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Development and Validation of a Stages of Change Algorithm for Calcium Intake for College Female Students

Department of Health and Fitness, American University, Washington, DC (L. J. T., A. M. S.)
Oklahoma State University, Stillwater, Oklahoma (T. B. A.)

Address reprint requests to: Anastasia M. Snelling, Ph.D., R.D., Assistant Professor, Department of Health and Fitness, American University, Washington, DC 20016. E-mail: Ssnelli{at}american.edu

	ABSTRACT

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
Objectives: The purpose of this study was to develop and validate a staging algorithm for calcium intake.

Methods: Three hundred seventy-six college-aged females at a private university were randomly selected to participate. After 8.5% of the data were omitted due to incomplete surveys, the sample consisted of 344 female participants. Calcium intake was measured as self-reported consumption with a 26-item food frequency questionnaire. Stages of change classifications were based on a four-item algorithm for calcium intake, and self-efficacy was measured with three items.

Results: Significant differences were found between calcium intake levels between precontemplation, contemplation/preparation and action/maintenance. Results also showed that 40% of the participants were in action/maintenance and were consuming the Dietary Reference Intake level of 1,000 mg of daily calcium. Participants in the action and maintenance stages had significantly higher self-efficacy than the preaction group.

Conclusion: The study suggests that the stages of change algorithm may be used as an effective tool in assessing daily calcium intake among a college female population.

Key words: transtheoretical model, stages of change, calcium intake, self-efficacy, college females

	INTRODUCTION

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
Osteoporosis is the gradual deterioration of bone leading to an increased risk of fractures as well as decreased mobility. The third National Health and Nutrition Examination Survey (NHANES III) found that four to six million older women in the United States suffer from osteoporosis. Another 13 to 17 million women suffer from osteopenia, the beginning stage of osteoporosis [1,2]. Results of this disease include more than 1.5 million fractures annually and a health care bill totaling more than $13 billion [3].

Osteoporosis prevention begins when an individual’s bones are forming. Studies have shown that attainment of peak bone mass early in life delays the effects of the bone deterioration process [3,4]. Because the body usually achieves its peak bone mass between the ages of 25 and 30 years of age [3], an ideal time for prevention may be during the college years. Calcium intake is one mechanism to promote bone growth and prevent loss of bone. Although studies have shown that desirable calcium intake has a positive influence on bone health [5,6], calcium consumption is also linked to maintaining optimal blood pressure [7] and appears to alter biomarkers of risk for colon cancer from a high risk to a lower risk level [8].

Despite the fact that there is documented evidence of the positive effects of calcium on health and calcium is easily found in milk and milk products, calcium consumption remains low, particularly among females. According to the NHANES III, only 19% of girls between the ages of 9 and 19 years and 40% of females between 20 and 49 years are meeting the recommended levels of calcium [2]. Furthermore, the lowest intake levels of calcium coincide with the peak period of bone development, ages 12 through 29 years [2,9].

The Transtheoretical Model of Behavior Change (TTM) developed by Prochaska and DiClemente is appealing to health professionals trying to change behaviors [10]. The stages of change, part of the model, classifies individuals into stages of readiness to change successfully a behavior. The five stages of this model are precontemplation, contemplation, preparation, action and maintenance. Precontemplation is the stage in which there is no recognition of the need for change nor is there any interest in it within the foreseeable future. Contemplation is recognition of the need for change with an intent to change within the next six months. Preparation is the stage in which a person is planning to take action, typically within the next month. The action stage is defined as when a person is adopting a change and has been doing so for less than six months. Lastly, the maintenance stage is when the behavior change has been adopted for more than six months and the individual is striving to prevent a relapse [10–12]. An appealing aspect of the stages of change is that interventions can be tailored to an individual’s readiness to change. Studies have shown that relative to generic interventions, some tailored interventions are more effective [13] and have been associated with more rapid behavior change [14,15].

The TTM has been found to be applicable to a wide range of dietary behaviors, including fat reduction and increased fruit, vegetable and fiber intake. Curry et al. [16] first published the study stating that individuals can be classified into stages of change in relation to dietary fat reduction. Prochaska et al. [11] again validated the applicability of the construct in dietary interventions such as high-fat diets. Since then, the stages of change have also been applied to fruit and vegetable intakes [17–20], fat intake [21–24], fiber intake [22,23] and, most recently, calcium intake [25].

Some dietary changes require initiation, some require modification and some cessation. Further, depending on the nutrient, the modification may be to either increase or decrease the nutrient consumed. Thus, an effective algorithm for fruit, vegetable, fiber and calcium intake will show a linear increase in nutrient intake from the precontemplation through maintenance stages. Alternatively, an effective algorithm for fat intake will show a linear decrease. In all instances, participants in the action and maintenance stages should be consuming the recommended level of the nutrient.

The purpose of this study was to develop and validate a stage of change algorithm for calcium intake. Measures of daily calcium intake and self-efficacy are used to validate both the behavioral and pre-behavioral components of the model.

	METHODS

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
Study Design
The study used a survey with random sampling. The study population was composed of students from a private university in a metropolitan area in the mid-Atlantic region. A large database of all undergraduate classes was used; classes that did not contain a minimum of ten students were eliminated from the database. The classes were then sorted two ways, first according to class level (100, 200, 300 or 400) and second by levels of enrollment (10 to 19, 20 to 29, greater than 30).

The class stratification method was used because the level of the class typically reflects the level of a student’s education. For example, 100-level classes tend to be composed of freshman, 200-level classes by sophomores, and so on. Four hundred-level courses were removed from the database because there were only three sections, and they were seminar classes that met irregularly.

The classes were randomly selected in a stratified fashion so that 520 students came from 100-level courses, 388 came from 200-level courses and 211 students came from 300-level courses. After the classes were selected, the professor of each selected class was phoned to request permission to survey his or her students.

Participants
Permission was obtained to survey 376 female students. Participants (n = 32, 8.5%) were omitted if they provided incomplete data, were outside the 18 to 24 year age range or reported calcium intake levels greater than four standard deviations from the mean. Therefore, the sample for this study consisted of 344 college-aged females between the ages of 18 and 24 years.

In order to select a sample size that would ensure adequate power, Cohen’s work was consulted [26]. With an alpha of 0.05 and an estimated effect size of 0.3, it was determined that a sample size between 350 and 400 would provide power sufficient to avoid type II error. To obtain the desired sample size, enough classes were randomly selected so that the initial population would contain about 1100 students. It was expected that some professors would not permit their classes to participate, and, based on university data, it was known that one-third of the students would be male.

Measure
The instruments used in this study included a demographic survey, the Calcium Score Sheet from the Oregon Dairy Council, and a stages of change algorithm developed for this study.

Demographic Survey
A seven-question survey assessed the age, gender, ethnicity, family history of osteoporosis, smoking history and frequency of vigorous physical activity of each participant. Age, gender, number of cigarettes smoked per day and frequency of physical activity were assessed by open-ended questions. The ethnicity question was a forced choice answer. The questions that inquired about participants’ smoking and history of osteoporosis were ‘yes’ or ‘no’ answers.

Calcium Intake
The Calcium Score Sheet was developed by the Oregon Dairy Council to assess daily calcium intake. The Calcium Score Sheet is widely used to measure calcium intake [27]. It contains 26 different food entries, including dairy and non-dairy sources, which are grouped from 25 mg per serving to 400 mg per serving, according to the amount of calcium in each entry. The participant was asked to mark the number of servings eaten of the food during the prior 24 hours.

The Calcium Score Sheet was scored by first summing the number of servings of each food entry then multiplying this number by the amount of calcium per serving. Finally, total calcium intake was computed by summing the above product.

Stage of Change
Using a four-question algorithm (Fig. 1), participants were classified into one of the five stages of change for calcium intake. This algorithm is based on a four-question algorithm previously used by Campbell et al. [18] that evaluated the relationship between stages of change and rural African-American church members’ consumption of fruits and vegetables.

View larger version (23K): [in this window] [in a new window]   Fig. 1. Stages of change algorithm for calcium consumption.

Participants who reported consuming three or more servings a day were asked how long they had engaged in this behavior. Participants who reported consuming fewer than three servings a day were asked if they were planning on increasing their consumption. Lastly, participants who were interested in increasing their consumption were then further classified by assessing when they were planning on increasing their consumption.

Participants who had been consuming three or more servings of dairy products a day for six months or more were considered in the maintenance stage. Participants who had been consuming three or more servings for fewer than six months were classified in the action stage. Participants who were planning to increase their consumption within one month were classified in the preparation stage, and those participants who were planning to increase consumption within six months were classified in the contemplation stage. Participants who were not planning to consume more servings of dairy were classified in the precontemplation stage.

Self-Efficacy
A three-question measure was used to assess the participants’ attitudes and beliefs regarding calcium consumption. The instrument was based on prior research measuring stages of change and self-efficacy for fruit and vegetable intake [18]. These questions included their confidence in their own personal knowledge of calcium, their ability to increase their intake and their ability to increase their intake of dairy products. Response options on a five-point scale ranged from 1, not confident, to 5, very confident. The self-efficacy items were summed to form a composite variable. The reliability of the scale = 0.70, which was deemed acceptable.

Data Analyses
Means and frequencies were first performed on the demographic data. Second, the relationship between the demographic variables and the measure of calcium intake was checked to determine whether any of the demographic variables might be confounders. There was no association between calcium intake and any of the demographic variables.

Of the 344 participants, frequency tests were conducted to determine the number of participants in each of the five stages of change. Analyses using a five-stage model were planned; however, there were few subjects in the contemplation (3.8%) and action (2.6%) stages. Thus, the five stages were collapsed into three stages, as shown in Table 1. Previous studies such as Campbell et al. [18] and Auld et al. [23] collapsed action and maintenance into one group. Campbell et al. [18] collapsed the stages because of the small number of participants in the action stage. Auld et al. [23] collapsed the stages because of the difficulty in distinguishing between people who had recently reduced fat and those who had sustained fat-reduction behaviors for months or years. In this study, the action and maintenance stages were similarly collapsed. Combining the action and maintenance stages is justifiable since both include individuals who have changed their behavior. In addition, the contemplation and preparation stages were combined using the rationale that both stages are defined by a participant’s intention to change and only differ by the amount of time until the intended change will be made. Precontemplation was left by itself because individuals in this stage have no intention of changing.

To determine whether self-efficacy increased across stages, a one-way analysis of variance with Tukey’s post hoc test was used with self-efficacy as the dependent variable and stage of change as the independent variable. Although all five stages were included, the primary interest was whether there would be any significant differences among the three pre-behavioral stages in terms of efficacy. SPSS 6.1.1 for Macintosh computers was used for all statistical analyses.

	RESULTS

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
The study sample consisted of 344 females between the ages of 18 and 24 years; however, 76.5% of the subjects were found to be between the ages of 18 to 20 years of age. Seventy-four percent of the students were Caucasian. The majority of the subjects were non-smokers (80.8%) and engaged in vigorous activity one to three times per week (52.9%) (Table 2).

The one-way analysis of variance revealed a significant difference in calcium intake among the three groups (F = 53.1, p = 0.0001). Post hoc analysis revealed that mean calcium consumption among groups differed significantly (Group 1 = 793.8 mg, Group 2 = 1018 mg, Group 3 = 1505.9 mg, p = 0.0001). Interestingly, the mean calcium intake from dairy sources for each group also differed significantly (Group 1 = 645.6 mg, Group 2 = 862 mg, Group 3 = 1312.9 mg, p = 0.0001) (Fig. 2). Importantly, the results indicate that participants placed in the action/maintenance stage of change were consuming a mean calcium intake of 1505.9 mg, thereby meeting the DRI level of 1,000 milligrams of total calcium.

View larger version (12K): [in this window] [in a new window]   Fig. 2. Mean dairy calcium and mean total calcium as a function of stage of change (p < 0.05).

	DISCUSSION

 
This study provides support for applying the stages of change algorithm to calcium intake for a sample of college age females. Data analysis indicated that a significant difference existed between the total and dairy calcium intake among precontemplation, contemplation/preparation, and action/maintenance. Further, as an individual progressed along the stages of change a linear increase was found for both the dairy and total calcium intake. Lastly, participants placed in the action/maintenance stage of change consumed the DRI level of 1,000 milligrams of total calcium.

Gulliver and Horwath [25] introduced dairy consumption using a stages of change algorithm to a group of women in New Zealand. The New Zealand dietary recommendation for dairy intake is two servings. Although the dairy recommendation is different from the US guideline, the results are similar. Mean calcium intake was significantly higher in action and maintenance than in the preaction stages.

Calcium intake, like fruits and vegetables intake, is a dietary behavior that needs to be increased. When comparing this study to other studies on fruit and vegetable intake, similar results can be seen in Brug et al. [17] and Campbell et al. [18]. Both studies found that consumption of both fruit and vegetable was significantly higher in the action stages than the preaction stages. In fact, Campbell et al. [18] found that participants in action and maintenance consumed approximately 6.5 servings of fruit and vegetables each day, which met the daily requirements. Brug et al. [17] found that participants met the requirement for fruit in action and maintenance, but not for vegetables. Glanz et al. [22] also found significant increases for fruit and vegetables for participants in the later stages.

Though lowering fat intake may be different than increasing a nutrient, such as calcium, the results of this study can also be compared to previous research done on fat. As this study found, calcium increased as the stage progressed, Greene et al. [13], Glanz et al. [22], Steptoe et al. [21] and Greene and Rossi [15] found similar results with fat intake. These studies found that the amount of fat in a person’s diet decreased as the stage of change progressed. However, unlike this study which found that most participants in the action and maintenance stages were consuming the recommended amounts of calcium, the findings of both Greene et al. [13] and Lechner et al. [19] contradicted this finding. Though people in action stages were consuming less than preaction stages, participants were not consuming the recommended less than 30 percent of their calories from fat.

As these studies demonstrate, fat consumption and fiber consumption are complex behaviors to quantify. People are not always able to say with certainty how much fat or fiber they are consuming. Fruit, vegetables and, now, calcium may be easier to quantify for individuals estimating their daily intake. A serving of fruits and vegetables may be easily visualized. However, the fat or fiber content of food tends to be less visible, more difficult to quantify and found in many different foods. Calcium intake based on dairy intake is similar to fruit intake because the servings are easier to visualize. For example, a serving of dairy is equal to one cup of milk or one slice of cheese.

The differences in stage distribution may be due to two factors, the nutrient being studied and the population. Consumption of dairy calcium has been studied and reported only once before employing the stages of change. Secondly, the college female population has not been previously used for stages of change. Study populations have included women older than 18 years; however, it has been in the worksite setting. The results of this study concur with Glanz et al. [22], which has shown women and higher education tend to be associated with better nutritional habits.

This study also assessed self-efficacy with regard to dietary calcium intake. The level of self-efficacy was positively correlated with stage, indicating that self-efficacy may influence behavior. These results corroborate other studies that have shown higher levels of self-efficacy are associated with advanced stages of change [18]. This provides further support for educational programs to use multiple strategies when designing programs. Programs naturally tend to focus on knowledge. However, self efficacy appears to play a crucial role in implementing this knowledge.

The challenge that appears to lie ahead is how health care professionals can use these results to create change. From the results of this study, knowledge, self-efficacy and stage of readiness appear to be an integral part to meeting the recommended intake of calcium. The question to be studied in the future is what are the essential pieces of the puzzle that precedes a successful behavior change. Many factors influence behavior including knowledge, self-efficacy, culture, convenience, economics, media, upbringing and other factors. Research studying the participants in the preaction stages may provide insight into the reasons optimal calcium consumption is not occurring and suggest strategies for nutrition interventions.

One of the limitations is the reliance on self-reported nutrient intake rather than detailed food diaries. Another potential limitation includes using only females from a private university, who may not be representative of all college women. Lastly, the public health campaigns on milk consumption may suggest to consumers the social desirability of drinking milk, and this may have influenced the participants’ reporting of dairy intake.

	CONCLUSION

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
Over the past decade there have been many research contributions in assessing a person’s stage of change with regard to dietary behavior. Fat, fiber, fruit, vegetable and, now, calcium have all been studied. These studies have demonstrated a challenge of the algorithm to measure both the attainment of a behavioral criterion for the last two stages and behavioral intent for the first three stages. Measuring behavioral intent and nutrient intake makes validation difficult.

Once a valid assessment tool exists, the next challenge is tailoring nutrition interventions to move people to the later stages. The results of this study suggest that this algorithm is a valid and useful tool for staging college-aged females in terms of their calcium consumption. Assessing dairy calcium may make it easier for participants to quantify their servings. However, this may be a limitation if individuals consume a vegan diet or are lactose intolerant. A comprehensive list of calcium-rich sources beyond dairy foods is important for the inclusion of all segments of the population.

This tool is a stepping stone for the promotion of calcium-rich foods. Using the stages of change construct as a starting point for a well-planned intervention may improve behavior change. Interventions such as Kristal et al. [28], Greene and Rossi [15], Glanz et al. [22] and Campbell et al. [14] used algorithms to test the effectiveness of programming. This same premise may be now applied to calcium promotion.

	FOOTNOTES

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 
Dr. Adams is now at Arizona State University East, Mesa, Arizona.

Received January 17, 2002. Accepted May 24, 2002.

	REFERENCES

TOP FOOTNOTES ABSTRACT INTRODUCTION METHODS RESULTS CONCLUSION REFERENCES

 

1. Looker AC, Orwoll ES, Johnston CC, Lindsay RL, Wahner HW, Dunn WL, Calvo MS, Harris TB, Heyse SP: Prevalence of low femoral bone density in older U.S. adults from NHANES III.J Bone Miner Res12 :1761 –1768,1997 .[Medline]
2. US Department of Health and Human Services: "Healthy People 2010," conference ed, 2 vols. Washington, DC: US Department of Health and Human Services,2000 .
3. Leslie M, St Pierre RW: Osteoporosis: implications for risk reduction in the college setting.J Am Coll Health48 :67 –71,1999 .[Medline]
4. Ausenhus MK: Osteoporosis: Prevention during the adolescent and young adult years.Nurse Pract13 :42 –48,1988 .[Medline]
5. Heaney RP: Calcium, dairy products and osteoporosis.J Am Coll Nutr19 :83S –99S,2000 .[Abstract/Free Full Text]
6. Heaney RP: Nutritional factors in osteoporosis.Annu Rev Nutr13 :287 –316,1993 .[Medline]
7. Miller GD, DIRienzo DD, Reusser ME, McCarron DA: Benefits of dairy product consumption on blood pressure in humans: summary of the biomedical literature.J Am Coll Nutr19 :147S –164S,2000 .[Abstract/Free Full Text]
8. Holt PR: Dairy foods and prevention of colon cancer: human studies.J Am Coll Nutr18 :379S –391S,1999 .[Abstract/Free Full Text]
9. Fleming KH, Heimbach JT: Consumption of calcium in the U.S.: food sources and intake levels.J Nutr124 :1426S –1430S,1994 .
10. Prochaska JO, DiClemente CC, Norcross JC: In search of how people change: applications to addictive behaviors.Am Psychol47 :1102 –1114,1992 .[Medline]
11. Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, Fiore C, Harlow LL, Redding CA, Rosenbloom D, et al.: Stages of change and decisional balance for 12 problem behaviors.Health Psychol13 :39 –46,1994 .[Medline]
12. Kristal AR, Glanz K, Curry SJ, Patterson RE: How can stages of change be best used in dietary inventions?J Am Diet Assoc99 :679 –684,1999 .[Medline]
13. Greene GW, Rossi SR, Rossi JS, Velicer WF, Fava JL, Prochaska JO: Dietary applications of the stages of change model.J Am Diet Assoc99 :673 –678,1999 .[Medline]
14. Campbell MK, Devellis BM, Strecher VJ, Ammerman AS, DeVellis RF, Sandler RS: Improving dietary behavior: The effectiveness of tailored messages in primary care settings.Am J Public Health84 :783 –787,1994 .[Abstract/Free Full Text]
15. Greene GW, Rossi SR: Stages of change for reducing dietary intake over 18 months.J Am Diet Assoc98 :529 –536,1998 .[Medline]
16. Curry SJ, Kristal AR, Bowen DJ: An application of the stage model of behavior change to dietary fat reduction.Health Educ Res7 :97 –105,1992 .[Abstract/Free Full Text]
17. Brug J, Glanz K, Kok G: The relationship between self-efficacy, attitudes, intake compared to others, consumption and stages of change related to fruit and vegetables.Am J Health Promot12 :25 –30,1997 .[Medline]
18. Campbell MK, Symons M, Demark-Wahnefried W, Polhamus B, Bernhardt JM, McClelland JW, Washington C: Stages of change and psychosocial correlates of fruit and vegetable consumption among rural African-American church members.Am J Health Promot12 :185 –191,1998 .[Medline]
19. Lechner L, Brug J, De Vries H, van Assema P, Mudde A: Stages of change for fruit, vegetable and fat intake: consequences of misconception.Health Educ Res13 :1 –11,1998 .[Free Full Text]
20. Laforge R, Greene G, Prochaska J: Psychosocial factors influencing low fruit and vegetable consumption.J Behav Med17 :361 –374,1994 .[Medline]
21. Steptoe A, Wijetunge S, Doherty S, Wardle J: Stages of change for fat reduction: associations with food intake, decisional balance and motives for food choice.Health Educ J55 :108 –122,1996 .
22. Glanz K, Patterson RE, Kristal AR, DiClemente CC, Heimendinger J, Linnan L, McLerran DF: Stages of change in adopting healthy diets: fat, fiber and correlates of nutrient intake.Health Educ Quart21 :499 –519,1994 .[Medline]
23. Auld GW, Nitzke SA, McNulty J, Bock MA, Bruhn CM, Gabel K, Lauritzen G, Lee YF, Medeiros D, Newman R, Ortiz M, Read M, Schurz H, Sheehan E: A stage of change classification system based on actions and beliefs regarding dietary fat and fiber.Am J Health Promot12 :192 –201,1998 .[Medline]
24. Sporny LA, Contento IR: Stages of change in dietary fat reduction: social psychological correlates.J Nutr Educ27 :191 –198,1995 .
25. Gulliver P, Horwath C: Women’s readiness to follow milk product consumption recommendations: design and evaluation of a "stage of change" algorithm.J Hum Nutr Dietet14 :277 –286,2001 .[Medline]
26. Cohen J:"Statistical Power Analysis for the Behavioral Sciences." New York: Academic Press,1977 .
27. Smith BA, Morgan SL, Vaughn WH, Fox L, Canfield GJ, Bartolucci AA: Comparison of a computer-based food frequency questionnaire for calcium intake with two other assessment tools.J Am Diet Assoc99 :1579 –1581,1999 .[Medline]
28. Kristal AR, Glanz K, Tiley BC, Li S: Mediating factors in dietary change: understanding the impact of a worksite nutrition intervention.Health Educ Behav27 :112 –125,2000 .[Abstract/Free Full Text]

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