Calcium knowledge, dietary calcium intake, and bone mineral content and density in young women.
Osteoporosis and low bone mass affects more than 44 million Americans and of those affected, 80% are women (National Osteoporosis Foundation, 2005). Osteoporosis is a degenerative bone disease characterized by low bone mass and deterioration of bone tissue. A major health concern associated with osteoporosis is an increased susceptibility to osteoporosis-related fractures (e.g., hip, spine, wrist). A major determinant of fracture risk in adults is bone mass at skeletal maturity or peak bone mass. Peak bone mass is typically attained during the later stages of puberty and a large percent of peak bone mass is accumulated during adolescence. By age 20, the average woman has accumulated 98% of her skeletal mass (National Osteoporosis Foundation, 2005). The National Osteoporosis Foundation (2005) has identified five steps to reduce the risk of osteoporosis: (1) consume a balanced diet rich in calcium and vitamin D, (2) participate in weight bearing exercise, (3) avoid smoking and excessive alcohol consumption, (4) talk to your doctor about bone health, and (5) bone density testing and medication when appropriate.
Dietary factors, in particular calcium intake, clearly impact bone mineral density. Dairy products provide the major source of calcium in the diet and calcium intake is a modifiable determinant of bone mass (Golden, 2000). The dietary reference intake (DRI) of calcium is 800 mg for children between the ages of 4 and 8; 1300 mg between the ages of 9 and 18; 1000 mg between the ages of 19 and 50; and 1200 mg for individuals age 50 and older (Institute of Medicine, 1997). Most adults would be able to meet the recommended calcium intake by consuming at least three servings of milk, cheese or yogurt daily (e.g., 1 cup skim milk, 8 oz. low fat yogurt, 1.5 - 2 oz cheese). Currently, only 19% of females aged 9-19 are meeting daily calcium recommendations, 40% of females aged 20-49 are meeting requirements, and 27% of females aged 50 and older are meeting calcium requirements (U.S. Department of Health and Human Services, 2000). In addition to reducing the risk of osteoporosis, adequate dietary calcium may reduce the risk of hypertension, colon cancer, kidney stones, and tooth decay. It is apparent that dietary calcium recommendations are not being met. Why are dietary calcium recommendations not being followed and dietary calcium intakes low?
Kristal, Bowen, Curry, Shattuck, and Henry (1990) suggested that nutritional knowledge influences food choices and dietary behavior. Inadequate nutritional knowledge may be a factor influencing the intake of low levels of dietary calcium. A major challenge in health promotion is translating knowledge about health issues into healthy behaviors (Rimal, 2000). In order for individuals to make bone-healthy food choices, they must have knowledge about foods that are good sources of calcium. Many women do not know how much calcium they need, may not feel susceptible to diseases associated with low levels of dietary calcium, or misperceive high calcium foods as fattening. Therefore enhancing knowledge related to calcium is critical for reducing rates of osteoporosis and low bone mass.
Several studies have demonstrated relationships among knowledge, attitudes, intentions, and behavior. Guiding frameworks and models that incorporate learning and knowledge include the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the Transtheoretical Model (Prochaska & DiClemente, 1983), and Social Cognitive Theory (Bandura, 1977). Although the correlation between knowledge and behavior may not be strong, Rimal (2000) states that a significant amount of variance in health behaviors can be accounted for by knowledge alone. Although the current study was not designed to examine a particular model or theoretical framework, it is highly unlikely that individuals will engage in health-promoting behaviors if they do not possess an adequate amount of knowledge related to respective health behaviors.
Several studies have demonstrated that knowledge and attitudes about osteoporosis influence osteoporosis-related preventive behaviors (e.g., Frederick & Hawkins, 1992; Terrio & Auld, 2002; Turner & Bass, 2001). To our knowledge, other than Frederick and Hawkins (1992), studies have not specifically examined associations among calcium knowledge, dietary calcium intake, and bone health in young women.
In addition to dietary calcium intake, physical activity positively impacts bone in young women (Rubin et al., 1999; Ulrich, Georgios, Gillis, & Snow-Harter, 1999). Studies also reveal that women spend less time in leisure-related activities, organized sports and heavy-intensity physical activity and more time in housework, caregiving and occupational activities (Ainsworth, Richardson, Leon, & Jacobs, 1993; Ainsworth, Sternfeld, Richardson, & Jackson, 2000). Therefore, several authors have suggested that physical activity surveys that omit the measurement of non-leisure activities may underestimate physical activity patterns among women (Ainsworth et al., 2000; Greendale et al., 2003). The Kaiser Physical Activity Survey (KPAS) (Sternfeld, Ainsworth, & Quesenberry, 1999) allows for the assessment of physical activity in sport, home, occupation and daily living domains.
The primary aim of this study was to examine associations among calcium knowledge, dietary calcium intake, and total body bone mineral content (TBMC) and density (TBMD) in young women. A secondary aim was to examine differences in calcium knowledge, TBMC and TBMD, between women meeting and not meeting the DRI for calcium. Because of the potential confounding influences of physical activity and anthropometric characteristics (e.g., fat mass, fat-free mass) on bone, these variables were also measured.
A convenience sample of fifty women aged 20-45 (M = 35.24, SD = 6.53 yrs) participated in a study examining associations among calcium knowledge, dietary calcium intake and bone health. Participants were recruited within the university and community via posters and the university's listserv. All testing procedures were approved by an Institutional Review Board for the Use of Human Subjects in Research. All participants provided written informed consent prior to participating in the study.
Participants completed a single testing session that lasted approximately one hour. During that session, physical measures and self-assessment surveys were completed. All bone densitometry scans were completed by the International Society for Clinical Densitometry (ISCD) certified technicians. Bone densitometry scans were completed at a regional medical center. A Hologic QDR 4500W bone densitometer was used for whole body scans. Scans were interpreted by a local endocrinologist.
Osteoporosis Risk Factor and Demographic Survey. A self-report descriptive demographic survey was developed by the principal investigators for this project. Several of the items included in the survey relate to known osteoporosis risk factors (e.g., ethnicity, family history of osteoporosis, previous/current medications, fracture history, menstrual history, smoking status, alcohol consumption).
Calcium Knowledge Survey. Participants completed an 18-item survey that was modified from a survey developed to assess calcium knowledge in adolescents (Harel, Riggs, Vaz, White, & Menzies, 1998). Response formats were modified so that correct/incorrect responses could be coded and a summary score derived based upon the number of correct/incorrect responses (score range 0-18). The survey consisted of multiple-choice and true or false items designed to assess knowledge of dietary calcium sources, health benefits of calcium, and calcium functions in the body. Cronbach alpha for the Calcium Knowledge Survey was 0.58.
Kaiser Physical Activity Survey (KPAS). A self-administered Kaiser Physical Activity Survey (Ainsworth, Sternfeld, Richardson, & Jackson, 2000) was used to classify physical activity levels. This questionnaire has demonstrated good reliability for measuring physical activity among women with a broad range of physical activity habits (Strenfeld, Ainsworth, Quesenberry, 1999). One-month adequate validity and test-retest reliability ranging between 0.79 and 0.91 has been reported for the KPAS (Ainsworth et al., 2000). The KPAS is a 75-item survey and consists of four summary activity indexes: household and care giving (HC); occupation (O); active living habits (AL); and sports/exercise (SE). Questions within the indexes were answered using a five level categorical response. Scores ranged from 1 for "never" or "none" to 5 for "always" or "more than once a week" in each physical activity domain. Scoring of the four activity indexes (HC, O, AL and SE) were based on the procedures outlined by Sternfeld et al. (1999). Higher scores indicated higher levels of activity in that index. The HC index included questions about child care, preparing and cleaning up from meals, routine household cleaning (e.g., dusting), heavy household cleaning (e.g., washing floors), garden and yard work, heavy outdoor work (e.g., chopping wood) and major home repair (e.g., painting). The O index included questions about being physically tired after work, how often did they sit, stand, walk, or lift heavy loads when they were working. The AL index included questions about the level of physical activity involved in their daily routine such as walking and/or biking to and from work, amount of television viewing, amount of walking or biking for at least 15 minutes at a time. The SE index asked general questions about participation in sports or exercise and if they sweat from exertion during sports and exercise. The SE index also allowed participants to specify up to three sports or exercises that they participated in and the number of hours per week and months per year of participation. Participants responded to each KPAS item based upon physical activity participation and habits in the past year. Although recalling activity over the past year may be challenging, the KPAS has demonstrated adequate test-retest reliability and validity (Ainsworth, Sternfeld, Richardson, & Jackson, 2000, Sternfeld & Ainsworth, 1999).
Diet Analysis. A 24-hr dietary recall was used to calculate energy intake, macronutrient and micronutrient levels. Dietary calcium was measured in milligrams per day. The use of food models and probing questions by a trained research nutritionist were used to obtain accurate and complete recall information. Analysis of nutrients was completed by using the Food Processor computer program (ESHA Research, Salem, OR). Recalled dietary record data was entered by the same research nutritionist to decrease the variability of interpretation.
Bone Measures and Anthropometric Data. Total body bone mineral content (TBMC) expressed as grams (g), total body bone mineral density (TBMD) expressed as grams per centimeter (g/cm2), and body composition were determined by dual energy x-ray absorptiometer (Hologic QDR 4500W). Fat-free body mass (FFM) and fat mass (FM) were expressed in kilograms (kg). Height (without shoes) was measured to the nearest 0.1 cm using a wall-mounted stadiometer. Weight (without shoes) was measured to the nearest 0.5 kg using a physician's balance beam scale. Height and weight measures were used to compute body mass index (BMI = W/H2), where W represents weight (kg) and H2 represents height in meters squared (m2).
Means and standard deviations were calculated for demographic and physical characteristics of the participants, daily dietary calcium intake, calcium knowledge survey scores, and KPAS scores. Frequency data were calculated for responses to the Calcium Knowledge Survey. MANOVA was conducted to examine differences in physical characteristics, dietary calcium intake, calcium knowledge, TBMC and TBMD, and KPAS scores between participants meeting and not meeting the DRI for calcium. Pearson correlations were conducted to examine associations among calcium knowledge, dietary calcium intake, TBMC and TBMD, and physical activity. Stepwise linear regression analysis was used to determine predictors of dietary calcium intake. Potential predictors included age and calcium knowledge. Stepwise linear regression analyses were conducted to examine predictors of TBMC and TBMD.
The 1st block of potential predictor variables considered for inclusion in the regression model were age, BMI, fat mass, and fat free mass. The 2nd block of potential predictors considered for inclusion was dietary variables that included calcium intake, and calcium knowledge. The 3rd and final block of potential predictors included KPAS scores. Type I error rate was p = .05 for analysis and all data were analyzed using SPSS for Windows (Version 11.0, SPSS, Inc., Chicago, IL).
Age, height, weight, body mass index, fat mass, fat-free mass, TBMC and TBMD, dietary calcium intake, and KPAS scores are reported in Table 1. All participants were Caucasian, not of Hispanic origin. Three participants (6%) reported that they currently smoke and 44 (88%) reported that they drink alcohol. Twelve participants (24%) reported a family history of osteoporosis and 21 participants (42%) reported a previous fracture. The mean score of the Calcium Knowledge Survey was 12.02 [+ or -] 2.43 (maximum score of 18).
Knowledge About Health Benefits of Calcium
All (100%) of the participants acknowledged that calcium is good for their health. Regarding the roles and function of dietary calcium; all of the participants knew that calcium was good for strong bones, 98% knew that calcium was important to prevent soft bones and fractures, 90% knew that calcium was important for good teeth, 48% knew that calcium was important in muscle and nervous system functioning, and 28% knew that calcium was important in blood pressure regulation. The majority of participants (72%) knew that adolescence is a critical period for achieving peak bone mass. If peak bone mass was not achieved, 26% felt that it could be achieved later on and 34% felt that bone mass could not be achieved later.
Knowledge About Dietary Calcium Requirements and Food Sources
Fifty-eight percent of the participants knew the dietary reference intake regarding the recommended amount of calcium needed each day. Thirty-two percent knew the amount of calcium in a serving of 1% (lowfat) milk. The following percentages relate to knowledge regarding calcium in dark green leafy vegetables: turnip greens (46%), kale (66%), spinach (72%), and broccoli (74%). A majority of respondents (70%) reported consuming juices enriched with calcium and 42% reported that they ate cereals enriched with calcium. Overall, the participants consumed an average of 1374.97 [+ or -] 556.86 mg of calcium daily. Sixty-six percent of the participants reported that they took a vitamin/mineral supplement each day and 60% of those respondents reported that their supplement included calcium.
Associations among Calcium Knowledge, Dietary Calcium Intake, TBMC and TBMD
Table 2 presents Pearson correlation coefficients for associations among calcium knowledge, dietary calcium intake, TBMC and TBMD, FM and FFM, and KPAS scores.
Results revealed a significant negative correlation between calcium knowledge and TBMC and TBMD. There were also significant positive relationships between dairy product intake and TBMC and TBMD. Results revealed significant positive correlations among fat free mass and TBMC and TBMD. Stepwise multiple regression analyses conducted to examine predictors of dietary calcium intake revealed age as the only significant predictor of dietary calcium intake (R = .33, adjusted [R.sup.2] = .09, p = .05). Stepwise linear regression analyses conducted to examine predictors of TBMC and TBMD revealed fat free mass and BMI as significant predictors of TBMC (R = .77, adjusted [R.sup.2] = .57, p = .05) and fat free mass as a significant predictor of TBMD (R = .38, [R.sup.2] = .12, p = .05).
Multivariate analysis of variance was conducted to examine differences in calcium knowledge score, TBMC and TBMD, fat mass and fat-free mass, and KPAS scores between participants meeting and not meeting the DRI for calcium revealed a significant multivariate effect, F (14, 27) = 4.59, p = .05. Univariate results revealed a significant effect for age, F(1,4) = 5.26, p = .05. Participants meeting the calcium DRI were older (M = 36.47, SD = 5.58) than participants not meeting the calcium DRI (M = 31.3, SD = 8.04). Results also revealed a significant effect for TBMD, F(1, 40) = 4.10, p = .05. Participants meeting the calcium DRI had a greater TBMD (M = 1.17, SD = 0.06) than participants not meeting the calcium DRI (M = 1.13, SD = 0.07). There were no significant differences in knowledge or KPAS scores between participants meeting and not meeting the calcium DRI.
Results of this study suggest that young adult women are generally aware of the roles and benefits of dietary calcium, dietary sources of calcium, and the recommended intake of calcium. Comparing calcium knowledge of participants in the present study is limited, as previous studies (e.g. Frederick & Hawkins, 1992; Terrio & Auld, 2002) have examined relationships among osteoporosis knowledge and attitudes with calcium intake rather than calcium knowledge. Harel and colleagues (1998) examined calcium knowledge in adolescents; compared to adolescents, calcium knowledge for participants in the present study was greater. Studies with adolescents have suggested that limited knowledge of calcium may be one factor influencing low intakes during adolescence (Barr, 1994; Harel, Riggs, Vaz, White, & Menzies, 1998). Average dietary calcium intake for women in this study met the DRI of 1000 mg/d.
Calcium knowledge scores were not significantly associated with dietary calcium intake. The lack of association between calcium knowledge and calcium intake is inconsistent with the positive relationship reported by Tepper and Nayga (1998) and consistent with results reported by Terrio and Auld (2002) and Turner and Bass (2001). A fact limiting generalization from the Tepper and Naygo (1998) study was that women were 50 years of age and over in that study. Examining the results of the present study, factors other than calcium knowledge may be influencing calcium intake in women. Interestingly, women meeting the DRI for calcium were slightly older and had greater total bone mineral density than women not meeting the DRI for calcium. Results revealed no differences in calcium knowledge between women meeting and not meeting the calcium DRI, suggesting that variables not measured in the present study may be influencing calcium intake, e.g., attitudes. It has been posited by Turner and Bass (2001) that concerns such as body weight and body fat may potentially be exerting influences on food choices as well.
Relative to predictors of total bone mineral content and density, fat free mass was the only significant predictor. Results are consistent with studies reporting physical parameters as determinants of bone mineral content and density (Chumlea, Wisemandle, Guo, & Siervogel, 2002; MacInnis, et al., 2003). Knowledge related to dietary calcium is not a sufficient condition or factor that independently predicts bone mineral content for women in this study.
Limitations of the present study need to be addressed. A primary limitation of the study is that dietary intake, physical activity and bone mineral content and density were assessed only once. We do not have retrospective or longitudinal data for this sample of women that might help clarify the underlying mechanism accounting for lower bone mineral content values among women with higher knowledge scores. Another limitation is that there is not a validated instrument available for assessing calcium knowledge in women. Development of an instrument for the assessment of calcium knowledge will allow further insight into relationships between knowledge and behavior. Also limiting results of this study is that educational and socioeconomic status were not assessed. Women participating in the present study were faculty, staff, or students at a northeastern university. Results may not be generalizable to women with differing educational and socioeconomic backgrounds.
Overall, this study further reveals the complexity of understanding mechanisms and the magnitude of factors influencing bone health. Although increased knowledge, increased dietary calcium and physical activity are clearly advocated as means of improving and maintaining bone health, other factors not assessed by the present study are contributing to the maintenance and quality of bone health in females. A variety of factors not assessed in this study such as genetic, neuroendocrine, hormonal, pharmacological, psychological (e.g., attitudes), medical history, previous nutritional status, and previous history of and types of physical activity all impact bone health and may have played a role in contributing to differences in bone health among participants in this study.
Further studies are needed to better understand demographic and health-related factors that influence the consumption or avoidance of milk other than lactose maldigestion or lactose intolerance. Several studies have revealed that many women do not know how much calcium they need, do not feel susceptible to diseases related to low calcium intakes, and have the misconception that milk is fattening (Bogan & DeWare, 1992; Brewer, Blake, Rankin, & Douglass, 1999; U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, 1996). Factors influencing calcium consumption and bone health in pre-menopausal women are important to understand in order to reduce the likelihood of osteoporosis and osteoporosis-related fractures. Additional knowledge will allow for the development of effective strategies to overcome resistance and barriers to behavioral change.
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Author info: Correspondence should be sent to: Christina M. Beaudoin, Ph.D., Department of Sports Medicine, University of Southern Maine, 37 College Ave., Gorham, ME 04038-1032. email@example.com
Christina M. Beaudoin and Janet Whatley Blum
University of Southern Maine
TABLE 1 Descriptive Characteristics of Participants Characteristic M [+ or -] SD Age (yr) 35.24 [+ or -] 6.53 Height (cm) 163.66 [+ or -] 7.70 Weight (kg) 67.96 [+ or -] 12.77 BMI (kg x [m.sup.-2]) 25.48 [+ or -] 5.18 Fat mass (kg) 21.04 [+ or -] 9.30 Fat free mass (kg) 44.93 [+ or -] 5.75 Total BMC (g) 2317.04 [+ or -] 282.72 Total BMD (g x [cm.sup.-2]) 1.16 [+ or -] 0.06 Calcium Intake (mg/d) 1374.97 [+ or -] 556.86 Dairy Intake (servings/day) Calcium Knowledge 12.02 [+ or -] 2.43 KPAS Housework/caregiving (a) 2.69 [+ or -] 0.53 Sports/exercise (a) 3.93 [+ or -] 0.87 Occupation (a) 2.58 [+ or -] 0.60 Active living (a) 3.13 [+ or -] 0.83 (a) Range = 1-5 points TABLE 2 Correlations among Calcium Knowledge, Dietary Calcium Intake, TBMC and TBMD, Fat and Fat Free Mass, and KPAS Scores (significant ones only) Variable Calcium Knowledge & Total Body Mineral Content (TBMC) -.41 ** Calcium Knowledge & Total Body Mineral Density (TBMD -.29 * Calcium Knowledge & Fat Free Mass (FFM) -.31 * Calcium Knowledge & KPAS-O .28 * Dietary Calcium Intake & Age .34 * TBMC & TBMD .81 ** TBMC & FFM .73 ** TBMD & FFM .39 * FFM & Fat Mass (FM) .41 ** FM & KPAS-SE -.41 ** Age & KPAS-HC .27 * KPAS-HC & KPAS-AL .41 ** KPAS-SE & KPAS-AL .46 ** * p <.05 ** p <.01 Note: Correlation coefficients in the upper twenties are marginally significant and should be interpreted cautiously in light of the many coefficients calculated. TABLE 3 Calcium Knowledge, Dietary Calcium and Dairy Intake, TBMC, Physical Characteristics and KPAS Scores for Women Meeting and Not Meeting the Calcium DRI Did Not Meet Calcium DRI Variable M [+ or -] SD Age (yr) 31.30 [+ or -] 8.04 Height (cm) 166.68 [+ or -] 6.89 Weight (kg) 70.90 [+ or -] 15.61 BMI (kg x [m.sup.-2]) 25.65 [+ or -] 6.27 Fat mass (kg) 23.40 [+ or -] 11.27 Fat free mass (kg) 45.51 [+ or -] 7.53 Total BMC (g) 2319.58 [+ or -] 345.27 Total BMD (g x [cm.sup.-2]) 1.13 [+ or -] 0.07 Dietary Calcium Intake (mg/d) 683.88 [+ or -] 297.19 Calcium Knowledge 11.10 [+ or -] 2.92 KPAS Housework/caregiving (a) 2.45 [+ or -] 0.42 Sports/exercise (a) 3.83 [+ or -] 0.77 Occupation (a) 2.41 [+ or -] 0.45 Active living (a) 3.15 [+ or -] 0.86 Met Calcium DRI Variable M [+ or -] SD F Age (yr) 36.47 [+ or -] 5.58 5.27 * Height (cm) 163.72 [+ or -] 7.80 2.06 Weight (kg) 67.04 [+ or -] 11.88 0.69 BMI (kg x [m.sup.-2]) 25.43 [+ or -] 4.91 0.01 Fat mass (kg) 20.30 [+ or -] 8.68 0.84 Fat free mass (kg) 44.75 [+ or -] 5.21 0.13 Total BMC (g) 2316.25 [+ or -] 266.65 0.00 Total BMD (g x [cm.sup.-2]) 1.17 [+ or -] 0.06 4.10 * Dietary Calcium Intake (mg/d) 1590.94 [+ or -] 426.93 38.90 * Calcium Knowledge 12.31 [+ or -] 2.24 1.93 KPAS Housework/caregiving (a) 2.76 [+ or -] 0.55 2.62 Sports/exercise (a) 3.97 [+ or -] 0.90 0.21 Occupation (a) 2.63 [+ or -] 0.63 1.04 Active living (a) 3.13 [+ or -] 0.84 0.00 Note: All univariate results have (1,40) degrees of freedom. (a) Range = 1-5 points * p <.05
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|Author:||Beaudoin, Christina M.; Blum, Janet Whatley|
|Publication:||North American Journal of Psychology|
|Date:||Jun 1, 2005|
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