Working Paper on Social-Ecological Resilience SeriesNo. 2012-017
Nutrient Intake, Physical Activity, and Travel Patterns of Adults
Living in Contrasting Ecological Zones in Rural Zambia
during the Less Labor-intensive Season for Farming
By
Sayuri Kon1, Thamana Lekprichakul2, Taro Yamauchi1
1

Hokkaido University, 2Research Institute for Humanity and Nature
March 2012

Vulnerability and Resilience of Social-Ecological Systems
RIHN Research Project E-04

Research Institute for Humanity and Nature (RIHN)Inter-University Research Institute Corporation, National Institutes for the Humanities

大学共同利用機関法人 人間文化研究機構
総合地球環境学研究所

Working Paper on Social-Ecological Resilience SeriesNo. 2012-017

Nutrient Intake, Physical Activity, and Travel Patterns of Adults
Living in Contrasting Ecological Zones in Rural Zambia
during the Less Labor-intensive Season for Farming
By

Sayuri Kon1, Thamana Lekprichakul2, Taro Yamauchi1
1

Hokkaido University, 2Research Institute for Humanity and Nature

E-mail: taroy@med.hokudai.ac.jp
March 2012

Vulnerability and Resilience of Social-Ecological Systems
RIHN Research Project E-04

Research Institute for Humanity and Nature (RIHN)Inter-University Research Institute Corporation, National Institutes for the Humanities

大学共同利用機関法人 人間文化研究機構
総合地球環境学研究所

Abstract
Objectives: To reveal the differences of food consumption, physical activity, and travel
patterns among contrasting ecological zones and between sexes in the Southern province ofZambia, and to determine whether the mechanism of sustaining nutritional status differs ineach zone.
Subjects and Method: A total of 26 men and 30 women aged 18 years and above, living in 3
zones (Lowland, Hillside, and Upland) was examined. Body measurements, a dietary survey
using a weighed-food method, a physical activity survey using an accelerometer, and a travel
patterns using a portable GPS were conducted.
Results: Although daily energy intake did not differ between the 3 groups for either sex, theproportions of specific food groups contributing to the intakes of energy and macronutrientswere significantly different in all zones. Subjects in the Lowland group had a high proportionof fish intake, and those in the Upland group tended to consume a high proportion of roots
and tubers. Sex difference in the proportional contribution of food groups for energy andnutrient intakes was identified only in the Lowland group. With regard to physical activity
and travel patterns, only traveled radius was significantly different between the 3 groups. The
Lowland group had the largest sex difference for energy balance, while the Upland group had
the smallest of the 3.
Conclusions: The findings suggest that the factors affecting the energy intake andexpenditure in adult villagers differed in every zone. Furthermore, it also showed that eachgroup has different characteristic of lifestyle between the sexes.

Key words: Nutritional Status, Dietary Intake, Physical Activity, Behavioral Pattern,Less Labor-intensive Season for Farming

要約

ザンビア南部州の生態学的に異なる３地域（Lowland、Hillside、Upland）に居住する成人において、農閑期における地域間および男女間の食事・身体活動・行動パ
ターンの違いを検討した。
男女ともに、体格およびエネルギー摂取量において有意な地域差は認められなかった。しかし、食品群別の摂取割合は地域間で異なっており、Lowland 男女では魚類の摂取割合が高く、Upland 男女では根茎類の摂取割合が高いことが明らかとなった。
身体活動はすべての地域の男女で「活動的」と判定された 。男女ともに総エネ
ルギー消費量、身体活動レベル、歩数、総移動距離においては地域差が認められなかったが、行動半径においてのみ有意な地域差がみられ、Hillside 男女が大きかった。
エネルギー出納に地域差は見られなかったものの食事内容と活動内容においては違いがみられたことから、地域ごとにエネルギー出納を適正に保つための戦略が異なることが示唆された。また副業を営む地域では日常の活動に性差が生じやすく、農業に従事する地域では男女類似した活動パターンであるというように、男女の役
割分担に地域差があることが示された。

キーワード：体格，栄養摂取，身体活動，行動パターン，農閑期

Contents
Introduction

・・・・・・・・・・・・・・・・・・・

1－3

Methods

・・・・・・・・・・・・・・・・・・・・

3－9

Results

・・・・・・・・・・・・・・・・・・・・

10－21

Discussion

・・・・・・・・・・・・・・・・・・・

22－28

Conclusion

・・・・・・・・・・・・・・・・・・・・

Acknowledgments

References

28

・・・・・・・・・・・・・・・・・

29

・・・・・・・・・・・・・・・・・・・

30－32

Introduction
Food consumption and physical activity are related to seasonal variations in
nutritional status. In many developing countries, seasonal variation in food availability
influences the nutritional and health conditions of populations (Wandel andHolmboe-Ottesen, 1992). Especially in rural areas, factors that affected nutritional
status were not only pre-harvest food shortages, but also high agricultural workloads(Wandel et al. 1992). Although many studies report on the variation of nutritional
status, food consumption, and physical activity among agricultural people during thebusy farming season (cf. Schofield, 1974; Wandel et al, 1992; Wandel andHolmboe-Ottesen, 1992), only a few studies have investigated these variations
specifically during the less labor-intensive season for farming (cf. Yamauchi et al,2006). Adult villagers actually lost body weight from the post-harvest to the
pre-harvest season. (Tanzania: Wandel and Holmboe-Ottesen 1992, Zambia: Yamauchi
and Kon 2010), and energy intake decreased after the harvest season (Zambia: Kumar
1988). Thus, knowledge of dietary and physical activity patterns from the post-harvestto the pre-harvest season when body weight is decreasing reveal the causes of the lossof body weight.
The Republic of Zambia is located in a semi-arid tropical zone in the southernpart of Africa. It has 2 seasons: a rainy season, which is December to March, and a dry-1-season, April to November. The average elevation within the country is about 1,300 m,
and the annual rainfall is about 500–1,500 mm. Traditional farming in rural villages
depends on rainfall, and food production is likely to be affected by climatic variationsuch as drought or heavy rainfall. In the Southern province of Zambia, where drought
occurs frequently, it was reported that there were differences in productivity of farm
produce (Shimono et al. 2010), meteorology (Kanno et al. 2010), food consumption at
the household level (Sakurai et al. 2010), and nutritional status (Yamauchi and Kon2010) between the upper flat land, middle slope, and lower flat land zones. Therefore,
it was expected that the lifestyles such as dietary or physical activity of the people
living in each zone were also different from each other.
During the busy farming season: from November to May, most households arefarming. While some have a second occupation, which include employment in
non-agricultural sectors, others continue farming after harvest of maize. The
differences in dietary and physical activity patterns are more likely to be observed
during the less labor-intensive season for farming when the diversity of occupation ishigher compared to the busy farming season. Furthermore, since gender-specificactivities resulting from the division of labor are more easily observed in lesslabor-intensive season for farming (Yamauchi et al, 2005), the lifestyle differences
between the sexes can be more easily observed during that season. In addition, a study
reported that variations of nutritional status between the pre-harvest and post-harvest-2-

seasons were identified only among women (Wandel and Holmboe-Ottesen, 1992).
Thus, the differences of lifestyle can lead to the difference of nutritional status
between the sexes. Therefore, identifying the differences of lifestyle such as dietaryand physical activity among the contrasting groups in terms of geography andmeteorology and between the sexes will show the relationship between body size orcomposition and lifestyle.
The purpose of this study was to reveal the differences of food consumption,
physical activity, and traveled patterns during the less labor-intensive season forfarming between the sexes of adults living in 3 contrasting ecological zones in theSouthern province of Zambia. Additionally, this study investigated whether themechanism of maintaining body weight differed in terms of location and sex.

Methods
Study area
The study was conducted in the Sinazongwe district of the Southern province ofZambia (Figure 1). The research area was divided into 3 contrasting ecological zones:
upper flat land zone (Upland), which is located on the plateau that continues to Choma
city; middle slope zone (Hillside), located on the decline down to Lake Kariba; andlower flat land zone (Lowland), located near Lake Kariba. These zones are locatedsouthwest along the causeway from the capital city Lusaka, in the direction of Lake
-3-

Kariba before Choma city. Four villages located in these zones: Sianemba and Siameja
located in the Lowland; Kanego in the Hillside; and Siachaya in the Upland, were
investigated. Table 1 shows the socio-environmental characteristics of each zone.

Design and subjects
Eight households comprised the sample for the Lowland group, and 7
households each comprised the Hillside and Upland groups. A total of 26 men and 30
women aged over 18 years old in the sample households were examined in the period
August―September 2010. Village meetings were held to explain the purpose andmethod of this study to all subjects before beginning the investigation. Informed
consent was obtained from the all subjects.

Table 1. Socio-environmental characteristics of the sample zones
Lowland
Total household number*

Hillside

UplandSiameja

Sianemba

KanegoSiachaya

37

34

16

87

Marriage status**
monogamy (%)

62.5

42.9

57.1

polygamy

37.5

57.1

42.9

Lower flat land zone

Middle slope zone

Upper flat land zone

Hand pump

StreamStream○―○Location
Source of drinking water(%)

Access by a vehicle* According to the census (Sakurai 2008)** Calculated based on the sample households of this study.
-4-

(a)

(b)

LusakaSouthern Province
Lake Kariba
Sample area
(Sinazongwe district)Figure 1. Location of the study area in Zambia(a) Location of Repubric of Zambia(b) Study area located in Southern Province of Zambia-5-Measurements
1) Anthropometry
Height, weight, mid-upper-arm circumference (MUAC), and the triceps andsubscapular skinfold thickness were measured following the standard protocol ofWeiner and Lourie (1981). Height was measured to the nearest 0.1 cm, using a portablestadiometer (SECA model 213, Germany). Weight was measured to the nearest 0.1 kg
using a battery-operated digital scale (TANITA HD-654, Japan). MUAC was measured
to the nearest 0.1 cm, using a plastic tape measure (ABBOTT Inser-Tape, Japan).
Skinfold thickness was measured to the nearest 1 mm, using a plastic adipometer(ABBOTT, Japan).
Body mass index (BMI; kg/m2 ) was calculated as weight (kg)/height (m2 ). The
sum of the 2-site skinfold thickness was applied to the equation of Durnin andWomersley (1974) to calculate body density, and the equation of Siri (1956) was used
to estimate body-fat percentage. Mid-upper-arm muscle area (MUAMA) was
calculated using the triceps skinfold thickness and MUAC (Frisancho, 1990).
2) Food Consumption
Food consumption was assessed with weighed-food method. A single day was
allocated to each household, and all subjects belonging to same household were
examined the same day. To measure meals, I visited sample households before thepreparation of breakfast (generally at 07.00 hours) and stayed until subjects had eaten-6-their supper (generally between 20.00 and 21.00 hours). Recall interviews were
performed to assess any foods that were consumed prior to the investigator’s visit. I
visited the household the next day again to interview and assess the foods consumed
after I left the household the previous day. The foods that subjects ate outside the homewere also estimated by recall interview. Subjects were asked to prepare meals as theyusually do.
The weight of food servings and ingredients ranging from 1 to 2,000 g wasmeasured by a digital cooking scale (TANITA KD-402, Japan) to the nearest 1 g. A
digital hanging scale (SHINWA 70109, Japan) was used to weigh food in the range of 2
to 20 kg to the nearest 10 g. Food consumption data were converted into quantities ofenergy and nutritional intakes using the Zambia Food Composition Tables, 4th edition(The National Food and Nutrition Commission of Zambia, 2009). In addition,
percentages of energy and macronutrients as derived from 7 food groups (cereals, roots& tubers, fruits & vegetables, beans & nuts, meat & dairy, fish, and others) were
calculated.
3) Physical activity
Physical activity was monitored using a uniaxial accelerometer sensor(Lifecorder Ex. Suzuken Co. Ltd, Japan). Subjects attached the device to their waist
during 5 consecutive weekdays and data recorded during the middle 3 days wereanalyzed because the data could not be gotten for 24 hours in 1st day and last day.
-7-

Subjects were asked to attach the device just after rising in the morning, to keep it on
until retiring for the evening, and to remove it when bathing.
Physical activities were categorized according to the level of activities using auniaxial accelerometry sensor. Activity levels were categorized into 1 of 11 activity
levels (0, 0.5, and 1–9) based on accelerometric signal patterns; the most frequentvalue per 2 min was recorded.
The total energy expenditure (TEE), which was automatically calculated by thedevice, was not used. The basal metabolic rate (BMR) based on sex, age, and weight ofa subject, was calculated first using the equation of WHO (FAO/WHO/UNU, 2004),
then, resting metabolic rate was calculated by 1.2 × BMR (FAO/WHO/UNU, 1985).
Estimated metabolic equivalent (MET) values were linked to the activity levels 1–9:
1.8, 2.3, 2.9, 3.6, 4.3, 5.2, 6.1, 7.1, and 8.3 MET, respectively (Kumahara et al. 2004).
Activity level of 0.5 was converted into 1.6 MET based on Kumahara’s equation: MET= 0.043x 2 + 0.379X + 1.361 (x = activity level). Zero activity was considered 0.9 MET
according to Ainsworth et al. (2000), because it was defined as the activity level during
sleep. Finally, TEE was calculated based on MET values and resting metabolic rate
(RMR) value.
4) Travel patterns
Daily travel patterns were investigated using the global positioning system(GPS: Wintec WPL-2000, Taiwan). Subjects wore a portable GPS along with an-8-

accelerometer sensor for 3 days corresponding to the middle 3 days of wearing theaccelerometer sensor. Location information (track points) were recorded at either 1
point every 20 s or 1 point every 5 m. The latitude, longitude, and elevation of eachsubject’s location, distance from previous point, cumulative distance, movement
bearing, and speed of movement were recorded. Traveled tracks were identified using
Google Earth software; then the traveled radius, the center of which was the subject’shome, was measured.

Data analysis
All data are expressed as means and standard deviation (SD). Regional
differences were examined by ANOVA and Tukey’s HSD test. Sex differences were
examined using the unpaired t-test. Difference of contribution of specific food groups
to daily energy and nutrient intakes among the 3 groups and between the sexes were
examined using the chi-square test. All analyses were performed with the JMP 8.0.2J
software package (SAS Japan), with statistical significance set at P < 0.05.

-9-Results
Anthropometry
Age and anthropometric measurements are shown in Table 2. Age, body size,and body composition did not differ significantly between the 3 groups for either sex.
BMI differences between the sexes were not identified in all groups. MUAMA washigher among men than women (P < 0.01), whereas %fat was higher among womenthan men in all groups (P < 0.001). Average BMI in all groups and for both sexes wereclassified as “normal” (18.5 ≤ BMI ≤ 25.0) according to WHO criteria (WHO, 2000).
The men and women of the Hillside group were characterized as having “lean”
physiques because BMI and %fat were the lowest, and MUAMA was the highest of the
3 groups (not significant).

- 10 -

Table 2. Age, body size, and body composition of adults living in 3 contrasting
ecological zones of Zambiaa) MaleLowland (n = 7)

Hillside (n = 10)

Mean

SD

Mean

SD

Mean

SD

Age (years)

47.3

18.1

36.9

13.1

36.7

18.6

Height (cm)

171.5

4.7

169.3

6.0

165.5

6.6

Weight (kg)

61.1

2.4

58.4

2.0

57.8

2.2

20.7

2.2

20.4

1.5

21.0

1.8

20.0

4.5

16.8

3.2

19.4

6.1

427.2

57.0

438.2

55.9

422.5

95.7

2 1

BMI（kg/m ）%fat (%)

2
2 3

MUAMA (cm )1Body mass index: weight (kg) / height (m)2

The equation of Siri (1956) and Durnin & Womersley (1974) was used.

3

Mid-upper-arm muscle area: The equation of Frisancho (1990) was used.

Upland (n = 9)2b) Female
Lowland (n = 10)

Hillside (n = 9)Upland (n = 11)

Mean

SD

Mean

SD

Mean

SD

Age (years)

36.5

15.2

37.9

8.2

35.9

11.9

Height (cm)

159.9

5.2

157.0

4.3

156.8

5.6

Weight (kg)

60.0*

9.8

50.3

4.0

54.2

7.4

23.4

3.9

20.4

1.2

22.1

2.9

33.5

5.5

28.7

3.9

32.5

4.2

346.5

43.4

362.0

45.7

322.8

44.4

2 1

BMI（kg/m ）%fat (%)

2
2 3

MUAMA (cm )1

2

Body mass index: weight (kg) / height (m)2

The equation of Siri (1956) and Durnin & Womersley (1974) was used.

3

Mid-upper-arm muscle area: The equation of Frisancho (1990) was used.

* Significantly higher than in Hillside and Upland (P < 0.05)- 11 -

Food Consumption
Table 3 shows the energy and nutrient intakes of men and women in the 3 zones.
Daily energy intake and daily energy intake per fat-free mass did not differ between
the 3 groups for either sex. Fat intake and fat energy percent among men in the
Lowland group were significantly lower than among men in the other 2 groups (P <0.01), while carbohydrate energy percent was the highest of the 3 groups (P < 0.01).
Among women, fat intake, carbohydrate intake, and energy percents of fat andcarbohydrate did not differ between the 3 groups. Only protein energy percent was
lower in the Upland group compared to the Lowland group (P < 0.05). Sex differenceswere not found in the intakes of energy and all nutrients in any of the groups.
The nutrient intakes of the subjects were compared with recommended dietary
allowance (RDA: FAO/WHO/UNU, 2004; FAO/WHO, 2002) and safe level(FAO/WHO/UNU, 1985). Percentages of sufficiency of RDA and safe level aredescribed in Figure 2. Table 4 shows RDA of each nutrient. Protein intake among the
subjects of this study was said to be mostly adequate, because protein intake of most of
the groups reached the safe level except for half of male subjects of the Lowland group.
Among men in the Lowland group, energy percent of fat intake of all subjects was also
below the proper range, and women in the same group had a high proportion ofsubjects whose fat intake was below the proper range. The proportions of the subjects
who did not meet the RDA for calcium intake was high for both sexes among all- 12 -

groups. While all men met the RDA for iron, a few women from each zone did. Only
women of the Lowland group had adequate intake of vitamin C, but among the othergroups, the proportions of the subjects who did not meet RDA were high. Most
subjects had adequate RDA of vitamin A, except for half of the men in the Lowlandgroup.
Figures 3, 4 and 5 show the proportional contribution of specific food groups tothe daily intake of energy, protein, and fat for the 3 groups and for each sex. The
proportions of specific food groups contributing to the intake of energy and all
macronutrients and micronutrients, except vitamin A and vitamin C for women, weresignificantly different in all zones (P < 0.05). In contrast, sex differences were notfound in the Hillside and Upland groups, but the proportional contribution of foodgroups to the daily intake of protein, fat, and carbohydrate were significantly different
between sexes in the Lowland group (P < 0.05). Furthermore, subjects in the Lowlandgroup had a high proportion of fish intake, which contributed to protein and fat intakes,
and those in the Upland group tended to have a high proportion of roots and tubers
contributing to energy and protein intakes.

- 13 -

Table 3. Comparison of daily energy and nutrient intakes of adults living in 3 contrasting
ecological zones of Zambiaa) MaleLowland (n = 6)

Hillside (n = 10)

Upland (n = 8)

Mean

SD

Mean

SD

Mean

SD

2266.7465.73032.0719.6

2863.4

539.2

Energy (kcal/FFM kg)

48.3

10.7

62.0

11.4

61.6

10.4

Protein (g)

53.7

21.1

75.4

24.5

62.0

13.4

9.1

2.1

9.9

1.6

8.6

0.3

20.3*

6.9

56.6

17.4

46.7

13.6

Energy %

7.8*

1.5

17.1

4.9

14.9

4.2

Carbohydrate (g)

472.1

87.0

557.1

140.3

540.4

99.2

Energy %83.8**

3.0

73.3

4.4

75.6

7.3

Calcium (mg)

299.9

210.0

432.7

299.1

634.5

405.4

Iron (mg)

16.8

3.4

21.0

5.3

20.2

4.1

Vitamin A (μg)

1850.6

2200.94532.73330.15163.22874.2

Vitamin C (mg)

44.5

28.1

26.8

24.6

32.6

21.8

Energy (kcal)
1

Energy %Fat (g)1

Fat free mass

* Significantly lower than in Hillside and Upland (P < 0.01)** Significantly higher than in Hillside and Upland (P < 0.01)b) Female
Lowland (n = 10)

Hillside (n = 9)Upland (n = 11)

Mean

SD

Mean

SD

Mean

SD

2681.1602.6

2595.4359.6

2476.9621.4

Energy (kcal/FFM kg)

68.4

16.0

72.6

9.3

68.0

15.3

Protein (g)

73.0

23.8

66.4

10.1

56.9

16.8

10.7*

1.8

10.3

1.2

9.1

0.8

35.6

18.8

43.1

10.1

42.4

20.0

Energy %

11.4

4.3

15.1

3.7

15.0

4.6

Carbohydrate (g)

505.6

74.2

487.6

78.4

464.3

102.5

Energy %

77.0

8.2

75.0

3.7

75.6

6.6

Calcium (mg)

668.0

395.3

369.5

133.0

516.6

416.8

Iron (mg)26.1**

11.7

20.3

5.7

16.6

3.7

Vitamin A (μg)

6140.46881.85044.92697.33819.12199.7

Vitamin C (mg)75.5***

39.2

26.3

17.1

21.1

16.7

Energy (kcal)
1

Energy %Fat (g)1

Fat free mass

* Significantly higher than in Upland (P < 0.05)** Significantly higher than in Hillside and Upland (P < 0.05)*** Significantly higher than in Hillside and Upland (P < 0.001)- 14 -

a) Protein
(%)

(%)

Male

300

300

250

250

200

200

150

150

100

100

95

95

Lowland Hillside UplandFemaleLowland Hillside Uplandb) Calcium
(%)
150

(%)

Male

150

125

125

100

100

75

75

50

50

25

25

0

0

Lowland Hillside Uplandc) Fat
(%)

35

(%)

Male

35

30

30

25

25

20

20

15

15

10

10

0

0

Lowland Hillside UplandFemaleLowland Hillside UplandFemaleLowland Hillside Upland

Figure 2. Comparison between nutrient intake and RDA value- 15 -

Table 4.

FAO/WHO Recommended Dietary Allowances
Male

Age (years)

18―65

Female>65

Protein (g/ kg/day)1
Fat % energy (%)
Calcium (mg)Iron (mg)3

Vitamin A (μg)

>504

0.75

2

3

18―50

4

10~30
1000

1300

1000

1300

11.4

11.4

24.5

9.4

3

600

600

500

500

3

45

45

45

45

Vitamin C (mg)1

Safe level (FAO/WHO/UNU, 1985)

2

FAO (1994)

3

FAO/WHO (2002)

4

50 years is the age of menopause defined by Morabia eta al.1998.

- 16 -

a) Lowland
Male

fish
fruits&ve 4%
getables1%

Femaleothers
12%

fish
2%
meat&
dairy
10%

cereals
83%

Femaleothers
45%

meat&
dairy
7%

cereals
39%

beans&
nuts
8%

fish
0.3%

fruits&ve
getables
0.3%

meat&
dairy
5%

beans&
nuts
9%

fruits&ve
getables0.4%

Femaleothers36%
cereals
55%

meat&
dairy
1%
beans&
nuts
4%

cereals40%

others46%c) Upland
Male

fish
1%

cereals
63%

beans&
nuts
6% fruits&ve
getables1%b) Hillside
Male

fish
1%

others18%

fruits&ve
getables2%roots&
tubers
1%

fish
1%
meat&
dairy
2%
beans&nuts
12%

others27%
cereals
55%

fruits&ve
getables1%roots&
tubers2%

Figure 3. Contribution of specific food groups to daily energy intake- 17 -

a) Lowland
Male

Fruits&ve
getables9%

FemaleFish
13%

Cereals78%

Meat&beans& dairy Fish
6%
nuts
5%
2%
Fruits&ve
getables13%b) Hillside
Male

FemaleMeat&dairy Fish Othersbeans&
5% 1%
4%
nuts
9%
Fruits&ve
getables9%
Cereals72%

beans&nuts
12%
Fruits&v
egetables
9%

Meat&
dairy
4%

Fish
2%

Cereals73%c) Upland
Male
Meat&
beans&
dairy
nuts
2%
Fruits&ve 5%getables8%
Roots&tu
bers
6%

Cereals
74%

FemaleFish
6%

Meat& Fishbeans& dairy 4%
nuts
5%
9%
Fruits&v
egetables
6%

Cereals
73%

Roots&tu
bers
6%

Cereals
70%

Figure 4. Contribution of specific food groups to daily protein intake- 18 -

a) Lowland
Male

Fruits&ve
getables
1%

Fish
4%

FemaleOthers
12%

Fish
2%
Meat&
dairy
10%

Cereals
83%

FemaleCereals
39%

Others
45%

Meat&
dairy
7%

beans&nuts
8%

Others
46%

Meat&
dairy
1%
beans&
nuts
4%

Cereals
40%

fish
0.3%

fruits&ve
getables
0.3%

Meat&
dairy
5%

c) Upland
Male

Fish
1%

Cereals
63%

beans&
nuts
6% Fruits&ve
getables1%b) Hillside
Male

Fish
1%

Others18%

beans&
nuts
9%

FemaleOthers36%
Cereals
55%

Fruits&v
egetables2%

Roots&tu
bers
1%

Fish
1%
Meat&
dairy
2%
beans&
nuts
12%

Others27%
Cereals
55%

Fruits&v Roots&tu
bers
egetables
2%
1%

Figure 5. Contribution of specific food groups to daily fat intake- 19 -

fruits&ve
getables0.4%

Physical activity and travel patterns
Table 5 shows the estimated BMR and indices of physical activity and travel
patterns. TEE, energy balance, and physical activity level (PAL) did not differ
significantly between the 3 groups for either sex. Energy balance was within ±1MJ
(239 kcal) for men in the Hillside group, and for men and women in the Upland group.
Mean PAL was classified as “active” (FAO/WHO/UNU, 2004) for all groups and both
sexes. Although there was no significant difference in daily steps or daily total travel
path between the 3 groups for either sex, the traveled radius was significantly larger
for both men and women in the Hillside group (P < 0.05 and P < 0.01, respectively).
Among the women in the Lowland group, PAL, traveled radius, and daily steps werethe lowest of the 3 zones, although not significantly so.
TEE values of men were significantly higher than those of women in the
Hillside and Upland groups (P < 0.001). In the Lowland and the Hillside group,
traveled radius were higher among men (P < 0.05 and P < 0.01, respectively), but there
was no sex difference among the Upland subjects.

- 20 -

Table 5.

Basal metabolic rate and indices of physical activity and travel patterns of adults

living in 3 contrasting ecological zones of Zambiaa) MaleLowland (n = 7)

Hillside (n = 10)

Upland (n = 9)

Mean

SD

Mean

SD

Mean

SD

61.1

2.4

50.3

4.0

57.8

2.2

BMR (kcal/day)

1479.0225.1

1552.7122.3

1513.5148.0

2TEE (kcal/day)

2535.4471.92790.3302.5

2728.6

279.4

Balance (kcal/day)3-345.2

699.4

241.7

649.4

177.0

534.2

PAL

1.71

0.08

1.79

0.09

1.81

0.13

Steps (steps/day)

12111

3980

15828

4932

17226

7208

Travel path (km/day)

9.5

6.3

20.6

13.0

11.3

5.5

Traveled radius (km)

4.9

3.7

21.8*

15.4

2.6

2.1

Weight (kg)1

4

1

Estimated basal metabolic rate (FAO/WHO/UNU 2004)

2

Total energy expenditure

3

TEI (energy intake shown in table2) - TEE

4Physical activity level; TEE/BMR* Significantly higher than in Lowland (P < 0.01)b)FemaleLowland (n = 10)

Hillside (n = 9)Upland (n = 11)

Mean

SD

Mean

SD

Mean

SD

60

9.8

50.3

4

54.2

11.9

1307.1128.9

1249.536.6

1288.762.3

2224.2253.9

2241.6113.1

2280.3

152.0

456.9

494.4

353.8

395.9

196.6

584.0

PAL

1.70

0.11

1.79

0.08

1.77

0.05

Steps (steps/day)

13681

7589

18643

5604

16414

3004

Travel path (km/day)

8.6

5.4

11.6

3.4

9.6

1.7

Traveled radius (km)

1.8

1.8

4.7*

3.3

2.9

1.6

Weight (kg)1

BMR (kcal/day)2

TEE (kcal/day)3

Balance (kcal/day)
4

1

Estimated basal metabolic rate (FAO/WHO/UNU 2004)

2

Total energy expenditure

3

TEI (energy intake shown in table2) - TEE

4Physical activity level : TEE/BMR* Significantly higher than in Lowland (P < 0.05)- 21 -

Discussion
Many studies have reported the variation of nutritional status, food consumption,and physical activity during the busy farming season, but nutritional status actuallyworsened during the less labor-intensive season for farming (Schofield, 1974; Wandel
et al, 1992; Wandel and Holmboe-Ottesen, 1992). It is important to know the actual
lifestyle during the less labor-intensive season for farming, particularly to identify thecause of the loss of body weight. This study investigated lifestyle such as dietary,physical activity, and travel patterns in contrasting ecological zones during the lesslabor-intensive season for farming; then, these were compared among the groups andbetween sexes. The results show that there are different lifestyles between the 3 groups
and sexes.

Difference of lifestyle among the 3 groups
Although there was no difference in energy balance, the proportional
contribution of specific food groups to the daily intake of energy and macronutrientswere different between the 3 zones. The high fish consumption, and therefore, the highintake of protein between both sexes in the Lowland group were expected because thelowland is adjacent to Lake Kariba. Most subjects in all zones satisfied the safe levelof protein, and there was no difference in protein intakes among both sexes between
the 3 groups; however, the contribution of fish to the daily intake of protein in the- 22 -

Lowland group was the highest of the 3 groups. This indicated that people who live inthe zones where less fish was consumed could have adequate intake of protein fromother food group (i.e. beans & nuts). On the other hand, there was a high proportion of
consumption of roots and tubers for intakes of energy and protein among both men andwomen in the Upland group (Figures 3, 4). This was attributed to the intake of sweet
potato only in the Upland group, where sweet potato eaten for breakfast in almost allcases; either porridge or wheat was eaten for breakfast in the Lowland and Hillside.
The quantity of protein that was consumed in a meal during the survey was higher inwheat than in sweet potato (data not shown). This could be the factors responsible forcause the low value of protein energy percent among women in the Upland group.
Furthermore, wheat is not grown among the sample population, but provided by anon-governmental organization (NGO) group. People may consume more protein from
wheat that was provided by the NGO group than from sweet potato, which were grownby them.
Among the Hillside group, distinctive dietary patterns did not appear, butcharacteristic patterns of physical activity were found. Although there was no
significant difference in daily steps or daily total travel path between the 3 groups for
either sex, the traveled radius was significantly larger for both men and women in the
Hillside group (P < 0.01 and P < 0.05, respectively). However, 5 male subjects in thisgroup attended a major Christian meeting in Batoka, a town about 35 km away from- 23 -

their village, where many Tongan Christians met during the survey period. This may
have expanded their traveled area compared to other subjects who did not attend themeeting. If we measured the mean traveled radius of the 5 male subjects excluding the
day they attended the meeting, the mean value and SD is 8.2 ± 8.9. It doesn’t show
significant difference between the 3 zones, but the mean traveled radius among men in
the Hillside group was still the largest of all groups. On the other hand, the subjects,
especially women, in the Hillside group had not only the largest traveled radius but
also the highest PAL, total daily steps, and daily total traveled path. This might berelated to the geographical situation of the Hillside, which is located on the side of amountain.
The lifestyle such as dietary, physical activity, and travel patterns of adultvillagers differed in every zone while the energy balance and the nutritional status
were not different. This showed that each group had a different strategy to maintain theproper energy balance and the nutritional status.

Difference of lifestyle between sexes
Many adult men in the Lowland group work in the “kapenta” (Tanganyika
sardine) industry and stay overnight near the lake in the less labor-intensive season
when there is less agricultural work at home. This might account for the clearer sex
difference in diet observed in the Lowland group because couples had meals separately.
- 24 -

Moreover, some male subjects in the Lowland group were absent for a long time during
our survey because of their second job, so their diets were assessed by recall interview.
Therefore, underestimation of food intake might occur only among the men in the
Lowland group. The fact that many male subjects in the Lowland group did not fill safe
level in protein, RDA of fat, and vitamin A supported the possibility of theirunderestimation of food intake. Furthermore, women in the Lowland group had thelowest values of PAL, traveled radius, and total daily steps. The data suggest thatwomen in the Lowland group are likely to be more sedentary than women in the other2 zones. Compared to women in the other 2 zones, who generally work (e.g. selling
fruits and vegetables at the market, farming) in the less labor-intensive season, most
women in the Lowland group did not have a specific job, except domestic work.
Furthermore, women in the Lowland group who used a hand pump could have fewerloads to carry when they drew water compared to women in the Hillside and Uplandgroups who drew from a stream.
Among the Upland subjects, there was no gender difference in daily steps, daily
total travel path, or traveled radius. In fact, most of the sample households in thisgroup farm even after the harvest of maize, many couples usually work together intheir field. This working together of spouses differs from the situation in the other 2zones, suggesting that both men and women in the Upland group have similar patternsof both amount of physical activity expended and travel patterns.
- 25 -

The more different dietary, physical activity, and travel patterns were between
sexes, the more different the body composition between sexes tend to appear. A
previous study that investigated the nutritional status of adults in the same areas
showed that sex difference of BMI was the largest in the Lowland group and thesmallest in the Upland group (Yamauchi and Kon, 2010). The results of this study,
where the largest differences of dietary patterns and energy balance were in the
Lowland group and there was little difference of physical activity patterns in theUpland group, could support the result of the previous study.

Validity and limitation of this study
Although we used weighed-food method for a single day in this study, themultiple day approach is generally recommended, which has been used in many
previous studies even for African population (e.g. Gewa et al, 2008; Kigutha et al,
1997; Mitchikpe et al, 2008). However, dietary patterns in most rural communities tend
to be monotonous and hardly vary from day to day or meal to meal (Beaton et al, 1979;
Hautvast et al, 1999), this could be applied to our sample populations. Moreover, we
explained to the subjects that they should prepare their usual food and they actually did
so. Thus, we could assess the usual dietary patterns as regards to kind of foods.
On the other hand, the assessment for quantity of food might not be accurate.
Women and children in the sample population usually surround 1 pot or plate, but I- 26 -

used side plates to weigh individual share of meals during the survey and it was not
usual way to eat foods for them. We could get more accurate data if we weighed thefood as the subjects usually ate without side plates, and carried out multiple day record
keeping.
Since there were reported differences of nutritional status among the 3 groups(Yamauchi and Kon, 2010), we expected to find not only the difference of lifestyle but
also a difference of nutritional status among the 3 groups. This, however, did notappear, perhaps because of the small sample size, which was as small as 56. However,previous studies which performed weighed-food method among African populations
had as few subjects as this study (cf. Mitchikpe et al., 2008). The weighed dietary
record was the most appropriate method for accurate dietary data in non-literate ruralcommunities (Kigutha, 1997), and the data obtained in this study had more accuracy
compared to the data from recall or questionnaire methods even with 1-day method.
In contrast, underestimations were likely in the assessment of energy
expenditure using an accelerometer sensor. In a previous report with parameters
similar to our study, August–September corresponded to the season when adults lost
body weight. Although it was expected that the energy balance of most subjects would
be negative values, most subjects actually had positive values. Not only theunderestimation of energy expenditure but the overestimation of food consumption
could promote bias to a positive value.
- 27 -

Lastly, because this study was a cross-sectional study that was conducted only
during the less labor-intensive season for farming, both the data of this study and thedata for food consumption, physical activity, and travel patterns for other seasons
could show how lifestyle connected to nutritional status. Further investigation with thesame type of survey during the busy farming season is needed to reveal the differencesof lifestyle between the less labor-intensive season for farming and the busy farming
season to show that the food consumption and physical activities during the lesslabor-intensive season for farming are more important factors for maintaining healthy
conditions.

Conclusions
Variations of food consumption, physical activity, and travel patterns between 3neighboring ecological zones in the Southern province of Zambia, and between thesexes were investigated. Food consumption, physical activity, and travel patterns ofadult villagers differed in every zone while the energy balance and the nutritional
status were not different. Therefore, it was suggested that each contrasting ecologicalzone had a strategy to maintain the proper energy balance and the health condition of
the people. It was also shown that each group has different characteristic of lifestyle
between the sexes. Further investigations during the busy farming season are needed toreveal that lifestyle during the less labor-intensive season for farming is more
important for maintaining healthy conditions.
- 28 -

Acknowledgments
This work was supported by the project “Vulnerability and Resilience ofSocial-Ecological Systems” of the Research Institute for Humanity and Nature (RIHN)
in Japan. I greatly thank the participants in the southern province of Zambia; ourexcellent assistant, Mr. Evans; and all project members of RIHN for cooperating mewith my study. I also appreciate my supervisor, Dr. Yamauchi, and all members of theLaboratory of Human Ecology, Graduate School of Health Sciences, Hokkaido
University.

- 29 -

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List of Working Paper

No. 2008-001

Moses Mwale, Synthesis of Soil Management Options for Better
Targeting of Technologies and Ecological Resilience under Variable
Environmental Conditions

No. 2008-002

Thamana Lekprichakul, Impact of 2004/2005 Drought on Zambia’s
Agricultural Production and Economy: Preliminary Results

No. 2008-003

Gear M. Kajoba, Vulnerability and Resilience of Rural Society in
Zambia: From the View Point of Land Tenure and Food Security

No. 2008-004

Lawrence S Flint, Socio-Ecological Vulnerability and Resilience inan Arena of Rapid Environmental Change: Community Adaptation to
Climate Variability in the Upper Zambezi Floodplain

No. 2008-005

Tetsuya Nakamura, The Livelihood of ‘Escarpment Tonga’: A Case
Study of One Village, Southern Zambia

No. 2008-006

Chihiro Ito, Re-thinking Labour Migration in Relation to Livelihood
Diversity in African Rural Area: A Case Study in Southern Province,
ZambiaNo. 2009-007

Matheaus Kioko Kauti, Rural Livelihood Security Assessment for
Smallholders Undergoing Economic Changes and Agro-Climatic
Events in Central Kenya

No. 2009-009

Gear M.Kajoba, Vulnerability of Food Production Systems of SmallScale Farmers to Climate Change in Southern Zambia: A Search for
Adaptive Strategies

No. 2009-010

Chileshe L. Mulenga, Resilience of Rural Households andCommunities to Economic Shocks, HIV/AIDS and Recurrent
Droughts: The Case of Households and Communities in the Mwami
Area, Chipata, Zambia

No. 2009-011

Bennett Siamwiinde Siamwiza, An Historical Analysis
Vulnerability and Resilience in a Semi-Arid Region of Zambia

No. 2009-012

Chewe M. Chabatama, Ecological Adversity and Food Supply inNorthwest Zambia

No. 2011-013

Elizabeth Colson, Resilience as a Way of Life in Gwembe Valley

No. 2011-014

Noriko Narisawa, Diversified Livelihood of Peasant Women in
Southern Zambia

No. 2012-015

Hidetoshi Miyazaki, Yudai Ishimoto and Ueru Tanaka, The
Importance of Sweet Potatoes in Rural Villages in Southern Province,
ZambiaNo. 2012-016

Yudai Ishimoto and Hidetoshi Miyazaki, Historical Change of
Neighborhood Community and Marriage Range of Gwembe Tonga inSouthern Zambia

ofList of Working Paper (continued)
No. 2012-017

Sayuri Kon, Thamana Lekprichakul, Taro Yamauchi, Nutrient Intake,Physical Activity, and Travel Patterns of Adults Living in Contrasting
Ecological Zones in Rural Zambia during the Less Labor-intensive
Season for Farming

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