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Some problems with nutritional analysis software
Some Problems with Nutritional Analysis Software Anne Rogan1 and Stella Yu 2 INutrition Program, Russell Sage College, Troy, New York 12180; and 2Nutrition Coordinated Undergraduate Program, Hood College, Frederick, Maryland 21701 Use of nutrient data can be complex and problematic. Indeed, articles and advertisements related to nutrient analysis software programs in popular computer journals may evoke the image of a number-eating dragon. This dragon feeds on information regarding an individual's size, shape, age, and exercise and eating habits; consumes this data in a split second; and expels hundreds of personalized tidbits of information related in some way to the dietary and exercise practices of the individual. With such technological power comes the potential for serious problems, including misuse of information by pseudo-nutritionists, inappropriate use of information by legitimate professionals, and misinterpretation of information by lay audiences. As nutrition educators, we must train ourselves and other health-related professionals to recognize these problems and to prevent them. Potential misuse by pseudo-nutritionists. The nutrition software dragon provides any owner of a computer with a tool that can generate nutrition information in a variety of forms. Our fear is that we are entering a stage of "nutrition quackery" in which some irresponsible entrepreneurs will exploit the public through the use of computers. It is conceivable that health food stores, health spas and clubs, and nontraditionally educated "nutrition consultants" could use these programs as a way of documenting the need for their services and nutrition products. Inappropriate use of nutrient data by legitimate professionals. As pointed out by Hoover and Pelican (1), many factors can contribute to wide variability of nutrient data for similar food items. Yet some legitimate professionals using nutrient analysis programs as teaching tools convey a false sense of precision to audiences by failing to indicate how substantial this variation can be. We offer a few examples. In the United States Department of Agriculture's Home and Garden Bulletin No. 72 (2), spaghetti with meatballs and tomato sauce has .25 mg thaimin for 248 grams of a home recipe. The thiamin content of any individual's spaghetti with meatball recipe VOLUME 16
NUMBER2
1984
may fall short of or exceed that value by 50-200070, depending upon the formulation of the recipe and the cooking time. Consider the home recipe that uses both ground beef and ground pork in the recipe for the meatballs compared to a recipe using only ground beef. The part pork recipe may contain closer to .8 mg thiamin (2). The thiamin content of two pork roasts of equal weight that are cooked at different temperatures, one at 300°F and the other at 450°F, will retain 64070 and 54070 of the original thiamin, respectively (3,4). Similarly, two steaks cut from the same round of beef but prepared differently, one using a moist cookery method such as braising and the other a dry cookery method as in broiling, will retain 40070 and 89070 of the original thiamin, respectively (3,4). Thus, the thiamin content of meats listed in any food composition handbook gives the user, at best, an approximation of the actual thiamin p(esent in any particular cut of meat prepared in the home or institutional kitchen. Similar variability can be found for most nutrients in foods (5-8). In light of this, the common practice of reporting computerized nutrient analyses results to the hundredth or thousandth of a milligram is highly questionable. It is certainly misleading for these programs to imply precision inconsistent with stateof-the-art chemical analyses of nutrients in foods. Output interpretations. Printouts of the nutritional analyses are indeed impressive in appearance. Many nutrient analysis software programs list the protein, carbohydrate, fat, vitamin and mineral content - as reported by USDA publications (2,9,10), depending upon the data base - for individual foods consumed during a day; most programs also provide nutrient totals for the entire intake. Printouts often provide the Recommended Dietary Allowances (RDA) based on the individual's sex and age, and usually compare the estimated nutrient intakes with the RDA. In most of the programs, these results appear without any interpretation. Interpretation of results becomes the burden of the software package users, who mayor may not be informed about appropriate use of
the RDA. Users may interpret an attainment of 80-100070 of the RDA for all nutrients as satisfactory or, what is more likely, they may view any intake less than 100070 as an indication of the need for various and sundry protein, vitamin and/or mineral supplements. We would not expect most consumers to know that the RDA were not established to provide nutrient requirements for a specific individual, or that for most nutrients, the RDA may exceed an individual's actual nutrient requirement. Indeed, it is extremely difficult, even for the professional, to make definitive statements regarding the nutritional adequacy of any individual's diet based on a nutrient analysis of any meal or set of meals. Some of the nutrient analysis software programs identify foods high in a given nutrient. For example, the users can request a list of foods high in iron. Although such a list may be generated quickly, its application again is left to the user's wisdom. Specifically, the user is not likely to understand the concept of bioavailability of nutrients. In the case of iron, bioavailability factors associated with iron utilization are not considered in the computer listing of high iron foods. The 5.8 mg iron in one cup of raisins and the 2.9 mg iron in hamburgers are in different chemical forms. If these differences are not noted, interpretation of foods high in a nutrient as reported by any one on the computer software packages can lead to a myriad of misinterpretations. Nutrition educators have an important task ahead: controlling the dragon. We must increase our knowledge of nutrient data and our understanding of the problems which accompany the educational use of information generated by nutrient analysis software. We must also educate ourselves as well as other health-related professionals to the potential benefits and pitfalls of nutrient analysis software. However, our ultimate goal should be to foster changes in the development of nutrient analysis software so that these programs function as useful tools with sound educational purposes rather than as unruly beasts. 0 LITERATURE CITED
1 Hoover, L. W., and S. Pelican. Nutrient data bases - considerations for educators. Journal
JOURNAL OF NUTRITION EDUCATION
65
of Nutrition Education 16:58-62, 1984. 2 U.S. Department of Agriculture. Nutritive value offoods. Home and garden bulletin no. 72. Washington, D.C.: Government Printing Office, 1981, p. 22. 3 Cover, S. E., E. M. Dilsaver, R. M. Hays, and W. H. Smith. Retention of B-vitamins after large scale cooking of meat. II. Roasting by two methods. Journal of the American Dietetic Association 25:949-51, 1949. 4 Tucker, R. E., W. F. Hinman, and E. G. Hollirlay. The retention of thiamin and riboflavin in beef cuts during braising, frying and
TWO
COMPUTER
LEARNING
I incorporated a series of microcomputer programs into a one-semester human nutrition course designed for nursing students and other health professionals who have studied biochemistry and physiology but have had no formal training in nutrition. I used these programs, which are related to energy and nutrient intakes, energy output, and weight status, to provide students with a supplemental, individualized, learning experience. Hardware consisted of an Apple II Plus microcomputer (DOS 3.3, 48K), two disk drives, an Epson MX-80 printer with graphics capability, and a BMC monitor. Students were expected to determine their weight status, and usual energy and nutrient intakes; and to estimate their energy output and total calorie needs. Based on the computer data received, on classroom discussions, and on material from the text, they prepared a report commenting on their own food intake pattern based on the following guidelines: a) discussion of present energy intakes and of energy needs with respect to weight status; b) evaluation of nutrient intakes (protein, calcium, phosphorus, iron, vitamin A, B-vitamins, vitamin C, cholesterol, potassium, and sodium); c) discussion of deficiencies or excesses in intakes with respect to foods presently eaten and foods that should be added to or deleted from the diet, taking into account personal preferences; d) qualifications in the use of the RDA for individual comparisons. I devised two approaches for achieving the assignment based on price of diet analysis software, as well as class size (40 to 75 students), and available student labor. Both methods involved a "hands-on" experience with the microcomputer. In the first approach, I used Diet Analysis (Javed Aslam and Tess Enterprises Inc. in conjunction with Apple Computer Inc., Cupertino, CA 94014, 1980), the least expensive software ($45). Students, with the help of a laboratory assistant, determined their usual food intakes from a data base of 66
broiling. Journal of the American Dietetic Association 22:877-81, 1946. 5 Charley, H. Food science. 2nd ed. New York: John Wiley, 1982, pp. 498-520. 6 Eheart, M. S., and C. Gott. Chlorophyll, ascorbic acid and pH changes in green vegetables by stir fry, microwave and conventional methods. Food Technology 19:867-70,1965. 7 Hertzler, A. A., and L. W. Hoover. Development of food tables and use with computers: Review of nutrient data bases. Journal of the American Dietetic Association 70:20-23, 1977.
JOURNAL OF NUTRITION EDUCATION
EXPERIENCES
FOR
8 Hoover, L. W. Computerized nutrient data bases: 1. Comparison of analysis systems. Journal of the American Dietetic Association 82:501-5, 1983. 9 U.S. Department of Agriculture. Composition of foods-Raw, processed, prepared. Agriculture handbook no. 8, by B. K. Watt and A. L. Merrill. Washington, D.C.: Government Printing Office, 1963, 190 pp. 10 U.S. Department of Agriculture. Nutritive value of American foods in common units. Agriculture handbook no. 456, Washington, D.C. 1975, 291 pp.
NON-NUTRITION
729 foods, accessed through a word-recognizing index displayed on the monitor. One week prior to their appointments for computer time, I provided students with printed lists of the foods in the data base to aid them in the proper identification and selection of food names from the word-recognizing index. Students determined their weight status (± ideal range) from an optional subprogram of Diet Analysis. They used a separate software program, Energy Output (Applied Nutrition Technologies, 86 Dana Street, Amherst, MA 01002), as a computer-administered interview to estimate individual energy output and required energy intake. No printout capability was available for either Diet Analysis or Energy Output, so students had to copy output values onto data sheets prepared for them. (Energy Output is presently being rewritten to incorporate more options, including a printout). I did not use the Diet Analysis menu option to compare nutrient levels to the RDA because with a large class, use of this option would have required too much computer time. Even though a laboratory assistant was available at all times to keep computer usage time to a minimum, total computer time per student ranged from 30 to 60 minutes. The second approach allowed more extensive data output with greater ease of data input and reduced computer time per student; however, this approach required a more expensive computer program ($150), Nutritionist I (N-Squared Computing, 5318 Forest Ridge Road, Silverton, OR 97381, 1982). Because the data base was Nutritive Value ojFoods, Home and Garden Bulletin No. 72 (1981), which was included in the course textbook, students were able to generate food files without using the computer. They submitted food files of usual food intake to the instructor and laboratory assistant, who entered the data into the computer and provided students with a hard copy of the output. This hard copy included the following: a list of the foods sub-
MAJORS
mitted by name with portion sizes; amounts of twenty nutritive components in the diet with accompanying percentages of the appropriate RDA; a graphic display of ten key nutritive components, with respect to the RDA; percentages of energy sources by weight and by calories; and sorting of problem nutrients by nutrient density from the submitted food list. The students still determined their weight status and energy status themselves by using the software packages described in the first approach. This second approach reduced by half the time required of a laboratory assistant and allowed data to be generated in one week compared to six weeks by the previous method. These learning experiences have stimulated interest in nutrition among nonnutrition majors. Initially, the computer, itself, elicited interest. However, in their reports, students repeatedly mentioned that relating nutrition concepts presented in class to personal experiences (food intake, weight status, energy needs) created a much greater impact than lecture alone could achieve. To write their reports, students had to use their knowledge of nutrition to explain differences between preconceived notions about their diets and their actual diet analysis. This understanding is especially important to the nurses who soon will be involved with patients who have various nutrition histories. The nutrition report also allowed the instructor to monitor how well concepts presented in class were understood by students, and where changes needed to be made. The approaches discussed here have been particularly successful given my constraints in time and money; other instructors are encouraged to examine all currently available programs to determine which of these can best meet their needs. Richard A. Cook, School oj Human Development, Division oj Human Nutrition, University oj Maine, Orono, Maine 04469 VOLUME 16
NUMBER 2
1984
NUMBER2
1984
may fall short of or exceed that value by 50-200070, depending upon the formulation of the recipe and the cooking time. Consider the home recipe that uses both ground beef and ground pork in the recipe for the meatballs compared to a recipe using only ground beef. The part pork recipe may contain closer to .8 mg thiamin (2). The thiamin content of two pork roasts of equal weight that are cooked at different temperatures, one at 300°F and the other at 450°F, will retain 64070 and 54070 of the original thiamin, respectively (3,4). Similarly, two steaks cut from the same round of beef but prepared differently, one using a moist cookery method such as braising and the other a dry cookery method as in broiling, will retain 40070 and 89070 of the original thiamin, respectively (3,4). Thus, the thiamin content of meats listed in any food composition handbook gives the user, at best, an approximation of the actual thiamin p(esent in any particular cut of meat prepared in the home or institutional kitchen. Similar variability can be found for most nutrients in foods (5-8). In light of this, the common practice of reporting computerized nutrient analyses results to the hundredth or thousandth of a milligram is highly questionable. It is certainly misleading for these programs to imply precision inconsistent with stateof-the-art chemical analyses of nutrients in foods. Output interpretations. Printouts of the nutritional analyses are indeed impressive in appearance. Many nutrient analysis software programs list the protein, carbohydrate, fat, vitamin and mineral content - as reported by USDA publications (2,9,10), depending upon the data base - for individual foods consumed during a day; most programs also provide nutrient totals for the entire intake. Printouts often provide the Recommended Dietary Allowances (RDA) based on the individual's sex and age, and usually compare the estimated nutrient intakes with the RDA. In most of the programs, these results appear without any interpretation. Interpretation of results becomes the burden of the software package users, who mayor may not be informed about appropriate use of
the RDA. Users may interpret an attainment of 80-100070 of the RDA for all nutrients as satisfactory or, what is more likely, they may view any intake less than 100070 as an indication of the need for various and sundry protein, vitamin and/or mineral supplements. We would not expect most consumers to know that the RDA were not established to provide nutrient requirements for a specific individual, or that for most nutrients, the RDA may exceed an individual's actual nutrient requirement. Indeed, it is extremely difficult, even for the professional, to make definitive statements regarding the nutritional adequacy of any individual's diet based on a nutrient analysis of any meal or set of meals. Some of the nutrient analysis software programs identify foods high in a given nutrient. For example, the users can request a list of foods high in iron. Although such a list may be generated quickly, its application again is left to the user's wisdom. Specifically, the user is not likely to understand the concept of bioavailability of nutrients. In the case of iron, bioavailability factors associated with iron utilization are not considered in the computer listing of high iron foods. The 5.8 mg iron in one cup of raisins and the 2.9 mg iron in hamburgers are in different chemical forms. If these differences are not noted, interpretation of foods high in a nutrient as reported by any one on the computer software packages can lead to a myriad of misinterpretations. Nutrition educators have an important task ahead: controlling the dragon. We must increase our knowledge of nutrient data and our understanding of the problems which accompany the educational use of information generated by nutrient analysis software. We must also educate ourselves as well as other health-related professionals to the potential benefits and pitfalls of nutrient analysis software. However, our ultimate goal should be to foster changes in the development of nutrient analysis software so that these programs function as useful tools with sound educational purposes rather than as unruly beasts. 0 LITERATURE CITED
1 Hoover, L. W., and S. Pelican. Nutrient data bases - considerations for educators. Journal
JOURNAL OF NUTRITION EDUCATION
65
of Nutrition Education 16:58-62, 1984. 2 U.S. Department of Agriculture. Nutritive value offoods. Home and garden bulletin no. 72. Washington, D.C.: Government Printing Office, 1981, p. 22. 3 Cover, S. E., E. M. Dilsaver, R. M. Hays, and W. H. Smith. Retention of B-vitamins after large scale cooking of meat. II. Roasting by two methods. Journal of the American Dietetic Association 25:949-51, 1949. 4 Tucker, R. E., W. F. Hinman, and E. G. Hollirlay. The retention of thiamin and riboflavin in beef cuts during braising, frying and
TWO
COMPUTER
LEARNING
I incorporated a series of microcomputer programs into a one-semester human nutrition course designed for nursing students and other health professionals who have studied biochemistry and physiology but have had no formal training in nutrition. I used these programs, which are related to energy and nutrient intakes, energy output, and weight status, to provide students with a supplemental, individualized, learning experience. Hardware consisted of an Apple II Plus microcomputer (DOS 3.3, 48K), two disk drives, an Epson MX-80 printer with graphics capability, and a BMC monitor. Students were expected to determine their weight status, and usual energy and nutrient intakes; and to estimate their energy output and total calorie needs. Based on the computer data received, on classroom discussions, and on material from the text, they prepared a report commenting on their own food intake pattern based on the following guidelines: a) discussion of present energy intakes and of energy needs with respect to weight status; b) evaluation of nutrient intakes (protein, calcium, phosphorus, iron, vitamin A, B-vitamins, vitamin C, cholesterol, potassium, and sodium); c) discussion of deficiencies or excesses in intakes with respect to foods presently eaten and foods that should be added to or deleted from the diet, taking into account personal preferences; d) qualifications in the use of the RDA for individual comparisons. I devised two approaches for achieving the assignment based on price of diet analysis software, as well as class size (40 to 75 students), and available student labor. Both methods involved a "hands-on" experience with the microcomputer. In the first approach, I used Diet Analysis (Javed Aslam and Tess Enterprises Inc. in conjunction with Apple Computer Inc., Cupertino, CA 94014, 1980), the least expensive software ($45). Students, with the help of a laboratory assistant, determined their usual food intakes from a data base of 66
broiling. Journal of the American Dietetic Association 22:877-81, 1946. 5 Charley, H. Food science. 2nd ed. New York: John Wiley, 1982, pp. 498-520. 6 Eheart, M. S., and C. Gott. Chlorophyll, ascorbic acid and pH changes in green vegetables by stir fry, microwave and conventional methods. Food Technology 19:867-70,1965. 7 Hertzler, A. A., and L. W. Hoover. Development of food tables and use with computers: Review of nutrient data bases. Journal of the American Dietetic Association 70:20-23, 1977.
JOURNAL OF NUTRITION EDUCATION
EXPERIENCES
FOR
8 Hoover, L. W. Computerized nutrient data bases: 1. Comparison of analysis systems. Journal of the American Dietetic Association 82:501-5, 1983. 9 U.S. Department of Agriculture. Composition of foods-Raw, processed, prepared. Agriculture handbook no. 8, by B. K. Watt and A. L. Merrill. Washington, D.C.: Government Printing Office, 1963, 190 pp. 10 U.S. Department of Agriculture. Nutritive value of American foods in common units. Agriculture handbook no. 456, Washington, D.C. 1975, 291 pp.
NON-NUTRITION
729 foods, accessed through a word-recognizing index displayed on the monitor. One week prior to their appointments for computer time, I provided students with printed lists of the foods in the data base to aid them in the proper identification and selection of food names from the word-recognizing index. Students determined their weight status (± ideal range) from an optional subprogram of Diet Analysis. They used a separate software program, Energy Output (Applied Nutrition Technologies, 86 Dana Street, Amherst, MA 01002), as a computer-administered interview to estimate individual energy output and required energy intake. No printout capability was available for either Diet Analysis or Energy Output, so students had to copy output values onto data sheets prepared for them. (Energy Output is presently being rewritten to incorporate more options, including a printout). I did not use the Diet Analysis menu option to compare nutrient levels to the RDA because with a large class, use of this option would have required too much computer time. Even though a laboratory assistant was available at all times to keep computer usage time to a minimum, total computer time per student ranged from 30 to 60 minutes. The second approach allowed more extensive data output with greater ease of data input and reduced computer time per student; however, this approach required a more expensive computer program ($150), Nutritionist I (N-Squared Computing, 5318 Forest Ridge Road, Silverton, OR 97381, 1982). Because the data base was Nutritive Value ojFoods, Home and Garden Bulletin No. 72 (1981), which was included in the course textbook, students were able to generate food files without using the computer. They submitted food files of usual food intake to the instructor and laboratory assistant, who entered the data into the computer and provided students with a hard copy of the output. This hard copy included the following: a list of the foods sub-
MAJORS
mitted by name with portion sizes; amounts of twenty nutritive components in the diet with accompanying percentages of the appropriate RDA; a graphic display of ten key nutritive components, with respect to the RDA; percentages of energy sources by weight and by calories; and sorting of problem nutrients by nutrient density from the submitted food list. The students still determined their weight status and energy status themselves by using the software packages described in the first approach. This second approach reduced by half the time required of a laboratory assistant and allowed data to be generated in one week compared to six weeks by the previous method. These learning experiences have stimulated interest in nutrition among nonnutrition majors. Initially, the computer, itself, elicited interest. However, in their reports, students repeatedly mentioned that relating nutrition concepts presented in class to personal experiences (food intake, weight status, energy needs) created a much greater impact than lecture alone could achieve. To write their reports, students had to use their knowledge of nutrition to explain differences between preconceived notions about their diets and their actual diet analysis. This understanding is especially important to the nurses who soon will be involved with patients who have various nutrition histories. The nutrition report also allowed the instructor to monitor how well concepts presented in class were understood by students, and where changes needed to be made. The approaches discussed here have been particularly successful given my constraints in time and money; other instructors are encouraged to examine all currently available programs to determine which of these can best meet their needs. Richard A. Cook, School oj Human Development, Division oj Human Nutrition, University oj Maine, Orono, Maine 04469 VOLUME 16
NUMBER 2
1984