and the aging process influence optimism.
With respect to cardiac care in middle age and late life, research has discovered that positive affect and attitudes may have protective effects in CHD. There is evidence that optimism is predictive of success in making health behavior changes that lower risk of CHD (Shepperd, Maroto, and Pbert 1996). Optimism appeared to be related to fewer hospital readmissions in CHD patients (Middleton and Byrd 1996). A comparison study found that patients with asymptomatic insulin-dependent diabetes and cardiovascular symptoms showed higher psychological distress and lower optimism than did the similar diabetic patients without these symptoms and healthy persons (Motivala et al. 1999). Buchanan 1995 tested the relationship between CHD, the Type A behavior pattern, and pessimistic explanatory style among 120 male participants who suffered a first heart attack; subsequent death by CHD was predicted by pessimism.
Optimism also predicts better outcomes from use of cardiac procedures. Among patients undergoing CABG, optimism was associated with better quality of life (Fitzgerald, Tennen, Affleck, and Pransky 1993). In a follow-up study of 51 male CABG patients, dispositional optimism on the day before the surgery was associated with a faster rate of physical recovery during the period of hospitalization, a faster rate of return to normal life activities subsequent to discharge, and better postsurgical quality of life at 6 months (Scheier, Matthews, Owens, and Magovern 1989). Helgeson and Fritz 1999 examined the relation of optimism and self-esteem with new coronary events (e.g., coronary artery bypass grafting, percutaneous transluminal coronary angioplasty [PTCA], myocardial infarction, or disease progression) within 6 months following the first PTCA. Patients at lower risk for a new cardiac event during this period were those who responded to their illness by perceiving control over their futures, having positive expectations about their futures, and having a positive view of themselves as measured in a previous study.
Despite the demonstrated relation of optimism to physical well-being, few researchers have investigated whether optimism originated in religious beliefs that involved spiritual ways of coping. Most psychologists have tended not to address which population was more optimistic, but rather focused on optimism as an individual psychological trait or resource (Taylor, Kemeny, Reed, Bower, and Gruenewald 2000). Researchers in religion, on the other hand, have long assumed that faith is a source of optimism, hope, and strength for adherents (Dull and Skokan 1995; James 1978). Yet, few empirical studies have investigated these connections. A study following 59 breast cancer patients did not find a clear relationship among religious coping, optimism, and distress (Carver et al. 1993). Because optimism does clearly predict better cardiac health, investigation of optimism in cardiac care should explore relations among optimism, belief, spiritual coping, and stress related to cardiac procedures.
The Use of Private Prayer for Health and Coping
Social and behavioral scientists have assumed a buffering role for religious involvement in moderating stress (Maton 1989a, Maton 1989b; Maton and Wells 1995). Religious involvement may facilitate adjustment as a source of a person's psychological resources. Johnson 1959 believed that "when the values of life are at stake, there is reason to be earnest. In time of crisis religion usually comes to the foreground. The more urgent the need the more man seeks for a response" (p. 82). Spiritual faith may function as a motivating force in people's lives and protect them from harm during crisis. This general notion provided the working hypotheses for the present study on the role of private prayer and other religious or spiritual factors in motivating positive attitudes such as optimism. The use of prayer has been popular in the United States, as shown by the consistent finding for four decades that over 80% of the American people engage in prayer to God (Gallup, and Jones 1989). Among multiple-choice responses to a question concerning seeking help in the face of crisis posed in a Gallup survey, prayer (80%) was second only to family support (87%).
In general, religiosity, including prayer, has been linked with positive health and mental health outcomes. There was evidence of (a) a survival advantage in a large sample of older adults who were involved in private religiosity (Helm, Hays, Flint, Koenig, and Blazer 2000), (b) a positive association between better behavioral and psychosocial resources and religious involvement among community-dwelling elders (Idler and Kasl 1997a), and (c) an inverse relation between public religiosity and risk of depression (Koenig et al. 1997, McCullough and Larson 1999). A meta-analysis of 42 independent samples showed an association between religious involvement and lower mortality from all causes (McCullough, Hoyt, Larson, Koenig, and Thoresen 2000). In cardiac patients, a lack of social participation or religious strength and comfort predicted death after cardiac surgery in older patients (Oxman, Freeman, and Manheimer 1995). Private prayer was associated with reduced levels of depression 1 year following CABG (Ai et al. 1998). However, there has been no empirical evidence about the function of this means of spiritual coping in sustaining optimism prior to a major medical procedure with life-or-death implications. Therefore, more investigation of the use of prayer for coping with medical stressors is necessary for the development of appropriate interventions.
Social science studies on aging and religion have confirmed the prayer dimension of private religiosity consisting in a threefold measure for general religiosity (e.g., private, public, and subjective) developed for well-designed population studies (Chatters, Levin, and Taylor 1992; Levin, Taylor, and Chatters 1994). The typical measure of private religiosity is the frequency of personal religious behaviors. There are a number of reasons that this frequency measure is not suitable for assessing coping in clinical situations. As Poloma and Pendleton 1991 suggested, the frequency of prayer may not be associated with either the measure of well-being or the comfort that comes from spiritual experiences. The frequency measure may reflect the relation of prayer to the degree of religiosity in general populations, but it provides little information about the rich subjective process of prayer life. Some measures of religious involvement may not be appropriate for assessing clinical cases such as cardiac surgery, when the sick person may not be able to enjoy religious reading or televison programming in the face of a medical crisis. Finally, even though the measure of public or private religiosity may be positively related to a given clinical outcome, the results will be too general to support a conclusion on which specific religious activity is uniquely effective for the purpose of intervention. Therefore, itemized effects of religious involvement should be addressed and scales for assessing spiritual coping should be developed for use in clinical practice and research so as to identify the needs of subpopulations.
The Present Study
The current study investigated the use of private prayer as a coping response among a middle-aged and older patient population and its predictive effect on optimism prior to cardiac surgery. We used the preliminary data from an ongoing longitudinal study on spirituality, religiousness, and recovery in middle-aged and older patients following cardiac surgery, initially funded by the National Institute on Aging, among other resources (Ory and Lipman 1998). Unlike most previous studies, optimism was addressed as an outcome rather than as a predictor. On the basis of the literature, two research questions were framed: (a) What were the prevalence and the types of prayer used for coping with distress related to cardiac surgery, and (b) does the use of prayer to cope predict optimism prior to the procedure after controlling for the effects of socioeconomic and psychological factors?
We attempted to develop a new measure of private prayer for clinical use. According to Lepore and Evans 1996, coping responses involve behavioral and cognitive efforts to adjust to stressful situations. A measure of private prayer in a medical crisis should reflect the mental process underlying the decision to cope by using prayer. In the face of uncertainty related to cardiac surgery, coping decisions tend to be based on the particular beliefs, faiths, and experiences of patients. At the very least, the measure of private prayer as an internal coping response should indicate whether an individual (a) believes that the use of private prayer is important, (b) has some faith in the efficacy of using prayer to cope on the basis of previous experiences, and (c) has the intention to use prayer for coping with difficulties and stressors associated with the medical procedure. A three-item measure of private prayer was designed to assess these factors prior to the surgery. It focused on the psychological process underlying this coping decision, presumably common to patients with various beliefs, rather than the content of prayers. To examine what patients would tend to do when they prayed, we addressed four independent types or activities of prayer in accordance with Poloma and Gallup 1991 national survey, and we included an "other types" option.
We hypothesized that the use of private prayer prior to cardiac surgery predicts optimism after controlling for other contributors. The effects of these five types of prayer and the accumulated number of types of prayer on optimism were also examined to test each type-specific effect independently. The influence of general religiosity on optimism was examined and designed to test Poloma and Pendleton 1991 suggestion that frequency of prayer may not be associated with comfort or well-being. We did not expect to find a faith difference in optimism, on the basis of Idler and Kasl 1997a, Idler and Kasl 1997b large sample study. Demographic effects by age, gender, or race on optimism were not anticipated, because of a lack of relevant research literature. Previous studies suggested the negative effect of depression and anxiety on optimism (Mineka et al. 1995). Also, positive attitudes are likely to develop in a more secure or affluent context. Therefore, we anticipated that patients who had more socioeconomic resources such as better education, income, or insurance and who had better general physical and emotional health would also be more optimistic. Because the primary interest in this study was the spiritually rooted predictors of optimism, contributors in demographic, socioeconomic, and health or illness sectors were identified as control variables.
Methods
Patients
The sample for this study was recruited for two sequential interviews from patients at the cardiac clinic at the University of Michigan Medical Center (Ann Arbor). Eligible candidates were patients who were admitted for their cardiac surgery during the period between May 3, 1999, and December 7, 2000. The types of surgeries included CABG, aneurysm repairs, and valve repair or replacement surgeries. The subject eligibility criteria were (a) aged 35 years or older, (b) scheduled for admission to the University of Michigan Health System for nonemergency, nontransplant cardiac surgery within the subsequent 8 weeks, (c) able to speak fluently and understand the English language, (d) cognitively and physically capable of providing informed consent, (e) not pregnant, (f) having provided informed consent, and (g) permitted to participate in the study by the surgeon.
Two hundred forty-six patients (74% of patients approached) completed the first face-to-face interview. Of these, 226 completed the second interview by telephone. The major reasons for nonparticipation were composed of various combinations of the patient's (a) time constraints due to the scheduled presurgical history and physical examination date, (b) reluctance to answer "personal" questions or to discuss religious matters, and (c) extreme anxiety about the surgery on the date of medical assessment. Among the 20 patients who did not join the second interview, 1 died before the procedure and another canceled the surgery. In the remaining 18 patients, the scheduled surgery—and, therefore, the second interview—was not encompassed in the time period of the data collection.
Procedure and Data Sources
Prior to the presurgical assessment, every patient received a general information packet containing a subject recruitment letter from the cardiac surgeon informing the patient of the opportunity to participate in this project. On the date of the medical assessment, a nurse screened the patient for availability and eligibility. A trained interviewer then interviewed the recruited patient. The first face-to-face interview lasted for 20 to 40 min. On average, it took place about 2 weeks prior to the cardiac surgery. The interview asked about (a) presurgical socioeconomic backgrounds, (b) health insurance, (c) the use of private prayer, (d) general religiousness, (e) impact and health status, and (f) mental health. At the end of first interview, participants were scheduled for the second interview around 1 day before their surgery and were provided with a sealed copy of the second set of questions. On the date of the second interview just prior to surgery, the interviewer instructed the patient to open the second questionnaire so that it could be read while the interview was conducted by telephone. Optimism was assessed at this time, when the stress level of the patient was presumably near its peak.
Optimism.
The Life Orientation Test (Scheier and Carver 1985) is a 12-item, self-report scale that assesses dispositional optimism, containing eight items plus four fillers. Patients were asked about the extent to which they agreed with each statement prior to their surgery on a 5-point Likert-type scale ranging from 0 (strongly disagree) to 4 (strongly agree). Cronbach's alpha was .76, and the test–retest reliability was .79 in other reports. Cronbach's alpha in the present study was also .76.
Prayer.
There were two measures of prayer. First, three items from the new scale, Using Private Prayer as a Means for Coping, asked about the extent to which patients agreed with each of the following statements: (a) "Prayer is important in my life," (b) "Prayer does not help me to cope with difficulties and stress in my life" (reverse scored), and (c) "I will use private prayer to cope with difficulties and stress associated with my cardiac surgery." All three questions were asked on a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly agree). Cronbach's alpha coefficient for the internal consistency of these three items was .85 in our sample. The score of this scale was the sum of the rating from the three items.
Second, a question addressed the content of private prayer ("What did you do when praying during the past month?") This question offered a yes/no response for four major types of prayer: (a) "reading a relevant book or reciting memorized prayers," (b) "making an informal conversation with God," (c) "addressing the accomplishment of spiritual or material needs," and (d) "meditating or thinking about or experiencing the divine, including hearing God's voice," as in Poloma and Gallup 1991 survey, as well as (e) other types of private prayer. Finally, we used one variable to sum up the types of prayer used by these patients to examine the extent of the variability in prayer.
Religious Affiliation and Religiousness.
Patients were questioned about whether they were Protestant, Catholic, Orthodox, Jewish, other religion, or had no preference. The answer to this question was then dichotomized into another variable, religious identification (religion vs. nonreligion). General religiosity was measured with a three-factor religiosity scale that assessed organizational, nonorganizational, and subjective religious involvement (Chatters et al. 1992; Levin et al. 1994). Likert-type questions assessed frequencies or levels (ranging from 1 to 5 or to 6) of religious activities and the importance of religion to the patients. With the permission of one of the scale's authors, we made a few changes suited to the culture of this population. Subjective religiosity included two items (importance of religion and the degree of religiousness) and was considered to be the patient's self-appraisal of their religiousness. Nonorganizational religiosity included private religious activities (using private prayer, watching religious programs, reading religious books, and asking others to pray for oneself). Organizational religiosity included public religious activities (attendance at church services, importance of going to church, the extent to which people in the religious agency helped the patient, and attendance at other religious activities). For the internal consistency of these three subscales, Cronbach's alpha coefficients were .85, .75, and .87, respectively.
Health Status/Illness Impact.
The five measures of this factor included (a) cigarette smoking (1 = current smoker, 2 = former smoker, 3 = never smoked), (b) alcohol consumption (1 = current drinker, 2 = former drinker, 3 = not a drinker), (c) body mass index (BMI) values (calculated as weight in kilograms divided by height in meters, squared), (d) an objective measure of illness—noncardiac chronic conditions (allergies, anemia, arthritis, back problems, cancer, chronic lung disease, diabetes, hypertension, liver and gallbladder problems, ulcers and digestive problems, incontinence of urine, kidney problems, nervous problems, sprains or strains, and others), and (e) general impact of illness, including subjective, functional, and psychosocial domains.
General impact of illness was assessed with the RAND 36-Item Health Survey 1.0 (RAND-36; Hays, Sherbourne, and Mazel 1993). The RAND-36 is a self-report instrument designed to assess respondents' health. Cronbach's alpha ranged between .78 and .93 across its subscales in previous reports. The RAND-36 includes single items that provide indication of perceived general health in the past month on a 5-point scale (excellent, very good, good, fair, and poor) and bodily pain (ranging from 1 [none] to 5 [severe]). Respondents were asked to indicate their answers using yes/no, forced-choice, and 3-, 5-, and 6-point Likert-type responses. Because of the schedule on the dates for medical assessment, the health condition of the sample, the size of this sample, and RAND-36 items overlapping with other measures in this study, we used only a part of this scale. The items chosen from the original measure were combined into three subscales: (a) impacted subjective health, including one single-item question on general health and four other items on the patient's self-appraisal of feeling healthy or ill (all with 5-level scores); (b) impacted functional status, involving eight items related to physical functioning limited by the patient's health (all with 3-level scores); and (c) impacted psychosocial well-being, combining five items on the patient's emotional well-being and one item on social functioning (all with 5-level scores). Cronbach's coefficient alphas for these three subscales were reasonably high, at .80, .88, and .77, respectively, in our sample.
Mental Health.
This factor was operationalized as depression and anxiety prior to cardiac surgery. Depression was measured with the Center for Epidemiologic Studies–Depression scale (CES-D). The CES-D is a 20-item, self-report instrument (Radloff 1977). The CES-D was developed for use in studies of the epidemiology of depressive symptomatology in the general population. For each item, patients were asked about "how often" they felt accordingly in the prior week on a 4-point Likert-type scale ranging from 0 (rarely or none of the time [< 1 day]) to 3 (most or all of the time [5–7 days]). The CES-D has adequate internal consistency (coefficient alpha and Spearman–Brown, split method) as high as about .90 in the patient sample and .85 in the general population, and adequate test–retest repeatability. Coefficient alpha was .80 or above in all subgroups in previous reports. In our sample, it was .89.
Anxiety was measured with the Trait Anxiety Inventory (STAI Form X-2). STAI is a part of the State–Trait Anxiety Inventory (Spielberger 1983). The scale was designed to assess relatively stable individual differences in anxiety proneness. The scale contains 20 statements concerning the general anxiety reaction under such conditions. For each item, patients were asked about "how often" they felt accordingly in the prior week on a 4-point Likert-type scale ranging from 1 (almost never) to 4 (almost always). The correlations of the STAI with the IPAT 8-Parallel-Form Anxiety Battery (Scheier and Cattell 1962; Cattell and Scheier 1963) and the Taylor Manifest Anxiety Scale (Taylor 1953) ranged from .85 to .73. The test–retest correlations were reasonably high, ranging from .73 to .86. Cronbach alpha coefficient approximated .90 in previous reports. In our sample, it was .91.
Statistical Analysis
Descriptive analysis was used to describe the sample and the prevalence of using prayer for coping. To examine the long-held assumption of the direct effect of faith on optimism (Dull and Skokan 1995; James, 1901–1902/1978), we first used a one-way analysis of variance (ANOVA) to identify whether religious affiliation predicted optimism. The multivariate ordinary least square regression models were then used for investigating the predictors of optimism, particularly the use of private prayer for coping prior to cardiac surgery, controlling for multilevel effects of demographic, socioeconomic, health/mental health, and religious measures. This analysis adopted the general strategy of previously planned hierarchical steps to examine the direct effects of various classes of factors. Variables that were considered as the same class were entered as a block at each step of regression analyses, with the most interesting variables, from both clinical and research points of view, entered last. Only significant and nearly significant factors were kept in the equation at the end of each step. The sequence of entering variables into the model followed an order of five steps: Step 1, demographic factors; Step 2, socioeconomic factors; Step 3, physical and mental health factors; Step 4, religious factors; and Step 5, prayer factors. In this plan, variables in the first three steps were considered as controls, and those in the last two focused on factors pertinent to the research questions.
Results
Sample Characteristics
Table 1 shows the information on demographic, socioeconomic, and insurance variables and religious affiliation of the 246 patients. The majority of the sample was White, married with spouse present, and Christian. Jewish participants, people practicing other religions (e.g., Eastern religion, Hindu, and Native American beliefs), and persons with no religious preferences counted for only one fifth of the sample. Male patients constituted slightly more than half the sample. The average age was 62.3 (ranging from 36 to 86), and 110 patients were above age 65. The average level of education was 14.2 years (ranging from 5 to 28). The average annual family income was $54,957 (ranging from $0 to $400,000). Most patients reported health insurance other than Medicare and Medicaid, and employed persons (full time or part time) made up nearly 30% of the sample. Of the 246 patients, only 14 (5.7%) were current smokers, whereas 146 (59.3%) were former smokers, and 85 (34.6%) had never smoked. However, 126 (51.2%) patients were current drinkers, whereas only 18 (7.3%) were former drinkers, and 102 (41.5%) never drank. Table 2 shows the means and standard deviations of variables concerning health, mental health, optimism, and religiosity.
Prevalence of Using Prayer for Coping
Table 3 includes information on the use of private prayer. The mean of the use of prayer as a means for coping was 9.97 (SD = 2.39). For the first research question concerning the prevalence of coping by using private prayer, our scale indicated that about 87.8% of the patients believed that private prayer was important in their lives, 83.9% had faith in the efficacy of using prayer in coping with stress in their lives, and 88.2% indicated their intention to use private prayer to cope with difficulties associated with cardiac surgery. These percentages were calculated from the sum of the proportion of patients who checked one response of four "agree" or "disagree" categories on each statement. The types of prayer used are also shown in Table 3 , with the most popular one as "conversation with God" used by three quarters of the sample, followed by three other types used by only half or less. Nearly half of the sample (47%) used more than three types of prayer and only 40 cases used the "other types" option, mostly reported as thanksgiving prayers.
Predictors of Optimism
As shown in Table 4 , the result of the one-way ANOVA did not support the long-held assumption of the direct effect of faith on optimism (Dull and Skokan 1995; James, 1901–1902/1978). No significant differences were indicated in optimism between patients with no religious preference and their religious counterparts. For the second research question, multiple regression analyses were performed in previous planned steps to identify predictors of optimism. A part of these steps is shown in Table 5 ; five nested models show the effects of the variables on optimism and the value R2 reveals information on variance in optimism explained by each model. All controlled factors were entered as blocks in the first three steps. Demographic factors (gender, age, race, and dichotomized marital status) were entered in Step 1, and socioeconomic factors (education, income, employment status, major occupation, and other health insurance) were entered in Step 2. Model 1 in Table 5 shows the predictive variables of optimism at the end of Step 2. Only age, years of education, employment status, and other health insurance were significant contributors of the variance in optimism and so were kept in the model.
In Step 3, nine factors were entered as indicators of health status, illness impact, and mental health. Joining the Step 2 variables of age, education, and employment, impacted psychosocial well-being positively predicted optimism, whereas depression and anxiety showed negative influence. Other insurance became insignificant and so was dropped from the equation with other noninfluential Step 3 variables, including alcohol consumption, BMI, bodily pain, number of noncardiac chronic conditions, impacted subjective health, and impacted functional status. Model 2 included only influential variables at the end of Step 3, including positive predictors (age, education, employment status, and impacted psychosocial well-being) and negative ones (depression and anxiety). Model 3 shows the effects of all religious variables that were entered into the equation in Step 4, with the effective variables remaining from Step 3. Although none of them (religious affiliation, subjective religiosity, private religiosity, and public religiosity) were influential and were dropped from the equation, they are listed here as factors of interest in this study.
Model 4 shows the effects of all variables related to the use of private prayer that were entered into the equation in Step 5, with the effective variables remaining from Step 4. Except for the effect of using private prayer as a means for coping, none of the other prayer variables (memorized prayer, conversation with God, accomplishment of needs, experience of the divine, other types of prayer, and the number of prayer types) showed predictive value for optimism. As shown by Model 5, the final regression analysis yielded a parsimonious model, F(7,245) = 16.12, p < .0001, R2 = 31.8, that included only predictors at the significance level below .05. This model explained about one third of the variance in optimism, and all predictors were highly significant. In this final model, optimistic patients tended to be those who intended to use private prayer for coping, who were older, who had more education and full-time or part-time employment status, and who were less depressed or anxious, as expected. However, patients whose psychological well-being was already affected prior to the surgery were also more likely to be optimistic.
Discussion
This study examined a measure of private prayer as a coping response and its ability to predict optimism prior to cardiac surgery. The results showed that the vast majority of middle-aged and older patients intended to use prayer and that this means of coping indeed predicted a positive attitude immediately before a major medical event. Because the past research linked both prayer and optimism to quality of life and cardiac health, the present study may suggest a pathway of healing through the link of active spiritual coping with a positive attitude. Although the use of private prayer predicted optimism prior to a medical crisis, optimism was not directly linked with any single specific type of prayer used by these patients. This finding implies that spiritually rooted active coping along with a patient's intention to survive may be more important than any content or type of prayer in sustaining their positive attitudes under the circumstance of a life crisis.
If the association between optimism and prayer is not surprising, then the absence of the traditionally assumed links between optimism and all religious measures may be a surprise. Although 90% of our sample was religious, this high prevalence did not predict optimism as shown in Model 3, Table 5 , after controlling for the effects of other demographic, socioeconomic, and health/mental health measures. By the same logic, the association of the prayer measure with optimism found in our study could not have occurred solely by chance. It should be noted that both organizational and nonorganizational religiosity was measured by frequency of religious activities. As mentioned earlier, Poloma and Pendleton 1991 speculated that the frequency of religious activities was not related to comfort. Our study provides a clinical case that these frequency measures, including that for prayer, were not associated with optimism. Thus, a general religiosity scale used in population studies may not be sensitive enough to identify the relationship between belief systems and successful coping outcomes in facing a medical crisis. Alternatively, the amount of time invested in religious activity may not guarantee one's positive attitudes in the face of life-altering events. These findings indicate the need for a better measure of religious practice that has sensitive predictive value to optimism and other outcome measures.
Optimism has been related positively to a religious root by social scientists (Dull and Skokan 1995; James, 1901–1902/1978). Scheier and Carver 1987 indicated that turning to religion was related to the generalized expectation that outcomes will be positive. In Freud's perspective, Freud 1928 however, religious optimism was tied to beliefs about an afterlife and came with the cost of the denial of reality. Whichever assumption holds, only a little research evidence of empirical nature has established the connection between optimism and religion. In a cross-sectional study, Idler and Kasl 1997a found that church attendance was marginally associated with optimism in a large, community-dwelling elderly adult sample. However, a small clinical sample of breast cancer patients did not show the associations among religious coping, optimism, and distress (Carver et al. 1993). Likewise, our multivariate analysis provides no evidence of direct religious impact on optimism, as measured by organizational, nonorganizational, and subjective religiosity or religious affiliation. These measures may not be sensitive indicators of faith-based optimism in relation to a clinical situation such as a major, life-affecting cardiac surgery. Although large-sample studies of the general population have the merit of better generalizability, in clinical research the use of more precise assessments to control for other influences can target specific at-risk populations. Our finding, contrary to common belief about religion and optimism, may not support rejection of the faith–optimism connection, but may rather suggest that this link is more complicated than has been theoretically assumed, or at least is not a straightforward one. Further research is needed to identify mediating and moderating factors, interaction among factors, and various pathways between belief systems and optimism as well as other health or mental health outcomes in clinical populations.
It is noteworthy that senior patients in this sample appeared more likely to hold a positive attitude in the face of a medical crisis than did their younger counterparts. This effect was independent of other sources of optimism: socioeconomic, psychological, and spiritual. Within a culture that believes negative stereotypes of old age, this strength deserves the attention of health and mental health providers as well as gerontological researchers. The older patients' positive attitude may be related to their better preparation for life-threatening events, to their spiritual maturity with life-or-death issues, to some reasons for appreciating a longer life course, or to their retired status, which is less affected by job-related medical conditions than is that of their younger, employed counterparts. Is there a possibility that older age is related to positive attitudes in the general population regardless of the distress level people face? This appealing hypothesis can only be evident through well-designed, large, randomized sample studies. Because the older a person is, the more likely chronic illness and disability become irreversible, more research should address the positive aspects of aging, spirituality, coping, and health. Researchers and clinicians should help identify and mobilize older persons' spiritual, psychosocial, and behavioral resources to enhance their quality of life and rehabilitation from a medical crisis and comorbidities. Despite this explicit age effect, the present study cannot explain whether the effect was due to a chronological or generational variation. Alternatively, this age effect could be a mortality effect (pessimistic people dying, leaving only the optimistic people). Should younger counterparts be expected to exhibit the same trend in their older age? These hypotheses need to be addressed in the future investigation of both general populations and clinical samples.
Our study also found that optimism does not exist in a vacuum but has spiritual, psychosocial, and socioeconomic contexts and resources. As anticipated, predictors of optimism, other than a spiritual way of coping, included the higher levels of education and employment status and lower levels of affect disorders. In a critical situation such as cardiac surgery, these factors might help secure the patients in multiple ways, such as information access, emotional stability and healthy affect, and anticipation for better care during the recovery process. Why did higher income not predict optimism but employment status did? Perhaps in this service region heavily influenced by the automobile industry, the labor-intensive, high-profit jobs held by midlife patients affected the patients' anticipation of returning to work after cardiac procedure (Ai et al. 1998). The positive effect of impacted psychosocial well-being on optimism was somewhat puzzling but not incomprehensible. One interpretation may be that this measure is an indicator of the quality of life rather than a measure of affective disorder among these cardiac patients. From a clinical point of view, not all cardiac patients facing surgery are chronically ill. Sometimes the decision for a cardiac surgery on the basis of the diagnosis of acute myocardial infarction can be a shock to some self-identified "healthy" persons. Accordingly, patients who had perceived a lower quality of life for a longer period because of diagnosed cardiac disease may have had higher levels of optimistic expectation prior to the procedure concerning the postoperative outcomes of surgery, including their improved psychosocial well-being. Alternatively, the literature provides similar evidence of unexpected findings concerning optimism, but in the opposite direction. In a follow-up study of pessimism as a potential risk factor for depressive mood among older adults (aged 64–94), it was the optimists rather than pessimists who were at higher risk for depressive symptoms after negative life events (Isaacowitz and Seligman 2001). In either case, these unexpected findings may add some theoretical implications for the future conceptualization of optimism, if the results of the present study are replicated under various circumstances.
Because all the predictors of optimism identified in the study will affect the outcome of major cardiac surgeries, health and mental health professionals may need to provide preoperative assessment and design intervention accordingly. In particular, physicians and nurses should collaborate with clinical social workers and other community health providers to support psychosocially disadvantaged patients. By an interdisciplinary effort and the connection between hospital and community care, the ultimate outcome of an expensive medical procedure might be improved through the pathway of patients' boosted positive attitudes.
Implications for Interdisciplinary Intervention by Clinicians
The major finding in this study underscores the importance of private spiritual coping in midlife and older patients in the face of a life-or-death crisis. From a clinical perspective, Rossiter-Thornton 2000 identified several benefits of using prayer: (a) it is easy to use, (b) patients are in charge, (c) there are no requirements for specific beliefs, (d) it is flexible, and (e) it is testable by the user. Given these advantages, it is no wonder that sicker or more disabled patients tended to pray more (Courtenay, Poon, Martin, and Clayton 1992; Koenig, Moberg, and Kvale 1988). In our sample, there were cases in which the patient asked the surgeon to pray with him or her before the surgery or for permission to play a tape of prayer throughout the procedure in the operation room. Patients who pray about their difficulties may have a spiritual means to achieve self-empowerment. Prayer may help them discover practical solutions for their problems related to a medical crisis. Prayer may also lead the patient to achieve a sufficient "distance" from the distress or a meditative state and to ruminate less about their health problems that may cause distress.
The use of prayer for coping in the face of a medical crisis appears to be a spiritual way of seeking help in general rather than a faith-specific religious ritual (Ai et al. 1998, Mackenzie, Rajagopal, Meibohm, and Lavizzo-Mourey 2000). Physicians, therefore, should give attention and respect to their patients' spirituality or religious means of coping, which may serve as a healing source for the patient, regardless of their particular religious tradition. By recognizing that spiritual or religious coping may reduce stress and improve optimism in the face of a life crisis, clinicians may begin to value an individual patient's prayer in the medical setting and permit time for this practice. The nurse's scheduling of rounds, medical procedures, and other physician's duties may need to take account of this practice, which may improve the effectiveness of the medical intervention. Hospital social workers should assess patients' spiritual needs with other psychosocial needs to help coordinate a variety of available resources. Because patients of different cultures or religious beliefs may use different means for spiritual coping, hospital chaplains need to develop diverse approaches to support each patient and family's unique requirement. Through an interdisciplinary effort approach to spiritual needs, the quality of health care and the relationship between providers and consumers can be improved substantially.
Limitations of the Study
A number of limitations of this study should be acknowledged. First of all, the study is correlational and does not rule out alternative interpretations. One alternative explanation is that optimism drives prayer: The more optimistic people are about their situation, the more likely it is that they will pray. This likelihood seems rather remote because most people tend to pray when they are in trouble. Prayer is practiced more frequently by people who have low socioeconomic status and low education, minority status, and oppression for any reason (including poor health), situations that tend to breed pessimism, not optimism. The negative relation between depression and anxiety with optimism may have been inverted if optimism had been measured in the first interview and anxiety in the second interview. In addition, if the relation between optimism and affective disorder is in fact reciprocal, perhaps some underlying genetic trait is associated with both optimism and prayer. Another plausible explanation is that the association between prayer and optimism was simply due to the fact that the majority of our patients confirmed the importance of prayer to them and their intention to use prayer to cope. Our multivariate analysis controlled for the influence of many factors that would support this second alternative explanation. For example, the sample was composed of nearly 90% religious people; however, none of the religiosity measures in our research design predicted optimism. Third, some patients with extreme anxiety may have self-selected out of the study. A fourth limitation is that the convenience sample does not permit generalizability of the finding to other groups of patients. Replications of the study in different populations will be desirable. Finally, because of the use of preliminary data from an ongoing longitudinal study, we are unable to identify outcome measures that may be predicted by optimism at this time. Despite these shortcomings, we hope that the finding is informative to health and mental health professionals. More important, we hope to spur more research on similar topics in medical settings and encourage the development of sensitive measures of spiritual/religious indicators.
Practice Concepts
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Variable/Label | N | % |
Gender | ||
Male | 137 | 56.7 |
Female | 109 | 44.3 |
Race | ||
Caucasian | 224 | 91.1 |
African American | 17 | 6.9 |
Asian/Pacific American | 2 | 0.8 |
Hispanic/Latino | 1 | 0.4 |
American Indian/Native | 1 | 0.4 |
Other | 1 | 0.4 |
Age (years) | ||
Below 65 | 136 | 53.3 |
Above 65 | 110 | 44.7 |
Education | ||
Grade school | 18 | 7.3 |
High school | 100 | 40.7 |
Some college | 86 | 35.0 |
Post college | 40 | 16.3 |
Missing | 2 | 0.8 |
Education dichotomized (college education above vs. the rest) | 122 | 49.6 |
Marital status at CABG | ||
Married, spouse present | 182 | 74.0 |
Married, spouse absent | 3 | 1.2 |
Widowed | 29 | 11.8 |
Divorced/separated | 21 | 8.5 |
Never married/single | 10 | 4.1 |
Missing | 1 | 0.4 |
Marital status dichotomized (married, spouse present vs. the rest) | 182 | 74.0 |
Income at CABG | ||
< $20,000 | 41 | 16.7 |
$20,000–$34,999 | 50 | 20.3 |
$35,000–$49,999 | 50 | 20.3 |
> $50,000 | 94 | 38.2 |
Missing | 11 | 4.5 |
Income dichotomized (< $35,000 vs. the rest) | 114 | 58.5 |
Employment at CABG | ||
Full time | 56 | 22.8 |
Part time | 17 | 6.9 |
Homemaker | 33 | 13.4 |
Unemployed | 5 | 2.0 |
Retired | 107 | 43.5 |
Medical leave | 24 | 9.8 |
Missing | 4 | 1.6 |
Employment dichotomized (full time or part time vs. the rest) | 73 | 29.7 |
Medicare | 122 | 49.6 |
Medicare Plan Part B | 114 | 46.0 |
Medicaid | 21 | 8.5 |
Government health insurance (e.g., railroad retirement, CHAMPUS, and military programs) | 7 | 2.8 |
Other health insurance (e.g., Blue Cross/Blue Shield, M-Care, and AETNA) | 221 | 89.8 |
Religious affiliation | ||
Protestant | 130 | 52.8 |
Catholic | 66 | 26.8 |
Orthodox | 5 | 2.0 |
Jewish | 9 | 3.7 |
Other religion | 9 | 3.7 |
No preference | 27 | 11.0 |
Variable/Label | N | % |
Gender | ||
Male | 137 | 56.7 |
Female | 109 | 44.3 |
Race | ||
Caucasian | 224 | 91.1 |
African American | 17 | 6.9 |
Asian/Pacific American | 2 | 0.8 |
Hispanic/Latino | 1 | 0.4 |
American Indian/Native | 1 | 0.4 |
Other | 1 | 0.4 |
Age (years) | ||
Below 65 | 136 | 53.3 |
Above 65 | 110 | 44.7 |
Education | ||
Grade school | 18 | 7.3 |
High school | 100 | 40.7 |
Some college | 86 | 35.0 |
Post college | 40 | 16.3 |
Missing | 2 | 0.8 |
Education dichotomized (college education above vs. the rest) | 122 | 49.6 |
Marital status at CABG | ||
Married, spouse present | 182 | 74.0 |
Married, spouse absent | 3 | 1.2 |
Widowed | 29 | 11.8 |
Divorced/separated | 21 | 8.5 |
Never married/single | 10 | 4.1 |
Missing | 1 | 0.4 |
Marital status dichotomized (married, spouse present vs. the rest) | 182 | 74.0 |
Income at CABG | ||
< $20,000 | 41 | 16.7 |
$20,000–$34,999 | 50 | 20.3 |
$35,000–$49,999 | 50 | 20.3 |
> $50,000 | 94 | 38.2 |
Missing | 11 | 4.5 |
Income dichotomized (< $35,000 vs. the rest) | 114 | 58.5 |
Employment at CABG | ||
Full time | 56 | 22.8 |
Part time | 17 | 6.9 |
Homemaker | 33 | 13.4 |
Unemployed | 5 | 2.0 |
Retired | 107 | 43.5 |
Medical leave | 24 | 9.8 |
Missing | 4 | 1.6 |
Employment dichotomized (full time or part time vs. the rest) | 73 | 29.7 |
Medicare | 122 | 49.6 |
Medicare Plan Part B | 114 | 46.0 |
Medicaid | 21 | 8.5 |
Government health insurance (e.g., railroad retirement, CHAMPUS, and military programs) | 7 | 2.8 |
Other health insurance (e.g., Blue Cross/Blue Shield, M-Care, and AETNA) | 221 | 89.8 |
Religious affiliation | ||
Protestant | 130 | 52.8 |
Catholic | 66 | 26.8 |
Orthodox | 5 | 2.0 |
Jewish | 9 | 3.7 |
Other religion | 9 | 3.7 |
No preference | 27 | 11.0 |
Note: CABG = coronary artery bypass graft surgery; CHAMPUS = Civilian Health and Medical Program of the Uniformed Services.
Variable/Label | N | % |
Gender | ||
Male | 137 | 56.7 |
Female | 109 | 44.3 |
Race | ||
Caucasian | 224 | 91.1 |
African American | 17 | 6.9 |
Asian/Pacific American | 2 | 0.8 |
Hispanic/Latino | 1 | 0.4 |
American Indian/Native | 1 | 0.4 |
Other | 1 | 0.4 |
Age (years) | ||
Below 65 | 136 | 53.3 |
Above 65 | 110 | 44.7 |
Education | ||
Grade school | 18 | 7.3 |
High school | 100 | 40.7 |
Some college | 86 | 35.0 |
Post college | 40 | 16.3 |
Missing | 2 | 0.8 |
Education dichotomized (college education above vs. the rest) | 122 | 49.6 |
Marital status at CABG | ||
Married, spouse present | 182 | 74.0 |
Married, spouse absent | 3 | 1.2 |
Widowed | 29 | 11.8 |
Divorced/separated | 21 | 8.5 |
Never married/single | 10 | 4.1 |
Missing | 1 | 0.4 |
Marital status dichotomized (married, spouse present vs. the rest) | 182 | 74.0 |
Income at CABG | ||
< $20,000 | 41 | 16.7 |
$20,000–$34,999 | 50 | 20.3 |
$35,000–$49,999 | 50 | 20.3 |
> $50,000 | 94 | 38.2 |
Missing | 11 | 4.5 |
Income dichotomized (< $35,000 vs. the rest) | 114 | 58.5 |
Employment at CABG | ||
Full time | 56 | 22.8 |
Part time | 17 | 6.9 |
Homemaker | 33 | 13.4 |
Unemployed | 5 | 2.0 |
Retired | 107 | 43.5 |
Medical leave | 24 | 9.8 |
Missing | 4 | 1.6 |
Employment dichotomized (full time or part time vs. the rest) | 73 | 29.7 |
Medicare | 122 | 49.6 |
Medicare Plan Part B | 114 | 46.0 |
Medicaid | 21 | 8.5 |
Government health insurance (e.g., railroad retirement, CHAMPUS, and military programs) | 7 | 2.8 |
Other health insurance (e.g., Blue Cross/Blue Shield, M-Care, and AETNA) | 221 | 89.8 |
Religious affiliation | ||
Protestant | 130 | 52.8 |
Catholic | 66 | 26.8 |
Orthodox | 5 | 2.0 |
Jewish | 9 | 3.7 |
Other religion | 9 | 3.7 |
No preference | 27 | 11.0 |
Variable/Label | N | % |
Gender | ||
Male | 137 | 56.7 |
Female | 109 | 44.3 |
Race | ||
Caucasian | 224 | 91.1 |
African American | 17 | 6.9 |
Asian/Pacific American | 2 | 0.8 |
Hispanic/Latino | 1 | 0.4 |
American Indian/Native | 1 | 0.4 |
Other | 1 | 0.4 |
Age (years) | ||
Below 65 | 136 | 53.3 |
Above 65 | 110 | 44.7 |
Education | ||
Grade school | 18 | 7.3 |
High school | 100 | 40.7 |
Some college | 86 | 35.0 |
Post college | 40 | 16.3 |
Missing | 2 | 0.8 |
Education dichotomized (college education above vs. the rest) | 122 | 49.6 |
Marital status at CABG | ||
Married, spouse present | 182 | 74.0 |
Married, spouse absent | 3 | 1.2 |
Widowed | 29 | 11.8 |
Divorced/separated | 21 | 8.5 |
Never married/single | 10 | 4.1 |
Missing | 1 | 0.4 |
Marital status dichotomized (married, spouse present vs. the rest) | 182 | 74.0 |
Income at CABG | ||
< $20,000 | 41 | 16.7 |
$20,000–$34,999 | 50 | 20.3 |
$35,000–$49,999 | 50 | 20.3 |
> $50,000 | 94 | 38.2 |
Missing | 11 | 4.5 |
Income dichotomized (< $35,000 vs. the rest) | 114 | 58.5 |
Employment at CABG | ||
Full time | 56 | 22.8 |
Part time | 17 | 6.9 |
Homemaker | 33 | 13.4 |
Unemployed | 5 | 2.0 |
Retired | 107 | 43.5 |
Medical leave | 24 | 9.8 |
Missing | 4 | 1.6 |
Employment dichotomized (full time or part time vs. the rest) | 73 | 29.7 |
Medicare | 122 | 49.6 |
Medicare Plan Part B | 114 | 46.0 |
Medicaid | 21 | 8.5 |
Government health insurance (e.g., railroad retirement, CHAMPUS, and military programs) | 7 | 2.8 |
Other health insurance (e.g., Blue Cross/Blue Shield, M-Care, and AETNA) | 221 | 89.8 |
Religious affiliation | ||
Protestant | 130 | 52.8 |
Catholic | 66 | 26.8 |
Orthodox | 5 | 2.0 |
Jewish | 9 | 3.7 |
Other religion | 9 | 3.7 |
No preference | 27 | 11.0 |
Note: CABG = coronary artery bypass graft surgery; CHAMPUS = Civilian Health and Medical Program of the Uniformed Services.
Variable | M | SD |
Health Status/Illness Impact | ||
BMI | 27.26 | 5.44 |
Noncardiac chronic conditions | 3.12 | 2.38 |
Subjective health | 19.98 | 4.14 |
Functional status | 15.04 | 4.9 |
Psychosocial well-being | 19.38 | 4.25 |
Mental Health/Optimism | ||
Depression | 13.41 | 10.33 |
Anxiety | 37.43 | 10.91 |
Optimism | 21.72 | 4.76 |
General Religiosity | ||
Subjective religiosity | 6 | 1.75 |
Private religiosity | 10.23 | 9.3 |
Public religiosity | 11.38 | 5.09 |
Variable | M | SD |
Health Status/Illness Impact | ||
BMI | 27.26 | 5.44 |
Noncardiac chronic conditions | 3.12 | 2.38 |
Subjective health | 19.98 | 4.14 |
Functional status | 15.04 | 4.9 |
Psychosocial well-being | 19.38 | 4.25 |
Mental Health/Optimism | ||
Depression | 13.41 | 10.33 |
Anxiety | 37.43 | 10.91 |
Optimism | 21.72 | 4.76 |
General Religiosity | ||
Subjective religiosity | 6 | 1.75 |
Private religiosity | 10.23 | 9.3 |
Public religiosity | 11.38 | 5.09 |
BMI = body mass index.
Variable | M | SD |
Health Status/Illness Impact | ||
BMI | 27.26 | 5.44 |
Noncardiac chronic conditions | 3.12 | 2.38 |
Subjective health | 19.98 | 4.14 |
Functional status | 15.04 | 4.9 |
Psychosocial well-being | 19.38 | 4.25 |
Mental Health/Optimism | ||
Depression | 13.41 | 10.33 |
Anxiety | 37.43 | 10.91 |
Optimism | 21.72 | 4.76 |
General Religiosity | ||
Subjective religiosity | 6 | 1.75 |
Private religiosity | 10.23 | 9.3 |
Public religiosity | 11.38 | 5.09 |
Variable | M | SD |
Health Status/Illness Impact | ||
BMI | 27.26 | 5.44 |
Noncardiac chronic conditions | 3.12 | 2.38 |
Subjective health | 19.98 | 4.14 |
Functional status | 15.04 | 4.9 |
Psychosocial well-being | 19.38 | 4.25 |
Mental Health/Optimism | ||
Depression | 13.41 | 10.33 |
Anxiety | 37.43 | 10.91 |
Optimism | 21.72 | 4.76 |
General Religiosity | ||
Subjective religiosity | 6 | 1.75 |
Private religiosity | 10.23 | 9.3 |
Public religiosity | 11.38 | 5.09 |
BMI = body mass index.
Variable/Label | N | % |
Use of Private Prayer for Coping | ||
Private prayer was important in one's life | ||
Strongly agree | 141 | 57.3 |
Moderately agree | 75 | 30.5 |
Moderately disagree | 17 | 6.9 |
Strongly disagree | 13 | 5.3 |
Faith in the efficacy of using prayer for coping | ||
Strongly agree | 130 | 52.8 |
Moderately agree | 52 | 21.1 |
Moderately disagree | 38 | 15.4 |
Strongly disagree | 25 | 10.2 |
Intention to use prayer to cope with surgery- related difficulties | ||
Strongly agree | 144 | 57.3 |
Moderately agree | 73 | 29.7 |
Moderately disagree | 16 | 6.5 |
Strongly disagree | 13 | 5.3 |
Types of Prayer Used | ||
Conversation with God | 188 | 76 |
Accomplishing spiritual or material needs | 141 | 57.3 |
Experiencing the divine | 106 | 43.1 |
Memorized prayer | 97 | 39.4 |
Other types of prayer | 40 | 16.3 |
Variable/Label | N | % |
Use of Private Prayer for Coping | ||
Private prayer was important in one's life | ||
Strongly agree | 141 | 57.3 |
Moderately agree | 75 | 30.5 |
Moderately disagree | 17 | 6.9 |
Strongly disagree | 13 | 5.3 |
Faith in the efficacy of using prayer for coping | ||
Strongly agree | 130 | 52.8 |
Moderately agree | 52 | 21.1 |
Moderately disagree | 38 | 15.4 |
Strongly disagree | 25 | 10.2 |
Intention to use prayer to cope with surgery- related difficulties | ||
Strongly agree | 144 | 57.3 |
Moderately agree | 73 | 29.7 |
Moderately disagree | 16 | 6.5 |
Strongly disagree | 13 | 5.3 |
Types of Prayer Used | ||
Conversation with God | 188 | 76 |
Accomplishing spiritual or material needs | 141 | 57.3 |
Experiencing the divine | 106 | 43.1 |
Memorized prayer | 97 | 39.4 |
Other types of prayer | 40 | 16.3 |
Variable/Label | N | % |
Use of Private Prayer for Coping | ||
Private prayer was important in one's life | ||
Strongly agree | 141 | 57.3 |
Moderately agree | 75 | 30.5 |
Moderately disagree | 17 | 6.9 |
Strongly disagree | 13 | 5.3 |
Faith in the efficacy of using prayer for coping | ||
Strongly agree | 130 | 52.8 |
Moderately agree | 52 | 21.1 |
Moderately disagree | 38 | 15.4 |
Strongly disagree | 25 | 10.2 |
Intention to use prayer to cope with surgery- related difficulties | ||
Strongly agree | 144 | 57.3 |
Moderately agree | 73 | 29.7 |
Moderately disagree | 16 | 6.5 |
Strongly disagree | 13 | 5.3 |
Types of Prayer Used | ||
Conversation with God | 188 | 76 |
Accomplishing spiritual or material needs | 141 | 57.3 |
Experiencing the divine | 106 | 43.1 |
Memorized prayer | 97 | 39.4 |
Other types of prayer | 40 | 16.3 |
Variable/Label | N | % |
Use of Private Prayer for Coping | ||
Private prayer was important in one's life | ||
Strongly agree | 141 | 57.3 |
Moderately agree | 75 | 30.5 |
Moderately disagree | 17 | 6.9 |
Strongly disagree | 13 | 5.3 |
Faith in the efficacy of using prayer for coping | ||
Strongly agree | 130 | 52.8 |
Moderately agree | 52 | 21.1 |
Moderately disagree | 38 | 15.4 |
Strongly disagree | 25 | 10.2 |
Intention to use prayer to cope with surgery- related difficulties | ||
Strongly agree | 144 | 57.3 |
Moderately agree | 73 | 29.7 |
Moderately disagree | 16 | 6.5 |
Strongly disagree | 13 | 5.3 |
Types of Prayer Used | ||
Conversation with God | 188 | 76 |
Accomplishing spiritual or material needs | 141 | 57.3 |
Experiencing the divine | 106 | 43.1 |
Memorized prayer | 97 | 39.4 |
Other types of prayer | 40 | 16.3 |
Dependent variable: optimism | |||
Predictors: Religious Affiliationsa | M | SD | |
Protestant | 22.09 | 4.81 | |
Catholic | 21.75 | 3.65 | |
Jewish | 23.50 | 5.63 | |
Orthodox | 18.20 | 8.11 | |
Other religion | 21.50 | 6.44 | |
No preference | 20.04 | 5.05 |
Dependent variable: optimism | |||
Predictors: Religious Affiliationsa | M | SD | |
Protestant | 22.09 | 4.81 | |
Catholic | 21.75 | 3.65 | |
Jewish | 23.50 | 5.63 | |
Orthodox | 18.20 | 8.11 | |
Other religion | 21.50 | 6.44 | |
No preference | 20.04 | 5.05 |
F(5,225) = 1.56.
Dependent variable: optimism | |||
Predictors: Religious Affiliationsa | M | SD | |
Protestant | 22.09 | 4.81 | |
Catholic | 21.75 | 3.65 | |
Jewish | 23.50 | 5.63 | |
Orthodox | 18.20 | 8.11 | |
Other religion | 21.50 | 6.44 | |
No preference | 20.04 | 5.05 |
Dependent variable: optimism | |||
Predictors: Religious Affiliationsa | M | SD | |
Protestant | 22.09 | 4.81 | |
Catholic | 21.75 | 3.65 | |
Jewish | 23.50 | 5.63 | |
Orthodox | 18.20 | 8.11 | |
Other religion | 21.50 | 6.44 | |
No preference | 20.04 | 5.05 |
F(5,225) = 1.56.
Model and Variable | R2 | F(df) | β |
Model 1 | .157 | 10.27*** | |
Age | .285**** | ||
Education | .128* | ||
Employment | .180* | ||
Other insurance | .211*** | ||
Model 2 | .321 | 17.21(6)**** | |
Age | .195*** | ||
Education | .100a | ||
Employment | .159* | ||
Impacted psychosocial well-being | .254*** | ||
Depression | −.236* | ||
Anxiety | −.411**** | ||
Model 3 | .367 | 12.41(10)**** | |
Age | .186*** | ||
Education | .129* | ||
Employment | .170** | ||
Impacted psychosocial well-being | .267**** | ||
Depression | −.253* | ||
Anxiety | −.411**** | ||
Religious affiliation | −.086 | ||
Subjective religiosity | −.036 | ||
Private religiosity | .125 | ||
Public religiosity | .093 | ||
Model 4 | .374 | 9.68(13)**** | |
Age | .193*** | ||
Education | .119* | ||
Employment | .171** | ||
Impacted psychosocial well-being | .286**** | ||
Depression | −.332*** | ||
Anxiety | −.353**** | ||
Prayer for coping | .285**** | ||
Memorized prayer | −.153 | ||
Conversation with God | −.135 | ||
Accomplishment of needs | −.150 | ||
Experiencing the divine | −.061 | ||
Other types of prayer | −.137 | ||
Sum of types of prayer used | .297 | ||
Model 5 | .363 | 17.67(7)**** | |
Age | .192*** | ||
Education | .113* | ||
Employment | .182*** | ||
Impacted psychosocial well-being | .268**** | ||
Depression | −.277* | ||
Anxiety | −.388**** | ||
Prayer for coping | .207**** |
Model and Variable | R2 | F(df) | β |
Model 1 | .157 | 10.27*** | |
Age | .285**** | ||
Education | .128* | ||
Employment | .180* | ||
Other insurance | .211*** | ||
Model 2 | .321 | 17.21(6)**** | |
Age | .195*** | ||
Education | .100a | ||
Employment | .159* | ||
Impacted psychosocial well-being | .254*** | ||
Depression | −.236* | ||
Anxiety | −.411**** | ||
Model 3 | .367 | 12.41(10)**** | |
Age | .186*** | ||
Education | .129* | ||
Employment | .170** | ||
Impacted psychosocial well-being | .267**** | ||
Depression | −.253* | ||
Anxiety | −.411**** | ||
Religious affiliation | −.086 | ||
Subjective religiosity | −.036 | ||
Private religiosity | .125 | ||
Public religiosity | .093 | ||
Model 4 | .374 | 9.68(13)**** | |
Age | .193*** | ||
Education | .119* | ||
Employment | .171** | ||
Impacted psychosocial well-being | .286**** | ||
Depression | −.332*** | ||
Anxiety | −.353**** | ||
Prayer for coping | .285**** | ||
Memorized prayer | −.153 | ||
Conversation with God | −.135 | ||
Accomplishment of needs | −.150 | ||
Experiencing the divine | −.061 | ||
Other types of prayer | −.137 | ||
Sum of types of prayer used | .297 | ||
Model 5 | .363 | 17.67(7)**** | |
Age | .192*** | ||
Education | .113* | ||
Employment | .182*** | ||
Impacted psychosocial well-being | .268**** | ||
Depression | −.277* | ||
Anxiety | −.388**** | ||
Prayer for coping | .207**** |
p < .05; **p < .01; ***p < .005; ****p < .001.
p < .08.
Model and Variable | R2 | F(df) | β |
Model 1 | .157 | 10.27*** | |
Age | .285**** | ||
Education | .128* | ||
Employment | .180* | ||
Other insurance | .211*** | ||
Model 2 | .321 | 17.21(6)**** | |
Age | .195*** | ||
Education | .100a | ||
Employment | .159* | ||
Impacted psychosocial well-being | .254*** | ||
Depression | −.236* | ||
Anxiety | −.411**** | ||
Model 3 | .367 | 12.41(10)**** | |
Age | .186*** | ||
Education | .129* | ||
Employment | .170** | ||
Impacted psychosocial well-being | .267**** | ||
Depression | −.253* | ||
Anxiety | −.411**** | ||
Religious affiliation | −.086 | ||
Subjective religiosity | −.036 | ||
Private religiosity | .125 | ||
Public religiosity | .093 | ||
Model 4 | .374 | 9.68(13)**** | |
Age | .193*** | ||
Education | .119* | ||
Employment | .171** | ||
Impacted psychosocial well-being | .286**** | ||
Depression | −.332*** | ||
Anxiety | −.353**** | ||
Prayer for coping | .285**** | ||
Memorized prayer | −.153 | ||
Conversation with God | −.135 | ||
Accomplishment of needs | −.150 | ||
Experiencing the divine | −.061 | ||
Other types of prayer | −.137 | ||
Sum of types of prayer used | .297 | ||
Model 5 | .363 | 17.67(7)**** | |
Age | .192*** | ||
Education | .113* | ||
Employment | .182*** | ||
Impacted psychosocial well-being | .268**** | ||
Depression | −.277* | ||
Anxiety | −.388**** | ||
Prayer for coping | .207**** |
Model and Variable | R2 | F(df) | β |
Model 1 | .157 | 10.27*** | |
Age | .285**** | ||
Education | .128* | ||
Employment | .180* | ||
Other insurance | .211*** | ||
Model 2 | .321 | 17.21(6)**** | |
Age | .195*** | ||
Education | .100a | ||
Employment | .159* | ||
Impacted psychosocial well-being | .254*** | ||
Depression | −.236* | ||
Anxiety | −.411**** | ||
Model 3 | .367 | 12.41(10)**** | |
Age | .186*** | ||
Education | .129* | ||
Employment | .170** | ||
Impacted psychosocial well-being | .267**** | ||
Depression | −.253* | ||
Anxiety | −.411**** | ||
Religious affiliation | −.086 | ||
Subjective religiosity | −.036 | ||
Private religiosity | .125 | ||
Public religiosity | .093 | ||
Model 4 | .374 | 9.68(13)**** | |
Age | .193*** | ||
Education | .119* | ||
Employment | .171** | ||
Impacted psychosocial well-being | .286**** | ||
Depression | −.332*** | ||
Anxiety | −.353**** | ||
Prayer for coping | .285**** | ||
Memorized prayer | −.153 | ||
Conversation with God | −.135 | ||
Accomplishment of needs | −.150 | ||
Experiencing the divine | −.061 | ||
Other types of prayer | −.137 | ||
Sum of types of prayer used | .297 | ||
Model 5 | .363 | 17.67(7)**** | |
Age | .192*** | ||
Education | .113* | ||
Employment | .182*** | ||
Impacted psychosocial well-being | .268**** | ||
Depression | −.277* | ||
Anxiety | −.388**** | ||
Prayer for coping | .207**** |
p < .05; **p < .01; ***p < .005; ****p < .001.
p < .08.
This work was supported by National Institute on Aging Grant 1 RO3 AGO 15686-01, National Center for Complementary and Alternative Medicine Grant P50 AT00011, and a grant from the John Templeton Foundation. The opinions expressed in this article are those of the authors and do not necessarily reflect the views of the National Institutes of Health and the John Templeton Foundation.
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