Introduction

Child undernutrition, caused by a lack of nutrients, remains a severe public health issue in developing countries, especially in Southeast Asia and Sub-Saharan Africa, leading to morbidity and mortality1.

Undernutrition can be measured using height-for-age (stunting), weight-for-age (underweight), weight-for-height (wasting), or body mass index (BMI)-for-age (thinness). The World Health Organization (WHO) recommends classifying nutritional status in children 5 years and older through age- and sex-specific thinness grades. Moderate thinness (MT) is a condition where the child’s BMI-for-age is two to three standard deviations (SD) below the median2.

Children’s nutritional status is influenced by several key factors. Household food insecurity3,4,5 and low household income6 are major contributors to undernutrition. Other determinants include inadequate child-feeding practice7, low level of maternal education8,9, poor maternal nutrition10,11, maternal age, residence12, antenatal care, and the duration of breastfeeding11 also play crucial roles in anthropometric failures, such as stunting, wasting, and underweight among children.

A 2024 global estimate revealed that 190 million children and adolescents aged 5 to 19 years are living with thinness13. Supporting this, a systematic review focusing on school-age children (6–12 y) living in low- and middle-income countries (LMICs) showed that underweight and thinness were most prevalent (21%–36%) in South-East Asian and African countries14.

Specifically in Ethiopia, the prevalence of wasting is largest in the rural area (24%) compared to the urban area (13%)15. Children and adolescents aged 5 to 19 have heightened nutritional needs due to rapid growth; persistent malnutrition during this time may therefore delay development16. Chronic malnutrition is associated with delayed motor and cognitive development, lower IQ, poor social skills, delayed physical growth, increased susceptibility to infections, and a higher risk of major health problems later in life17,18,19,20, muscle wasting21,22, causing deterioration in muscle strength23,24 and impairments in muscle growth, leading to poor motor abilities in infants and children23.

Muscle strength is integral to physical fitness and plays an important role in motor skill-related physical fitness and physical activity25. Motor skill-related physical fitness encompasses the neuromuscular components that enable children to engage successfully in various games, activities, and motor skills26. A recent study in Jimma, Southeast Ethiopia, showed that children with MT in the same age group have significantly lower fat-free mass (muscle wasting) and muscle strength (hand grip, elbow flexors, knee extenders, and plantar flexors)27.

The nutrition of school-aged children, especially during middle childhood and adolescence, has received relatively little scientific attention due to the prevailing belief that early growth deficits are irreparable28. However, in recent years, researchers, public health advocates, and scholars have increasingly highlighted this developmental period as a crucial opportunity for intervention29,30,31,32. There is a growing understanding that childhood malnutrition has both immediate and long-lasting consequences that extend beyond infancy33. Particularly, children aged 5 to 7 experience significant brain development, impacting cognitive and behavioral advancement, including motor skills34,35,36.

Malnourished children aged five years and above have often been overlooked in research and policy. Furthermore, records of a comprehensive assessment of motor skills in children with MT are still lacking in the literature, particularly in children aged 5–7. Additionally, there is limited evidence regarding the motor skill-related physical fitness profile among 5- to 7-year-old children with MT. Hence, the aims of this study are to (1) determine the motor skill-related physical fitness profile in children with MT compared to NW peers; (2) determine which factors are associated with motor skill-related fitness in these children and which of them predict the overall performance, finally we hypothesized that MT children especially in rural area have significantly poorer motor skill-related physical fitness compared to their NW peers.

Methods

Sample size and sampling techniques

The sample size was determined using G*power version 3.1.9.2 statistical software with the assumption of a difference between two independent samples (t-test) for comparing the mean. Based on the mean of the overhead throw performances (MT group mean (SD) = 121.7 (23.9) and NW group mean (SD) = 138.3 (31.8)), which were derived from a pilot study conducted in Jimma town, a sample size of 76 children in each group would be required to reach an effect size of 0.5 with a precision of 0.05 and power (1- β) of 80%, with a 95% level of confidence, and an allocation ratio of 1:1. After applying a design effect of 2 and adding 10% non-response rate, the final sample size was estimated at 168 children, 84 NW and 84 MT. Since the sample size was estimated using an independent t-test, and the formal analysis was more complex, an a post-hoc power calculation was conducted and found to be effective (power: 0.984).

Study design and participants

This school-based comparative cross-sectional study was conducted in six kindergartens (grade 0) and elementary schools (grade 1) within the Kersa district, which is the rural area of Jimma Zone, between December 2023 and January 2024. Participants were eligible if: (1) they were between ages 5 and 7, with a BMI-for-age either indicating MT (-3z ≤ BMI-for-age < -2z, without bilateral pitting edema or other complications) or NW (-1z ≤ BMI-for-age < + 1), (2) they lived in a rural district, Kersa, in Ethiopia for at least 6 months and attended a local regular public kindergarten or primary schools, and (3) whose parents or caregivers were willing to participate in the face-to-face interview. The children were excluded from the study if they had any overt disability or musculoskeletal disorder, and (edematous) body, a neurodevelopmental disorder, e.g., cerebral palsy, autism spectrum disorder, medical complications, or if they were on an Outpatient Therapeutic Program (OTP).

Sampling procedure

A multistage sampling technique was applied. Six schools were selected randomly using a lottery method. The sample size was based on the proportion of the school-age group. The school’s registration book was used to select study units for eligible children. Finally, a simple random sampling technique was applied to select the final sample after screening the children for their nutritional status as either MT or NW children. Figure 1 depicts the selection process.

Data quality management

Before the actual data collection, a pretest was conducted on 10% of the study participants at a nearby school (Serxie primary school) who were not included in this study. The trained data collectors, performed all the anthropometric measurements. The weight scale was calibrated to zero in the absence of any objects and positioned on a flat surface prior to taking measurements. Continuous checks were conducted to verify the reliability of the scales. By adhering to these protocols, standardization was upheld throughout the data collection process, ensuring consistent and accurate measurements. The data collectors were fluent in the local language. A training session of 12 h took place to ensure that all data collectors reliably administered the PERF-FIT. Following the training, evaluators practiced under supervision and were required to demonstrate mastery of the administration and scoring procedures before participating in data collection. The first author (YMD) supervised the data collection. Each questionnaire was reviewed for completeness.

Data collection and procedure

We initially conducted a screening to identify children within the 5–7 age group at each primary school, followed by two consecutive days of anthropometric measurements in each school. On the third day, we began administering the PERF-FIT to those who fulfilled the inclusion criteria. This test took place in a designated classroom prepared by the teachers, where each child was brought individually for testing. During the test administration, a circuit system was implemented to ensure that each data collector consistently handled the same portion of the PERF-FIT test. Socio-demographic, socio-economic, child interaction and child health data were collected through face-to-face interviews with the parents/caregivers using a semi-structured questionnaire. Between January 4th and 28th, 2024, in-person interviews were conducted in the rural Kersa district, Ethiopia, as part of a data collection initiative.

Anthropometric measurements

Each child’s height (cm) and weight (kg) were recorded after the standardization of measures and calibration of equipment. All measurements were taken three times, and an average value was recorded. Weight was measured with shoes taken off and in light clothing (underwear and t-shirts only) using a Seca digital weighing scale (Seca, Hamburg, Germany, Model 770) and recorded to the nearest 0.1 kg. Height was measured using a stadiometer Seca 213 (Seca Germany) and recorded at the nearest 0.1 cm. The subject’s head was aligned in the Frankfurt plane during the height measurement. The ankle, calf, buttocks, and shoulders touched the stadiometer’s vertical stand to measure the height accurately.

Motor skill-related physical fitness measurement

Motor skill-related physical fitness for children was measured with the PERF-FIT test battery. This reliable and valid assessment tool was specifically developed to evaluate motor skill-related physical fitness in 5- to 12-year-old children living in low-income settings37,38,39,40. Motor skill-related physical fitness combines fundamental motor skills and muscular-skeletal fitness in functional activities. PERF-FIT has cross-cultural applicability and can be used in diverse resource-limited environments. The normative reference values used for comparison were derived from the African reference PERF-FIT dataset, which includes data from Ethiopian children who participated in the development of the norms. This ensures cultural relevance and appropriateness for our study population41.

The PERF-FIT comprises 10 items, five tapping into agility and power during functional activities and five skill item series (SIS) addressing motor competence. All items are described in detail in (Annex Table A1). African reference data have been developed based on 2800 children between ages 6 and 12. The reference data have been established for boys and girls separately in each chronological age band between 6 and 12. Using these reference data, raw data can be converted to scaled scores (SS: 1–19) at item level. Next the scaled item scores can be summed and converted into a scaled Agility and Power subscale score, a scaled Motor skills subscale score, and scaled total scores. The SS (at the item, subscale and total level) implies the mean/median corresponds with a value of 10 and a standard deviation (SD) of 3. Since the normative data are age- and sex-specific and start from age 6, the performances of the 5-year-old children were compared to the reference data of 6-year-olds. Since this was done for both groups (MT and normal weight peers), the impact is similar.

Wealth index

The wealth index, derived from the Ethiopian Demographic and Health Survey (EDHS), serves as a comprehensive indicator of household socioeconomic status42. This index is computed using data on durable assets of households, housing quality, and access to amenities such as electricity, with weights determined through principal component analysis (PCA). Subsequently, households were grouped into quintiles, from poorest to wealthiest, ranging.

Household food insecurity access scale measurement

Household food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS), which was developed by the Food and Nutrition Technical Assistance (FANTA) project43. It is widely used to assess the food insecurity status of households. It measures the level of food insecurity by asking a series of questions about food access, focusing on the frequency and severity of food-related issues. Data were collected through the administration of the HFIAS questionnaire, which inquired about various aspects of the household’s food access over a specified period, such as the previous 30 days. The total score for each household was calculated by summing the scores from all the questions. The data were analyzed based on food insecurity access classification criteria on a predetermined threshold score that distinguished between the two groups, as either ‘food secure’ or ‘food insecure43.

Muscle strength

Hand grip strength was assessed using a Takei Digital Grip Strength Dynamometer (Model TKK 5401, Tokyo, Japan). Prior to the assessment, the child completed two warm-up exercises for the hands and fingers. The warm-up routine involved shaking hands three times and bending and stretching all fingers three times. The dynamometer was adjusted to fit the participant’s hand size, if necessary. Participants were instructed to exert maximum force while squeezing the dynamometer with their preferred or dominant hand, holding it away from the body, maintaining the wrist in a neutral position, and extending the elbow. The Measurement was recorded to the nearest 0.1 kg based on three trials, and the average value was used for analysis.

Statistical analysis

The data were checked for completeness, coded, entered in EPI Data Version 4.6.0.6, and subsequently exported to SPSS Version 29.0 for data cleaning and analysis. The PERF-FIT data were analyzed in three steps. First an analysis of variance (ANOVA) was used to explore differences in the PERF-FIT total scores, subscale scores (Agility & Power and Motor Skills) and SIS between BMI group, age group and sex (fixed factors). Assumptions for applying ANOVA were checked: the residuals’ normality (the Shapiro-Wilk test, visual inspections of the normal Q-Q plots and histograms) and homoscedasticity (Levene’s test). Age and sex were considered as variables, since the raw performances of the 5-year-olds were converted into SS using the 6-year-old sex-specific references. Sex was not a significant factor and was removed from the analysis. If the assumptions for ANOVA were not met, a similar non-parametric generalized linear model (generalized estimating equations model) was conducted using Wald Chi-square statistics. Statistical significance was set at alpha less than 0.05.

Next, relationships were explored between PERF-FIT performances and food insecurity, wealth index, education mother, occupation mother, head of household, mode of delivery, child immunization, exclusive breastfeeding (EBF), initiation of complementary feeding, and grip strength using a Spearman rank correlation. Finally, significant sociodemographic variables (sex of the child, food security scale), significant variables in the ANOVA (age, BMI category) or significant correlation analysis (grip strength, education of the mother) were added to a multivariable linear regression model using backward elimination to identify how much of the variance in total PERF-FIT scores can be explained by these predictors.

Results

Participants

A total of 167 out of 168 children and their mothers or primary caregivers provided complete data, giving a response rate of 99.4%. The average age of mothers or caregivers was 30.7 (6.0) years. Notably, 79.6% of households faced food insecurity, while 59.5% of the children were males. The mean and SD weight was significantly lower for MT children (17.0 ± 2.0 kg) compared to the NW children (19.8 ± 2.6) kg), but they had a similar height (MT: 115.6 ± 6.9) cm; NW: 115.7 ± 7.1) cm). The mean value for grip strength was significantly lower for MT children (7.0 kg) compared to the NW children (8.0 kg); a description of the sample is provided in Table 1.

Table 1 Sociodemographic characteristics of parents/caregivers and children in rural area Kersa district Jimma zone Southwest Ethiopia.
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Impact of BMI and age group on motor skill-related physical fitness

The PERF-FIT performances for the BMI groups (MT vs. NW), considering their age group, are presented in Table 2.

Table 2 Post hoc comparisons (performances adjusted for grip strength).
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A significant interaction effect between BMI and age groups was identified for the PERF-FIT total score (F2,161=5.69, p = 0.004). At ages 5 and 6, children with MT performed similarly to the NW group (p > 0.05), whereas in age group 7, NW children outperformed those with MT (p < 0.001) (Fig. 2). Other interaction effects between BMI and age groups were identified for the Motor skill subscale (F2,161=7.01, p = 0.001), Dynamic balance (W(2) = 18.06, p < 0.001), Static balance (W(2) = 8.67, p = 0.013), Bounce and catch (F2,161=3.09, p = 0.048), and Running (F2,161=7.07, p = 0.001). The Bounce and catch item showed similar performances as the total score: at age 5 and 6, children with MT performed similarly as their NW peers (p > 0.05), but at age 7 NW children outperform those with MT (p = 0.003). For the Motor skill subscale, Running, Static Balance and Dynamic balance, at age 5, children with MT outperform NW peers (p = 0.043, p = 0.018, p = 0.034, p = 0.006 respectively), at age 6 they perform similarly (p > 0.05) and at age 7, NW peers outperform those with MT (p < 0.001, p = 0.003, p = 0.026, p < 0.001, respectively).

Fig. 1
figure 1

Random selection, process of study participants. G1: grade 1; KG: kindergarden.

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Fig. 2
figure 2

Performance on the agility and power subscale, the motor skills subscale and the total score.

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For the Agility and Power subscale, and the item scores of Stepping, Side jump, Long jump, Overhand throw, Throw and catch and Jumping and hopping no significant interaction effects were identified. Significant main effects of BMI group were identified on the Agility and Power subscale (F1,163=5.58, p = 0.019), the Overhand throw (F1,163=4.99, p = 0.027), Throw and catch (F1,161=4.58, p = 0.034) and Jumping and hopping (W(1) = 5.17, p = 0.023) in favor of the NW group. Significant main effects of age group were seen on the Agility and Power subscale (F2,163=9.76, p < 0.001), the Side jump (W(2) = 18.55, p < 0.001), the Long jump (F2,163=6.00, p = 0.003), the Overhand throw (F2,163=12.87, p < 0.001), Throw and catch (F2,163=14.43, p < 0.001) and Jumping and hopping (W(2) = 41.93, p < 0.001). The performances increased with increasing age for all these variables (age 5 < age 6 < age 7), except for the Throw and catch item where children aged 5 and 6 performed similarly and were outperformed by those aged 7.

Relationships between sociodemographic variables and motor skill-related fitness

The correlation coefficients are presented in Table 3. Grip strength correlates moderately with the PERF-FIT total scores (rho = 0.554, p < 0.001), Agility and Power subscale scores (rho = 0.482, p < 0.001), and Motor skills subscale scores (rho = 0.502, p < 0.001). Weak (rho < 0.4) but significant correlations were found between the education of the mother and the PERF-FIT total scores and motor skill subscale scores.

Table 3 Spearman rank correlation matrix between sociodemographic variables, grip strength and PERF-FIT performances (subscales and total).
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Predictors of motor skills-related physical fitness

The final model explained 32.4% of the variance in total PERF-FIT scores (F4,163=19.536, p < 0.001), comprising the following predictors: food security scale (HFIAS), age group, grip strength, and education of the mother. When food insecurity is present, the PERF-FIT total score will decrease with 1.33 SS (B=-1.329, t=-2.242, p = 0.026), whereas an increase in age with one unit induces an increase in PERF-FIT total score of approximately 1 SS (B = 0.913, t = 2.525, p = 0.013) and a one-unit increase in grip strength induces an increase of 0.53 SS on the PERF-FIT total score (B = 0.533, t = 4.414, p < 0.001). Although significant in the model, the education of mother as a single variable was not significant (B=-0.285, t=-1.580, p = 0.116).

Discussion

This study offers new insights into the motor skill-related fitness profile of MT children compared to their NW peers. Our findings indicate that the differences in performance between MT and NW children varied with age, suggesting that performance patterns shifted according to developmental stages. Furthermore, grip strength significantly correlated with the PERF-FIT total score and both subscales, and together with the age groups and food security scale predicted the PERF-FIT total score.

Agility and power in MT children

Children in the MT group scored lower than their NW peers on the Agility and Power subscale, particularly in the overhand throw. No other item-level differences were noted. The evidence regarding agility and power in underweight children is mixed; however, our meta-analysis indicates that tasks related to power may be particularly impacted, especially in African contexts44. However, the dispersion of the pooled results also indicates that new research may reveal (in)significant results, as shown in the current study. Consistent with this, some studies have reported that underweight children performed worse than NW peers on overhand throwing tasks40,45 and the standing long jump40,46,47,48, while others found no difference46,49. In contrast, overweight children often outperform peers on these strength tasks, highlighting the influence of body mass50,51,52,53.

The effects of age and sex were examined, and no differences between sexes were found. However, age-related effects were observed in the overhand throw, standing long jump, and side jump. These findings are consistent with European studies that developed age- and sex-specific benchmarks for the standing long jump and reported significant differences in sideways jumping among individuals aged 6 to 1354,55. Similarly, research conducted in Xi’an, China, confirmed that age influences all motor components of the overarm throw56.

At age 5, MT children outperformed NW peers in running, but by age 7, NW peers performed better. Consistent with Ethiopian findings that MT children show better running performance at younger ages57, likely due to lower body mass. However, this interaction effect in agility, absent in power tasks, suggests that thinness may impair coordination and accuracy. For stepping, no group differences were found, likely reflecting the task’s difficulty for this age range, as indicated by low mean scores in both groups (Table 2). This aligns with reports of age-related changes in movement patterns, indicating that tasks requiring coordination, such as stepping, can be especially challenging for younger children58.

Motor skills competence

For the Motor skills subscale, At age 5, MT children outperformed their NW peers in balance; however, by age 7, the NW peers performed better. For the bounce and catch activities, no differences were observed between ages 5 and 6, but by age 7, NW children outperformed MT children. For the Throw and Catch and the Jumping and Hopping, regardless of their age, children with NW outperformed their MT peers. This finding aligns with research on 5–7‑year‑olds with MT, showing that undernourished children performed worse in jumping and hopping than NW peers57. Due to lower muscle mass, they generate less power, leading to reduced jumping and hopping abilities. Furthermore, results demonstrate a positive correlation between height and jumping performance, with taller children generally achieving superior results59.

Our recent systematic review also showed that the literature on motor skill competence in underweight children is very scarce60 and usually indicates that underweight children perform significantly, but only slightly lower than their NW peers45,54,61,62,63,64.

In the PERF-FIT assessment, the activities Bounce and Catch, and Throw and Catch primarily evaluate catching skills. Our results indicate that bouncing and catching were more challenging than throwing and catching, as indicated by lower scores in both nutritional groups (see Table 2). The quicker movement of a bouncing ball places greater demands on eye-hand coordination, and catching itself requires precise spatial and temporal coordination to successfully intercept a moving object65. Therefore, the ability to throw and catch a ball appears to be more indicative of identifying motor coordination difficulties in children with MT66. The evidence aligns with prior studies showing that underweight children (ages 5–12) perform notably worse than well-nourished children in the Upper limb coordination subtest of the Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2), which includes bouncing and catching a ball67. Undernutrition affects motor development by depriving UW children of essential nutrients needed for muscle growth and coordination. Lower energy levels also make it harder for them to engage in physical activities that develop their motor skills. Additionally, a lower socio-economic status has been linked to inadequate motor competence, as indicated in previous research68,69. However, the socio-economic background of the children included in our study is well balanced, as no differences were identified in the wealth index between the nutritional groups. Furthermore, the limited opportunities for children to participate in physical activities that contribute to the development of motor skills can be attributed to a lack of parental or caregiver involvement and less favorable environments. These factors may impede them of reaching their full potential70. The 2018 Report Card on Physical Activity for Children and Youth in Ethiopia highlighted a concerning lack of physical activity participation within this demographic. This lack of physical activity may be linked to diminished motor competence, indicating that alongside energy shortage due to nutritional status, physical inactivity could be an additional factor influencing the motor skills of children and youth in Ethiopia71. This association might be considered an additional reason for lower motor competence.

We also identified poorer static and dynamic balance in MT children compared to their NW peers (Table 2), which aligns with previous research62. This could be attributed to decreased muscle mass and reduced body stability, both of which are essential for maintaining balance. Other studies suggest body composition, including BMI, impacts children’s balance72. Yet a recent study in 6–9‑year‑olds found moderate negative correlations between BMI and Y‑Balance performance, indicating contradictory results73. This inconsistency may stem from prior studies focusing on lower‑limb movement, whereas ours involved more complex upper‑body control during single‑leg stance. Despite high scores for both groups relative to the reference, the item proved to be more sensitive towards the MT group.

Furthermore, in contrast to our findings, some previous studies showed that undernourished children can outperform their normal-weight peers in specific motor tasks, depending on their age74. This reflects the complex interplay of nutrition, socioeconomic factors, and healthcare in relation to BMI and motor skills68. Our results also suggest the need for diverse anthropometric indicators to better understand motor development across different populations.

Impact of grip strength on motor skill-related physical fitness

In this study, children with MT exhibit poorer grip strength than their NW counterparts, but the grip strength also moderately correlates with the PERF-FIT total score and subscales and significantly predicts the PERF-FIT total scores. This indicates that strength is an important requisite for many skills and may therefore be seen as a general measure predicting poor performances in undernourished children. A previous study conducted by our research group on children aged 5–7 years demonstrated that children with MT who live in urban areas also have weaker grip strength, elbow flexors, quadriceps, and gastrocnemius regardless of their age groups57. It, therefore, seems to be a key feature, specifically in this group. In the literature, diverging results have been reported75,76,77,78. Nutritional status impacts grip strength, with undernourished children typically underperforming. However, some studies find no clear or consistent relationship, especially regarding BMI. Further investigation is necessary to fully understand the potential significance of grip strength in the screening process of this specific patient group.

Limitations and strengths of the study

The study’s primary strength is its pioneering focus on motor skill-related physical fitness in children with MT within a specific geographic area and age range, addressing an important gap in the literature. Nevertheless, the cross-sectional design entails a single assessment, which may not fully capture children’s abilities over an extended period, as motor skills can fluctuate significantly day-to-day, potentially rendering a single assessment less reliable. Furthermore, the cross-sectional, descriptive design does not establish causal relationships, and longitudinal studies featuring multiple assessments over time are necessary, especially since undernourishment seems to have a detrimental impact on motor skills by the age of 7. Prior research indicated that children might exhibit poorer performance on physical and motor tests compared to their normal-weight peers. However, the NW group also tends to perform poor on several items and subscales, raising doubts about the appropriateness of using normal-weight children as control groups. This may reflect selection bias, as the normal-weight children who participated may not fully represent the broader population of typically developing children. for example, given that the food security scale significantly predicts the PERF-FIT total score, and Table 1 clearly shows that even in the normal weight group, a large portion of the children are food insecure, control groups based solely on weight and height metrics may not be fully representative. Children in this age range, regardless of weight status, may benefit from an increased focus on improving motor skills with sufficient nutrition. Future research on typically developing children and considering other screening criteria, such as body composition, could offer additional insights, as the study underscores the need for more nuanced approaches when assessing children’s muscular fitness and motor skills. Another limitation of this study is the use of PERF-FIT normative data, which begins at age 6. Since our sample includes 5-year-olds, their performance was compared to the reference values of 6-year-olds. Despite applying this approach uniformly, it may introduce measurement bias due to differences in developmental readiness and task comprehension between these age groups. Therefore, results for the youngest cohort should be interpreted cautiously, and future research would benefit from age-appropriate normative data in this specific age group. In addition, we did not gather detailed information on the children’s usual physical activity levels, which may have affected the outcomes. Future studies should use objective measures of physical activity to address this potential confounding factor.

Conclusion

The study highlights that children with MT generally perform more poorly on the PERF-FIT, including the Agility and Power subscale and the Motor skills subscale. However, the differences between the MT and NW children vary by age. Younger children with MT tend to outperform NW peers, whereas by age 7, they are being outperformed by them. Food insecurity is the most important predictor for motor skill-related physical fitness performances, rather than the nutritional status alone. Future research, preferably longitudinal in design, is needed to better understand the developmental trajectories of MT children compared to their NW peers. Such studies will help determine which metrics are most appropriate to identify malnourished children and more accurately describe their motor skill-related physical fitness profiles.

The findings underscore the need for public policies that prioritize early intervention. Timely support for children at risk of malnutrition and food insecurity is vital to prevent declines in physical fitness and promote healthy development. Policies should integrate nutrition programs, school feeding initiatives, and parental support to mitigate the long-term effects of food insecurity on child health and development.