Measuring human energy expenditure - Wiley Online Library

AMERICAN JOURNAL OF HUMAN BIOLOGY 15:479–489 (2003)

Measuring Human Energy Expenditure: What Have We Learned From the Flex-Heart Rate Method? WILLIAM R. LEONARD* Laboratory for Human Biology Research, Department of Anthropology, Northwestern University, Evanston, Illinois 60208

ABSTRACT The measurement of daily energy expenditure is an important aspect of research on human health and nutrition. Over the last 30 years, G.B. Spurr has been a leader in developing and implementing methods for more effectively assessing energy expenditure and physical activity in traditional and modernizing populations. One of his most notable contributions has been the development of the ‘‘Flex Heart Rate’’ (flex-HR) method. Since its inception in the late 1980s, the flex-HR method has become a standard tool for measuring daily energy expenditure in freeliving human populations. This article reviews the initial development and validation of the flexHR technique, and examines recent refinements of the method and its application to research in biomedicine and human population biology. The review and analyses highlight how the flex-HR technique has improved on earlier methods of assessing energy expenditure and greatly advanced our understanding of variability in human energy requirements. Am. J. Hum. Biol. 15:479–489, # 2003 Wiley-Liss, Inc. 2003.

The measurement of energy expenditure (EE) is central to much research in human biology and biological anthropology. (Ulijaszek, 1995). Information on energy expenditure is critical for assessing dietary adequacy and the likelihood of both overand undernutrition (James and Schofield, 1990). Additionally, anthropologists have long used the measurement of EE as a way of understanding how traditional subsistence-level populations adapt to their environments (e.g., Hawkes et al., 1982; Smith, 1981; Thomas, 1973; Dufour, 1983; Galvin, 1985). Yet, while information on activity patterns and EE provides important insights into the ecology and health of human groups, such data are difficult to obtain on traditional, ‘‘free-living’’ populations. Over the course of his career, G.B. Spurr has been a leader in developing and implementing methods for more effectively assessing EE and physical activity in traditional and modernizing populations. One of his most notable contributions has been the development of the ‘‘Flex Heart Rate’’ (flexHR) method. Since its inception in the late 1980s (Spurr et al., 1988), the flex-HR method has become a standard tool for measuring daily EE in free-living human populations (Livingstone, 1997). This article reviews the development of the flex-HR technique and examines recent refinements and applications of the method. I first review the initial development and

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validation of the flex-HR technique in the late 1980s and early 1990s. I then examine the more recent applications and refinements of the technique, highlighting the growth of the method into a standard technique for human energetics research. Finally, I consider the application of flexHR method for research in human biology and biological anthropology, specifically focusing on how this technique has advanced our understanding of the energetics of traditional and ‘‘modernizing’’ human populations. BACKGROUND AND INITIAL DEVELOPMENT Researchers have long recognized the potential of heart rate (HR) monitoring for measuring EE (Booyens and Hervey, 1960). The utility of HR as a proxy measure for Contract grant sponsor: the Natural Sciences and Engineering Council (NSERC) of Canada; Contract grant number: OGP-0116785; Contract grant sponsor: the US National Science Foundation; Contract grant number: BSR-99101571. *Correspondence to: William R. Leonard, Laboratory for Human Biology Research, Department of Anthropology, Northwestern University, 1810 Hinman Avenue, Evanston, IL 60208. E-mail: [email protected] Received 17 November 2002; Revision received 5 March 2003; Accepted 18 March 2003 Published online in Wiley InterScience (www.interscience. wiley.com). DOI: 10.1002/ajhb.10187

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EE stems from the fact that there is a linear relationship between HR and EE (or oxygen consumption) over a large range of activity levels (Booyens and Hervey, 1960; Christensen et al., 1983). That is, as work or exercise levels increase, HR increases as a direct function of energy demands. With the development of portable HR recorders in the late 1970s, scholars in human physiology and nutrition increasingly saw daily HR monitoring as a potentially useful and relatively low-cost technique for quantifying daily EE in ‘‘real world’’ situations (so-called ‘‘free-living’’ populations). However, there still remained key problems with using daily HR-monitoring to accurately quantify EE. Two of the most critical problems were that: 1) the linear relationship between EE and HR breaks down at resting levels and at very high work levels (nearmaximal work capacity [VO2max]); and 2) HR vs. EE relationships show considerable interindividual variation. In other words, because of differences in fitness levels and genetic background, the relationship between HR and EE is not the same for everyone. In the 1980s, Spurr and colleagues directly addressed these problems in developing what is now known as the ‘‘flex-HR’’ method. This method involves establishing individual-level calibrations between HR and EE expenditure for each subject to address the problem of between-subject variability in HR responses. Thus, for each subject HR and EE (oxygen consumption) are measured under resting conditions and during a standardized submaximal exercise protocol (e.g., step test, treadmill, bicycle ergometer) (Spurr, 1990). Figure 1a shows an example of an individual EE vs. HR relationship. This approach gets around the problem of the nonlinear relationship between HR and EE at low intensity levels by identifying HR thresholds that discriminate between ‘‘resting’’ and ‘‘active’’ levels of EE. This point of differentiation is known as the ‘‘flex-HR’’ and is typically defined as the average of the highest resting HR value and the lowest exercising HR. For HRs greater than the flex point, EE is predicted based on the individual linear regression of HR vs. EE fit to the exercise values. For HRs less than or equal to the flex point, EE is determined as the average for the three resting postures (Spurr, 1990). Figure 1b shows a daily HR profile for the same subject whose EE-HR relationship is

shown in Figure 1a. Using the subject’s calibration data, individual minute-by-minute HRs are converted into energy equivalents to determine the subject’s total energy expenditure (TEE) over the course of the day. In 1988, Spurr and colleagues presented the first validation study of the flex-HR method in the American Journal of Clinical Nutrition. In this study, they compared estimates of TEE derived from HR monitoring to those obtained from whole body indirect calorimetry. The results showed high concordance between the two methods, with no significant differences in estimates of TEE. At a group level, the method was found to be quite accurate (2–3%), whereas at the individual level, higher error levels of up to 15–20% were observed.

VALIDATION OF THE FLEX-HR METHOD AND APPLICATION FOR BIOMEDICAL RESEARCH Over the subsequent 5 years, several other validation studies of the method were published (see Table 1). These studies confirmed the results obtained from the initial 1988 validation. Figure 2 presents a compilation of the individual data (n ¼ 101 subjects) from five of the early validation studies published between 1988 and 1993. Estimates of EE as determined by the flex-HR method are highly correlated (r ¼ 0.88; P < 0.001) with those obtained from the ‘‘Standard’’ methods (either whole body calorimetry or doubly labeled water [DLW]), and the best fit regression line does not significantly depart from the line of identity (slope ¼ 0.93 0.05). The flex-HR method thus provides an accurate and unbiased estimate of TEE. Figure 3 presents a plot of the differences between the two estimates of TEE (HR – ‘‘Standard’’) vs. the mean of both estimates using the statistical approach of Bland and Altman (1986). The mean difference between the HR and ‘‘Standard’’ estimates is –1% (–0.12 MJ), and the difference between the techniques does not vary with the level of EE (r ¼ 0.12; n.s.). In other words, the method is equally accurate at high and low levels of daily energy expenditure. Although there are two cases of extreme differences between the techniques, 70% of the subjects (70 of 101) had HR estimates that were within 10% of the ‘‘Standard’’ value.

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FLEX-HR METHOD FOR MEASURING EE

Fig. 1. a: Energy expenditure (EE; kJ/min) vs. heart rate (HR; beats/min) relationship. b: 24-hour heart rate trace for a subject being studied using the flex-HR protocol. Based on the EE–HR relationship, daily HR values are converted to energetic equivalents to determine total energy expenditure.

TABLE 1. Initial (1988–93) validation studies of the flex-HR method for measuring total energy expenditure (TEE)1 Study Spurr et al. (1988) Ceesay et al. (1989) Livingstone et al. (1990) Livingstone et al. (1992) Lovelady et al. (1993) 1

Sample

Reference standard

TEE-HR (MJ/d)

TEE-St (MJ/d)

Difference (%)

22 adults (16 M; 6 F) 20 adults (11 M; 9 F) 14 adults (9 M; 5 F) 36 children (19 M; 17 F) 9 adults (9F)

Calorimetry

9.72 1.81

9.49 1.71

þ2.7

Calorimetry

7.93 1.33

8.06 1.45

1.2

DLW

12.99 3.83

12.89 3.80

þ2.0

DLW

8.86 2.02

9.15 1.77

3.4

DLW

9.48 1.36

10.11 0.99

þ5.8

All total energy expenditure (TEE) values are mean SD. Comparative reference methods are: whole body indirect calorimetry (Calorimetry) or doubly labeled water (DLW).

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Fig. 2. Relationship between estimates of total energy expenditure (MJ/day) determined by the flex-HR method (TEE-HR) and a reference standard method (TEE-Standard) for the five validation studies presented in Table 1. Estimates of EE from the flex-HR method are highly correlated with those derived from the ‘‘Standard’’ methods (r ¼ 0.88; P < 0.001), and the best-fit regression line does not significantly depart from the line of identity (y ¼ 0.93x þ 0.51).

Fig. 3. Difference between total energy expenditure (TEE; MJ/day) estimates (flex-HR – ‘‘Standard’’) vs. mean TEE for the data presented in Figure 2. The flex-HR estimates average 0.12 MJ less than the ‘‘Standard’’ estimates, a difference of –1%.

Since the initial validation studies, the technique has become more broadly applied to a variety of different populations of clinical interest (see Table 2). For example, recent validation studies have found that

the flex-HR method provides accurate estimates of TEE among disabled children (Vandenberg-Emons et al., 1996), the obese (Maffeis et al., 1995), and the elderly (Morio et al., 1997; Rothenberg et al., 1998). Other

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TABLE 2. Recent (1995–present) validation studies of the flex-HR method for assessing energy expenditure in biomedical research1 Study Maffeis et al. (1995) Maffeis et al. (1995) VandenbergEmons et al. (1996) Bitar et al. (1996) Davidson et al. (1997) Morio et al. (1997) Morio et al. (1997) Rothenberg et al. (1998) Treuth et al. (1998) Rennie et al. (2000)

Sample 6 obese children (4 M; 2 F) 7 nonobese children (3 M; 4 F) 9 disabled children (5 M; 4 F) 19 children (10 M; 9 F) 9 adults (9 M) 6 elderly adults (6 M) 6 elderly adults (6F) 12 elderly adults (3 M; 9 F) 20 children (10 M; 10 F) 8 adults (5 M; 3 F)

Reference standard

TEE-HR (MJ/d)

TEE-St (MJ/d)

Difference (%)

DLW

9.47 0.84

8.99 0.63

þ5.3

DLW

8.43 2.02

8.42 2.30

þ0.1

DLW

7.40 2.20

7.40 2.10

0.0

—

—

þ7.6

DLW

15.29 2.93

14.49 2.69

þ6.0

DLW

13.50 2.70

12.80 3.10

þ4.7

DLW

10.20 1.50

9.60 0.80

þ5.9

DLW

8.94 1.96

9.90 1.43

9.7

Calorimetry

—

—

2.9

Calorimetry

6.47 1.22

6.46 0.90

þ0.8

Calorimetry

1

All total energy expenditure (TEE) values are mean SD. Comparative reference methods are: whole body indirect calorimetry (Calorimetry) or doubly labeled water (DLW).

groups of researchers have attempted to improve the accuracy of the technique at the individual level by refining the calibration procedures and by incorporating physical activity (PA) monitors into the protocol (Bitar et al., 1996; Davidson et al., 1997; Treuth et al., 1998; Rennie et al., 2000). Recent work by Nancy Butte and colleagues (Moon and Butte, 1996; Treuth et al., 1998) has shown that the combination of HR and PA monitoring produced accurate estimates of TEE at both the individual and group levels. Replication of these findings on other populations is still necessary; however, the results bode well for the broader application of HR monitoring to the study of energy balance in biomedical and epidemiological research. APPLICATIONS OF THE FLEX-HR METHODS FOR RESEARCH IN HUMAN POPULATION BIOLOGY In addition to the increasing application of the method for standard biomedical research, the last decade has also seen the flex-HR method increasingly used in field studies of non-Western populations. HR monitoring has now become a standard tool for addressing such fundamental questions in human population biology as: 1) the

energy costs of alternative human subsistence strategies and the nature of seasonal variation in energy demands; 2) the energetic of reproduction, specifically looking at how women of the developing world accommodate the high energy demands of pregnancy and lactation under conditions of marginal food availability; and 3) the influence of lifestyle change on activity patterns and EE. Table 3 and Figure 4 present data on TEE of 11 non-Western groups that have been studied with the flex-HR method. These include studies of tropical agricultural populations from Gambia (Heini et al., 1991, 1996), Thailand (Muryama and Otsuhka, 1999), Papua New Guinea (Yamauchi et al., 2001), highland Bolivia (Kashiwazaki, 1999), highland and coastal regions of Ecuador (Leonard et al., 1995), and herding and fishing populations from Siberia (Katzmarzyk et al., 1994; Leonard et al., 2002). In addition to the rural, subsistence-level populations, studies have also been done on urban groups from Papua New Guinea (Yamauchi et al., 2001), Bangladesh (Rashid and Ulijaszek, 1999), Colombia (Spurr et al., 1996; Dufour et al., 2002), and Siberia (Leonard et al., 1997, 2002). The range of daily EE in these samples is 9.7–16.1 MJ/day (2,300–3,850 kcal/day)

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TABLE 3. Body weight (kg), total energy expenditure (MJ/d) and physical activity levels of non-Western populations studied using the flex-HR method Urban/ rural

Sample1

Body weight (kg)

TEE (MJ/d)

PAL

Gambia

R R R

8M 37 F (P) 12 F

61.2 57.7 50.3

16.10 10.44 10.46

2.40 1.86 2.03

Heini et al. (1996) Heini et al. (1991)

Thailand–Lao

R R R R U U

8M 8F 15 M 12 F 14 M 15 F

55.1 57.7 63.6 53.3 70.0 59.7

12.10 9.28 13.13 11.04 12.20 9.31

2.20 1.83 1.84 1.88 1.71 1.63

Murayama and Ohtsuka (1999)

Bangladesh

U

68 F (L)

47.3

8.47

1.67

Rashid and Ulijaszek (1999)

Colombia

U U U

23 F 29 F 15 F (L)

56.4 52.2 58.2

9.99 8.83 9.84

1.90 1.83 1.84

Spurr et al. (1996)

Bolivia–Aymara

R R

5M 5F

54.6 50.5

10.91 10.34

1.92 2.12

Kashiwazaki (1999)

Highland Ecuador

R R

11 M 11 F

61.3 55.7

15.94 10.29

2.39 1.97

Leonard et al. (1995)

Coastal Ecuador

R R

5M 5F

55.6 47.8

10.11 8.34

1.58 1.63


Siberia–Evenki

R R U U

43 M 51 F 3M 5F

58.6 51.6 56.4 63.7

11.33 8.81 9.69 6.96

1.76 1.67 1.55 1.23


Siberia–Ket

R R

9M 2F

62.3 50.1

11.42 7.79

1.69 1.51

Katzmarzyk et al. (1994)

Siberia–Russian

U U

10 M 22 F

72.4 64.7

10.92 8.26

1.39 1.53


Population

PNG–Huli

Reference

Yamauchi et al. (2001)

Dufour et al. (2002)

1

P ¼ pregnant; L ¼ lactating.

Fig. 4.

Total energy expenditure (MJ/day) of adult men and women from 11 non-Western populations.


in men and 7.0–11.0 MJ/day (1,700–2,600 kcal/day) in women. Figure 5 examines the differences in TEE between the rural and urban samples. For both men and women, the rural, subsistence-level groups have higher expenditure levels. Men of the rural groups have average TEEs of 12.5 MJ/day (3,000 kcal), compared to 10.9 MJ

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(2,600 kcal) in the urban groups. Among women the differences are more modest, with the rural and urban samples averaging 9.6 and 8.8 MJ/day, respectively (2,300 vs. 2,100 kcal). To correct for differences in body mass between the urban and rural groups, Figure 6 shows the EE data as physical activity

Fig. 5. Total energy expenditure (MJ/day; Mean SEM) of adult men and women from rural and urban nonWestern populations. Among men, TEE averages 12.5 0.7 MJ/day in the rural groups and 10.9 0.7 MJ/day in the urban groups. Among women, TEE averages 9.6 0.03 and 8.8 0.04 MJ/day in the rural and urban groups, respectively.

Fig. 6. Physical activity levels (TEE/BMR; mean SEM) of adult men and women from rural and urban non-Western populations. Rural men have significantly higher PALs than urban men (2.02 0.10 vs. 1.55 0.09; P < 0.05). PAL differences between rural and urban women are less marked (1.83 0.06 vs. 1.66 0.09; P ¼ 0.11).

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TABLE 4. Physical activity levels (PALs) associated with different daily work categories1 Daily work level Sex

Light

Moderate

Heavy

Male Female

1.55 1.56

1.78 1.64

2.10 1.82

1

From: FAO/WHO/UNU (1985:78).

levels (PAL), the ratio of TEE to basal metabolic rate (TEE/BMR; FAO/WHO/UNU, 1985). When expressed as PALs, the urban–rural differences are much larger and statistically significant in men. Rural men have daily expenditure levels that are more than double basal rates and substantially greater than men of the urban samples (PAL ¼ 2.02 vs. 1.55; P < 0.05). Among women, mean PALs for the rural and urban samples are 1.83 and 1.66, respectively (P ¼ 0.11). These results suggest that the declines in physical activity and EE associated with ‘‘modernization’’ are more pronounced in men than women. What is also striking about these data is how high the average level of physical activity is for the rural, subsistence-level groups. Table 4 presents the PALs associated with different levels of daily work based on the FAO/WHO/UNU (1985) recommendations. Note that the PAL thresholds for ‘‘moderate’’ and ‘‘heavy’’ work are higher in males than in females. These sex-specific PAL thresholds for different work levels reflect the fact that energy costs of specific activities appear higher in men than in women (see Durnin and Passmore, 1967; James and Schofield, 1990).* Thus, the PALs of both men and women of the rural subsistence groups (2.02 and 1.83, respectively) are commensurate with high levels of physical work according to the 1985 WHO Norms (FAO/WHO/UNU, 1985; James and Schofield, 1990). These levels of energy expenditure are quite high in comparison to many of the earlier anthropological studies of EE in subsistence level populations—most notably the results on the !Kung foragers of the Kalahari (Lee, 1979), the Andean Quechua (Thomas, 1973; Leonard, 1991), and Turkana Pastoralists (Galvin, 1985). Such differences raise the question of whether the traditional time allocation (TA) (or factorial) methods for

measuring EE under field situations may systematically underestimate daily expenditure levels. To explore this question, several field studies have recently compared estimates of TEE obtained by HR monitoring and TA methods. Spurr, Dufour, and colleagues (1996, 1997) have addressed this issue using data from their work in urban Colombia. Our research group has also examined this topic, drawing on our data from Ecuador and Siberia (Leonard et al., 1995, 1997; Katzmarzyk et al., 1996). All of this work shows that the standard WHO time allocation protocol significantly underestimates daily EE relative to both the HR monitoring and DLW methods. Moreover, it appears that the level of underestimation increases with higher levels of EE. Figure 7 presents a summary of recent studies that have compared TA estimates of EE (shown on the Y-axis) to those obtained by either the flex-HR or DLW methods (on the X-axis). In all of the 12 samples shown in Figure 7, the TA method gives a lower estimate of activity and energy costs, with the mean difference being statistically significant (PAL ¼ 1.77 vs. 1.55; P < 0.01). Subsequent work has also explored the reasons for the underestimates with the TA methods. Spurr et al. (1997) found that in their sample of Colombian women the WHO values for energy costs of particular activities underestimated the actual measured costs for those activities. They also demonstrated that Ainsworth et al.’s (1993) more recent standards for activity-specific energy costs produce more accurate estimates of EE than the WHO and Schofield norms. Additionally, Katzmarzyk et al. (1996) have shown that energy costs of resting postures may also be a source of error for TA methods. Among rural populations that do not live in ‘‘temperature-controlled’’ surroundings, thermoregulatory costs may result in significantly higher levels of resting EE than predicted based on the WHO standards. Thus, it appears that the WHO may need to include adjustment for climate in their method for estimating EE.

*Panter-Brick (1996, 2002) has raised questions about the utility of the sex-specific PAL thresholds of the FAO/WHO/ UNU (1985) recommendations. She notes that the sex differences in activity-specific energy costs noted in the Durnin and Passmore (1967) data have not been found in many subsequent studies (e.g., Ainsworth et al., 1993).


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Fig. 7. Relationship between time allocation/factorial (TA) and doubly labeled water (DLW) or flex-heart rate (flex-HR) estimates of physical activity level (PAL) for 12 population samples (adapted from Leonard et al., 1997). The TA estimates are significantly lower than those obtained from DLW or flex-HR (PAL ¼ 1.55 vs. 1.77; P < 0.001).

CONCLUSIONS The flex-HR method has contributed substantially to our understanding of variability in human energy expenditure. Since its initial development in the 1980s, the method has been validated in a number of settings; it is now a standard research tool for biomedical research, being used to study health issues such as: the origins of childhood obesity, activity patterns and energy balance in the disabled, and changes in EE among the elderly. Ongoing research, including examination of variability in the flex point (Panter-Brick et al., 1996) and the incorporation of activity monitors into the protocol (Treuth et al., 1998), are helping to improve the accuracy and expand the utility of the technique. For human biologists and biological anthropologists the technique has revolutionized the study of energetics among traditional and non-Western populations. The technique has proven to be practical for studying human populations in a broad range of environmental contexts—from the tropics to the arctic. The method is also an improvement on the standard time allocation approach in terms of greater accuracy and minimizing participant burden. Indeed, the insights that we have gained from the flex-HR method are now causing us to

reevaluate earlier studies on subsistence effort and energetic efficiency in traditional foraging and agricultural populations. ACKNOWLEDGMENTS I thank Dr. Darna Dufour for the opportunity to participate in the symposium honoring the contributions of G.B. Spurr. I thank Dr. Spurr for his help and encouragement in applying the flex-HR method to my own research. I also thank my research collaborators, Drs. Peter Katzmarzyk, Michael Crawford, Victoria Galloway, and Ludmila Osipova.

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