Research in Nursing & Health, 1990, 13, 421 -428
Breast Self-Examination and the Health Belief Model: Variations on a Theme Mary Alexandra Wyper
Variables derived from the Health Belief Model (HBM) were studied in relation to breast self-examination (BSE) performance, which was measured in terms of both frequency and thoroughness. Data were collected from 202 adult women via self-administered questionnaires. Susceptibility and Seriousness were combined to form a “threat of breast cancer” variable, and two approaches were used to compute “net perceived efficacy of BSE.” However, barriers and susceptibility in their original form explained more variance in BSE practice than did the combined variables. The negative relationship found between perceived barriers and BSE performance (r= - .44) is consistent with previous findings. Implications for research and practice are presented.
Breast self-examination (BSE), correctly and regularly performed, is one means of detecting breast cancer at an early stage when treatment is most likely to result in a favorable outcome. This is especially important for women who do not regularly participate in other modes of breast cancer screening. However, despite the widespread endorsement of BSE by “virtually all of those practicing in the field of preventive health care” (Eggertsen & Bergman, 1983, p. 713), less than 30% of American women do this examination on a regular basis (Gallup Organization, 1974; Holleb, 1977; Kegeles & Grady, 1982; Knobf, 1984; Morra, 1985; Public attitudes, 1980). Information on the factors that influence the performance of BSE may suggest nursing strategies to minimize deterrents and to enhance the likelihood that women will learn this behavior and practice indefinitely. The purpose of this study, therefore, was to examine the relationships of variables derived from the Health Belief Model (HBM) to the performance of BSE.
The Health Belief Model The HBM was originally formulated to explain the public’s unwillingness to accept disease pre-
ventives or screening tests offered by the U.S. Public Health Service for the early detection of asymptomatic disease (Rosenstock, 1974b). This model has undergone a number of evolutionary changes, but most research has been based on the conceptualizations summarized by Mikhail(l98 1): The HBM proposes that the likelihood that a person will take action relative to a health condition is determined both by the individual’s psychological state of readiness to take that action and by the perceived benefit of the action weighed against the perceived cost of barriers involved in the proposed action. (p. 67) Psychological state of readiness to take action is determined by both perceived susceptibility to the particular health condition (subjective risk of contracting the condition or disease) and perceived severity (the degree of emotional arousal created by the thought of the disease as well as by the kinds of difficulties the individual believes that particular disease will create). Perceived susceptibility and perceived severity collectively form the variable “threat of disease X ” and thought to be at least partly dependent on knowledge (Rosenstock, 1974b).
Mary A. Wyper, PhD, RN, is an assistant professor in the School of Nursing, Kent State University, Kent, OH. The study reported here was a doctoral dissertation and was supported by a Joseph Silber Student Fellowship from the American Cancer Society, Cuyahoga County Unit. This article was received on December 4, 1989, was revised, and accepted for publication June 11, 1990. Requests for reprints can be addressed to Dr. Mary A. Wyper, Kent State University School of Nursing, Kent, Ohio 44242-0001.
0 1990 John Wiley & Sons, Inc. 0160-6891/90/060421-08 $04.00
422
RESEARCH IN NURSING & HEALTH
Modifying factors (demographic, sociopsychological, and structural) are thought to affect the likelihood of action only indirectly but to have a direct influence on individual perceptions. No mathematical formulations for the interactions among the variables have been described although a multiplicative relationship has been suggested (Maiman & Becker, 1974). The Health Belief Model has been widely used in the study of preventive health behavior in spite of the fact that few studies have supported the usefulness of the model in its entirety (Janz & Becker, 1984; Mikhail, 1981; Rosenstock, 1974a). Considerable support has accumulated for the influence of certain variables included in the model, particularly perceived barriers to health action. Janz and Becker (1984) found that when the behavior of interest could be classified as a preventive health behavior such as BSE, statistically significant findings related to the influence of barriers were present in 93% of the reports that provided significance levels. Statistically significant findings related to susceptibility were present in 86% of such reports whereas benefits were significant in 74% and seriousness in only 50%. Inconclusive results of previous studies may have been caused by deficiencies in design, measurement, and data analysis, or they may be the result of deficiencies in the model itself. The absence of information concerning the validity and reliability of tools used to measure health beliefs has been noted repeatedly (Janz & Becker, 1984; Rosenstock, 1974a). In addition, many studies have examined the isolated effects of only selected components of the model rather than the combined effects or their interactions (Mikhail, 1981; Pender, 1987).
Studies of BSE Based on the Health Belief Model Although studies of BSE based on the HBM have contributed information on the factors influencing BSE, many have exhibited the deficiencies noted above. The findings, therefore, are difficult to interpret. Several early studies (Hallal, 1982; Manfredi, Wamecke, Graham, & Rosenthal, 1977; Stillman, 1977) did not correlate the health beliefs measured with reported BSE performance and did not report information on barriers to BSE or on reliability and validity of the tools used. Massey (1986) found a positive relationship between reported susceptibility to breast cancer and BSE frequency, but susceptibility was the only HBM variable that she studied.
Two studies provided useful information concerning possible barriers to BSE. Women surveyed by Turnbull (1978) identified forgetting, feeling unmotivated, being unsure how to do the procedure, and feeling that this practice would cause unnecessary worry as barriers. Additional barriers such as receiving little or no positive reinforcement for regular BSE, the need to be one’s own diagnostician, and the need to carry out the exam on a regular, continuous basis were proposed by Edwards (1980). Multiple regression analysis of the combined influence of all HBM variables on BSE performance has explained approximately one-fourth of the variance in behavior in three correlational studies (Champion, 1984, 1987; Trotta, 1980). In each study, barriers accounted for nearly all of the explained variance, and BSE was operationally defined as frequency of performance. Although Trotta obtained data related to the thoroughness of the exam performed by the subjects, she did not report factors related to thoroughness or on the reliability and validity of her tool. Champion’s (1984) approach to the development of valid and reliable scales for measuring health beliefs related to breast cancer and BSE is the most comprehensive reported to date. The instrument was developed based on the assumption that the HBM was accurate in predicting behavior. Internal consistency and test-retest reliability for scales measuring five variables from the HBM were established, and construct validity was tested through factor analysis and multiple regression analysis on frequency of BSE. Despite this increased rigor in tool development and the use of multivariate data analysis procedures, approximately 75% of the variance in frequency of BSE remains unexplained. In addition, several methodological issues concerning measurement of health beliefs indicate the need for continued study. For example, attempts to achieve unidimensional scales to measure these beliefs may be at cross purposes with the fact that some of the variables (e.g., seriousness) are, by definition, clearly multidimensional. Of more concern, HBM variables have been entered into multiple regression analyses as though each influenced BSE independently whereas analysis of the relationships proposed by the HBM suggests that the four variables should be combined to form two independent constructs. Susceptibilityand seriousness have been described as collectively forming the construct “perceived threat” (of disease X). While the second construct has not been specifically named, it has been described as “benefits weighed against barriers.” One possible term for this construct might
BREAST SELF-EXAMINATION / WYPER
I
I I
423
INDIVIDUAL PERCEPTIONS Perceived susceptibility to b r e a s t cancer
I
combined w l t h
BREAST CANCER
Perceived seriousness b r e a s t cancer MODIFYING
-
\\ I
\‘I
Perceived b e n e f i t s o f BSE weighed against
+
NET PERCEIVED EFFICACY OF BSE
erceived b a r r i e r s t o BSE
FIGURE 1. Major variables and constructs of the Health Belief Model related to breast cancer and breast selfexamination.
be “net perceived efficacy” of the recommended health behavior (see Fig. 1). Because the HBM variables are discussed from a theoretical perspective as having a collective influence on health behavior and because no study of BSE was found in which HBM variables were mathematically combined to form two collective constructs, this study was undertaken to answer the following research question: What is the combined influence of perceived threat of breast cancer and net perceived efficacy of BSE on BSE performance?
METHOD A cross-sectional survey design was used to answer the research question. A self-administered questionnaire was given to women who agreed to participate in the study, and data were coded and analyzed using a variety of multivariate techniques.
Sample The study sample consisted of women who were at least 18 years old and who could read and write English. Subjects were recruited from women’s groups at local churches, a group practice of physicians at a university-affiliated health center, health-related events open to the public, and participants in a hospital-sponsored breast evaluation and education program. Women were recruited
from the BSE education program on the premise that voluntary participation in such programs may indicate greater likelihood of BSE performance. This strategy was intended, therefore, to increase the proportion of women in the sample who regularly performed BSE beyond the 30% or fewer expected by chance.
Instruments
HBM variables. A 33-item modified version of Champion’s tool (1987) was used to measure perceived susceptibility to breast cancer, perceived seriousness of breast cancer, perceived benefits of BSE, and perceived barriers to BSE. Modifications included addition of items worded “in reverse” to each scale and expansion of the seriousness scale. Subjects indicated, on a 6-point scale, the extent to which they agreed with the 33 statements. Responses were anchored with “Not at all” and “Completely.” There was no neutral category. Information on the stability and internal consistency of the HBM scales may be found in Table 1. Construct validity was examined through the use of factor analysis of responses to the 33 items. A four-factor solution with orthogonal rotation of the factors explained 40.8% of the variance in responses. All but four of the 33 items had loadings of more than .30 on their respective factors. BSE performance. Information was requested on both the frequency and thoroughness of BSE. Fourteen BSE steps were presented in a
424
RESEARCH IN NURSING & HEALTH
Table 1. Reliability Data for Health Belief Model Scales ~
HBM Scale Susceptibility Seriousness Benefits Barriers
Items
Cronbach’s Alphaa
7 8 9 9
.80
.90’
.74
.81* .75* .61*
.73 .72
Test-Retestb
‘.N = 202. bN = 66.
‘p
i,001
chart, and subjects were asked to indicate if they knew how to do each step and whether they performed the step usually, sometimes, or never. The 14 steps and the format for the questions about them were based on information in the literature about BSE and questions used in previous studies (Celentano & Holtzman, 1983; Trotta, 1980; National Cancer Institute, 1984). Stability of the tool in relation to the components of BSE performance was determined by comparing test-retest responses. Correlation coefficients for the 66 subjects who completed the retest were .87 (frequency) and .89 (thoroughness).
Procedure Except for subjects recmited at public health-related events, the questionnaires were distributed by persons other than the investigator (e.g., private physicians, leadership persons in church groups, etc.). Potential subjects received a copy of the questionnaire, a cover letter, and a self-addressed, stamped envelope. Return of the questionnaire was considered consent to participate. Human subjects approval was obtained from the sponsoring academic institution and all agencies involved in data collection. Data to determine test-retest reliability were collected by sending a second questionnaire to all subjects who provided their names and addresses. The second questionnaire was mailed to these subjects 2 weeks after receipt of their original questionnaire.
RESULTS The proportion of returned questionnaires varied from setting to setting. Sixty-eight percent (n = 122) of the questionnaires distributed to patients of a private physicians’ group were returned whereas only 13% (n = 8) of those distributed to Black church groups were returned. Overall
the return rate of 50.47% yielded 215 questionnaires; of these, 202 were suitable for analysis. The majority of subjects in the final sample (60%) were patients of private physicians. Subjects from the breast evaluation/education center comprised 29% of the sample, and 11% were from other settings.
Sample Characteristics Approximately one-third of the subjects were under 40 years old, one-third were between 40 and 59 years, and one-third were 60 years or older. The subjects were primarily white (n = 185; 92%), married (n = 169; 85%), and well educated. Only 5 subjects had less than a high school education; 165 (82%) had completed more than 12 years of school. The most frequent religious preference reported by the subjects was Protestant (n = 95; 47%).
Health Beliefs Related to Breast Cancer and BSE Mean scores were computed for the four HBM scales because of the variation in number of items per scale. Although each of the 33 items used to measure HBM variables exhibited the full range of possible responses, only the Susceptibility Scale scores were normally distributed. There was moderate variation among subjects in the extent to which they perceived breast cancer as “serious,” but the sample as a whole perceived many benefits and few barriers to BSE. Descriptive statistics for each of the four scales are found in Table 2. Three variables were computed from scores on the HBM scales. THREAT was computed as susceptibility plus seriousness. The THREAT scores were normally distributed with a mean of 6.83. As no mathematical formulation was found in the literature for benefits weighed against barriers, net perceived efficacy was analyzed both as NETBEN (benefits minus barriers) and COSTBEN
425
BREAST SELF-EXAMINATION / WYPER
Table 2.
Descriptive Statistics for HBM Scales (N = 202)
HBM Scale
Mean
Standard Deviation
Range of Subjects’ Mean Scores
Susceptibility Seriousness Benefits Barriers
3.62 3.21 5.43 1.76
1.04 .96 .66 .71
1.OO-6.00 1.OO-5.75 3.22-6.00 1.OO-5.67
Note: The possible range of subjects’ mean scores was 1.OO to 6.00 The higher the score, the greater the subjects’ perceptions of susceptibility tolseriousness of breast cancer and benefits ofibarriers to BSE.
(the ratio of benefits to barriers). The mean scores on NETBEN and COSTBEN were similar, 3.67 and 3.58 respectively, but only the COSTBEN scores were normally distributed. COSTBEN, therefore, was used as the measure of net perceived efficacy in subsequent analyses.
BSE Performance The reported Erequency of BSE performance ranged from “never” (14%) to “at least once a month” (45%). The remaining subjects (41%) reported performing BSE less often than once a month. The average thoroughness score was 16.21 (SD= 8.59); a score of 28 would indicate that the subject reported “usually” performing all 14 BSE steps. An additional variable, BSE PRACTICE, was computed by multiplying frequency times thoroughness. This provided a more comprehensive indicator of the subjects’ performance of BSE than could be obtained by measuring frequency alone and it allowed for considerable variation in response. The BSE PRACTICE scores were normally distributed and ranged from 0 to 140 (A4 = 68, SD = 45.67).
Relationship of HBM Variables to BSE Simple correlations of HBM variables (original and computed) with components of BSE per-
formance are shown in Table 3. The correlations between all HBM variables andfrequency of BSE were very similar to findings reported by Champion (1984, 1987) and Rutledge (1987). The details of these comparisons are presented in Table 4. The effect of combining susceptibility and seriousness into the variable THREAT was attenuation of the slight positive correlations of the former (especially with thoroughness) and the very slight negative correlations of the latter. There were, therefore, no significant relationships between THREAT of breast cancer and any measure of BSE performance obtained in this study. There were significant, positive correlations between net perceived efficacy (COSTBEN) and all measures of BSE, but these correlations were slightly smaller than those of barriers, one of its component variables. In a study reported as these data were being analyzed, Rutledge (1987) created a THREAT variable by multiplying standardized susceptibility scores by standardized seriousness scores rather than adding the raw scores, and her findings were similar to those reported here. THREAT did not enter into an explanatory model at all. She also created a net effect of benefits and barriers variable (BENBAR) by subtracting standardized barriers scores from standardized benefits scores (similar to NETBEN in this study). BENBAR had a simple correlation of .51 with BSE frequency compared with .32 for NETBEN in the current study.
Table 3. Correlations between HBM Variables and BSE Breast Self Exam HBM Variables Susceptibility Seriousness THREAT (Sus + Ser) Benefits Barriers COSTBEN (Ben/Bar)
Frequency
Thoroughness
PRACTICE
,168
,224’ -.079 .lo6 ,266” -.386“’ .380**’
,211‘ -.091 .089 ,254” - .440”’ .427’**
- ,098
,054
,212’ -.443**’ ,401* * *
Note:N = 194. ‘ p < .05;“ p < .Ol;“ * p < ,001.
426
RESEARCH IN NURSING & HEALTH
Table 4. Comparison ot Correlations between HBM Variables and Frequency of BSE in Four Nursing Research Studies Correlations with BSE Frequency Study ~~~
~~~
Susceptibility
Seriousness
Benefits
Barriers
- .03 .01 .17
-.lo
.18 .26 - .22e .21
- .48
.06'
-.11 - .09 .l 6e
~~~
Champion, 1985a Champion, 1987' Ruttedge, 1987' Wyper, 198711988*
- .47 ,458 - .44
"N =
301. b N = 585. 'N = 93. d N = 194. 'Scoring on these scales was reversed from the other three studies which explains the difference in direcfion of the correlations.
The HBM variables, as measured in this study, were not independent. There was a marked negative relationship (r = - S 9 ) between benefits and barriers, and a modest positive relationship ( r = .30) between seriousness and barriers.
Combined Influence of HBM Variables on BSE
Original HBM variables. Multiple regression of the HBM variables in their original form on all three measures of BSE (frequency, thoroughness, and PRACTICE) revealed that only susceptibility and barriers contributed to the model. The greatest proportion of explained variance (22%) was associated with the combined variable, BSE PRACTICE, and the details of this regression analysis are presented in Table 5. Stepwise multiple regression indicated that regardless of the measure of BSE, barriers accounted for most of the explained variance (see Table 6 ) . Computed HBM variables. Stepwise multiple regression of THREAT and COSTBEN on
the dependent variable BSE PRACTICE revealed that both constructs contributed to the regression model. However, THREAT contributed only 2% to the overall explained variance of 20%. Seriousness and benefits, therefore, contributed to a prediction model for BSE practice only when combined with susceptibility (THREAT) and barriers (COSTBEN).
DISCUSSION The findings of this study must be evaluated in light of the fact that the scales used to measure HBM variables resulted in scores that were normally distributed for only one HBM variable, SUSceptibility. It is difficult, therefore, to attribute the findings to deficiencies in the model without considering possible deficiencies in the measurement process and the homogeneity of the sample. Because the data were collected through a selfreport measure, it is also possible that the findings were affected by a social desirability response set.
Table 5. Multiple Regression of HBM variables on BSE PRACTICE Dep Var: Practice N: 194 Adjusted Squared Multiple R: ,204 Variable Coefficient STD Error
Multiple R: ,470
Squared Multiple R: ,220 Standard Error of Estimate: 41.224 Tolerance t p ( 2 Tail)
STD Coef
~
Constant
sus
SER BEN BAR
101.111 7.281 0.504 - 1.225 -30.804
38.381 2.924 3.286 5.708 5.666
1 .ooooooo
0.000 0.165 0.01 0 -0.01 7 -0.434
.9414619 .8921557 .6718545 ,6468450
~~~
2.634 2.490 0.153 -0.215 -5.437
,009 ,014 .878 ,830
,000
Analysis of Variance Source Regression Residual
Sum-of-Squares
DF
Mean-Square
F-Ratio
P
90839.445 321 197.545
4 189
22709.861 1699.458
13.363
Breast Self-Examination and the Health Belief Model: Variations on a Theme Mary Alexandra Wyper
Variables derived from the Health Belief Model (HBM) were studied in relation to breast self-examination (BSE) performance, which was measured in terms of both frequency and thoroughness. Data were collected from 202 adult women via self-administered questionnaires. Susceptibility and Seriousness were combined to form a “threat of breast cancer” variable, and two approaches were used to compute “net perceived efficacy of BSE.” However, barriers and susceptibility in their original form explained more variance in BSE practice than did the combined variables. The negative relationship found between perceived barriers and BSE performance (r= - .44) is consistent with previous findings. Implications for research and practice are presented.
Breast self-examination (BSE), correctly and regularly performed, is one means of detecting breast cancer at an early stage when treatment is most likely to result in a favorable outcome. This is especially important for women who do not regularly participate in other modes of breast cancer screening. However, despite the widespread endorsement of BSE by “virtually all of those practicing in the field of preventive health care” (Eggertsen & Bergman, 1983, p. 713), less than 30% of American women do this examination on a regular basis (Gallup Organization, 1974; Holleb, 1977; Kegeles & Grady, 1982; Knobf, 1984; Morra, 1985; Public attitudes, 1980). Information on the factors that influence the performance of BSE may suggest nursing strategies to minimize deterrents and to enhance the likelihood that women will learn this behavior and practice indefinitely. The purpose of this study, therefore, was to examine the relationships of variables derived from the Health Belief Model (HBM) to the performance of BSE.
The Health Belief Model The HBM was originally formulated to explain the public’s unwillingness to accept disease pre-
ventives or screening tests offered by the U.S. Public Health Service for the early detection of asymptomatic disease (Rosenstock, 1974b). This model has undergone a number of evolutionary changes, but most research has been based on the conceptualizations summarized by Mikhail(l98 1): The HBM proposes that the likelihood that a person will take action relative to a health condition is determined both by the individual’s psychological state of readiness to take that action and by the perceived benefit of the action weighed against the perceived cost of barriers involved in the proposed action. (p. 67) Psychological state of readiness to take action is determined by both perceived susceptibility to the particular health condition (subjective risk of contracting the condition or disease) and perceived severity (the degree of emotional arousal created by the thought of the disease as well as by the kinds of difficulties the individual believes that particular disease will create). Perceived susceptibility and perceived severity collectively form the variable “threat of disease X ” and thought to be at least partly dependent on knowledge (Rosenstock, 1974b).
Mary A. Wyper, PhD, RN, is an assistant professor in the School of Nursing, Kent State University, Kent, OH. The study reported here was a doctoral dissertation and was supported by a Joseph Silber Student Fellowship from the American Cancer Society, Cuyahoga County Unit. This article was received on December 4, 1989, was revised, and accepted for publication June 11, 1990. Requests for reprints can be addressed to Dr. Mary A. Wyper, Kent State University School of Nursing, Kent, Ohio 44242-0001.
0 1990 John Wiley & Sons, Inc. 0160-6891/90/060421-08 $04.00
422
RESEARCH IN NURSING & HEALTH
Modifying factors (demographic, sociopsychological, and structural) are thought to affect the likelihood of action only indirectly but to have a direct influence on individual perceptions. No mathematical formulations for the interactions among the variables have been described although a multiplicative relationship has been suggested (Maiman & Becker, 1974). The Health Belief Model has been widely used in the study of preventive health behavior in spite of the fact that few studies have supported the usefulness of the model in its entirety (Janz & Becker, 1984; Mikhail, 1981; Rosenstock, 1974a). Considerable support has accumulated for the influence of certain variables included in the model, particularly perceived barriers to health action. Janz and Becker (1984) found that when the behavior of interest could be classified as a preventive health behavior such as BSE, statistically significant findings related to the influence of barriers were present in 93% of the reports that provided significance levels. Statistically significant findings related to susceptibility were present in 86% of such reports whereas benefits were significant in 74% and seriousness in only 50%. Inconclusive results of previous studies may have been caused by deficiencies in design, measurement, and data analysis, or they may be the result of deficiencies in the model itself. The absence of information concerning the validity and reliability of tools used to measure health beliefs has been noted repeatedly (Janz & Becker, 1984; Rosenstock, 1974a). In addition, many studies have examined the isolated effects of only selected components of the model rather than the combined effects or their interactions (Mikhail, 1981; Pender, 1987).
Studies of BSE Based on the Health Belief Model Although studies of BSE based on the HBM have contributed information on the factors influencing BSE, many have exhibited the deficiencies noted above. The findings, therefore, are difficult to interpret. Several early studies (Hallal, 1982; Manfredi, Wamecke, Graham, & Rosenthal, 1977; Stillman, 1977) did not correlate the health beliefs measured with reported BSE performance and did not report information on barriers to BSE or on reliability and validity of the tools used. Massey (1986) found a positive relationship between reported susceptibility to breast cancer and BSE frequency, but susceptibility was the only HBM variable that she studied.
Two studies provided useful information concerning possible barriers to BSE. Women surveyed by Turnbull (1978) identified forgetting, feeling unmotivated, being unsure how to do the procedure, and feeling that this practice would cause unnecessary worry as barriers. Additional barriers such as receiving little or no positive reinforcement for regular BSE, the need to be one’s own diagnostician, and the need to carry out the exam on a regular, continuous basis were proposed by Edwards (1980). Multiple regression analysis of the combined influence of all HBM variables on BSE performance has explained approximately one-fourth of the variance in behavior in three correlational studies (Champion, 1984, 1987; Trotta, 1980). In each study, barriers accounted for nearly all of the explained variance, and BSE was operationally defined as frequency of performance. Although Trotta obtained data related to the thoroughness of the exam performed by the subjects, she did not report factors related to thoroughness or on the reliability and validity of her tool. Champion’s (1984) approach to the development of valid and reliable scales for measuring health beliefs related to breast cancer and BSE is the most comprehensive reported to date. The instrument was developed based on the assumption that the HBM was accurate in predicting behavior. Internal consistency and test-retest reliability for scales measuring five variables from the HBM were established, and construct validity was tested through factor analysis and multiple regression analysis on frequency of BSE. Despite this increased rigor in tool development and the use of multivariate data analysis procedures, approximately 75% of the variance in frequency of BSE remains unexplained. In addition, several methodological issues concerning measurement of health beliefs indicate the need for continued study. For example, attempts to achieve unidimensional scales to measure these beliefs may be at cross purposes with the fact that some of the variables (e.g., seriousness) are, by definition, clearly multidimensional. Of more concern, HBM variables have been entered into multiple regression analyses as though each influenced BSE independently whereas analysis of the relationships proposed by the HBM suggests that the four variables should be combined to form two independent constructs. Susceptibilityand seriousness have been described as collectively forming the construct “perceived threat” (of disease X). While the second construct has not been specifically named, it has been described as “benefits weighed against barriers.” One possible term for this construct might
BREAST SELF-EXAMINATION / WYPER
I
I I
423
INDIVIDUAL PERCEPTIONS Perceived susceptibility to b r e a s t cancer
I
combined w l t h
BREAST CANCER
Perceived seriousness b r e a s t cancer MODIFYING
-
\\ I
\‘I
Perceived b e n e f i t s o f BSE weighed against
+
NET PERCEIVED EFFICACY OF BSE
erceived b a r r i e r s t o BSE
FIGURE 1. Major variables and constructs of the Health Belief Model related to breast cancer and breast selfexamination.
be “net perceived efficacy” of the recommended health behavior (see Fig. 1). Because the HBM variables are discussed from a theoretical perspective as having a collective influence on health behavior and because no study of BSE was found in which HBM variables were mathematically combined to form two collective constructs, this study was undertaken to answer the following research question: What is the combined influence of perceived threat of breast cancer and net perceived efficacy of BSE on BSE performance?
METHOD A cross-sectional survey design was used to answer the research question. A self-administered questionnaire was given to women who agreed to participate in the study, and data were coded and analyzed using a variety of multivariate techniques.
Sample The study sample consisted of women who were at least 18 years old and who could read and write English. Subjects were recruited from women’s groups at local churches, a group practice of physicians at a university-affiliated health center, health-related events open to the public, and participants in a hospital-sponsored breast evaluation and education program. Women were recruited
from the BSE education program on the premise that voluntary participation in such programs may indicate greater likelihood of BSE performance. This strategy was intended, therefore, to increase the proportion of women in the sample who regularly performed BSE beyond the 30% or fewer expected by chance.
Instruments
HBM variables. A 33-item modified version of Champion’s tool (1987) was used to measure perceived susceptibility to breast cancer, perceived seriousness of breast cancer, perceived benefits of BSE, and perceived barriers to BSE. Modifications included addition of items worded “in reverse” to each scale and expansion of the seriousness scale. Subjects indicated, on a 6-point scale, the extent to which they agreed with the 33 statements. Responses were anchored with “Not at all” and “Completely.” There was no neutral category. Information on the stability and internal consistency of the HBM scales may be found in Table 1. Construct validity was examined through the use of factor analysis of responses to the 33 items. A four-factor solution with orthogonal rotation of the factors explained 40.8% of the variance in responses. All but four of the 33 items had loadings of more than .30 on their respective factors. BSE performance. Information was requested on both the frequency and thoroughness of BSE. Fourteen BSE steps were presented in a
424
RESEARCH IN NURSING & HEALTH
Table 1. Reliability Data for Health Belief Model Scales ~
HBM Scale Susceptibility Seriousness Benefits Barriers
Items
Cronbach’s Alphaa
7 8 9 9
.80
.90’
.74
.81* .75* .61*
.73 .72
Test-Retestb
‘.N = 202. bN = 66.
‘p
i,001
chart, and subjects were asked to indicate if they knew how to do each step and whether they performed the step usually, sometimes, or never. The 14 steps and the format for the questions about them were based on information in the literature about BSE and questions used in previous studies (Celentano & Holtzman, 1983; Trotta, 1980; National Cancer Institute, 1984). Stability of the tool in relation to the components of BSE performance was determined by comparing test-retest responses. Correlation coefficients for the 66 subjects who completed the retest were .87 (frequency) and .89 (thoroughness).
Procedure Except for subjects recmited at public health-related events, the questionnaires were distributed by persons other than the investigator (e.g., private physicians, leadership persons in church groups, etc.). Potential subjects received a copy of the questionnaire, a cover letter, and a self-addressed, stamped envelope. Return of the questionnaire was considered consent to participate. Human subjects approval was obtained from the sponsoring academic institution and all agencies involved in data collection. Data to determine test-retest reliability were collected by sending a second questionnaire to all subjects who provided their names and addresses. The second questionnaire was mailed to these subjects 2 weeks after receipt of their original questionnaire.
RESULTS The proportion of returned questionnaires varied from setting to setting. Sixty-eight percent (n = 122) of the questionnaires distributed to patients of a private physicians’ group were returned whereas only 13% (n = 8) of those distributed to Black church groups were returned. Overall
the return rate of 50.47% yielded 215 questionnaires; of these, 202 were suitable for analysis. The majority of subjects in the final sample (60%) were patients of private physicians. Subjects from the breast evaluation/education center comprised 29% of the sample, and 11% were from other settings.
Sample Characteristics Approximately one-third of the subjects were under 40 years old, one-third were between 40 and 59 years, and one-third were 60 years or older. The subjects were primarily white (n = 185; 92%), married (n = 169; 85%), and well educated. Only 5 subjects had less than a high school education; 165 (82%) had completed more than 12 years of school. The most frequent religious preference reported by the subjects was Protestant (n = 95; 47%).
Health Beliefs Related to Breast Cancer and BSE Mean scores were computed for the four HBM scales because of the variation in number of items per scale. Although each of the 33 items used to measure HBM variables exhibited the full range of possible responses, only the Susceptibility Scale scores were normally distributed. There was moderate variation among subjects in the extent to which they perceived breast cancer as “serious,” but the sample as a whole perceived many benefits and few barriers to BSE. Descriptive statistics for each of the four scales are found in Table 2. Three variables were computed from scores on the HBM scales. THREAT was computed as susceptibility plus seriousness. The THREAT scores were normally distributed with a mean of 6.83. As no mathematical formulation was found in the literature for benefits weighed against barriers, net perceived efficacy was analyzed both as NETBEN (benefits minus barriers) and COSTBEN
425
BREAST SELF-EXAMINATION / WYPER
Table 2.
Descriptive Statistics for HBM Scales (N = 202)
HBM Scale
Mean
Standard Deviation
Range of Subjects’ Mean Scores
Susceptibility Seriousness Benefits Barriers
3.62 3.21 5.43 1.76
1.04 .96 .66 .71
1.OO-6.00 1.OO-5.75 3.22-6.00 1.OO-5.67
Note: The possible range of subjects’ mean scores was 1.OO to 6.00 The higher the score, the greater the subjects’ perceptions of susceptibility tolseriousness of breast cancer and benefits ofibarriers to BSE.
(the ratio of benefits to barriers). The mean scores on NETBEN and COSTBEN were similar, 3.67 and 3.58 respectively, but only the COSTBEN scores were normally distributed. COSTBEN, therefore, was used as the measure of net perceived efficacy in subsequent analyses.
BSE Performance The reported Erequency of BSE performance ranged from “never” (14%) to “at least once a month” (45%). The remaining subjects (41%) reported performing BSE less often than once a month. The average thoroughness score was 16.21 (SD= 8.59); a score of 28 would indicate that the subject reported “usually” performing all 14 BSE steps. An additional variable, BSE PRACTICE, was computed by multiplying frequency times thoroughness. This provided a more comprehensive indicator of the subjects’ performance of BSE than could be obtained by measuring frequency alone and it allowed for considerable variation in response. The BSE PRACTICE scores were normally distributed and ranged from 0 to 140 (A4 = 68, SD = 45.67).
Relationship of HBM Variables to BSE Simple correlations of HBM variables (original and computed) with components of BSE per-
formance are shown in Table 3. The correlations between all HBM variables andfrequency of BSE were very similar to findings reported by Champion (1984, 1987) and Rutledge (1987). The details of these comparisons are presented in Table 4. The effect of combining susceptibility and seriousness into the variable THREAT was attenuation of the slight positive correlations of the former (especially with thoroughness) and the very slight negative correlations of the latter. There were, therefore, no significant relationships between THREAT of breast cancer and any measure of BSE performance obtained in this study. There were significant, positive correlations between net perceived efficacy (COSTBEN) and all measures of BSE, but these correlations were slightly smaller than those of barriers, one of its component variables. In a study reported as these data were being analyzed, Rutledge (1987) created a THREAT variable by multiplying standardized susceptibility scores by standardized seriousness scores rather than adding the raw scores, and her findings were similar to those reported here. THREAT did not enter into an explanatory model at all. She also created a net effect of benefits and barriers variable (BENBAR) by subtracting standardized barriers scores from standardized benefits scores (similar to NETBEN in this study). BENBAR had a simple correlation of .51 with BSE frequency compared with .32 for NETBEN in the current study.
Table 3. Correlations between HBM Variables and BSE Breast Self Exam HBM Variables Susceptibility Seriousness THREAT (Sus + Ser) Benefits Barriers COSTBEN (Ben/Bar)
Frequency
Thoroughness
PRACTICE
,168
,224’ -.079 .lo6 ,266” -.386“’ .380**’
,211‘ -.091 .089 ,254” - .440”’ .427’**
- ,098
,054
,212’ -.443**’ ,401* * *
Note:N = 194. ‘ p < .05;“ p < .Ol;“ * p < ,001.
426
RESEARCH IN NURSING & HEALTH
Table 4. Comparison ot Correlations between HBM Variables and Frequency of BSE in Four Nursing Research Studies Correlations with BSE Frequency Study ~~~
~~~
Susceptibility
Seriousness
Benefits
Barriers
- .03 .01 .17
-.lo
.18 .26 - .22e .21
- .48
.06'
-.11 - .09 .l 6e
~~~
Champion, 1985a Champion, 1987' Ruttedge, 1987' Wyper, 198711988*
- .47 ,458 - .44
"N =
301. b N = 585. 'N = 93. d N = 194. 'Scoring on these scales was reversed from the other three studies which explains the difference in direcfion of the correlations.
The HBM variables, as measured in this study, were not independent. There was a marked negative relationship (r = - S 9 ) between benefits and barriers, and a modest positive relationship ( r = .30) between seriousness and barriers.
Combined Influence of HBM Variables on BSE
Original HBM variables. Multiple regression of the HBM variables in their original form on all three measures of BSE (frequency, thoroughness, and PRACTICE) revealed that only susceptibility and barriers contributed to the model. The greatest proportion of explained variance (22%) was associated with the combined variable, BSE PRACTICE, and the details of this regression analysis are presented in Table 5. Stepwise multiple regression indicated that regardless of the measure of BSE, barriers accounted for most of the explained variance (see Table 6 ) . Computed HBM variables. Stepwise multiple regression of THREAT and COSTBEN on
the dependent variable BSE PRACTICE revealed that both constructs contributed to the regression model. However, THREAT contributed only 2% to the overall explained variance of 20%. Seriousness and benefits, therefore, contributed to a prediction model for BSE practice only when combined with susceptibility (THREAT) and barriers (COSTBEN).
DISCUSSION The findings of this study must be evaluated in light of the fact that the scales used to measure HBM variables resulted in scores that were normally distributed for only one HBM variable, SUSceptibility. It is difficult, therefore, to attribute the findings to deficiencies in the model without considering possible deficiencies in the measurement process and the homogeneity of the sample. Because the data were collected through a selfreport measure, it is also possible that the findings were affected by a social desirability response set.
Table 5. Multiple Regression of HBM variables on BSE PRACTICE Dep Var: Practice N: 194 Adjusted Squared Multiple R: ,204 Variable Coefficient STD Error
Multiple R: ,470
Squared Multiple R: ,220 Standard Error of Estimate: 41.224 Tolerance t p ( 2 Tail)
STD Coef
~
Constant
sus
SER BEN BAR
101.111 7.281 0.504 - 1.225 -30.804
38.381 2.924 3.286 5.708 5.666
1 .ooooooo
0.000 0.165 0.01 0 -0.01 7 -0.434
.9414619 .8921557 .6718545 ,6468450
~~~
2.634 2.490 0.153 -0.215 -5.437
,009 ,014 .878 ,830
,000
Analysis of Variance Source Regression Residual
Sum-of-Squares
DF
Mean-Square
F-Ratio
P
90839.445 321 197.545
4 189
22709.861 1699.458
13.363
