Printer Friendly

Use of electronic self-monitoring for food and fluid intake: a pilot study.


To describe a study of hemodialysis patients self-monitoring nutrient intake.


1. Enumerate the positive outcomes of self-monitoring as a behavioral intervention strategy.

2. Summarize the findings from a study of individuals self-monitoring their intake of various nutrients.

3. Generate ideas for ways to track nutrient intake by hemodialysis patients.

Kidney failure is a growing problem in the United States, with the total number over 450,000. Of these, over 298,000 receive hemodialysis (U.S. Renal Data System, 2005). Although dialysis is a life-saving procedure for individuals with kidney failure, quality of life is greatly reduced (Dwyer et at., 2002; Sagawa, Oka, Chaboyer, Satoh, & Yamaguchi, 2001; Simmons & Abress, 1988; Wolcott & Nissenson, 1988). Food and fluid limitations and the management of multiple diets are frequently reported stressors (Welch & Austin, 1999). Individuals treated with hemodialysis often find that the strict dietary and fluid limitations interfere with their ability to participate in desired social interactions (Sagawa et. at., 2001).

Hemodialysis is a complex therapy that regulates electrolytes and removes waste products. To avoid excessive accumulation of electrolytes and waste products, patients are prescribed a therapeutic diet that frequently limits fluid, phosphorus, sodium, and potassium. Understandably, nonadherence to these strict fluid and dietary limitations is common in patients on dialysis. Estimates of fluid nonadherence range from 41% to 93% (Betts & Cotty, 1988; Cummings, Becker, Kirscht, & Levin, 1982; Welch, 2001; Welch, Perkins, Evans, & Bajpai, 2003), and dietary nonadherence estimates from 20%-78% (Durose, Holdsworth, Watson, & Przygrodzka, 2004; Kaplan De Nour & Czaczkes, 1981; Kugler, Vlaminck, Haverich, & Maes, 2005; Lee & Molassiotis, 2002; Schmicker & Baumbach, 1990).

Although protein-energy malnutrition is one of the strongest predictors of morbidity and mortality in adults receiving hemodialysis (National Kidney Foundation [NKF], 2000), patients are often protein malnourished. Estimates of the prevalence of protein-energy malnutrition in the dialysis population range from 18%-70% (NKF, 2000). In one cross-sectional study of 1,000 patients receiving hemodialysis, approximately 90% of men and 50% of women had insufficient intake of energy and protein compared to the recommended daily protein and energy requirements of the K/DOQI guidelines of 1.2 mg/kg of protein and 35 kcal/kg of calories daily (Rocco et al., 2002).

Nonadherence to the dietary and fluid limitations compromises the outcomes of adults receiving hemodialysis. Nonadherence can lead to detrimental long-term outcomes including deterioration of the cardiovascular system, heart failure, hypertension, and pulmonary edema as well as short-term problems of edema, itching, bone pain, and breathlessness (Brady, Tucker, Alfino, Tarrant, & Finlayson, 1997; Durose et. al., 2004; Lee & Molassiotis, 2002; Welch, 2001).

Behavioral intervention strategies, such as self monitoring, may be associated with improved dietary and fluid adherence among individuals receiving hemodialysis (Welch & Thomas-Hawkins, 2005). Self-monitoring is defined as recognizing the occurrence of a behavior, such as eating and drinking, and recording it (Korotitsch & Nelson-Gray, 1999). Benefits of self-monitoring include an emphasis on individual control over behavior, continuous and immediate feedback to the client, and a more complete and thorough account of behavior (Bornstein, Hamilton, & Bornstein, 1986). Self-monitoring has been successfully used in a variety of chronically ill populations, including those who are obese or have diabetes mellitus (Quinn, Goka & Richardson, 2003; Schwedes, Seiebolds, & Mertes, 2002; Sperduto, Thompson, & O'Brien, 1986; Stone, Shiffman, Schwartz, Brodrick, & Hufford, 2002; Wilson & Vitousek, 1999). For example, Sperduto, Thompson, and O'Brien (1986) found that 15 weeks of self-monitoring resulted in 64% more weight loss compared to a control group. Schwedes, Siebolds, and Mertes (2002) found that individuals who self-monitored blood glucose had significant improvement in their hemoglobin A1C levels when compared to controls.

In this pilot study, the use of self-monitoring to change diet and fluid intake is based on Bandura's social cognitive theory (1986). Bandura emphasizes that human beings are individual agents proactively involved in their own development and can make changes through their own actions (Parjares, 2002). A major construct in Bandura's theory is self-efficacy. Self-efficacy beliefs can be defined as "people's judgments of their capabilities to organize and execute courses of action required to attain designated types of performances" (Bandura, 1986, p. 391). He proposes that self-efficacy beliefs are developed though four sources of influence: enactive attainment, verbal persuasion, vicarious experiences, and physiological state (Bandura, 1986). Enactive attainment or mastery experience is the most influential source of efficacy information (Bandura, 1986; Coffman, Shellman, & Bernal, 2004) and self-monitoring in this study is conceptualized as a mastery experience. By self-monitoring one's intake, one is able to continually assess intake and work resourcefully toward a personal goal. Self-monitoring allows a patient to continually receive feedback on an ongoing basis and possibly to identify changes needed. This can create an environment in which the patient feels he or she has more control, which may lead to better outcomes.

Paper and pencil self monitoring has been investigated in four studies of adults receiving hemodialysis (N=10-103) for its effects on fluid adherence among adults receiving hemodialysis (Christensen, Weibe, Moran, Ehlers, & Lawton, 2002; Sagawa et al., 2001; Tsay, 2003; Tucker, 1989). Three of these studies found the group using self-monitoring had significant decreases in interdialytic weight gain at the completion of self-monitoring (Sagawa et al., 2001; Tsay, 2003; Tucker, 1989), and Christensen and colleagues (2002) reported a decrease in interdialytic weight gain 8 weeks following self-monitoring. Unfortunately, all four of these studies bundled self-monitoring with other interventions, making it difficult to determine to the extent to which if self-monitoring independently contributed to the effects (Welch & Thomas-Hawkins, 2005). The effects of self-monitoring on adherence to dietary recommendations are not known.

Electronic self-monitoring of dietary and fluid intake may be superior, however, to paper and pencil self-monitoring. The benefits of electronic monitoring include increased compliance, the fact that recording compliance cannot be counterfeited because of automatic time entry functions, alarm triggering capability for an entry, and the ability to plot data to visually demonstrate to the patient emerging trends (Kerkenbush & Lansome, 2003). For example, one study (Stone et al., 2002) compared the compliance rates of paper diaries versus electronic diaries among patients with chronic pain. Although participants reported compliance to completion of paper diaries at 90%, when actual compliance was assessed, it was only 11%. Conversely, actual compliance to recording in electronic diaries was 94%. Another study conducted by a company that explores electronic diary technology found that, after completing 24 trials, patients responded in a timely manner to 93% of all data collection prompts (Shiffman & Hufford, 2001). A third study found that electronic diaries had 31% less missing data than paper diaries and required 81% less time for entering and cleaning data (Johannes et al., 2000).

Interventions to promote adherence to the therapeutic diet are needed to improve patient outcomes. The purpose of this pilot study was to describe intake patterns of fluid, sodium, potassium, phosphorus, protein, and calories over a three-month period as electronically recorded using a PDA.


Sample and Setting

A convenience sample of four adults receiving hemodialysis therapy from a metropolitan Midwestern University Hospital was recruited to participate in the pilot study. Inclusion criteria for the study included: (a) 18 years of age or older; (b) alert and oriented to time, place, and self; (c) able to read and speak the English language; (d) currently receiving hemodialysis treatment; and (e) a willingness to use technology. Participants were excluded from the study if they were (a) living in an assisted-living or extended care facility, (b) receiving hemodialysis as a temporary treatment following a peritoneal dialysis complication or an episode of transplant rejection, or (c) receiving hemodialysis at home. One consenting adult withdrew from the study because he felt inputting data was too demanding on his time.

The mean age of the remaining 3 participants was 54 (range 46-70; SD = 13.57). One participant was an African American female and 2 were African American males. The causes of chronic kidney failure were hypertension (n=1), diabetes (n=1), and unknown (n=1). Two of the participants had previously had a kidney transplant, but had to reinitiate dialysis as a result of graft failure.


This small pilot study examined the use of electronic self-monitoring of dietary and fluid intake using a descriptive longitudinal design. Electronic self-monitoring was completed by the use of a personal digital assistant (PDA).


Patients were approached by the nurse practitioner in the dialysis unit and asked if they would be interested in participating in this study. They were given the opportunity to say no without any further explanation. If a patient indicated he or she would like more information, the investigator then approached the patient and explained the purpose of the study, obtained informed consent, and determined eligibility. Those patients found to be ineligible to participate were thanked for their time.

Each consenting adult was taught how to use the PDA, including navigation, input of fluid and dietary intake, and interpretation of information that the PDA provided. The participants were instructed to input all fluid and dietary intake, including snacks, and to be specific about portion sizes. The participants were instructed to input the information immediately following consumption. Each participant received a written instruction sheet that included examples.

Participants were asked to monitor intake for 3 months. During these

3 months, the investigator met with each participant weekly to discuss the fluid and dietary intake for the previous week. The participant and investigator discussed areas where the participant was not meeting recommendations. The participant was praised when he or she met the prescribed individual values. During the weekly meeting the investigator downloaded information from the PDA to a database that maintained all of the participant's fluid and dietary information. The daily values for each individual component (fluid, sodium, potassium, phosphorus, protein, calories) were calculated as a weekly total and divided to figure a daily average. At the conclusion of the study, two participants had a total of 12 weekly average values for calcium, potassium, sodium, phosphorus, protein, and calories. The third participant had seven weekly intake values due to hospitalizations during the data collection period.


Each participant was given a Palm Pilot Zire 31 PDA with an application designed by Diet Mate Pro[R] ( to monitor their dietary intake. The Diet Mate Pro[R] application contained nutrient information on 6600 food items and 120 food components. The participants entered the food consumed and the Diet Mate Pro[R] application provided the nutrition information for that type of food. For this study, the amount of fluid, sodium, potassium, phosphorus, protein, and calories was monitored and recorded on the PDA. Totals for fluid, sodium, potassium, phosphorus, protein, and calories were stored daily and intake was downloaded weekly.

The information entered into the PDA by the participants was downloaded to a password-protected web-based database provided by Diet Mate Pro[R] which converted the data into an Excel file. Weekly intake of sodium, potassium, phosphorus, protein, and calories for each participant was computed in Excel. Mean daily intake was then computed.


Mean weekly dietary intakes of calories, protein, sodium, potassium, and phosphorus for each participant were plotted on line graphs to examine trends (see Figures 1-6). These figures are based on 12 weeks of data for participant one (P1) and participant two (P2) and 8 weeks of data on participant three (P3). P3 was unable to record during 3 of the 12 weeks due to a hospitalization for 2 weeks and malfunction of the PDA for one of the weeks. The daily intake ranges and means are shown in Table 1.



Although many studies have been conducted to examine correlates of dietary and fluid adherence in the patients receiving hemodialysis, little research has tested the effects of self monitoring on fluid and dietary adherence. The findings from this small pilot study are important because they provide us with preliminary data concerning patterns of intake obtained by electronic self monitoring. Overall, the patients in this study were fairly adherent with their dietary intake of sodium, potassium, and phosphorus. Most likely these patients had been adherent pre-self monitoring as well, although we cannot be sure of this. These patients, however, had severely reduced intake of protein and calories.

For the most part, the participants' sodium and potassium intake fell within the normal recommendations. This small sample may be different than most patients in their sodium intake patterns. Although there is little information in the literature about adherence to sodium limitations by patients on dialysis, estimates are that 75% of healthy men and women between the ages 31-50 consume over 5.8 grams of sodium daily (Institute of Medicine, 2004). The sample in this pilot study may be like other patients on dialysis when the results are compared to previous research. The literature suggested that between 61 and 95% of patients on dialysis were adherent to potassium limitations (Bame Petersen, & Wray, 1993; Durose et al., 2004; Lee & Molassiotis, 2001).

Similar to sodium and potassium, the participants' intake of phosphorus rarely was above the recommended guideline of 8-17mg/kg. This finding is not consistent with a study of 6,151 patients which found that 22% of the patients were nonadherent with their phosphorus limitations (Leggat et al., 1998). Another study by Saran and colleagues (2003) found that 15.4% of patients from a sample of 3,359 patients from 145 different facilities in the United States were nonadherent to phosphorus limitations. Durose and colleagues (2004) found 69% of the patients in their sample (n=71) were adherent with phosphorus limitations. Many times patients do not have to restrict their phosphorus if their phosphorus levels are within the recommended range; however, the dialysis center where the study took place advised patients to restrict high phosphorus foods regardless of serum levels.

Interestingly, the three participants fell far below the recommended guideline for protein and caloric intake. Mean daily intake of protein was 50% or less of the recommendation. Individuals with chronic kidney failure require more protein than healthy persons due to multiple catabolic processes and a diminished utilization of ingested protein (Bergstrom, 1995; Ikizler, 2004). The patients' caloric intakes were insufficient compared to current recommendations (NKF, 2000). The literature suggests that up to 70% of patients receiving hemodialysis are malnourished (Bergstrom, 1995). This study found that all three of the participants were ingesting insufficient amounts of protein and calories during the three months of self monitoring.

Fluid intake was remarkably close to clinical recommendations of 1000 ml/day, although there were periods of fluctuations. Typically, in the literature, fluid intake is indirectly assessed by interdialytic weight gain (Durose et. at, 2004; Leggat et. al, 1998) and based on intake in this study would have fallen within the normal limit of 1 kg/day.

The results of this study are not generalizable; however, it was interesting to note that dietary and fluid intake patterns were dynamic and fluctuating. The K/DOQI guidelines (NKF, 2000) suggest that dieticians routinely meet with patients to analyze their nutritional status using 24-hour recall or 3-day written journals. In this small pilot study, 24-hour recall or a 3-day journal would not have adequately captured diet and fluid intake patterns. The results of this preliminary study indicate that patients' intakes are not consistent week to week and may vary greatly. For example, P1 was experiencing a relapse of metastasic prostate cancer in the final month of the study. His physical capability to make dinner and feed himself had decreased substantially. During the final 2 weeks of data collection, he reported that his energy level was extremely low and he could not stand long enough to cook a meal. His dietary intake suffered greatly in the last week of data collection. He reported many missed meals and an increasing inability to eat. We are unable to capture these oscillations of dietary intake by using our current means of data collection.


The findings of this study are limited because the sample is small and the findings rely on self-reports of the participants and the participants' ability to input accurate and complete data. Comparing electronic intake patterns with periodic 24-hour dietary recall in a larger sample would help establish the reliability of data entry. Each week, however, the participants verbally reported that they recorded 100% of their intake. There were times when participants verbalized that they were unable to record exactly what they consumed, but recorded something similar. This inability to record the exact product might have affected intake data. Perhaps a different application with more dietary options to choose from would be more beneficial to use in this patient population.


This study provides a foundation for a larger study to assess the effects of electronic self monitoring on dietary and fluid intake. This study also shows the need for a more accurate examination of dietary and fluid intake as evidenced by the wide fluctuations in each participant's dietary intake. Future studies may want to include nonadherence to more than one dietary recommendation as an inclusion criteria because the participants in this study appeared to be adherent prior to self monitoring.

Acknowledgment: This study was completed in partial fulfillment of the requirements for the Master's Degree as an adult health clinical nurse specialist. The authors would like to acknowledge the Indiana University School of Nursing Fellowship Committee for funding this study. We appreciate their support and generosity.


Bame, S.I., Petersen, N., & Wray, N.P. (1993). Variation in hemodialysis patient compliance according to demographic characteristics. Social Science & Medicine, 37(8), 1035-1043.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bergstrom, J. (1995). Why are dialysis patients malnourished? American Journal of Kidney Diseases, 26(1), 229-241.

Betts, D.K., & Crotty, G.D. (1988). Response to illness and compliance of long-term hemodialysis patients. ANNA Journal, 15(2), 96-100.

Bornstein, P.H., Hamilton, S.B., & Bornstein, M.T. (1986). Self monitoring procedures. In A.R. Ciminero, K.S. Calhoun, & H.E. Adams (Eds.), Handbook of behavioral assessment (Vol. 2) (pp. 176-222). New York: Wiley and Sons.

Brady, B.A., Tucker, C.M., Alfino, P.A., Tarrant, D.G., & Finlayson, G.C. (1997). An investigation of factors associated with fluid adherence among hemodialysis patients: A self efficacy theory based approach. Annals of Behavioral Medicine, 19(4), 339-343.

Christensen, A.J., Moran, P.J., Weibe, J.S., Ehlers, S.L., & Lawton, W.J. (2002). Effect of a behavioral self-regulation intervention on patient adherence in hemodialysis. Health Psychology, 27(4), 393-397.

Coffman, M.J., Shellman, J., & Bernal, H. (2004). An integrative review of American nurses' perceived cultural self-efficacy. Journal of Nursing Scholarship, 36(2), 180-185.

Cummings, K.M., Becker, M.H., Kirscht, J.P., & Levin, N.W. (1982). Psychosocial factors affecting adherence to medical regimens in a group of hemodialysis patients. Medical Care, 20(6), 567-580.

Durose, C.L., Holdsworth, M., Watson, V., & Przygrodzka, F. (2004). Knowledge of dietary restrictions and the medial consequences of noncompliance by patients on hemodialysis are not predictive of dietary compliance. Journal of the American Dietetic Association, 104(1), 35-41.

Dwyer, J.T., Larive, B., Leung, J., Rocco, M., Burrowes, J.D., Chumlea, W.C., et al. (2002). Nutritional status affects quality of life in hemodialysis (HEMO) study patients at baseline. Journal of Renal Nutrition, 12(4), 213-223.

Ikizler, T.A. (2004). Protein and energy: Recommended intake and nutrient supplementation in chronic dialysis patients. Seminars in Dialysis, 17(6), 471-478.

Institute of Medicine. (2004). Dietary reference intakes: Water, potassium, sodium, chloride, and sulfate. Retrieved May 2, 2006, from ?id=18495&redirect=0

Johannes, C., Woods, J., Crawford, S., Cochran, M., Tran, D., & Schuth, B. (2000). Electronic versus paper instruments for daily diary data collection. Annals of Epidemiology, 10(7), 457.

Kaplan De Nour, A., & Czaczkes, J.W. (1981). Prediction of adjustment to chronic hemodialysis (Vol. 1). In K. Levy (Ed.), Psychonephrology (pp. 117-132). New York: Plenum Press.

Kerkenbush, N.L., & Lansome, C.E. (2003). The emerging role of electronic diaries in the management of diabetes mellitus. AACN Clinical Issues, 14(3), 371-380.

Korotitsch, W.J., & Nelson-Gray, R.O. (1999). An overview of self monitoring and research in assessment and treatment. Psychological Assessment, 11, 415-425.

Kugler, C., Vlaminck, H., Haverich, A., & Maes, B. (2005). Nonadherence 1with diet and fluid restrictions among adults having hemodialysis. Journal of Nursing Scholarship, 37(1), 25-29.

Lee, S.H., & Molassiotis, A. (2002). Dietary and fluid compliance in Chinese hemodialysis patients. International Journal of Nursing Studies, 39(7), 695-704.

Leggat, J.E., Orzol, S.M., Hulbert-Shearon, T.F., Goplper, T.A., Jones, C.A., Held, P.J., et al. (1998). Noncompliance in hemodialysis: Predictors and survival analysis. American Journal of Kidney Diseases, 32(1), 139-145.

National Kidney Foundation (NKF). (2000). Clinical practice guidelines for nutrition in chronic renal failure, K/DOQI. American Journal of Kidney Diseases, 35(suppl), S1-S140.

Parjares, F. (2002). Overview of social cognitive theory and of self-efficacy. Retrieved June 3, 2004, from TION/mfp/eff.html

Quinn, P., Goka, j., & Richardson, H. (2003). Assessment of an electronic daily diary in patients with overactive bladder. British Journal of Urology International, 91(7), 647-652.

Rocco, M.V., Paranandi, L., Burrowes, J.D., Cockram, D.B., Dwyer, J.T., Kusek, J.W., et al. (2002). Nutritional status in the HEMO Study cohort at baseline. Hemodialysis. American Journal of Kidney Diseases, 39(2), 245-256.

Sagawa, M., Oka, M., Chaboyer, W., Satoh, W., & Yamaguchi, M. (2001). Cognitive behavioral therapy for fluid control in hemodialysis patient. Nephrology Nursing Journal, 28(1), 37-39.

Saran, R., Bragg-Gresham, J.L., Rayner, H.C., Goodkin, D.A., Keen, M.L., Van Dijk, P.C., et al. (2003). Nonadherence in hemodialysis: Associations with mortality, hospitalization, and practice patterns in the DOPPS. Kidney International, 64(1), 254-262.

Schwedes, U., Siebolds, M., & Mertes, G. (2002). Meal-related structured self monitoring of blood glucose: Effect on diabetes control in non-insulin-treated type 2 diabetic patients. Diabetes Care, 25(11), 1928-1932.

Schmicker, R., & Baumbach, A. (1990). Dietary compliance in hemodialysis patients. Contributions to Nephrology, 81, 115-123.

Shiffman, S., & Hufford, M. (2001). Methods of measuring patient experience: Paper versus electronic patient diaries [White paper]. Scotts Valley, CA. Retrieved May 2, 2006, from pdf/whitepaper2.pdf

Simmons, R.G., & Abress, L. (1988). Quality of life and rehabilitation differences among alternate end stage renal disease therapies. Transplantation Proceedings, 20, 379-380.

Sperduto, W.A., Thompson, H.S., & O'Brien, R.M., (1986). The effect of target behavior monitoring on weight loss and completion rate in behavior modification program for weight reduction. Addictive Behaviors, 17(3), 337-340.

Stone, A.A., Shiffman, S., Schwartz, J.E., Broderick, J.E., & Hufford, M.R. (2003). Patient compliance with paper and electronic diaries. Controlled Clinical Trials, 24(2), 182-199.

Tsay, S.L. (2003). Self-efficacy training for patients with end-stage renal disease. Journal of Advanced Nursing, 43(4), 370-375.

Tucker, C.M. (1989). The effects of behavioral intervention with patients, nurses, and family members on dietary non-compliance in chronic hemodialysis patients. Transplantation Proceedings, 21(6), 3985-3988.

U.S. Renal Data System. (2005). USRDS 2005 annual data report: Atlas of end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes, and Digestive and Kidney Diseases.

Welch, J.L. (2001). Hemodialysis patient beliefs by stage of fluid adherence. Research in Nursing & Health, 24(2), 105-112.

Welch, J.L. & Austin, J.K. (1999). Factors associated with treatment related stressors in hemodialysis patients. ANNA Journal, 26(3), 318-325.

Welch, J.L., Perkins, S.M., Evans, J.D., & Bajpai, S. (2003). Differences in perceptions by stage of fluid adherence. Journal of Renal Nutrition, 13(4), 275-281.

Welch, J.L., & Thomas-Hawkins, C. (2005). Psycho-educational strategies to promote fluid adherence in adult hemodialysis patients: A review of intervention studies. International Journal of Nursing Studies, 42(5), 597-608.

Wilson, G.T., & Vitousek, K.M. (1999). Self monitoring in the assessment of eating disorders. Psychological Assessment, 11, 480-489.

Wolcott, D.L., & Nissenson, A.R. (1988). Quality of life in chronic dialysis patients: A critical comparison of continuous ambulatory peritoneal dialysis (CAPD) and hemodialysis. American Journal of Kidney Diseases, 11(5), 402-412.

Shannon Dowell, MSN, RN, CNS, CCTN, is a clinical nurse specialist, Transplant Center, Clarian Heath Partners, Indianapolis, IN. She is a member of the Hoosier Hills Chapter of ANNA.

Janet Welch, DNS, RN, CNS, is an associate professor, Indiana School of Nursing, Indianapolis, IN. She is a member of the Hoosier Hills Chapter of ANNA.
Table 1 Daily Intake Ranges and Means

             P1                                    P2
             Range                 Mean            Range

Sodium       1.21-3.09 gm          2.30 gm         1.73-4.94 gm
Potassium    .87-1.94 gm           1.40 gm         .72-2.92 gm
Phosphorus   7.60-15.80 mg/kg      10.58 mg/kg     2.69-7.94 mg/kg
Protein      .43-.81 mg/kg         .61 mg/kg       .33-.74 mg/kg
Calories     10.62-25.41 kcal/kg   18.00 kcal/kg   7.95-15.81 kcal/kg
Fluid        444.75-1474.50 ml     1065.28 ml      767.54-1475.00 ml

             Mean            Range                Mean

Sodium       3.21 gm         1.19-2.23 gm         1.72 gm
Potassium    1.79 gm         .46-1.56 gm          .92 gm
Phosphorus   6.03 mg/kg      3.81-6.53 mg/kg      5.22 mg/kg
Protein      .51 mg/kg       .33-.55 mg/kg        .41 mg/kg
Calories     10.94 kcal/kg   9.71-15.90 kcal/kg   10.73 kcal/kg
Fluid        1044.59 ml      387.70-728.34 ml     534.36 ml
COPYRIGHT 2006 Jannetti Publications, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2006 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Continuing Education; nephrology nursing research; includes statistical table
Author:Dowell, Shannon A.; Welch, Janet L.
Publication:Nephrology Nursing Journal
Geographic Code:1USA
Date:May 1, 2006
Previous Article:Human immunodeficiency virus-1 associated nephropathy (HIVAN): epidemiology, pathogenesis, histology, diagnosis, and medical management.
Next Article:Quality of life: subjective descriptions of challenges to patients with end stage renal disease.

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |