Habitual physical activity and cardio-respiratory fitness (CRF) are important regulators of metabolism and major determinants of modern lifestyle diseases such as Type 2 diabetes and cardiovascular disease (Gill et al.,2006; Helmrich et al.,1991). Several studies have reported a reduction in CRF among ethnic groups in different continents as a consequence of a shift from traditional to more modern lifestyle (Celis-Morales et al.,2011; Ekblom and Gjessing,1968; Rode and Shephard,1984).
In East Africa, a secular increase in metabolic disease burden has been observed over recent decades, with diabetes prevalence of rural dwellers in Tanzania estimated at 0.9% in 1989 (McLarty et al.,1989) and 2.2% in rural Kenyan dwellers in 2009 (Christensen et al.,2009). Similarly, cholesterol levels in the Maasai have increased by about 12% over the last four decades (Biss et al.,1970; Mbalilaki et al.,2010). However, there is a paucity of data on free-living physical activity and CRF. Further, most epidemiological studies in African populations in which the association between physical activity and chronic diseases have been examined have used questionnaire-based exposure measurement (Aspray et al.,2000; Ezenwaka et al.,1997; Forrest et al.,2001; Kruger et al.,2002; Mbalilaki et al.,2007; Sobngwi et al.,2002). However, the validity of questionnaires to assess quantitative measures of physical activity such as its associated energy expenditure (PAEE) has been questioned (Wareham,2001).
Population-based free-living PAEE has only been objectively assessed in one African study, which was conducted in Cameroon (West Africa) using combined accelerometry and heart rate (HR) monitoring (Assah et al.,2011), a method which demonstrated good agreement with the doubly labeled water method (Assah et al.,2011). Rural dwellers of Cameroon were found to be more active than urban residents in both men (66.5 vs. 53.4 kJ day−1 kg−1) and women (55.7 vs. 38.9 kJ day−1 kg−1), a difference which was also reflected in CRF levels.
In East Africa, rural Maasai men were reported to have high CRF (=55 mlO2·kg−1 min−1), and markedly lower CRF from age 44 years onward (Mann et al.,1965). However, a similar age-related difference in CRF could not be found in rural Kenyan men and women of different ethnic groups (di Prampero and Cerretelli,1969). Both of these studies from the 1960s used objective methods to estimate CRF but were relatively small and, apart from reports in specific occupational groupings as for example sugarcane cutters in Tanzania (Davies and Van Haaren,1973), more recent population data are missing.
Rural East Africa is dominated by ethnic clustering and economies based on traditional lifestyle. Agriculture is the most widespread economy but some ethnic groups depend also on fishing or pastoralism for their livelihood (Hansen et al.,2011). In addition, these populations live at different altitude, and taken together these differences may result in diverse levels of fitness and physical activity.
The purpose of this study was to compare objective measures of CRF and physical activity, including PAEE, in three population-based samples of rural Luo, Kamba and Maasai in Kenya, representing predominantly agro-fishing, agricultural, and agro-pastoralist lifestyles, respectively.
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- STUDY POPULATION AND METHODS
- LITERATURE CITED
Participant characteristics are shown in Table 1. Maasai participants were slightly younger, and the Kamba had the lowest weight and height. Hb level was lowest in the Luo and highest in the Maasai.
Table 1. Biological background characteristics stratified by ethnicity and gender
| ||Luo (n = 381)||Kamba (n = 377)||Maasai (n = 341)||P valuea|
|Age (years)|| || || || |
| Women||39.0 (9.7)||40.3 (9.5)||34.6 (10.4)b,c||<0.001|
| Men||39.9 (10.5)||42.2 (10.5)||38.5 (11.6)c||0.029|
|Weight (kg)|| || || || |
| Women||58.1 (56.5; 59.6)||55.6 (54.2; 56.9)||59.2 (57.4; 60.9)c||0.004|
| Men||64.2 (62.5; 65.9)||56.0 (53.7; 58.4)b||64.0 (62.2; 65.8)c||<0.001|
|Height (cm)|| || || || |
| Women||163.2 (162.4; 164.0)||156.7 (156.0; 157.4)b||161.4 (160.5; 162.3)b,c||<0.001|
| Men||174.2 (173.2; 175.2)||167.9 (166.6; 169.3)b||174.2 (173.2; 175.2)c||<0.001|
|BMI (kg m−2)|| || || || |
| Women||21.8 (21.2; 22.4)||22.6 (22.1; 23.1)||22.7 (22.1; 23.4)||0.063|
| Men||21.1 (20.6; 21.6)||19.8 (19.1; 20.5)b||21.1 (20.5; 21.6)c||0.009|
|Hemoglobin (g dl−1)|| || || || |
| Women||12.1 (11.8; 12.3)||12.6 (12.4; 12.8)b||12.7 (12.4; 13.0)b||0.001|
| Men||14.1 (13.8; 14.3)||14.5 (14.2; 14.9)||15.7 (15.4; 15.9)b,c||<0.001|
The median (interquartile range) step test duration was 7.0 (5.0–8.0) min, with 1049 participants stepping >2 min and 904 stepping ≥4 min. The Actiheart was worn over an average (range) of 3.9 (1.0–8.1) days, slightly shorter among the Maasai (3.8 days) compared to the Luo (4.0 days, P = 0.050), and the Kamba (4.0 days, P = 0.032), with 1099 participants accumulating >24 h and 1,039 accumulating >72 h of valid activity data.
CRF and PAEE in the three ethnic groups are presented in Table 2. Kamba women had the highest sleeping HR. Maasai had the lowest and Luo the highest recovery HR (above sleep) in women and men, respectively but no differences were found in CRF between the three ethnic groups in neither gender. Similar patterns were observed when stricter criteria for inclusion (>4-min step test duration) were applied.
Table 2. Estimations of cardio-respiratory fitness and physical activity for ethnic groups by sex
| ||Luo (n = 381)||Kamba (n = 377)||Maasai (n = 341)||P valuea|
|Sleeping HR (beats min−1)|| || || || |
| Women||59.1 (58.1; 60.1)||61.0 (60.1; 61.8)b||59.2 (58.1; 60.3)c||0.007|
| Men||54.4 (53.3; 55.5)||55.2 (53.7; 56.8)||55.6 (54.4; 56.7)||0.342|
|Recovery HR above sleepd (beats min−1)|| || || || |
| Women||30.1 (28.0; 32.2)||27.7 (25.9; 29.5)||23.4 (21.1; 25.7)b,c||<0.001|
| Men||26.3 (24.3; 28.4)||20.9 (18.2; 23.6)b||21.4 (19.4; 23.4)b||<0.001|
|Cardio-respiratory fitnesse (ml O2 min−1 kg−1)|| || || || |
| Women||37.8 (36.8; 38.7)||37.7 (36.9; 38.5)||38.9 (37.8; 39.9)||0.212|
| Men||42.9 (42.0; 43.9)||43.1 (41.7; 44.4)||43.1 (42.1; 44.1)||0.971|
|Habitual HR above sleep (beats mini−1)|| || || || |
| Women||19.4 (18.9; 19.9)||20.4 (20.0; 20.9)b||21.2 (20.6; 21.8)b||<0.001|
| Men||20.1 (19.4; 20.7)||20.1 (19.3; 21.0)||19.1 (18.5; 19.8)||0.087|
|Accelerometric movement (m s−2)|| || || || |
| Women||0.13 (0.09; 0.17)||0.18 (0.15; 0.22)||0.15 (0.10; 0.19)||0.134|
| Men||0.17 (0.15; 0.18)||0.20 (0.19; 0.22)b||0.21 (0.20; 0.23)b||<0.001|
|PAEEf (kJ day−1 kg−1)|| || || || |
| Women||58.9 (55.0; 62.9)||67.4 (64.0; 70.8)b||74.5 (70.1; 78.9)b,c||<0.001|
| Men||74.4 (70.8; 78.1)||80.9 (75.9; 85.9)||78.0 (74.2; 81.7)||0.110|
|PALf (TEE/RMR)|| || || || |
| Women||1.81 (1.76; 1.86)||1.90 (1.86; 1.94)b||1.99 (1.94; 2.05)b,c||<0.001|
| Men||1.93 (1.89; 1.96)||1.95 (1.90; 2.00)||1.95 (1.91; 1.99)||0.592|
|% Time spent <1.5 METf|| || || || |
| Women||60.2 (58.8; 61.6)||59.2 (58.0; 60.5)||55.2 (53.6; 56.8)b,c||<0.001|
| Men||59.4 (57.9; 60.8)||57.7 (55.7; 59.6)||58.9 (57.5; 60.4)||0.381|
|% Time spent >3 METf|| || || || |
| Women||9.0 (8.2; 9.9)||11.4 (10.7; 12.2)b||13.2 (12.3; 14.2)b,c||<0.001|
| Men||12.8 (11.9; 13.6)||13.3 (12.1; 14.5)||13.5 (12.6; 14.4)||0.501|
Accelerometric movement was lowest in Luo men and women but only in the women was this significantly reflected in PAEE, PAL, and proportion of time spent in MVPA. These measures also suggested that Maasai women were most physically active, and spent less time sedentary. Adjusting for Hb as a proxy for oxygen-binding capacity did not change the group mean estimates (data not shown); however, Hb was positively associated with CRF in women (P = 0.001) but not in men (P = 0.574), whereas men with higher Hb had higher PAEE (P = 0.038) but this association was not significant in women (P = 0.620).
CRF was positively correlated with PAEE (r = 0.59, P < 0.001); the relationship being a 1.9 kJ day−1 difference in PAEE for every 1 ml O2 min−1 difference in CRF (similar in men and women and across ethnic groups).
Using PAL <1.6 to denote prevalence of physical inactivity and adjusted to 40 years of age using logistic regression, mean (95% CI) prevalence was highest in Luo women (23.1%, 16.3; 32.7%), followed by the Kamba (13.2%, 9.1; 18.9%) and the Maasai (6.3%, 3.3; 12.0%). In men, corresponding estimates were 12.8% (7.9; 20.7%) in Luo, 8.6% (4.2; 17.4%) in Kamba, and 6.9% (3.6; 12.9%) in Maasai. Using stricter criteria for inclusion (>72-h activity data) did not materially change observed differences between ethnic groups.
CRF as well as PAEE were inversely associated with age in all three ethnic groups (Figs. 1 and 2). For each 10-year difference in age, CRF was about 2 mlO2·min−1 kg−1 lower in women, while in the men (with over 10% higher mean levels), the age-related differences were of similar magnitude in Kamba but more pronounced in Luo and Maasai (−3.0 and −4.6 mlO2·min−1 kg−1 per 10 years, respectively) men. For PAEE, age-related differences were highest in the Maasai at −6.0 and −11.9 kJ day−1 kg−1 per 10 years in women and men, respectively (Table 3).
Figure 1. (a) The relationship between cardio-respiratory fitness (ml min−1 kg−1) and age among women, stratified by ethnic group (polynomial regression lines). For reference, a decline of ∼2 ml O2/10 years denote the expected difference in CRF induced by using age-estimated maximal HR. (b) The relationship between cardio-respiratory fitness (ml min−1 kg−1) and age among men, stratified by ethnic group (polynomial regression lines). For reference, a decline of ∼2 ml O2/10 years denote the expected difference in CRF induced by using age estimated maximal HR.
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Figure 2. (a) The relationship between activity energy expenditure (kJ day−1 kg−1) and age among women, stratified by ethnic group (polynomial regression lines). (b) The relationship between activity energy expenditure (kJ day−1 kg−1) and age among men, stratified by ethnic group (polynomial regression lines). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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Table 3. Age-related differences in cardio-respiratory fitness and physical activity energy expenditure for ethnic groups by sex
| ||Women||P value||Men||P value|
|CRFa (ml O2 min−1 kg−1)|| || || || |
| Luo||−1.6 (−2.6; −0.7)||0.001||−3.0 (−4.0; −2.1)||<0.001|
| Kamba||−1.9 (−2.9; −1.0)||<0.001||−1.9 (−3.2; −0.4)||0.001|
| Maasai||−2.1 (−3.0; −1.2)||<0.001||−4.4 (−5.2; −3.6)||<0.001|
|PAEEb (kJ day−1 kg−1)|| || || || |
| Luo||−1.9 (−4.7; 1.0)||0.204||−10.1 (−13.7; −6.5)||<0.001|
| Kamba||−2.0 (−6.5; 2.6)||0.391||−3.9 (−9.5; 1.7)||0.169|
| Maasai||−6.0 (−9.4; -2.7)||<0.001||−11.9 (−14.8; −9.0)||<0.001|
- Top of page
- STUDY POPULATION AND METHODS
- LITERATURE CITED
We observed relatively low levels of CRF in men (∼43 mlO2·kg−1 min−1) and women (∼38 ml O2·kg−1 min−1), respectively, among all three ethnic groups (adjusted to 40 years of age). In comparison with earlier studies of rural populations, and keeping method differences in mind, the Maasai had 10–25% higher CRF levels four decades earlier for the same age (di Prampero and Cerretelli,1969; Mann et al.,1965). However, the average CRF level of the Kamba in our study was only 5% lower, compared to the biologically and culturally related Kikuyu and Meru measured in the 1960s (di Prampero and Cerretelli,1969). Current fitness levels are also lower than those measured in Tanzanian sugarcane cutters in the 1970s (Davies,1973; Davies and Van Haaren,1973). Thus, the overall trend suggests a decline in fitness of rural East Africans over time and for the Maasai population in particular.
Bearing in mind that age differences in CRF estimated from submaximal tests should be interpreted with caution, the age-related difference was evident in all three ethnic groups but more pronounced in men compared to women, and especially in the Maasai. A similar inverse relationship between CRF and age was observed in rural Maasai from Tanzania four decades earlier (Mann et al.,1965). Culturally determined changes in physical activity with increasing age as well as age-related sarcopenia are possible explanations for what seems a fairly large difference in CRF between young and old adults.
Age-related differences in prevalence of overweight and obesity in the same study population (Christensen et al.,2008) may at least partly explain the parallel differences in CRF levels. These findings are also in line with our previous observations on lower glucose tolerance in older adults, although the rural population as a whole has a relatively low diabetes prevalence of 2.2% (Christensen et al., 2009). This is, however, still higher than earlier estimates of 0.9% (McLarty et al.,1989), which is paralleled by a secular increase in population cholesterol levels (Biss et al.,1970; Mbalilaki et al.,2010). This places the suggested decline in physical activity and fitness over time in a noncommunicable disease context.
Sleeping HR and recovery HR also reflect CRF in an inverse manner (Brage et al.,2007; Darr et al.,1988). In the present study, recovery HR was highest in the Luo among both women and men, while sleeping HR was similar among the three ethnic groups. CRF is to some extent dependent on the Hb level, which in turn depends on iron status and intake of other micronutrients, history of inflammation, as well as exposure to altitude. These environmental influences were reflected in the Hb values in men and women with a tendency among the Maasai to have the highest and the Luo to have the lowest values. Given that CRF levels were similar within each gender in the three ethnic groups, this may reflect that fitness-enhancing activity is lower in the Maasai population and higher in the Luo.
A recent study found a strong dose-response relationship between volume of exercise and change in CRF (Church et al.,2007), and in the current study a strong cross-sectional relationship was also observed between PAEE and CRF. However, increasing physical activity levels will not necessarily increase CRF if the activity is predominantly of low intensity (Gill,2007). We measured PAEE over ∼4 days and expressed it relative to RMR to get an impression of the relative energy cost of daily activities. These results showed that the men spent roughly 13% of their time, or about 3 h day−1, in MVPA (>3 METs). The same was true for the Maasai women and MVPA was still over 2 h day−1 in the Luo women, who were the least active. This suggests that the intensity distribution of habitual physical activity in rural Kenyans of today may be different than it was decades ago.
We also observed lower PAEE with in older compared to younger in both genders. As for CRF, the difference was most pronounced in the Maasai, and more so in men compared to women. These differences point towards more pronounced age-related and gender-specific cultural changes in the Maasai, compared to the Luo and Kamba people.
An accelerometer on the trunk as was used in the present study cannot meaningfully measure physical activity while an individual is carrying out work with his or her arms (Hendelman et al.,2000) but picks up walking very well (Brage et al.,2007). The lower values obtained in accelerometric movement in the Luo compared to the Kamba and Maasai participants may therefore be a result of differences in mode of physical activity with more walking taking place among the Kamba and Maasai, as PAEE was higher in these populations.
In general, the PAL values have to be interpreted with caution in this study, as RMR and dietary-induced thermogenesis are both estimated. Of particular note is RMR, which has been estimated using only participants' age, sex, height, and weight (Henry,2005). According to Henry and Rees (Henry and Rees,1991), there is some evidence of an overestimation of basal metabolic rate in people from tropical countries in general by using estimated values obtained from mainly other population groups. Basal metabolic rate was overestimated by 6.5% in African males—no data exist on African females—when compared to the Schofield equations (Schofield,1985), and the newer equations only make up for part of this discrepancy (Henry,2005). A similar bias would impact on amount of time spent between <1.5 METs and >3 METs.
The present study showed no difference in the prevalence of physical inactivity (PAL < 1.6) between the ethnic groups among the men. However, Maasai women had the lowest and the Luo women the highest prevalence of physical inactivity. These dichotomized data correspond well with the mean values for PAEE, especially in the women.
The average PAL values in the present study populations ranged from 1.81 to 1.99. In contrast, the energy expenditure of Gambian women and men measured during peak agricultural activity using the doubly labeled water method and HR measurement showed a mean PAL value >2.3 in nonpregnant women (Singh et al.,1989) and 2.4 in men (Heini et al.,1996). Compared with our results this dramatic difference may indicate a difference in activity levels as a result of seasonal variation as we collected data following but not during the harvest season. Nonetheless, these activity levels from rural East African populations are 15–20% higher than recent estimates from rural West Africans (Assah et al.,2011) and >50% higher than levels measured in 55-year-old Europeans (Eur J Epidemiol,2012), both studies using the same assessment method for physical activity. To obtain a more accurate picture of habitual PAEE, future work should adopt a repeated measurement design to reflect possible seasonal variation.
The lower average PAL among the women compared to the men (except in the Maasai) may be a result of an energy-saving mechanism, and thus not necessarily less physical activity per se as Maloiy et al. (1986) found low energy costs of carrying heavy loads on the heads among Luo and Kikuyu women. These findings have been confirmed later by others (Cavagna et al.,2002; Heglund et al.,1995). To some extent, similar results have been found in Gambian men, who had a 3% greater net efficiency of walking at level and 10% elevation on a treadmill compared to European men (Minghelli et al.,1990). Nevertheless, the combined overall lower accelerometric movement, lower PAEE and PAL measurements in women provide some evidence that the females in our study were less physically active than the males.
No questionnaire-based studies on physical activity in African populations have investigated ethnic differences (Aspray et al.,2000; Ezenwaka et al.,1997; Mbalilaki et al.,2007; Sobngwi et al.,2002) but confirm our findings that men are more physically active than women (Ezenwaka et al.,1997; Kruger et al.,2002).
Certain limitations of this study should be acknowledged. First of all, potential selection bias cannot be ruled out due to not being able to use a random sampling frame. Second, the key variables, CRF and PAEE, although based on objective measurements, are still the results of estimations by modeling; they are not measured directly.
In conclusion, this is the first epidemiological study to objectively measure habitual physical activity in East African populations. In combination with other changes in lifestyle, age-related declines in physical activity and CRF, especially in the Maasai and in the men, this may increase the risk of noncommunicable diseases in rural populations of low income.