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Longitudinal changes in energy expenditure in an elderly German population: a 12-year follow-up



This study investigates age-dependent changes in different components of energy expenditure (EE) within the longitudinal study on nutrition and health status in an aging population in Giessen, Germany (GISELA).


Between 1994 and 2006, data obtained at a total of 3033 visits from 363 women and 153 men with a mean initial age of 67.4±5.9 and 66.9±5.2 years, respectively, were evaluated. The mean duration of follow-up was 8 years. Resting metabolic rate (RMR) was assessed by indirect calorimetry and physical activity patterns were assessed by questionnaire. EE of physical activity and total EE (TEE) were calculated using multipliers for RMR. Energy intake was determined through a validated 3-day estimated dietary record. Linear mixed models were used to analyze the influence of age on EE adjusted for covariates.


Resting metabolic rate decreased in women and men by 158 and 326 kJ/d per decade, respectively; after considering changes in body composition and fat distribution, respective decreases were 81 and 286 kJ/d per decade. EE of physical activity decreased similarly in both sexes (472 kJ/d per decade). TEE dropped in women and men by 540 and 823 kJ/d per decade, respectively. No statistically significant changes in energy intake and body weight were observed in the course of follow-up.


The age-dependent decrease in TEE is mainly due to a decrease in physical activity. The stable energy intake and body weight of the GISELA subjects may be indicators for a relatively good health status.


During the last two decades numerous surveys were conducted to investigate energy expenditure (EE) of free-living elderly people, suggesting that resting metabolic rate (RMR), EE of physical activity, total energy expenditure (TEE), as well as energy intake decline with advancing age. Most of these studies were carried out as cross-sectional studies (Black et al., 1996; Klausen et al., 1997; Elia et al., 2000; Tooze et al., 2007). Cross-sectional surveys, however, have only limited value for analyzing trends. For accurate conclusions on age effects, longitudinal surveys are mandatory because they make it possible to assess changes in individual subjects, cohort-specific and secular trends and other influencing factors (Grimes and Schulz, 2002).

Data concerning long-term changes in the different components of EE in elderly people are still lacking. The Baltimore Longitudinal Study (Tzankoff and Norris, 1978; Hallfrisch et al., 1990) and the investigation by Keys et al. (1973) are two of the few longitudinal surveys. However, women were not considered in these studies and only a few of the male participants were older than 60 years. Other investigations were only carried out over a relatively short period of time (4 to 6 years) with a minor group of elderly people (Murray et al., 1996; Rothenberg et al., 2003), and/or investigated only single components of EE, like physical activity (Hughes et al., 2002) or energy intake (Sjögren et al., 1994; Moreiras et al., 1996; Nicolas et al., 2000).

Against this background in 1994, a longitudinal study in a larger group of free-living women and men over 60 years of age (GISELA study) was initiated, aiming to investigate EE in the course of advanced aging, whereas simultaneously taking a variety of potential influencing factors into consideration. The objective of this study is therefore to use the extensive data of the GISELA study collected over a period of 12 years (1994–2006) to analyze age trends in physical activity patterns, RMR in consideration of body composition, EE of physical activity, as well as in energy intake.

Subjects and methods

Study design

The GISELA study is a prospective cohort study in which the nutritional and health status of free-living elderly citizens in Giessen have been observed at annual intervals since 1994 and at every second year since 1998. Within the scope of this study anthropometric data, body composition, EE, food intake and corresponding energy and nutrient intake of the study participants are examined. All investigations took place in the Institute of Nutritional Science in Giessen, Germany between July and October from 6:00 to 10:00 AM after an overnight fast by using always the same methods and equipment. The GISELA study is observational, non-intervening, and non-invasive. The study protocol was approved by the Ethical Committee of the faculty of medicine at the Justus-Liebig-University, Giessen, Germany, and a written informed consent was obtained from each study participant.


At enrollment, study participants had to be at least 60 years of age, physically mobile, and available around Giessen on a long-term basis. In 1994 and 1995 220 and 107 subjects respectively, were recruited by physicians, notices, senior citizens’ meetings, advertisements in local newspapers, as well as by recruitment through subjects who had already participated. From 1996 to 2002, in each follow-up, cohorts of about 40–60 subjects were recruited for the study by the participants themselves. Since 2004 no further subjects have been recruited. Not all subjects participated in each follow-up. In the present analyses, data of those elderly subjects who returned for at least one follow-up visit are considered. Between 1994 and 2006, a total of 3011 visits from 516 subjects (363 women and 153 men) took place and were evaluated. Between two and nine visits per study participant were available. On average, six visits per participant were evaluated in both women and men. The duration of the follow-up period for the subjects is shown in Table 1. The mean duration of follow-up was 8 years.

Table 1 Number of GISELA subjects by duration of follow-upa

Anthropometric data and body composition

After shoes, coats, and sweaters had been removed, body weight was measured with a calibrated digital scale (Seca, Vogel & Halke, Hamburg, Germany) to the nearest 0.1 kg. Body height was determined by a height measurement device integrated in the scale to the nearest 0.5 cm. Waist-to-hip ratio was used as a marker for body fat distribution and measured with a tape to the nearest 1 cm in an upright position. Body composition was investigated by using a single frequency (50 kHz) bioelectrical impedance analyzer (Akern-RJL BIA 101/S, Data Input, Frankfurt, Germany). Fat-free mass and fat mass were calculated by applying the equation derived from the cross-validation study (Akern-RJL BIA 101 vs dual-energy X-ray absorptiometry) from Kyle et al. (2001).

Resting metabolic rate

RMR was determined by an open-circuit indirect calorimeter (Deltatrac MBM-100, Hoyer, Bremen, Germany). Oxygen uptake and carbon dioxide production were measured for 25–35 min at intervals of 1 min by respiratory gas analysis using a ventilated-hood system, with the subjects in a supine position and completely at rest in a thermoneutral environment. Data collected during the initial 10 min of the measurements were discarded. RMR was determined by using the equation of Weir (1949). With regard to the short-term precision of the measurement method the mean coefficient of variation for measured RMR in our laboratory was 1.1%. As measurements were made on an outpatient basis, inpatient RMR was calculated according to Berke et al. (1992) by multiplying measured RMR by 0.93. This correction was applied to avoid an overestimation of EE of physical activity and of TEE, which was calculated by using the multipliers for RMR of the WHO (1985), of which it is assumed that they are based on inpatient measurements.

Physical activity patterns, total energy expenditure, and physical activity level

Study participants were asked about their physical activity patterns by a questionnaire. EE of the different activities was calculated using multipliers for RMR according to the WHO (1985), as described in detail elsewhere (Krems et al., 2004). TEE was calculated by the sum of EE for the different activities. The difference between TEE and RMR represents the EE of physical activity. The physical activity level (PAL) of the subjects was calculated as TEE divided by RMR.

Energy intake

To determine food intake, a 3-day estimated dietary record was especially developed and validated for the GISELA study (Lührmann et al., 1999). Energy and nutrient contents of the food items were calculated by means of the German Food Code and Nutrient Data Base version II.3 (Bundesinstitut für gesundheitlichen Verbraucherschutz und Veterinärmedizin, Berlin, Germany).

Statistical analyses

To analyze the influence of age on anthropometric data, body composition, physical activity patterns, and EE, linear mixed models were used with subject as random effect (Cnaan et al., 1997). In contrast to conventionally used linear models for repeated measurements, mixed models do not require all subjects to have the same number of measurements. Instead of eliminating subjects with missing data, it was therefore feasible to use all the available data (Littell et al., 1998). For parameter estimation, the maximum likelihood method was used with unstructured covariance matrices (SAS, 1999). In these models, age was analyzed as fixed effect beside the random subject effect. Sex group was analyzed as fixed effect to investigate whether significant differences in age trends between sex groups exist. Differences in age trends between sex groups were expressed as interaction terms between age and sex group.

Regarding RMR, first the influence of age, sex, and the interaction between age and sex were investigated in model 1. As RMR is determined by fat-free mass, fat mass, and fat distribution (Lührmann et al., 2001a) in model 2, the influence of body composition and fat distribution (waist-to-hip ratio) was additionally considered. Results are considered statistically significant when P-values are <0.05. Owing to the explorative character of this study, no adjustments for multiple hypotheses testing have been carried out (Bender and Lange, 2001). For computations, SAS PROC MIXED (SAS version 8.02, SAS Institute, Cary, NC, USA) was used.


At baseline (visit 1), the age of the female and male subjects ranged from 60 to 90 years. The age, anthropometric data, and body composition of the GISELA subjects at baseline are presented in Table 2. Baseline values of physical activity patterns, EE, and its components, as well as energy intake of the subjects are shown in Table 3.

Table 2 Age, anthropometric data, and body composition of GISELA subjects at baselinea
Table 3 Physical activity patterns, energy expenditure and its components, and energy intake of GISELA subjects at baselinea

Results of the linear mixed models regarding age-dependent changes in anthropometric data and body composition are presented in Table 4. Regarding body height a significant decline with increasing age was observed, which is significantly influenced by sex (age × sex interaction, P=0.025). Body height falls by 0.126 cm per year (P<0.001) in females and only by 0.094 cm per year (P<0.001) in males. Whereas body weight does not change significantly in the course of aging, body mass index increases in both sexes (0.036 kg/m2 per year, P=0.029). With increasing age in both females and males, fat-free mass decreases (−0.105 kg per year, P<0.001) and fat mass increases (0.126 kg per year, P<0.001). Waist-to-hip ratio also diminishes significantly with increasing age. This decrease is significantly influenced by sex (P=0.012) and amounts to 0.001 per year in males (P<0.001) and 0.002 per year in females (P<0.001).

Table 4 Results of the linear mixed model for age-dependent changes in anthropometric data and body compositiona

Results of the linear mixed models regarding age-dependent changes in RMR are shown in Table 5. Results of model 1 show a significant decline in RMR with increasing age, which is significantly different between females and males (P<0.001). RMR falls by 32.6 kJ/d per year (P<0.001) in males and only by 15.8 kJ/d per year (P<0.001) in females. Results of model 2 show a significant decline in RMR with increasing age, even after considering body composition and fat distribution, which again is significantly different between the sexes (P<0.001) and amounts to 28.6 kJ/d per year in males (P<0.001) and to 8.1 kJ/d per year in females (P<0.001).

Table 5 Results of linear mixed models for age-dependent changes in resting metabolic ratea

Age-dependent changes in physical activity patterns are presented in Table 6. With increasing age, females (P<0.001), but not males (P=0.721), spent significantly less time on housework and gardening, the decrease being 5.2 min/d per year. In both sexes time spent on walking does not change significantly in the course of aging. With regard to the time spent on sports, there is a small, but significant decline with increasing age. This decrease is also significantly different between the sexes (P<0.001). Whereas in males the time spent on sports falls by 1.4 min/d per year (P<0.001), in females there is only a decline of 0.4 min/d per year (P=0.002).

Table 6 Results of the linear mixed model for age-dependent changes in physical activity patternsa

In consequence of these changes in physical activity patterns, there is a significant drop in EE of physical activity with increasing age (47.2 kJ/d per year, P<0.001), which is not significantly affected by sex (P=0.179) (Table 7). TEE also diminishes significantly with increasing age. This decrease is significantly influenced by sex (P=0.007) and amounts to 82.3 kJ/d per year in males (P<0.001) and 54.0 kJ/d per year in females (P<0.001). Referring to baseline values, this decrease corresponds to a decline of about 7.5 and 6.0% per decade in males and females, respectively. There is also a small, but significant decline in the PAL with increasing age (P<0.001), which is not significantly influenced by sex (P=0.365). Regarding energy intake (P=0.445), no significant changes in the course of aging are observed.

Table 7 Results of the linear mixed model for age-dependent changes in energy expenditure and energy intakea


A special feature of the GISELA study is the long period of investigation with a high frequency of follow-ups in a relatively large sample of elderly subjects by applying a constant methodology. To the best of our knowledge the GISELA sample is the largest group of older subjects investigated extensively up to now on a long-term basis with regard to age-related changes in EE.

The observed decline in RMR (5.0 and 3.0% per decade in males and females, respectively) confirms findings from several cross-sectional (Black et al., 1996; Elia et al., 2000; Müller et al., 2004) and a few longitudinal studies (Keys et al., 1973; Tzankoff and Norris 1978). Changes in RMR depend mainly on changes in body composition, especially in the metabolic active fat-free mass, but also in fat mass and fat distribution (Lührmann et al., 2001a). As aging is accompanied by changes in fat-free mass, fat mass, and body fat distribution (Guo et al., 1999; Hughes et al., 2004), it is unclear at present whether the decrease in RMR is entirely a consequence of the age-related changes in body composition or whether it is additionally due to a decline in the metabolic rate per unit of tissue mass. This question has been addressed in several cross-sectional studies with inconsistent results. Although some authors could not find significant differences in RMR of young and elderly subjects after adjustment for body composition (Welle et al., 1996; Bosy-Westphal et al., 2003), other studies showed that RMR in elderly subjects was significantly lower in comparison with young adults even after correcting for body composition (Klausen et al., 1997; Gallagher et al., 2000; Hunter et al., 2001; Krems et al., 2005). Results of our investigations show that the decline in RMR is not entirely due to changes in body composition. Daily RMR falls by 286 kJ per decade in males (4.4%) and by 81 kJ per decade (1.5%) in females, independent of changes in body composition. This indicates a mass-specific decline in RMR with aging, which could be due to several factors such as a decline in Na–K pump activity (Poehlman et al., 1993), a reduced mitochondrial volume density, and oxidative capacity per mitochondrial volume (Conley et al., 2000), as well as a blunted response to sympathetic activation (Heinsimer and Lefkowitz, 1986). Furthermore, the decline in RMR could be caused by qualitative changes in organs like infiltration with fat, edema, or cystic structures (Gallagher et al., 1998). Interestingly, the age-dependent decline in RMR is much higher in males than in females. Studies investigating sex differences regarding the decline in RMR in subjects over 60 are not yet available.

As a result of changes in physical activities, GISELA subjects show an age-dependent reduction in EE of physical activity of 472 kJ/d per decade. Whereas women reduce their time spent on housework and gardening, men take less exercise. Our results regarding sports are in accordance with the findings of Hughes et al. (2002) who examined 53 men and 78 women with an initial age of 60 years over a mean period of nine years. They also reported a reduction in EE of sports and recreational activity in males (about 34 kJ/d per year), but not in females. In accordance with our results, initial EE on sports was significantly higher in males than in females, but adapted to that of the females in the course of aging. As a consequence of the reduced EE of physical activity, PAL of the GISELA subjects also decreases in the course of aging. As baseline PAL is relatively high and the decrease is rather small, mean PAL is still above the WHO (1985) proposed PAL and the Dietary References Intake (Institute of Medicine, 2002) recommendations estimated PAL range of 1.4 to 1.6 for the elderly.

The age-dependent reduction in RMR and EE of physical activity leads to a decrease in TEE of the GISELA subjects. This decrease is lower in females than in males (540 and 823 kJ/d per decade, respectively) due to the lower reduction in RMR. These results are similar to those found in the large cross-sectional investigation of Elia et al. (2000) who analyzed 568 doubly labeled water measurements in healthy subjects including 184 subjects over 65 years. Their results suggest that in adulthood there is an average decrease in TEE of 430 and 690 kJ/d per decade in women and men, respectively. However, data of Elia et al. (2000) were not analyzed separately for the older age group.

The mean energy intake of the GISELA subjects meets the RDA (Institute of Medicine, 2002). Baseline data as presented in Table 3 reveal that energy intake is about 10 % lower than EE, which may indicate an apparent negative energy balance of the subjects or, most likely, a certain degree of underreporting among GISELA subjects as is discussed elsewhere (Lührmann et al., 2001b). However, mean energy intake of participants of the GISELA study does not change in the course of aging. This trend is supported by a French study in which a stable energy intake was observed in 82 healthy elderly subjects aged more than 55 years over a period of 4 years (Nicolas et al., 2000). In contrast, other longitudinal studies like the Baltimore Longitudinal Study of Aging (Hallfrisch et al., 1990), as well as the population-based nutritional survey on 70- and 76-year-old Swedish people (Sjögren et al., 1994) observed a gradual decline in the energy intake at old age. Results of the SENECA study on energy intake of elderly subjects over a period of 4 years in nine European towns are also inconsistent. Although in some centers a significant decrease in energy intake was observed, in other centers no significant changes occurred (Moreiras et al., 1996). Interestingly, body weight of the GISELA subjects did not change in the course of the investigation, although the decrease in TEE was accompanied by a stable energy intake. Theoretically, weight should be gained on that condition. We anyhow think that our results are plausible and not due to inaccurate data collection for the following reasons: We always applied the same standardized methods, used the same measuring instruments, and carried out measurements in the same subjects during the entire study period. Therefore, it can be assumed that reproducibility of our measuring methods was high, which is essential for detecting long-term changes. Even if our methods for assessment of energy intake or of physical activity would not yield accurate results, potential error in measurement would have remained the same over time and thus, would not explain changes over time. In addition, the instrument for assessing food and nutrient intake was also validated with regard to energy intake (Lührmann et al., 1999), thus, it can be also assumed that the results for energy intake are fairly reliable. In our opinion the most likely explanation why GISELA subjects did not increase their weight may be as follows: it is well known that increasing age is associated with a higher prevalence of diseases, as well as a higher intake of medicines (Bergmann and Wiholm, 1981; Van den Akker et al., 1998). Both diseases and drugs can reduce appetite, resulting in a reduction of food and energy intake (Schiffman and Zevakis, 2002). Against this background we assume that GISELA subjects experience phases of inadequate energy intake accompanied by some weight loss more frequently with increasing age. For this assumption we have some evidence from occasional conversations with the subjects during their follow-up visits. After recovery, food intake may normalize and energy intake may return to the initial level. Certainly GISELA subjects recorded their energy intake in phases of good health and thus, interim phases of reduced energy intake are not detected in this survey.

Some limitations of this study have to be considered when interpreting the findings. First, the GISELA study sample is self-selected and not representative of the elderly German population. Although anthropometric data of GISELA subjects are comparable with those of the average elderly German population, GISELA subjects have a higher educational level and are more health conscious (Mensink, 2002; Krems et al., 2004; Jungjohann et al., 2005). Second, the small, but existent drop-out rate may lead to selectivity and thus influence the results. Therefore, the observed changes in EE cannot be generalized to the entire elderly German population. Furthermore, in our study TEE and PAL are determined by a factorial method based on a physical activity questionnaire, which has limited application to estimate EE (Conway et al., 2002). However, as the doubly labeled water method (Speakman, 1998) is very expensive and time consuming it is unsuitable for large population studies. In addition, mean initial PAL values of the GISELA subjects are very close to those found in studies measuring TEE by doubly labeled water in Caucasian elderly subjects (Elia et al., 2000; Blanc et al., 2004).

In summary, our results indicate a decline in RMR with advancing age that cannot be totally explained by changes in body composition and fat distribution. However, compared with this decline in RMR, EE of physical activity declines to a greater extent and is therefore, mainly responsible for the age-dependent reduction in TEE. Energy intake and body weight of the GISELA subjects stay fairly stable in the course of aging, which may be indicators for a relatively good health status.


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Correspondence to M Neuhäuser-Berthold.

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Lührmann, P., Bender, R., Edelmann-Schäfer, B. et al. Longitudinal changes in energy expenditure in an elderly German population: a 12-year follow-up. Eur J Clin Nutr 63, 986–992 (2009).

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  • longitudinal changes
  • resting metabolic rate
  • physical activity
  • total energy expenditure
  • energy intake
  • elderly

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