AJCN EB Program 2010 Early Registration
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R
Agricola
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R
American Journal of Clinical Nutrition, Vol. 73, No. 3, 549-553, March 2001
© 2001 American Society for Clinical Nutrition

Original Research Communication

Use of a triaxial accelerometer to validate reported food intakes1,2

Annelies HC Goris, Erwin P Meijer, Arnold Kester and Klaas R Westerterp

1 From the Departments of Human Biology and Methodology and Statistics, Maastricht University, Maastricht, Netherlands.

2 Address reprint requests to AHC Goris, Department of Human Biology, Maastricht University, PO Box 616, 6200 MD Maastricht, Netherlands. E-mail: a.goris{at}hb.unimaas.nl.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: An easy and cheap method for validating reported energy intake (EI) is needed.

Objective: Reported EI was compared with calculated energy expenditure (EEcalc) and with energy expenditure measured by the doubly labeled water method (EEDLW).

Design: EE was calculated on the basis of basal metabolic rate (BMR) measured with the ventilated-hood technique and physical activity (PA) measured with a triaxial accelerometer (EEVH+PA) and on the basis of BMR estimated by using World Health Organization equations and PA (EEWHO+PA): EEcalc = -1.259 + 1.55 x BMR + 0.076 x counts/min (r2 = 0.90, P = 0.0001). Subjects [n = 12 men and 12 women aged 60 ± 3 y; body mass index (in kg/m2): 26 ± 4] reported their food intakes for 7 d and EEDLW, EEVH+PA, and EEWHO+PA were assessed over the same 7 d.

Results: Reported EI (9.0 ± 2.1 MJ/d) was lower (P < 0.0001) than were EEDLW (11.3 ± 2.3 MJ/d), EEVH+PA (10.8 ± 1.7 MJ/d), and EEWHO+PA (10.8 ± 1.8 MJ/d). Underreporting was 19.4 ± 14.0%, 16.7 ± 13.6%, and 16.4 ± 15.5% on the basis of EEDLW, EEVH+PA, and EEWHO+PA, respectively. The difference of 2.7 ± 8.0% between EEDLW and EEVH+PA was not related to the average of both percentages and was not significantly different from zero. The percentage of underreporting calculated with EEWHO+PA was not significantly different from that calculated with EEDLW.

Conclusions: The use of a combination of BMR (measured or estimated) and PA is a good method for validating reported EI. There was no significant difference between the percentage of underreporting calculated with EEVH+PA, EEWHO+PA, or EEDLW.

Key Words: Energy intake • energy expenditure • triaxial accelerometer • doubly labeled water • basal metabolic rate • physical activity • elderly • underreporting • underrecording


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Food intake by humans is often related to health or disease indexes in epidemiologic studies and is used as a measure (outcome) in intervention studies. Measurements of food intake with use of dietary records, food-frequency questionnaires, dietary histories, or 24-h recalls are mostly unreliable because of underrecording, undereating, or both (1, 2). A reporting bias in measured food intake can attenuate or exaggerate associations with nutrition and obesity-related disease or disturb the outcome of intervention studies (3, 4). Underreporting of habitual food intake was measured in both obese and lean subjects (2, 5, 6–8). Thus, measured food intake in nutrition research needs to be validated to ensure that the right conclusions are drawn.

The reporting of total food intake can be checked by validating reported energy intake (EI). Several methods for validating reported EI have been used with varying degrees of success. The doubly labeled water method, which measures the total energy expenditure (EE) of subjects in free-living situations, is the most reliable method for validating reported EI. However, it is very expensive (H218O is not readily available) and is thus not practical in large studies. Total EE is the sum of basal metabolic rate (BMR), physical activity (PA), and diet-induced EE (a constant fraction of {approx}10% of EI and, when subjects are in energy balance, of total EE) and can serve to validate reported EI. BMR can be measured with a ventilated hood or calculated with formulas that use age, sex, height, and weight or with formulas that use body composition. The most variable part is the PA, which can be measured with activity diaries, questionnaires, heart rate monitors, or accelerometers. The triaxial accelerometer seems to give the most objective measure of individual PA in free-living situations (9, 10).

The ratio of EE to BMR is known as the PA level (PAL); if there is no underreporting, EE:BMR equals EI:BMR. Goldberg et al (11) set cutoff limits for EI:BMR to recognize underreporting at the group level. However, the proposed cutoff limits for EI:BMR have failed to identify underreporters of food intake at the individual level (2, 12). The cutoff limits are based on minimum PALs for subjects and do not take variation in individual PALs into account.

An easy and cheap method for validating individual reported EIs in large surveys is needed. Estimated or measured BMR in combination with PA assessed with a triaxial accelerometer might be such a method. We present the results of a validation study in which the reported EI of elderly subjects was compared with calculated EE (EEcalc) and with EE measured by the doubly labeled water method (EEDLW).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Thirty subjects (14 women and 16 men) with a mean (±SD) age of 60 ± 4 y (range: 55–74 y) and a mean body mass index (BMI; in kg/m2) of 26.3 ± 3.6 (range: 19.4–34.1) participated in the study. They were recruited for an exercise intervention by advertisements in local newspapers. Results presented here are baseline measurements, which were made before the intervention began. The protocol was approved by the Medical Ethical Committee of the University of Maastricht, Maastricht, Netherlands.

Food and water intakes
Food and water intakes were measured with a 7-d dietary record. Subjects received instructions from a dietitian on how to keep a food record and were asked not to change their habitual food intakes. The data on the food records were used to calculate intakes of total energy, protein, fat, carbohydrate, and water with a computer program based on food tables (BECEL NUTRITION PROGRAM, 1988; Nederlandse Unilever Bedrijven BV, Rotterdam, Netherlands). Total water intake was calculated from reported food and water intakes and the calculated amount of metabolic water. The amount of metabolic water was calculated by multiplying EE by the fraction of energy from protein, fat, and carbohydrate (from the 7-d food record). The oxidation of protein, fat, and carbohydrate gives 0.41, 1.07, and 0.6 mL water/g, respectively (13).

Energy expenditure and water loss
EEDLW was measured according to Westerterp and Bouten (14). Water balance was assessed to measure the percentage of underrecording (ie, the failure to record in a food diary everything that is consumed; 2).

The estimated CV for EEDLW was 6% (15). Water loss was calculated by using the deuterium elimination method, which has an estimated CV of 7% (15). In the evening on day 0, subjects were given a weighed dose of a mixture of 99.84 atom% 2H2O in 10.05 atom% H218O, such that 2H and 18O increased from baseline by >=150 and >=300 ppm, respectively. A background urine sample was collected in the evening of day 0. Additional urine samples were collected on day 1 (from the second void of the day and during the evening) and in the morning and evening of days 8 and 15. EEDLW was calculated over the second week, during which subjects recorded their food intakes.

BMR was measured with an open-circuit, ventilated-hood system (BMRVH) in the morning while subjects lay for 30 min in a supine position. Subjects had slept at the university the night before this measurement was made and were in a fasting state. The estimated CV for BMRVH is 4% (16). Gas analyses were made with a paramagnetic oxygen analyzer (Servomex type 500A; Crowborough, United Kingdom) and with an infrared carbon dioxide analyzer (Servomex type 500A) similar to the system described by Schoffelen et al (17). Weir's (18) equations were used to calculate BMR. BMR was also estimated by using equations from the World Health Organization (BMRWHO), in which age, sex, body mass, and height are incorporated (19).

PA was assessed with a triaxial accelerometer for movement registration (Tracmor; Philips Research, Eindhoven, Netherlands). The triaxial accelerometer was an improved version (smaller, 7 x 2 x 0.8 cm; weight: 30 g) of the triaxial accelerometer used in previous studies (14, 20). Subjects wore the triaxial accelerometer on a belt at the back of their waist during waking hours and recorded the times at which they awoke in the morning, put the triaxial accelerometer on and off, and went to bed. Total counts per day were divided by the amount of time the subjects wore the triaxial accelerometer to get an average number of counts/min. The triaxial accelerometer measures accelerations in the anteroposterior, mediolateral, and vertical directions of the trunk and was validated against the doubly labeled water method in another study (K Westerterp, U Ekelund, unpublished observations, 2000) in 16 men and women with a mean (±SD) body mass of 92.2 ± 25.2 kg, PAL of 1.76 ± 0.14, and age of 20 y. This validation resulted in the following equation in which triaxial accelerometer counts and BMR were used for total EEcalc:


(regression analysis: r2 = 0.90, P = 0.0001; residual SD: 0.809 MJ/d). Other validations of the triaxial accelerometer output with the doubly labeled water method showed similar correlations (14, 20). EE calculated on the basis of BMRVH and PA is referred to as EEVH+PA and EE calculated on the basis of BMRWHO and PA is referred to as EEWHO+PA.

Body mass
Energy balance was checked by measuring changes in body weight over each of the 2 wk separately. Thus, possible weight changes resulting from a change in diet while reporting food intakes could be compared with normal weight fluctuations. Subjects were weighed (in underwear) in the morning on day 1, before consuming any food or beverages and after voiding, with a digital balance accurate to 0.01 kg (Sauter, Ebingen, Germany) and on days 8 and 15 with a digital balance accurate to 0.1 kg (Seca, Almere, Netherlands). Both balances were calibrated with four 20-kg weights.

Statistics
Means and SDs were calculated for subjects who had 7 d of PA data and who recorded their food intake for 7 d. Reported EI was compared with EEDLW, EEVH+PA, and EEWHO+PA by using paired t tests with Bonferroni correction. Prediction margins for reported EI versus EEVH+PA were calculated as ±2 times the residual SD of the value obtained in a previous validation study (K Westerterp, U Ekelund, unpublished observations, 2000). The percentage underreporting of food intake was calculated by using EEDLW, EEVH+PA, and EEWHO+PA (%underreportingDLW, %underreportingVH+PA, and %underreportingWHO+PA) as follows:


The calculated %underreporting values were compared by using Bland-Altman analysis and t tests (21).

Changes in body mass over the recording week were compared with changes over the nonrecording week by using a paired t test with Bonferroni correction and were compared with zero by using a one-sample group t test. Total water intake was compared with water loss by using a paired t test with Bonferroni correction. The percentage underrecording (%underrecording) and undereating were calculated if a significant difference was found between water intake and water loss and if a significant change was found in body mass over the recording week:



Significance was set at P < 0.05. The STATVIEW program (1992–1998; SAS Institute Inc, Cary, NC) was used for the statistical analysis.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Thirty subjects participated in the baseline measurements; 6 subjects were excluded from the statistical analysis because of missing PA data during the recording week. The 24 subjects with complete data had a mean (±SD) age of 60 ± 3 y (range: 55–65 y) and a BMI of 26.1 ± 3.5 (range: 19.4–32.7). Values for EI, EE, BMR, PA, water loss, and water intake are presented in Table 1Go. BMRWHO was not significantly different from BMRVH. The mean differences of 0.45 MJ/d between EEDLW and EEVH+PA and of 0.47 MJ/d between EEDLW and EEWHO+PA were not significant. The reported EI of 9.0 MJ/d was significantly lower than EEDLW, EEVH+PA, and EEWHO+PA. The correlation between reported EI and EEVH+PA is shown in Figure 1Go; the line of identity and {approx}95% prediction margins are also shown. Twelve of the 24 subjects were identified as underreporters; ie, they reported an EI below the prediction margin.


View this table:
[in this window]
[in a new window]
  		TABLE 1. Energy intake (EI), energy expenditure (EE), basal metabolic rate (BMR), physical activity (PA), water intake, water loss, and metabolic water values of the total group of subjects1
 


View larger version (18K):
[in this window]
[in a new window]
  		FIGURE 1. . Correlation between reported energy intakes and energy expenditure (calculated by using measured basal metabolic rate and triaxial accelerometer–assessed physical activity) in elderly men (•) and women ({circ}). The line of identity and 95% prediction margins are shown.

 
Values for %underreportingDLW, %underreportingVH+PA, and %underreportingWHO+PA were 19.4 ± 14%, 16.7 ± 13.6%, and 16.4 ± 15.5%, respectively. A Bland-Altman plot of the difference between %underreportingDLW and %underreportingVH+PA against the mean %underreporting is shown in Figure 2Go. The difference was not related to the mean in a simple regression analysis (r = 0.06, P = 0.78). The relative bias was 2.7% (NS) and the SD of the mean difference was 8.0%. The 4 subjects clustered in the bottom left of Figure 2Go were men with a relatively high PAL (2.0 compared with 1.6 for the other 20 subjects). The difference between %underreportingDLW and %underreportingWHO+PA was also not related to the mean of both percentages (simple regression analysis: r = 0.15, P = 0.49). The relative bias was 3.0% (NS) and the SD of the mean difference was 11%.



View larger version (18K):
[in this window]
[in a new window]
  		FIGURE 2. . Difference between the percentage underreporting by elderly men (•) and women ({circ}) calculated on the basis of energy intake and energy expenditure measured with the doubly labeled water method (%underreportingDLW) and the percentage underreporting calculated on the basis of energy intake and energy expenditure measured by using triaxial accelerometer–assessed physical activity (PA) and basal metabolic rate measured with a ventilated-hood (VH) technique (%underreportingVH+PA) plotted against the mean of %underreportingDLW and %underreportingVH+PA values.

 
The means of the 3 body mass measurements were 76.5 ± 11.4 kg on day 1, 77.1 ± 11.7 kg on day 8, and 76.8 ± 11.7 kg on day 15. The change in body mass over the nonrecording week was 0.62 ± 0.74 kg and that over the recording week was -0.24 ± 0.66 kg (P = 0.01). The change in body mass over the recording week was not significantly different from zero and indicated no undereating. The energy equivalent of the change in body mass over the recording week was -0.8 ± 2.6 MJ/d (1 kg body mass was assumed to be 30 MJ; 22). The reported water intake plus calculated metabolic water was 2.6 ± 0.7 L/d and was significantly different from the measured water loss of 3.2 ± 0.7 L/d (P = 0.0007). The calculated %underrecording was 19.5 ± 11.2%.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The use of a combination of PA and BMRWHO or BMRVH values was found to be a good validation method for reported EI given that no significant difference was found between %underreportingVH+PA , %underreportingWHO+PA, and %underreportingDLW. The relative bias between %underreportingDLW and %underreportingVH+PA might be higher for subjects with PALs > 2.0 than for subjects with PALs < 2.0. However, because only 4 subjects in this study had a PAL of {approx}2.0, no conclusions can be drawn from this.

The triaxial accelerometer used in this study is a miniaturized version (based on same principle) of the triaxial accelerometer used in previous studies (14, 20). Thus, the smaller triaxial accelerometer required a new EE equation, which was developed in a previous, unpublished study. The equation was derived from a younger population than that in the present study but, as shown in the present study, is also valid for older populations. A few of the accelerometers used in the present study did not register any activity because of problems with battery contact; the data of 6 subjects had to be excluded from the statistical analysis.

In previous studies, the output of the triaxial accelerometer for movement registration was found to be a good discriminator between PALs (14). However, additional studies with groups of subjects with a wider range of PALs than in the subjects of the present study is advised to assess the use of a combination of BMR and PA to validate reported EI.

The triaxial accelerometer gives an objective measurement of the PA of subjects in their natural environment, which is not true of diaries or questionnaires (10). Subjects completing activity diaries may alter their normal activity patterns or overreport their activity. In a group of obese subjects who failed to lose weight under a physician's care, overreporting of up to 51% of PA, assessed with activity diaries, was found (23).

In the present study, %underreporting ({approx}18%) was comparable with values found in other studies (2, 5, 6–8). In these other studies, the %underreporting was explained on a group level by underrecording (ie, the compliance with food recording) and not by undereating as in our 2 previous studies (2, 8). In the present study, %underrecording was calculated by using the water balance method, but water balance was not corrected for eventual water input from atmospheric exchange. The water input from atmospheric exchange can account for 3–10% of water turnover, depending on the inspired air volume and absolute humidity, which would result in an increase in total water intake (15). A higher total water intake would mean a smaller difference between water intake and water loss and the %underrecording would be reduced to 17% or 10%.

A reported EI outside the prediction interval of the calculated EE (Figure 1Go) indicated underreporting. A simple adjustment of food intake for these underreporters is no solution because of selective underreporting (3). Recent studies showed selective underreporting of fat intake by both obese and lean subjects (8, 24, 25). The reported protein intake can be validated with urine nitrogen analysis (26), but there is no validation method for reported carbohydrate and fat intakes (27). A possible method to solve the problem of underreporting is to confront subjects with the accuracy of their recordings: whether they ate less during the recording period (undereating) or whether they did not write down everything they consumed (underrecording). The subjects would then be asked to rerecord their food intake. In a previous study of lean women, this method resulted in improved food recording by 16 of 18 subjects (28). More studies in other populations are necessary to validate this method for improving the accuracy of food records.

This study and other studies of underreporting of food intake make it clear that the design of a nutrition study should include a validation method for reported food intakes as well as a strategy for dealing with possible underreporting of food intake (2, 5, 6–8). The triaxial accelerometer for the assessment of physical activity proved to be an easy and valid method for the determination of underreporting of food intake at the individual level.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Bingham SA. Limitations of the various methods for collecting dietary intake data. Ann Nutr Metab 1991;35:117–27.[Medline]
  2. Goris AHC, Westerterp KR. Underreporting of habitual food explained by undereating in motivated lean women. J Nutr 1999; 129:878–82.[Abstract/Free Full Text]
  3. Bellach B, Kohlmeier L. Energy adjustment does not control for differential recall bias in nutritional epidemiology. J Clin Epidemiol 1998;51:393–8.[Medline]
  4. Lissner L, Heitmann BL, Lindroos AK. Measuring intake in free-living human subjects: a question of bias. Proc Nutr Soc 1998;57:333–9.[Medline]
  5. Bandini LG, Schoeller DA, Cyr HN, Dietz WH. Validity of reported energy intake in obese and nonobese adolescents. Am J Clin Nutr 1990;52:421–5.[Abstract/Free Full Text]
  6. Schoeller DA. How accurate is self-reported dietary energy intake? Nutr Rev 1990;48:373–9.[Medline]
  7. Westerterp KR, Verboeket-van de Venne WPHG, Meijer GAL, ten Hoor F. Self-reported intake as a measure for energy intake. A validation against doubly labelled water. Obes Eur 1991;91:17–22.
  8. Goris AHC, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: selective underreporting of fat intake. Am J Clin Nutr 2000;71:130–4.[Abstract/Free Full Text]
  9. Westerterp K. Assessment of physical activity level in relation to obesity: current evidence and research issues. Med Sci Sports Exerc 1999;31:S522–5.[Medline]
  10. Melanson EL, Freedson PS. Physical activity assessment: a review of methods. Crit Rev Food Sci Nutr 1996;36:385–96.[Medline]
  11. Goldberg GR, Black AE, Jebb SA, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 1991;45:569–81.[Medline]
  12. Black AE, Bingham SA, Johansson G, Coward WA. Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements. Eur J Clin Nutr 1997;51:405–13.[Medline]
  13. Fjeld CR, Brown KH, Schoeller DA. Validation of the deuterium oxide method for measuring average daily milk intake in infants. Am J Clin Nutr 1988;48:671–9.[Abstract/Free Full Text]
  14. Westerterp KR, Bouten CVC. Physical activity assessment: comparison between movement registration and doubly labeled water method. Z Ernahrungswiss 1997;36:263–7.[Medline]
  15. Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol 1982;53: 955–9.[Abstract/Free Full Text]
  16. Goran MI. Variation in total energy expenditure in humans. Obes Res 1995;3:59–66.
  17. Schoffelen PFM, Westerterp KR, Saris WHM, ten Hoor F. A dual-respiration chamber system with automated calibration. J Appl Physiol 1997;83:2064–72.[Abstract/Free Full Text]
  18. de Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949;109:1–9.
  19. FAO/WHO/UNU. Energy and protein requirements. Geneva: World Health Organization, 1985.
  20. Bouten CV, Verboeket-van de Venne WP, Westerterp KR, Verduin M, Janssen JD. Daily physical activity assessment: comparison between movement registration and doubly labeled water. J Appl Physiol 1996;81:1019–26.[Abstract/Free Full Text]
  21. Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician 1983;32:307–17.
  22. Westerterp KR, Donkers JH, Fredrix EW, Boekhoudt P. Energy intake, physical activity and body weight: a simulation model. Br J Nutr 1995;73:337–47.[Medline]
  23. Lichtman SW, Pisarska K, Berman ER, et al. Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med 1992;327:1893–8.[Abstract]
  24. Tomoyasu N, Toth M, Poehlman E. Misreporting of total energy intake in older African Americans. Int J Obes Relat Metab Disord 2000;24:20–6.[Medline]
  25. Schaefer EJ, Augustin JL, Schaefer MM, et al. Lack of efficacy of a food-frequency questionnaire in assessing dietary macronutrient intakes in subjects consuming diets of known composition. Am J Clin Nutr 2000;71:746–51.[Abstract/Free Full Text]
  26. Bingham SA, Cummings JH. Urine nitrogen as an independent validatory measure of dietary intake: a study of nitrogen balance in individuals consuming their normal diet. Am J Clin Nutr 1985;42: 1276–89.[Abstract/Free Full Text]
  27. Goris AHC, Westerterp KR. Postabsorptive respiratory quotient and food quotient—an analysis in lean and obese men. Eur J Clin Nutr 2000;54:546–50.[Medline]
  28. Goris AHC, Westerterp KR. Improved reporting of habitual food intake after confrontation with earlier results on food reporting. Br J Nutr 2000;83:363–9.[Medline]
Received for publication May 5, 2000. Accepted for publication July 27, 2000.




This article has been cited by other articles:


Home page
 Am. J. Clin. Nutr.Home page
R. Hursel and M. S Westerterp-Plantenga
Green tea catechin plus caffeine supplementation to a high-protein diet has no additional effect on body weight maintenance after weight loss
Am. J. Clinical Nutrition, March 1, 2009; 89(3): 822 - 830.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Clin. Nutr.Home page
K. Duval, I. Strychar, M.-J. Cyr, D. Prud'homme, R. Rabasa-Lhoret, and E. Doucet
Physical activity is a confounding factor of the relation between eating frequency and body composition
Am. J. Clinical Nutrition, November 1, 2008; 88(5): 1200 - 1205.
[Abstract] [Full Text] [PDF]

Home page
 Arch DermatolHome page
M. M. Heinen, C. van der Vleuten, M. J. M. de Rooij, C. J. T. Uden, A. W. M. Evers, and T. van Achterberg
Physical Activity and Adherence to Compression Therapy in Patients With Venous Leg Ulcers
Arch Dermatol, October 1, 2007; 143(10): 1283 - 1288.
[Abstract] [Full Text] [PDF]

Home page
 Eur Respir JHome page
F. Pitta, T. Troosters, V. S. Probst, M. A. Spruit, M. Decramer, and R. Gosselink
Quantifying physical activity in daily life with questionnaires and motion sensors in COPD.
Eur. Respir. J., May 1, 2006; 27(5): 1040 - 1055.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Clin. Nutr.Home page
A. M. Joosen, M. Gielen, R. Vlietinck, and K. R Westerterp
Genetic analysis of physical activity in twins
Am. J. Clinical Nutrition, December 1, 2005; 82(6): 1253 - 1259.
[Abstract] [Full Text] [PDF]

Home page
 J. Nutr.Home page
W. T. Donahoo, D. H. Bessesen, D. R. Higbee, S. Lei, G. K. Grunwald, and J. A. Higgins
Serum Lithium Concentration Can Be Used to Assess Dietary Compliance in Adults1
J. Nutr., November 1, 2004; 134(11): 3133 - 3136.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Clin. Nutr.Home page
M. A van Baak, E. van Mil, A. V Astrup, N. Finer, L. F Van Gaal, J. Hilsted, P. G Kopelman, S. Rossner, W P. James, and W. H. Saris
Leisure-time activity is an important determinant of long-term weight maintenance after weight loss in the Sibutramine Trial on Obesity Reduction and Maintenance (STORM trial)
Am. J. Clinical Nutrition, August 1, 2003; 78(2): 209 - 214.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R
Agricola
Right arrow Articles by Goris, A. H.
Right arrow Articles by Westerterp, K. R

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS