Osteoporos Int (2007) 18:981–989 DOI 10.1007/s00198-007-0326-z
ORIGINAL ARTICLE
Psychometric properties of the “Osteoporosis and You” questionnaire: osteoporosis knowledge deficits among older community-dwelling women S. M. Cadarette & M. A. M. Gignac & D. E. Beaton & S. B. Jaglal & G. A. Hawker
Received: 11 September 2006 / Accepted: 20 December 2006 / Published online: 28 February 2007 # International Osteoporosis Foundation and National Osteoporosis Foundation 2007
Abstract Summary In older women, knowledge about risk factors for osteoporosis was good, with over 75% responding correctly to questions about lifestyle factors, family history, height loss, and menopausal status. However, significant knowledge deficits were identified regarding osteoporosis “consequences” and “prevention and treatment.” Introduction We examined osteoporosis knowledge by testing the psychometric properties of the 10-item knowledge component of the “Osteoporosis and You” questionnaire. Several knowledge domains were hypothesized. Methods Community-dwelling women aged 65–90 years residing within two regions of Ontario, Canada were studied (N=869). Data were collected by standardized telephone interviews in 2003 and 2004. Items to which 75% or more responded correctly were identified as having S. M. Cadarette : S. B. Jaglal : G. A. Hawker Osteoporosis Research Program, Women’s College Hospital, Toronto, ON, Canada S. M. Cadarette : D. E. Beaton : S. B. Jaglal : G. A. Hawker Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada M. A. M. Gignac Division of Outcomes and Population Health, University Health Network, Toronto, ON, Canada M. A. M. Gignac : S. B. Jaglal Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada M. A. M. Gignac : D. E. Beaton Institute for Work and Health, Toronto, ON, Canada
a low index of difficulty; the remaining items identified areas of knowledge deficit. Confirmatory factor analysis was used to test scale structure. Results Six of the ten items had a low index of difficulty. These items largely examined osteoporosis risk factors. The remaining four items identified significant knowledge deficits in the areas of osteoporosis consequences, prevention, and treatment. Confirmatory factor analysis identified four distinct osteoporosis knowledge domains. However, the internal consistency was low for all but one domain, which examined “prevention and treatment.” Conclusion Although older women appear to be aware of osteoporosis risk factors, knowledge deficits regarding the consequences of osteoporosis and that treatment exists to prevent bone loss were identified. Better understanding of the multi-dimensional aspects of osteoporosis knowledge
D. E. Beaton Mobility Program Clinical Research Unit, St. Michael’s Hospital, Toronto, ON, Canada S. B. Jaglal Department of Physical Therapy, University of Toronto, Toronto, ON, Canada G. A. Hawker Division of Rheumatology, Women’s College Hospital, Toronto, ON, Canada S. M. Cadarette (*) Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120, USA e-mail:
[email protected]
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may help to inform the development of effective educational interventions. Keywords Factor analysis, Statistical . Knowledge . Osteoporosis
Introduction Health beliefs are defined by attitudes, values and knowledge about health and health services [1]. An underlying knowledge of osteoporosis as a disease is important in developing attitudes about the condition. These attitudes may in turn impact health care and treatment behaviors [1, 2]. A recent review of the literature identified significant limitations in the measurement and conceptualization of osteoporosis knowledge [3]. For example, examining osteoporosis knowledge as a single domain may obscure the more multi-dimensional aspects of disease knowledge, which include knowledge about the causes or risk factors of the disease, knowledge about prevention, knowledge about disease consequences, and knowledge about treatment. The majority of prior studies have also relied on convenience samples, which may not be generalizable beyond those under study [3]. We studied osteoporosis knowledge within the larger context of examining osteoporosis management. The current study aimed to address the call for research to more closely examine osteoporosis knowledge [3] by examining the psychometric properties of the knowledge component of the “Osteoporosis and You” questionnaire [4] in a population-based cohort of older women. This scale was developed for use as a uni-dimensional assessment of osteoporosis knowledge overall [4]. We hypothesized that osteoporosis knowledge would be multi-dimensional, including distinct aspects of knowledge about risk factors, sequelae (consequences), prevention, and treatment. In studying the psychometric properties of the scale, we also aimed to identify distinct areas of osteoporosis knowledge deficits.
Materials and methods Recruitment and data collection A short screener questionnaire focusing on musculoskeletal health [5, 6] was sent between 1995 and 1997 to all persons 55 years and older residing within two regions of Ontario, Canada. The main aim of this screener was to identify those with moderate to severe arthritis who were then contacted for longitudinal follow-up [5]. Women aged 65 to 89 years who completed the short screener questionnaire and were ineligi-
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ble for follow-up were randomly sampled (n=750 per region) for the current study. Information was mailed prior to recruitment by telephone. Although the focus of the current study was on osteoporosis management [7], the study was referred to as the “Women’s Healthy Bones and Joints Study” to provide participants context, but without a focus on osteoporosis that may influence osteoporosis health beliefs assessed during the telephone interview. Data were collected by standardized telephone interviews in 2003 and 2004. Community-dwelling women residing in the two sampled communities were eligible. Women were deemed ineligible if they were unable to complete the standardized telephone interview due to hearing impairment, language barrier, dementia or illness (e.g., aggressive cancer treatment); proxy respondents were not permitted. The questionnaire included information about personal and family history of osteoporosis and fractures, health status, health services use, medication and supplement use, osteoporosis health beliefs, osteoporosis management, comorbidity, drug coverage and demographic factors. We framed the section examining osteoporosis health beliefs by providing a definition of osteoporosis: “Osteoporosis is a condition in which the bones become excessively thin and weak so that they break easily. Osteoporosis is not the same as osteoarthritis, which is the wear and tear type of arthritis, but is defined as brittle bones.” Providing this definition and distinction was important for our overall study objective of examining osteoporosis management, to help ensure that responses to health belief questions were relevant to osteoporosis, and avoid confusion with osteoarthritis [8]. We administered the Osteoporosis Health Belief Scale (OHBS) [9, 10] prior to the knowledge component of the “Osteoporosis and You” questionnaire. The OHBS measures the following domains of osteoporosis-related health beliefs: susceptibility, severity, exercise benefits and barriers, calcium benefits and barriers, as well as general health motivation. Each item has a 5-point response option: strongly disagree, disagree, neutral, agree, strongly agree. Developers of the OHBS also developed the Osteoporosis Knowledge Test, a 24-item scale with multiple choice response options targeting knowledge about calcium and exercise [11]. However, with our overall objective of examining osteoporosis management, the two domains were too restrictive in focus, and we felt that the multiple choice response options may be difficult for our target study group of women aged 65 or more years; the Osteoporosis Knowledge Test was developed with 201 women aged 35 years or older. Of the remaining osteoporosis knowledge scales that did not have multiple choice response options, the knowledge component of the “Osteoporosis and You” questionnaire was selected: fewest items (10 items compared with at least 20 items [3]), appeared to be tapping into relevant constructs
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of osteoporosis knowledge based on item wording (face validity), and had a 5-point response option similar to the OHBS. We thus felt that inclusion of the knowledge component of the “Osteoporosis and You” questionnaire would minimize participant burden during the standardized telephone interview, yet conceptually be able to measure osteoporosis-related knowledge of interest for our overall objective of examining osteoporosis management. Details regarding the “Osteoporosis and You” questionnaire were identified from an abstract and poster presented at a scientific meeting [4]. Specifically, the developers described that the scale’s 20 items were derived from previously used and unvalidated questionnaires. Qualitative testing was used to test and modify wording for readability and comprehension. The questionnaire was then validated in a convenience sample of 500 consecutive women aged 20 or more years self-selected for screening at a Preventive Medicine Health Appraisal Center, including 100 women from each age group: 20–39, 40–49, 50–59, 60–69, and 70 or more years. Developers reported that confirmatory factor analysis supported the use of three domains: knowledge, attitude/actions, and health behavior; internal consistency of the 10-item knowledge domain measured by the Cronbach’s alpha coefficient was 0.73 [4]. The knowledge items were coded by developers on a 5-point Likert scale with the following response options: strongly agree, somewhat agree, no opinion, somewhat disagree, and strongly disagree. To simplify responses by telephone administration in this study, responses were modified to: strongly agree, agree, neutral, disagree, and strongly disagree, consistent with other health belief questions asked [9, 10]. Statistical analysis Language to describe the analysis of multi-item scales varies by discipline, e.g., latent variable (statistics), hypothetical construct (psychology). To simplify interpretation by the reader, the glossary summarizes the terminology adopted in this paper. Item analysis Item response frequency and measures of central tendency were summarized. The response distribution of items was then collapsed into three categories: agree (strongly agree and agree), neutral, and disagree (strongly disagree and disagree). This permitted an assessment of the degree of difficulty for each item, and direct comparison to developer responses. As examined by others [12], all items in which over 75% of participants responded correctly (neutral responses were coded as incorrect) were deemed to have a low index of difficulty. The remaining items identified areas of knowledge deficit.
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Inter-item correlation Inter-item correlations were examined using polychoric correlation coefficients that estimate what the correlation between two ordinal variables would be if each were continuous and normally distributed [13]. These analyses were completed using PRELIS 2.72 [14]. Inter-item correlation coefficients within a domain should exceed 0.3 [15]. Factor analysis Factor analysis is a multivariate interdependence technique describing a category of approaches to determining the underlying structure in a data matrix [13, 15]. Factor analysis can be used for either an exploratory or confirmatory purpose. In exploratory factor analysis, one may not know beforehand how many factors best describe the structure of a set of variables, and thus the objective is to determine the number of factors. Confirmatory factor analysis is used to test hypotheses about the underlying scale structure when an a priori theory exists. Confirmatory factor analysis was used to test scale structure in LISREL 8.72 [16]. Factor loadings ≥0.40 were considered important and loadings ≥0.5 significant [15]. First, the developers’ suggested structure of a single factor was tested. This was then compared with the structure hypothesized based on face validity and the inter-item correlations observed in this study. Three goodness-of-fit statistics were used to compare the single factor solution with the multi-dimensional solution: the root mean square error of approximation (acceptable values are less than 0.05 with the upper boundary of the 90% confidence interval being less than 0.08), the root mean square residual (values less than 0.10 are considered favorable), and the non-normed fit index (ranges between 0 and 1 with values of 0.9 or higher being recommended; when comparing between models, differences between 0.06 to 0.09 are proposed to be indicative of substantial model differences) [15, 17, 18]. In the last step of examining how well the proposed scale structure fit the data, standardized residual matrices were reviewed. When there is a good fit, the distribution of standardized residuals will be centered on zero, will be symmetrical, and will contain no or few large residuals [17, 18]. Standardized residuals greater than 2.58 were considered significant at the 0.05 level [15] pinpointing associations between items that were inadequately represented in the structure under analysis. Internal consistency (reliability) Once scale structure as recommended by factor analysis was determined, the reliability of each domain (factor) was tested. Reliability is an assessment of the degree of
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consistency between multiple measurements of a variable (e.g., items in a domain) and should be analyzed for appropriateness before proceeding to an assessment of validity. Direct measures of reliability include Cronbach’s alpha, and measures produced from confirmatory factor analysis: composite reliability and the average variance extracted [15]. Composite reliability is preferred over Cronbach’s alpha if data are skewed. To provide a consistent metric for comparison, Cronbach’s alpha coefficients were also estimated using the Spearman–Brown prophecy formula [19] to scales with 10 items each. Cronbach’s alpha coefficients and composite reliability values of 0.70 or higher are generally deemed acceptable for group comparisons [15, 18]. The total variance extracted is the amount of variance in the items accounted for by the domain and should exceed 0.50 [15]. Construct validity Construct validity of domain scores calculated as the proportion of correct responses was tested by theoretical hypotheses using unpaired t test statistics (two-group comparison) or ANOVAs (comparing more than two groups). Specifically, it was hypothesized that: 1. Those with higher education would have higher knowledge scores in all areas
2. Those having been screened for osteoporosis by having had a bone mineral density test would have higher knowledge scores in all areas 3. Those taking non-estrogen bone-sparing medications (bisphosphonate, calcitonin, and/or raloxifene) would have higher knowledge scores regarding the prevention and treatment of osteoporosis 4. No differences in osteoporosis-related knowledge would be observed comparing hormone replacement therapy users with non-users because hormone replacement therapy is indicated for several conditions and not only for bone health
Results Sixty-nine percent (n=1,042) of the 1,500 women sampled for this study were located and presumed eligible. Of these, 84% (n=871) participated. The overall response rate was 72% after adjusting for those who could not be located (10%) or reached by telephone before the end of data collection (2%). Participants were similar to non-responders in terms of body mass index, drug use (etidronate and hormone replacement therapy), and self-report of physician-diagnosed osteoporosis and fracture history collected between 1995 and 1997. Participants were similar in age to
Table 1 Characteristics of study participants (N=869)
a
Proportions adjusted for missing data b East York within Toronto, metropolitan area, population density = 5,418/km2 , Oxford County in southwestern Ontario, population density = 49/km2 [24]. c Determined by consensus among three authors (DB, GH, SJ) based on participant responses to questions about fractures (site and cause) occurring since the age of 40.
White Primary language English Annual household income
