Variation in Child Stunting and Association with Maternal and Child Dietary Intakes in Rural Kenya: A One-Year Prospective Study
by
, , , ,
Madoka Kishino
1,2
,
Azumi Hida
3,*,
Kazuko Ishikawa-Takata
3,
Yuki Tada
3,
Lucy Kariuki
4,
Patrick Maundu
4,5,
Hirotaka Matsuda
6,
Kenji Irie
7 and
Yasuyuki Morimoto
4 1
Department of Food and Nutritional Science, Graduate School of Applied Bioscience, Tokyo University of Agriculture, Setagaya, Tokyo 156-8502, Japan
2
Research Fellow of Japan Society for the Promotion of Science, Chiyoda, Tokyo 156-8502, Japan
3
Department of Nutritional Science, Faculty of Applied Bioscience, Tokyo University of Agriculture, Setagaya, Tokyo 156-8502, Japan
4
Alliance of Bioversity International and International Center for Tropical Agriculture—CIAT, Nairobi P.O. Box 823-00621, Kenya
5
Kenya Resource Center for Indigenous Knowledge, National Museum of Kenya, Nairobi P.O. Box 823-00621, Kenya
6
Department of Agricultural Innovation for Sustainable Society, Faculty of Agriculture, Tokyo University of Agriculture, Atsugi 243-0034, Japan
7
Department of International Agricultural Development, Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Setagaya, Tokyo 156-8502, Japan
*
Author to whom correspondence should be addressed.
Dietetics 2025, 4(4), 46; https://doi.org/10.3390/dietetics4040046
Submission received: 30 June 2025
/
Revised: 25 July 2025
/
Accepted: 18 September 2025
/
Published: 13 October 2025
Abstract
Objectives: Few studies have examined maternal dietary intakes in relation to children’s malnutrition status. We examined variations in children stunting status and their association with maternal and child dietary intakes. Methods: This one-year prospective study (conducted from November 2021 to December 2022) consisted of up to four surveys carried out in rural Kenya. It included 135 pairs of children aged 12–59 months and their non-pregnant mothers, all of whom had received nutrition guidance during the study. Dietary intakes were assessed in four non-consecutive 24 h dietary recalls during the first two surveys. Anthropometric measurements were taken at most four times, and variations in children stunting status (not-stunted, recovered-from-stunting, or persistent/worsened stunting) were assessed. Maternal and child dietary intakes, based on variations in stunting status, were compared using one-way analysis of covariance adjusted for socio-demographic variables. Results: Of the 135 children studied, 40 (29.6%) were stunted at baseline, whereas 85, 20, and 30 had no stunting, recovered from stunting, or had persistent/worsened stunting. Children with persistent/worsened stunting had a significantly lower energy intake than other children; however, maternal energy intake did not differ by children’s stunting status. Milk intake was significantly lower among children with persistent/worsening stunting than other children. A similar difference based on variations in stunting was also observed for maternal milk intake. Conclusions for Practice: The mothers of rural Kenyan children who had recovered from stunting consumed the most milk, while the mothers of children with persistent/worsening consumed the least milk. Further research is needed to confirm the factors behind the observed intake differences.
1. Introduction
Globally, more than 149 million children under the age of five are stunted, with 91% living in low- and middle-income countries [1]. Child stunting and growth retardation can lead to increased morbidity and mortality, poor child development, and lower learning capacity in the short term, and increased risk of non-communicable diseases, lowered working capacity, and unfavourable maternal reproductive outcomes in the long term [2]. This highlights the need for effective strategies to prevent stunting in young children.
Infants with stunting can recover during childhood and adolescence, and recovery from early growth failure, sometimes termed catch-up growth, remains beneficial for cognitive development [3,4,5]. However, few studies have assessed variations in children’s growth status and associated factors, including distinguishing between recovery from growth failure and remaining stunted.
Maternal diet plays a crucial role in optimising child nutrition. Maternal dietary intake can affect child development and nutrition [6]. Moreover, mothers are often responsible for preparing the household meals, and maternal and young children’s diets are closely correlated [7,8,9,10]. However, very limited studies have examined maternal diets in relation to child malnutrition [11]. A Bangladesh study showed that children whose mothers consumed less than five food groups were 1.7 times more likely to be stunted [11]. Children of mothers with nutrient-rich diets may be more likely to have nutrient-rich diets and less likely to be malnourished.
Here, we aimed to examine variations in children’s stunting status (no stunting, recovered-from-stunting, or persistent/worsened stunting) and associations with maternal and child dietary intakes (assessed quantitatively using dietary recall methods). We discussed the observed differences in dietary intakes and the potential causes of these differences based on the local contexts.
2. Methods
2.1. Study Design and Participants
This one-year prospective study, conducted from November 2021 to December 2022, was originally aimed to investigate the effect of nutritional guidance on knowledge, perceptions, and attitudes toward a balanced diet, using a newly developed mobile app [12]. The study sites were two rural areas in eastern and western Kenya: Kitui and Vihiga counties, the former semi-arid and the latter humid, with different dominant ethnic groups. The overall study design is shown in Figure 1.
Eligible households with children under the age of five, representative of each study area, were identified from 10 village clusters, using community-based health information systems [13]. If two or more children under the age of five lived in the same household, children who were most likely to be found at home during the day were selected. Of 200 randomly selected households at registration in August 2021, 191 completed a socio-demographic survey. The primary respondents were women caregivers of the target children. In November and December 2021 (survey 1, rainy season) and February and March 2022 (survey 2, dry season), dietary and anthropometric data were collected, followed by nutritional guidance. In June 2022 (survey 3, dry season) and October and November 2022 (survey 4, rainy season), the third and fourth anthropometric datasets were collected. Dietary intake data (dietary recalls for 4 days over two seasons [Surveys 1 and 2]) and anthropometric data from Surveys 1, 2, and 3 were available for the 182 mother–child pairs. From eligible households, 14 pairs of children under the age of 12 months (including those with unknown birth dates) were excluded because they were more likely to depend on milk, not foods, for their nutrition. Additionally, 33 caregivers (not the children’s mothers or who were pregnant at survey time) were excluded. Finally, the study included 135 pairs of children aged 12 to 59 months and their non-pregnant mothers (Figure 2).
All the participants in the present study received personalised nutrition guidance. In brief, respondents received visualised dietary assessment and nutrition education according to their dietary problems (visualisation of current dietary intake using Kenya’s food-based dietary guidelines) immediately after completing the app-based food frequency questionnaire [12].
All data were collected by enumerators through face-to-face interviews with the respondents in their homesteads. Enumerators had previous experience with nutrition surveys and were proficient in English, Swahili, and local languages spoken in the study area. Before the surveys, enumerators underwent a two-day training and two-day field pre-test to standardise survey protocols and minimise reporting errors such as recall bias.
This study was conducted in accordance with the principles of the Declaration of Helsinki. All procedures involving human participants were approved by the Ethics Committee of Tokyo University of Agriculture (code no. 2113, 27 September 2021) and Amref Health Africa in Kenya (P947/2021, 4 May 2021). Written informed consent has been obtained from participants and the children’s parents or primary caregivers.
2.2. Assessment of Variation in Children’s Stunting Status
Children’s heights were measured using a height gauge with a measurement tape strapped to the bars, and weights were measured using a digital health metre (HD-660; TANITA, Japan). A height board was used to measure the length of children aged < 2 years or unable to stand alone, regardless of age. For children aged ≥ 2 years whose height was measured on the board, to derive the height, we subtracted 0.7 cm from the length [14]. If the height was shorter than a previous measurement, the current value was treated as missing or unreliable.
Using the World Health Organization (WHO) growth standards, child stunting was defined as height-for-age z-score (HAZ) < –2 and underweight as weight-for-age z-score (WAZ) -2 [14]. In the present study, we assessed variations in children’s stunting status as follows: Children without stunting during the measured points were grouped as “not stunted”. Those without stunting at survey end but stunted prior were grouped as “recovered-from-stunting”. Regardless of stunting status at the first survey, those with stunting at the end of the survey (Surveys 3 or 4) were grouped as having “persistent/worsened stunting”.
2.3. Assessment of Maternal and Child Dietary Intakes
The children’s and mothers’ dietary intakes were assessed by 24 h dietary recall method (24 HR) in two seasons. The 24HRs were conducted on two non-consecutive days per season. The second recall was conducted approximately 5 days (3–8 days) after the first recall. The 24HRs were performed according to a standardised protocol with prior training and pretesting [15]. First, the enumerators asked the caregivers to recall all the food and beverages they had consumed the previous day, starting with the children’s diets. Secondly, the caregivers described mixed dishes’ ingredients and cooking methods, including beverages and portion sizes. To estimate the amount or size of very common ingredients (salt, sugar, tomato, and onion), actual foods were mentioned. To estimate the portion of common cooked dishes consumed per participant, such as ugali (Kenyan staple food made mostly from maize flour), stir-fried green leafy vegetables, and stewed small fish, plastic food samples of standard portions were used as reference. For others, market prices of ingredients were asked for, and household plates or cups were used to estimate the volume (substituted with water) of cooked dishes. The reported market prices were converted to gram weights based on data collected in market surveys during the survey period. Conversion rates from water volumes to gram weights were calculated using actual cooked dishes prepared by field staff during the survey period. The amount of ingredients consumed by each participant was estimated as the ratio of the portion consumed by each participant to the total volume of the dish cooked.
Nutritional calculations were performed using the Kenyan food composition tables [16]. The Tanzanian composition tables were used to compensate for data on cassava leaves and carbonated beverages, and the West African composition tables for data on tea infusion and instant noodles [17,18]. Kenyan food recipes were used to estimate dishes consumed outside the home [19].
Food items were categorised based on the Kenya food composition tables [16] and the minimum dietary diversity score for women [20]. From 4 days of dietary recalls, obtained during two different seasons, the usual intake of each participant was estimated using the multiple source method (MSM) programme, a web-based statistics package [21]. The MSM estimates the usual individual nutrient and food intake, including episodically consumed foods in the study population. In this study, food group intake was estimated to be the usual intake among consumers.
2.4. Other Variables
Data on children’s age and sex were obtained from certified documents in every survey. Maternal pregnancy and lactation status were confirmed in each survey as this might have changed between surveys. Information on family structure, household socioeconomic status, and respondent educational level was collected during registration in August 2021. Educational level was categorised into three groups: lower than primary (<8 years), completed primary (between 8 and 12 years), and completed secondary or higher (>12 years). Using principal component analysis, a composite wealth index was created for each household based on the number of people per room; ownership or possession of electricity, smartphones, television, and livestock; and land size, and grouped into three categories: poorer, middle, and richer [22].
2.5. Statistical Analysis
Data are presented as median and interquartile range for continuous variables and numbers and percentages for categorical variables. To compare the characteristics of participants and dietary intake based on variations in children’s stunting status (not-stunted, recovered-from-stunting, or persistent/worsened stunting), chi-square test or Fisher’s exact test for categorical variables, and one-way analysis of variance (ANOVA) or Kruskal–Wallis test based on Shapiro–Wilk test of normality for continuous variables were performed. One-way analysis of covariance (ANCOVA) was used to compare maternal and children dietary intakes based on variations in children’s stunting status adjusted for covariates. Variables skewed to the extreme right (skewness > 2) were transformed into their natural logarithms to apply ANCOVA. Covariates for the comparison of children’s dietary intake included region (Kitui or Vihiga), sex (girl or boy), and age in months (continuous). Covariates for maternal dietary intake included region (Kitui or Vihiga), age in years (continuous), and lactation status (yes or no).
Analyses were performed to compare energy-adjusted nutrient and food group intakes based on variations in children’s stunting status. Energy-adjusted nutrients were calculated using the residual method, and energy-adjusted intake for food groups was calculated using the density method expressed in grams per 1000 kcal [23].
All statistical analyses were performed using SPSS version 28 (IBM Corp., Armonk, NY, USA). All reported p-values were two-tailed. For multiple comparisons, the significance levels were adjusted using the Bonferroni method.
3. Results
3.1. Variation in Children’s Stunting Status
Table 1 shows the mean HAZ, number of children with stunting, and missing values by survey. The percentages of children stunting were 29.6, 25.0, 20.8, and 23.4% in surveys 1, 2, 3, and 4, respectively (Table 1). Eighty-five (63.0%), 20 (14.8%), and 30 (22.2%) children were classified as ‘not-stunted’, ‘recovered-from-stunting’, and ‘persistent/worsened stunting’, respectively. Of 30 children with persistent/worsened stunting, 27 remained stunted while three developed stunting during surveys 1 to 4.
3.2. Characteristics of Participants
Table 2 shows the characteristics of households, children, and mothers. The proportion of participants from each study area, family structure, household wealth index, children’s age, and maternal education did not differ according to children’s stunting status. Boys were more likely to recover from stunting. Mothers of not-stunted children were significantly older and taller. When examining mothers’ lactating status by groups, there was no significant difference between groups (Table S1). At the time of the first surveys, the percentages of children aged 12–23 months whose mothers were lactating were 90.5%, 75.0% and 100% for never stunted, recovered from stunting, and consistent/worsened stunting groups, respectively. Those of older children (24–59 months) were 29.7%, 33.3% and 27.3%, respectively.
3.3. Children’s and Mothers’ Energy and Nutrient Intakes
Table 3 presents crude energy and nutrient intakes by stunting status. The median children’s energy intakes were 1124, 1068, and 972 kcal for the not-stunted, recovered-from-stunting, and persistent/worsened stunting groups, respectively. The corresponding maternal energy intakes were 1931, 1902, and 1749 kcal. After adjusting for study area, sex, and age, significant differences occurred in children’s energy intake (p = 0.002); and energy intake of the persistent/worsened stunting group was the lowest relative to those of the other two groups. However, no significant group differences occurred for maternal energy intake (p = 0.14). Children with persistent/worsened stunting had lower total protein (p = 0.005) and vitamin B2 (p < 0.001) intakes than the other two groups. They also had lower intakes of animal protein (p = 0.003), fat (p = 0.004), calcium (p = 0.012), and vitamin B12 (p = 0.018) than the recovered-from-stunting group. Animal protein intake among mothers of the recovered-from-stunting group was significantly higher than for the persistent/worsened stunting group (p = 0.036).
After energy adjustment, there were still significant differences in children’s fat, animal protein, calcium, and vitamins B2 and B12 intake and mothers’ protein and vitamin B12 intake (Table S2).
3.4. Children’s and Mothers’ Food Group Intakes
Table 4 presents the crude food group intakes by stunting status. Significant group differences occurred in intakes of other vegetables (p < 0.001), milk and dairy products (p = 0.004), and beverages (p = 0.018) for children. Significant group differences occurred in intakes of other vegetables (p = 0.003) and milk and dairy products (p = 0.011) for mothers. The persistent/worsened stunting group and their mothers had significantly lower dairy intakes than the recovered-from-stunting group and their mothers. Almost all children consumed milk and dairy products, and the median intakes were 108, 118, and 79 g for the not-stunted, recovered-from-stunting, and persistent/worsened stunting groups, respectively. The corresponding intakes for mothers were 120, 164, and 108 g, respectively. In addition, the recovered-from-stunting group and their mothers were less likely to consume dark green leafy vegetables. They consumed other vegetables more than those in the other two groups.
Even after energy adjustment, although there were no statistically significant differences between the two groups, the persistent/worsened stunted group and their mothers tended to consume milk and dairy products less than the recovered-from-stunting group (Table S3).
Table 3.
Energy and nutritional intake of children and their mothers by child stunting status.
| Never Stunted (n = 85) | Recovered from Stunting (n = 20) | Persistent/Worsened Stunting (n = 30) | p-Value (crude) * | p-Value (adjusted) † | |
| Children’s intake | |||||
| Energy (kcal) | 1124 (962, 1343)a | 1068 (923, 1265)b | 972 (840, 1150)ab | 0.013 | 0.002 |
| Energy (kcal/kgBM) | 91 (76, 109) | 95 (82, 117) | 94 (77, 101) | 0.596 | 0.460 |
| Protein (g) | 27.8 (22.7, 35.3)a | 25.0 (22.9, 32.2)b | 23.1 (19.8, 28.8)ab | 0.034 | 0.005 |
| Protein (g/kgBM) | 2.2 (1.9, 2.7) | 2.4 (2.1, 2.9) | 2.2 (1.9, 2.7) | 0.451 | 0.436 |
| Animal protein (g) | 5.3 (3.4, 8.1) | 6.4 (3.3, 11.3)a | 2.8 (1.4, 5.8)a | 0.005 | 0.003 |
| Plant protein (g) | 21.5 (17.4, 27.6)a | 19.9 (17.1, 23.2) | 20.2 (17.4, 21.4)a | 0.232 | 0.018 |
| Fat (g) | 27.7 (23.7, 34.2) | 25.6 (22.5, 40.7)a | 22.6 (18.4, 30.3)a | 0.039 | 0.004 |
| Carbohydrate (g) | 179.4 (154.3, 210.9)a | 173.3 (146.8, 190.4) | 150.8 (131.9, 172.8)a | 0.006 | 0.002 |
| Fibre (g) | 23.0 (18.8, 29.5) | 22.3 (17.9, 25.1) | 20.4 (18.1, 25.1) | 0.198 | 0.119 |
| Calcium (mg) | 419 (325, 526) | 435 (290, 611)a | 306 (268, 450)a | 0.044 | 0.012 |
| Magnesium (mg) | 192 (150, 231) | 177 (158, 207) | 164 (138, 195) | 0.086 | 0.076 |
| Iron (mg) | 9.4 (7.5, 11.8) | 9.0 (7.0, 12.6) | 8.2 (6.9, 10.5) | 0.317 | 0.163 |
| Zinc (mg) | 5.1 (4.2, 6.1) | 4.9 (4.3, 6.0) | 4.3 (3.6, 5.3) | 0.082 | 0.062 |
| Vitamin A (μgRAE) | 169 (140, 235) | 196 (129, 240) | 146 (112, 190) | 0.141 | 0.050 |
| Vitamin B1 (mg) | 0.65 (0.53, 0.78) | 0.56 (0.49, 0.72) | 0.53 (0.48, 0.70) | 0.105 | 0.060 |
| Vitamin B2 (mg) | 0.74 (0.58, 0.93)a | 0.84 (0.51, 1.06)b | 0.51 (0.39, 0.81)ab | 0.002 | <0.001 |
| Niacin (μg) | 7.1 (6.2, 8.5)a | 6.7 (4.9, 8.1) | 5.9 (5.2, 7.4)a | 0.059 | 0.049 |
| Vitamin B12 (μg) | 1.6 (0.8, 2.7)a | 2.3 (0.8, 3.9)ab | 0.9 (0.4, 1.8)b | 0.028 | 0.018 |
| Folate (μg) | 267 (214, 326) | 252 (197, 336) | 238 (151, 302) | 0.140 | 0.157 |
| Vitamin C (mg) | 60 (49, 78) | 65 (41, 83) | 52 (39, 80) | 0.307 | 0.268 |
| Mothers’ intake | |||||
| Energy (kcal) | 1931 (1669, 2231) | 1901 (1633, 2229) | 1749 (1477, 2074) | 0.052 | 0.140 |
| Energy (kcal/kgBM) | 31 (25, 39) | 32 (27, 38) | 30 (24, 36) | 0.697 | 0.614 |
| Protein (g) | 50.2 (43.1, 56.3) | 48 (41.5, 57.7) | 46.2 (38.6, 52.7) | 0.197 | 0.392 |
| Protein (g/kgBM) | 0.8 (0.7, 1) | 0.8 (0.7, 1) | 0.8 (0.7, 0.9) | 0.763 | 0.748 |
| Animal protein (g) | 6.7 (3.8, 9.8) | 9.5 (5.9, 13.1)a | 5.1 (2.2, 10.1)a | 0.014 | 0.036 |
| Plant protein (g) | 42.1 (35.6, 47.7) | 38.1 (33.4, 43.1) | 39.4 (36.1, 42.8) | 0.161 | 0.191 |
| Fat (g) | 42.5 (36.5, 52.6) | 44.2 (37.4, 59.6) | 38.6 (28, 45.5) | 0.037 | 0.057 |
| Carbohydrate (g) | 309.9 (263.1, 361.1) | 291.7 (250.7, 347.6) | 273.3 (232.5, 315.2) | 0.080 | 0.175 |
| Fibre (g) | 44.9 (36.7, 50.9) | 40.5 (36.2, 45.7) | 42.7 (33.5, 49.4) | 0.313 | 0.377 |
| Calcium (mg) | 615 (508, 816) | 679 (456, 890) | 538 (412, 733) | 0.091 | 0.330 |
| Magnesium (mg) | 328 (273, 381) | 300 (266, 375) | 289 (241, 328) | 0.176 | 0.438 |
| Iron (mg) | 16.2 (13.9, 18.1) | 14.7 (12.2, 18) | 14.4 (12.4, 18.6) | 0.225 | 0.571 |
| Zinc (mg) | 9 (8, 10.6) | 8.6 (7.4, 10.7) | 8.7 (6.8, 10.3) | 0.383 | 0.696 |
| Vitamin A (μgRAE) | 237 (186, 304) | 222 (184, 330) | 216 (170, 259) | 0.212 | 0.405 |
| Vitamin B1 (mg) | 1.16 (0.98, 1.32) | 1.00 (0.88, 1.2) | 1.08 (0.86, 1.23) | 0.153 | 0.209 |
| Vitamin B2 (mg) | 1.07 (0.84, 1.32) | 1.05 (0.77, 1.66) | 0.92 (0.68, 1.25) | 0.073 | 0.267 |
| Niacin (μg) | 12 (10.3, 13.5) | 10.8 (9.1, 12.9) | 10.8 (9.4, 12.2) | 0.090 | 0.200 |
| Vitamin B12 (μg) ‡ | 2.2 (1.4, 3.4) | 3.5 (1.6, 5) | 2 (1.1, 2.9) | 0.093 | 0.355 |
| Folate (μg) | 475 (391, 538) | 377 (337, 528) | 427 (325, 534) | 0.136 | 0.481 |
| Vitamin C (mg) | 77 (61, 98) | 80 (61, 103) | 72 (56, 90) | 0.486 | 0.391 |
BM; body mass, RAE; retinol activity equivalents. Values are expressed as median (25 percentiles, 75 percentiles). * Kruskal–Wallis test or ANOVA, † ANCOVA (adjusted with region, age, sex (only for children), and lactating status (only for mothers). ‡ Log-transformed for ANCOVA. a,b Values marked with the same superscript in the same column differ significantly based on the Bonferroni adjustment.
Table 4.
Food group intake of children and their mothers by child stunting status.
| Never Stunted (n = 85) | Recovered from Stunting (n = 20) | Persistent/Worsened Stunting (n = 30) | p-Value | ||||||
| Consumers (%) | Median (IQR) (g) | Consumers (%) | Median (IQR) (g) | Consumers (%) | Median (IQR) (g) | Consumers (%) * | Intake † | Intake (adjusted) ‡ | |
| Children’s intake | |||||||||
| Grains and cereals total | 100.0 | 326 (251, 409) | 100.0 | 327 (214, 366) | 100.0 | 303 (252, 375) | - | 0.711 | 0.137 |
| Maize | 100.0 | 212 (150, 270) | 100.0 | 186 (160, 291) | 100.0 | 186 (145, 253) | - | 0.739 | 0.510 |
| Bread/Wheat flour § | 67.1 | 77 (56, 95) | 55.0 | 57 (54, 68) | 63.3 | 58 (49, 77) | 0.592 | 0.067 | 0.200 |
| Rice | 83.5 | 188 (152, 232) | 70.0 | 170 (144, 212) | 76.7 | 189 (141, 247) | 0.328 | 0.523 | 0.491 |
| Other cereals | 43.5 | 32 (27, 44) | 50.0 | 32 (30, 36) | 46.7 | 35 (30, 40) | 0.857 | 0.906 | 0.975 |
| Potatoes, tubers and starches | 71.8 | 138 (97, 216) | 85.0 | 96 (83, 164) | 60.0 | 131 (101, 160) | 0.157 | 0.164 | 0.110 |
| Sugar | 100.0 | 22 (14, 32) | 100.0 | 27 (21, 35) | 100.0 | 14 (10, 28) | - | 0.018 | 0.105 |
| Nuts and seeds | 7.1 | 53 (21, 120) | 15.0 | 17 (15, 44) | 3.3 | - | 0.309 | - | - |
| Dark green leafy vegetables ** | 96.5 | 61 (49, 75) | 85.0 | 59 (50, 91) | 100.0 | 57 (52, 77) | 0.056 | 0.869 | 0.867 |
| Vitamin A rich fruits and vegetables †† | 57.6 | 166 (80, 222) | 55.0 | 162 (144, 256) | 36.7 | 160 (115, 228) | 0.137 | 0.703 | 0.714 |
| Other vegetables ‡‡ | 100.0 | 47 (36, 64)a | 100.0 | 65 (48, 86)ab | 100.0 | 48 (33, 67)b | - | 0.040 | <0.001 |
| Other fruits §§ | 57.6 | 131 (107, 180) | 65.0 | 118 (108, 160) | 63.3 | 135 (92, 184) | 0.762 | 0.803 | 0.512 |
| Pulses | 75.3 | 93 (58, 133) | 80.0 | 83 (51, 106) | 86.7 | 83 (51, 97) | 0.468 | 0.321 | 0.147 |
| Fish, meat and egg total | 57.6 | 40 (30, 51) | 55.0 | 40 (36, 48) | 40.0 | 43 (34, 64) | 0.247 | 0.784 | 0.788 |
| Milk and dairy products | 97.6 | 108 (79, 152)a | 100.0 | 118 (69, 218)b | 93.3 | 79 (48, 100)ab | 0.312 | 0.003 | 0.004 |
| Oils and fats | 100.0 | 14 (10, 17) | 100.0 | 14 (9, 19) | 100.0 | 13 (9, 14) | - | 0.433 | 0.078 |
| Confectioneries | 62.4 | 55 (42, 72) | 70.0 | 62 (53, 73) | 53.3 | 55 (48, 74) | 0.477 | 0.267 | 0.175 |
| Beverages | 95.3 | 328 (167, 573)a | 95.0 | 232 (205, 580) | 100.0 | 206 (129, 289)a | 0.530 | 0.024 | 0.018 |
| Seasonings | 100.0 | 3 (2.3, 4.2) | 100.0 | 3.4 (2.9, 5.1) | 100.0 | 2.8 (2.2, 4.3) | - | 0.254 | 0.053 |
| Mothers’ intake | |||||||||
| Grains and cereals total | 100.0 | 667 (594, 762) | 100.0 | 607 (541, 752) | 100.0 | 648 (535, 720) | - | 0.319 | 0.386 |
| Maize | 100.0 | 535 (414, 632) | 100.0 | 486 (437, 591) | 100.0 | 491 (394, 639) | - | 0.806 | 0.697 |
| Bread/Wheat flour | 69.4 | 108 (85, 137) | 60.0 | 97 (84, 109) | 66.7 | 108 (85, 138) | 0.718 | 0.411 | 0.285 |
| Rice § | 75.3 | 324 (273, 395) | 70.0 | 304 (221, 336) | 70.0 | 321 (274, 367) | 0.798 | 0.444 | 0.393 |
| Other cereals | 24.7 | 31 (22, 39) | 20.0 | 27 (26, 28) | 16.7 | 36 (30, 38) | 0.726 | 0.613 | 0.547 |
| Potatoes, tubers and starches | 61.2 | 250 (147, 352) | 75.0 | 181 (144, 252) | 56.7 | 181 (149, 280) | 0.402 | 0.420 | 0.098 |
| Sugar | 100.0 | 30 (20, 51) | 100.0 | 36 (22, 50) | 100.0 | 21 (16, 36) | - | 0.038 | 0.430 |
| Nuts and seeds | 9.4 | 42 (31, 90) | 10.0 | 48 (9, 87) | 3.3 | - | 0.650 | - | - |
| Dark green leafy vegetables ** | 97.6 | 113 (98, 129) | 85.0 | 115 (108, 127) | 93.3 | 105 (92, 123) | 0.035 | 0.245 | 0.855 |
| Vitamin A rich fruits and vegetables †† | 41.2 | 169 (143, 292) | 30.0 | 205 (145, 268) | 26.7 | 240 (162, 299) | 0.298 | 0.637 | 0.972 |
| Other vegetables ‡‡ | 100.0 | 77 (58, 100)a | 100.0 | 108 (65, 140)ab | 100.0 | 71 (49, 101)b | - | 0.085 | 0.003 |
| Other fruits §§ | 35.3 | 148 (126, 197) | 55.0 | 186 (112, 203) | 46.7 | 162 (126, 242) | 0.205 | 0.810 | 0.675 |
| Pulses | 82.4 | 195 (140, 253) | 75.0 | 177 (130, 220) | 93.3 | 181 (150, 227) | 0.187 | 0.613 | 0.649 |
| Fish, meat and egg total | 54.1 | 61 (49, 84) | 65.0 | 70 (54, 83) | 40.0 | 66 (55, 94) | 0.200 | 0.689 | 0.910 |
| Milk and dairy products § | 96.5 | 120 (91, 156) | 95.0 | 164 (109, 198)a | 96.7 | 108 (80, 128)a | 0.825 | 0.003 | 0.011 |
| Oils and fats | 100.0 | 21 (16, 28) | 100.0 | 23 (16, 32) | 100.0 | 20 (14, 24) | - | 0.405 | 0.176 |
| Confectioneries § | 57.6 | 86 (78, 96) | 60.0 | 75 (53, 99) | 43.3 | 80 (78, 87) | 0.352 | 0.369 | 0.243 |
| Beverages | 100.0 | 515 (365, 837) | 100.0 | 536 (350, 792) | 100.0 | 407 (306, 600) | - | 0.086 | 0.266 |
| Seasonings | 100.0 | 5.9 (4.8, 7.3) | 100.0 | 6 (5.6, 8.9) | 100.0 | 5 (3.8, 6.8) | - | 0.091 | 0.053 |
Abbreviations: IQR, Interquartile range. Intakes were for consumers only. * Chi-Square or Fisher’s exact test (underlined values are significantly lower). † Kruskal–Wallis Test or ANOVA. ‡ ANCOVA (adjusted with region, age, sex (only for children), and lactating status (only for mothers). § Log-transformed for ANCOVA. ** Dark green leafy vegetables include kale, amaranth leaves, black nightshade, cowpea leaves, pumpkin leaves, etc. †† Vitamin A-rich fruits and vegetables include carrots, pumpkin fruit, mango, papaya, etc. ‡‡ Other vegetables include tomato, cabbage, onion, etc. §§ Other fruits include avocado, banana, guava, orange, watermelon, etc. a,b Values marked with the same superscript in the same column are significantly different based on the Bonferroni adjustment.
4. Discussion
The present study identified children who were not stunted, had recovered from stunting, and those whose stunting status persisted/worsened in the one-year follow-up period during which two nutritional guidance sessions were provided. We compared the dietary intakes of children and mothers by variations in stunting status. There were significant group differences in children’s intakes of energy and most nutrients, as well as in maternal and children’s intakes of milk and dairy products and vegetable types.
Our results showed a significant difference in children’s energy intake according to variations in stunting status. Less energy intake among children with stunting is plausible. This is because providing adequate energy, protein, and all other nutrients is essential for catch-up growth [24]. In contrast, there was no significant difference in energy intake between mothers of children with and without stunting, even though studies showed close correlations between mothers and young children’s diets [7,8,9,10]. This suggests that insufficient energy intake in children with stunting might result not from household food insecurity but from inappropriate complementary feeding practices, including poor allocation of family diets to children, and children’s lack of appetite [25]. Lower intake of dairy products may be the primary cause of lower energy intake. In addition, compared to mothers, the intake of bread and vitamin A-rich fruit and vegetables among stunted children was relatively lower. These minor differences in intake between mothers and children could contribute to significantly lower energy intake in children. Further research is warranted to elucidate the factors influencing lower energy intake among children.
Maternal and child consumption of milk and dairy products and related nutrients, such as animal protein, calcium, and vitamins B2 and B12, were lower in the persistent/worsened stunting group than in the not-stunted or those recovered-from-stunting groups. These findings are consistent with those from a cross-country panel data, which showed that increased national milk supply over time, which correlated with child-level consumption, was associated with a significant reduction in children’s stunting [26]. The results also agree with other studies’ findings in high-, low-, and middle-income countries, showing a link between milk intake and improved children’s growth [27,28,29,30,31,32]. In addition, a Bangladesh study showed a higher percentage of mothers of not-stunted children had consumed dairy more than mothers of children with stunting [11]. Given that >95% of the participants consumed milk and dairy products, these foods appear to be a central part of their diet and are of nutritional importance to this population [33]. Dairy products contain energy, essential nutrients, and growth-promoting factors, and their high nutrient density is beneficial for improving nutrient adequacy in resource-limited environments [34]. Therefore, promoting dairy product intake for mothers and children would contribute to addressing stunting in children by improving dietary quality and foetal nutrition.
Given the differences in dairy consumption by groups for both children and mothers in this study, their lower consumption may result from limited access to sufficient amounts of milk at the household level. This limited access could be attributed to inadequate availability and limited purchasing capability. The milk consumed in the study area was home-produced to some extent (approximately 30% in average). Specifically, the ownership of cows was high in Vihiga, and the ownership of goats was high in Kitui due to their respective climatic conditions [33]. The underlying factors of insufficient milk access may differ between study areas. Since goats produce less milk than cows, households owning goats would have had to buy milk to access sufficient amounts. In fact, lower dairy intake was significant among poor households in Kitui. On the other hand, in Vihiga, lower per-individual dairy intake was observed among families with many young children. A lower milk allocation might be due to limited cow milk production and large family size. Altogether, interventions and policies are recommended to improve household dairy production [31] and decrease the market price of dairy products [35]. Future studies that incorporate household-level milk yield and intra-household milk allocation would provide a better understanding of factors that contribute to insufficient milk access and consumption among children and mothers.
The way dairy products are consumed can also affect dairy consumption. Tea with milk, consumed by mothers and children in most households, contains a small amount of milk and a large quantity of sugar. Indeed, the median sugar intake exceeded 5% of the total energy intake for both children and mothers (Table S3). Furthermore, some children who recovered from stunting consumed packaged plain milk and yoghurt besides tea. Therefore, nutrition education is recommended to ensure children receive plain milk or yoghurt and to recommend recipes of tea with higher quantities of milk and reduced sugar.
Differences in vegetable consumption between groups could reflect differences in socio-economic conditions facilitating the purchase of foods. This study showed that the recovered-from-stunting group consumed more other vegetables but less dark green leafy vegetables. As there were no significant group differences in total vegetable intake, it is unlikely that vegetable consumption independently affected children’s nutrition. Instead, families of recovered-from-stunting children may have been more dependent on purchased foods. This is because other vegetables, such as tomatoes, onions, and cabbage, are more likely to be bought from the market, and dark green leafy vegetables are more likely to be home-grown in the study area [33]. In a cohort study of preschool children in Kenya, children with worse growth tended to have a subsistence-dependent traditional diet, characterised by high maize foods with low dietary diversity and few animal-source foods [36]. Similarly, this study showed that the persistent/worsened stunting group consumed less dairy, and their consumption rate of fish, meat, and eggs was the lowest. Although there was no significant difference in wealth index between the groups, unmeasured socio-economic conditions could be related to market accessibility. Nevertheless, our data on self-reported market dependency and food expenditure showed no significant differences between the groups. More robust evidence could be generated through future studies incorporating various socio-economic status indicators, food purchasing patterns, and market accessibility. Enhancing livelihoods for food affordability, especially for animal-source foods, should be an essential strategy for tackling child stunting.
Random sampling in different agroecological regions of Kenya and quantitative dietary assessment in different seasons are the strengths of this study. However, the present study has several limitations. First, some measurement errors may exist because of the self-reported recall method. Enumerators followed a standardised protocol to minimise measurement errors and used the participants’ familiar languages. Secondly, dietary intake and food security during surveys 3 and 4, which may also affect nutrition status, are unknown. The children’s medical history was examined during each survey, but no association with nutrition status was found. Thirdly, the children’s ages and study periods may have been unsuitable for accurately identifying children’s growth patterns. For instance, some children in the persistent/worsened stunting group may have recovered from stunting after the study period. Nonetheless, the follow-up period in the present study may be considered sufficient to speculate on the influence of dietary intake as the effect of nutritional intake on height gain requires at least 90 days of monitoring [37]. Fourthly, we did not assess children’s consumption of breastmilk, which affects children’s growth and dietary intake. However, as there was no significant difference in the breastfeeding status of the mothers between the groups, we do not believe there were significant group differences in breastmilk intake. Fifthly, the personalised dietary assessment might have influenced this study’s results, and we could not distinguish seasonality and intervention effects. For instance, a significant decrease in mothers’ energy and sugar intakes was observed before (survey 1) and after (survey 2) the intervention in the not-stunted and persistent/worsened stunting groups. However, little change occurred in the recovered-from-stunting group. Thus, the intervention could have had a different impact between groups. Nonetheless, the results of separate analyses of surveys 1 and 2, before and after the intervention, were similar to those of the present study. Finally, categorising child stunting status based on anthropometric measures could induce screening errors in growth outcomes. This is because there were some difficulties in measuring height due to children just starting to walk and uneven floors, non-vertical walls, etc. Nonetheless, measuring anthropometric factors multiple times and observing changes made it possible to identify measurement errors. Future studies incorporating biomarker-based assessments can provide a more accurate assessment.
This study showed that children’s energy intake, and maternal and child dairy intakes were significantly lower in the persistent/worsened stunting group. There were also significant differences in vegetable types consumed between groups, which could reflect the disparities in socio-economic conditions of food affordability. These findings indicate the importance of improving children’s feeding practices and their families’ livelihoods and creating food environments with sufficient milk and dairy products to address children’s stunting. Further research is needed to confirm the factors behind the observed differences in intake.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/dietetics4040046/s1, Table S1: Percentage of children whose mothers were lactating during each survey period; Table S2: Energy-adjusted nutritional intake of children and their mothers by child stunting status; Table S3: Energy-adjusted food group intake (g/1000 kcal/day) of children and their mothers by child stunting status.
Author Contributions
Conceptualization, M.K.; methodology, M.K., A.H., K.I.-T., Y.T., H.M., P.M., K.I., and Y.M.; formal analysis, M.K.; data collection, M.K., L.K., P.M., and Y.M.; data curation, M.K. and Y.M.; writing—original draft preparation, M.K.; writing—review and editing, A.H., K.I.-T., Y.T., L.K., and Y.M.; supervision, A.H.; project administration, A.H., H.M., K.I., P.M. and Y.M.; funding acquisition, M.K. and Y.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Japan Society for the Promotion of Science, grant number 21J20326, and Ministry of Agriculture, Forestry and Fisheries, grant number L21ROM109.
Data Availability Statement
Data will be provided upon reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Study design. This one-year prospective study was conducted from November 2021 to November 2022. All the participants in the present study received nutrition guidance after diet and anthropometric data were collected in November–December 2021 and February–March 2022. Children’s growth pattern was assessed during follow-up to identify variation in stunting status.
Figure 1.
Study design. This one-year prospective study was conducted from November 2021 to November 2022. All the participants in the present study received nutrition guidance after diet and anthropometric data were collected in November–December 2021 and February–March 2022. Children’s growth pattern was assessed during follow-up to identify variation in stunting status.
Figure 2.
Flowchart of the study participant selection.
Table 1.
Height-for-age z scores, number (%) of stunted children, and missing data by groups of variation in child stunting.
Table 1.
Height-for-age z scores, number (%) of stunted children, and missing data by groups of variation in child stunting.
| Survey Timing | Overall (n = 135) | Variation in Child Stunting During the Study Period | ||||||||||
| Not Stunted (n = 85) | Recovered from Stunting (n = 20) | Persistent/Worsened Stunting (n = 30) | ||||||||||
| HAZ | Stunted | Missing | HAZ | Stunted | Missing | HAZ | Stunted | Missing | HAZ | Stunted | Missing | |
| Survey 1 (Nov.–Dec. 2021) | −1.25 ± 1.25 | 40 (29.6) | 0 (0.0) | −0.51 ± 0.85 | 0 (0.0) | 0 (0.0) | −2.35 ± 0.77 | 15 (75.0) | 0 (0.0) | −2.61 ± 0.60 | 25 (83.3) | 0 (0.0) |
| Survey 2 (Feb.–Mar. 2022) | −1.27 ± 1.15 | 33 (25.0) | 3 (2.2) | −0.63 ± 0.85 | 0 (0.0) | 3 (3.5) | −1.98 ± 0.71 | 10 (50.0) | 0 (0.0) | −2.54 ± 0.69 | 23 (76.7) | 0 (0.0) |
| Survey 3 (Jun. 2022) | −1.11 ± 1.15 | 27 (21.1) | 7 (5.2) | −0.49 ± 0.85 | 0 (0.0) | 6 (7.1) | −1.66 ± 0.84 | 9 (45.0) | 0 (0.0) | −2.42 ± 0.68 | 18 (62.1) | 1 (3.4) |
| Survey 4 (Oct.–Nov. 2022) | −1.20 ± 1.03 | 29 (23.4) | 11 (8.1) | −0.62 ± 0.73 | 0 (0.0) | 8 (9.4) | −1.46 ± 0.43 | 0 (0.0) | 2 (10.0) | −2.57 ± 0.48 | 29 (100.0) | 1 (3.4) |
Abbreviation: HAZ, Height-for-age z score. Values are expressed as the mean ± SD for HAZ and number (%) for other variables. Participating children were measured at least three times during the study period and assessed for variations in their stunting status. If the height was shorter than the previous measurement, the value was treated as a missing value as it was not reliable.
Table 2.
Characteristics of participants by children’s stunting status.
| Not Stunted (n = 85) | Recovered from Stunting (n = 20) | Persistent/Worsened Stunting (n = 30) | p-Value † | ||||
| County | |||||||
| Vihiga | 43 | 50.6% | 12 | 60.0% | 11 | 36.7% | 0.237 |
| Kitui | 42 | 49.4% | 8 | 40.0% | 19 | 63.3% | |
| Characteristics of households | |||||||
| Family size (number of persons) | 6.0 | (5.0, 7.0) | 6.0 | (4.0, 6.0) | 6.0 | (5.0, 7.0) | 0.366 |
| Number of children under five | 1.0 | (1.0, 2.0) | 1.5 | (1.0, 2.0) | 2.0 | (1.0, 2.0) | 0.217 |
| Wealth index | |||||||
| Poorer | 27 | 31.8% | 9 | 45.0% | 13 | 43.3% | 0.641 |
| Middle | 25 | 29.4% | 6 | 30.0% | 8 | 26.7% | |
| Richer | 33 | 38.8% | 5 | 25.0% | 9 | 30.0% | |
| Characteristics of Children | |||||||
| Male | 40 | 47.1% | 15 | 75.0% | 14 | 46.7% | 0.068 |
| Female | 45 | 52.9% | 5 | 25.0% | 16 | 53.3% | |
| Age (months) 1 | 26.0 | (20.0, 37.0) | 22.5 | (19.5, 32.5) | 28.0 | (20.0, 39.0) | 0.456 |
| The last-born 1 | 78 | 91.8% | 18 | 90.0% | 27 | 90.0% | 0.829 |
| Exclusive breastfeeding during the first 6 months | 67 | 78.8% | 12 | 60.0% | 24 | 80.0% | 0.198 |
| Characteristics of Mothers | |||||||
| Age (years) 1 | 32.0 | (28.0, 36.0) a | 31.0 | (29.0, 35.5) | 29.0 | (23.0, 31.0) a | 0.013 |
| Maternal education | |||||||
| Less than primary (less than 8 years) | 36 | 42.4% | 5 | 25.0% | 20 | 66.7% | 0.553 |
| Completed primary (between 8 and 12 years) | 34 | 40.0% | 9 | 45.0% | 2 | 6.7% | |
| Higher than secondary (more than 12 years) | 15 | 17.6% | 6 | 30.0% | 7 | 23.3% | |
| Height (cm) 1 | 159.3 | (155.5, 163.0) | 156.6 | (154.1, 159.1) | 156.5 | (152.3, 161.6) | 0.030 |
| BMI (kg/m2) 1 | 23.6 | (21.5, 26.9) | 23.5 | (22.1, 25.2) | 22.1 | (19.9, 28.2) | 0.260 |
| Underweight | 2 | 2.4% | 0 | 0.0% | 2 | 6.7% | 0.290 |
| Normal weight | 48 | 56.5% | 15 | 75.0% | 19 | 63.3% | |
| Overweight | 26 | 30.6% | 3 | 15.0% | 4 | 13.3% | |
| Obesity | 9 | 10.6% | 2 | 10.0% | 5 | 16.7% | |
| Lactating 2 | 39 | 45.9% | 11 | 55.0% | 14 | 46.7% | 0.760 |
WAZ; weight-for-age z score, BMI; body mass index. Values are expressed as number (%) of participants or median (25 percentiles, 75 percentiles). † Chi-square test or Fisher’s exact test for categorical variables or Kruskal–Wallis test for continuous variables; values marked with the same superscript in the same column are significantly different based on the Mann–Whitney test with Bonferroni adjustment. 1 Information at the time of the first survey (November–December 2021). 2 Those who were lactating during either or both periods of the first and second surveys.
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Kishino, M.; Hida, A.; Ishikawa-Takata, K.; Tada, Y.; Kariuki, L.; Maundu, P.; Matsuda, H.; Irie, K.; Morimoto, Y. Variation in Child Stunting and Association with Maternal and Child Dietary Intakes in Rural Kenya: A One-Year Prospective Study. Dietetics 2025, 4, 46. https://doi.org/10.3390/dietetics4040046
AMA Style
Kishino M, Hida A, Ishikawa-Takata K, Tada Y, Kariuki L, Maundu P, Matsuda H, Irie K, Morimoto Y. Variation in Child Stunting and Association with Maternal and Child Dietary Intakes in Rural Kenya: A One-Year Prospective Study. Dietetics. 2025; 4(4):46. https://doi.org/10.3390/dietetics4040046
Chicago/Turabian StyleKishino, Madoka, Azumi Hida, Kazuko Ishikawa-Takata, Yuki Tada, Lucy Kariuki, Patrick Maundu, Hirotaka Matsuda, Kenji Irie, and Yasuyuki Morimoto. 2025. "Variation in Child Stunting and Association with Maternal and Child Dietary Intakes in Rural Kenya: A One-Year Prospective Study" Dietetics 4, no. 4: 46. https://doi.org/10.3390/dietetics4040046
APA StyleKishino, M., Hida, A., Ishikawa-Takata, K., Tada, Y., Kariuki, L., Maundu, P., Matsuda, H., Irie, K., & Morimoto, Y. (2025). Variation in Child Stunting and Association with Maternal and Child Dietary Intakes in Rural Kenya: A One-Year Prospective Study. Dietetics, 4(4), 46. https://doi.org/10.3390/dietetics4040046
