Methodological quality and reporting characteristics of anthropometric measurements in studies assessing the nutritional status of children in Ethiopia: A scoping review

Mekdes Tigistu Yilma; Alemselam Zebdewos Orsango; Mehretu Belayneh; Ingunn Marie Stadskleiv Engebretsen

doi:10.1371/journal.pone.0341038

. 2026 Feb 17;21(2):e0341038. doi: 10.1371/journal.pone.0341038

Methodological quality and reporting characteristics of anthropometric measurements in studies assessing the nutritional status of children in Ethiopia: A scoping review

Mekdes Tigistu Yilma ^1,^2,^*, Alemselam Zebdewos Orsango ¹, Mehretu Belayneh ¹, Ingunn Marie Stadskleiv Engebretsen ³

Editor: Ayodeji Babatunde Oginni⁴

PMCID: PMC12912574 PMID: 41701763

Abstract

Background

Accurate anthropometric data is essential for assessing nutritional status. To ensure data quality, careful planning of instruments, training and supervision of enumerators are mandatory. In Ethiopia, where malnutrition rates are high, investigating the methodological quality of anthropometric measurements in primary studies is crucial for ensuring the credibility of reports. Therefore, this review assesses the reporting characteristics and methodological quality of anthropometric measurements in primary studies conducted in Ethiopia, focusing on the nutritional status of school children.

Methods

A comprehensive systematic search was conducted to include primary studies that reported on children’s growth from Medline, AJOL, Embase, and CINAHL. Additional sources, such as Google Scholar, ProQuest, Addis Ababa, and Jimma University’s repositories were also accessed. Studies whose primary objective was to evaluate the nutritional status of children using anthropometric measurements were included in the review. The results were organized in EndNote, screened in Covidence, extracted in Excel and analyzed in Stata.

Results

Of the 678 retrieved records, 30 (n = 18,059) studies were included in the review. The studies used different exclusion criteria: 14 (46.7%) excluded children with physical deformities, seven (23.3%) excluded children who received anti-parasitic treatment within a specified time and four (13.3%) excluded children who were taking nutritional supplements. About 22 (73.3%) studies did not mention standardization, while 17 (56.7%) did not mention the calibration of instruments. Furthermore, about 12 (40%) studies did not report the setting where the measurements were obtained.

Conclusion

Inconsistencies in reporting key methodological details of anthropometric measurements were identified, highlighting a potential gap or loose interpretations of the STROBE-nut reporting checklist for studies that measure anthropometry to assess the nutritional status of children. Therefore, we recommend strengthening the STROBE-nut by giving emphasis to the quality assurance aspect of anthropometric measurements including standardization, calibration, material, setting, number of measurements taken and measurer qualifications.

Introduction

Anthropometric measurements are used to indicate the nutritional and health status, dietary adequacy, growth and development of children. These measurements include weight, head circumference, skinfold thickness, height (or length for children under two years or those unable to stand), mid-upper arm circumference and body mass index [1,2]. Accurate measurement of these indicators is essential for understanding the nutritional status of children. However, these measurements could be biased in a situation where there are physical deformities, equipment issues, such as a malfunctioning weight scale, stadiometer, tape meter and skinfold caliper, and skill gaps among individuals taking measurements [1,3].

Anthropometric measurement is a simple, safe, cost-effective and non-invasive procedure used to assess children’s growth. However, generating valid anthropometric data requires careful planning on the selection, calibration, and maintenance of instruments, as well as the recruitment, training, standardization, and supervision of enumerators. Therefore, the credibility of the data depends on the thorough reporting of all procedures and quality control activities [4]. A recent study suggested that dietary and food-based measures should supplement anthropometry for measuring child nutritional status, as anthropometry alone may not fully capture the dietary adequacy and clinical and biochemical status of children [5]. Additionally, potential random errors in the methods and procedures for obtaining anthropometric data can affect the accuracy of growth estimates [4].

Malnutrition, particularly undernutrition and obesity, is a global public health concern [6]. In recent years, the coexistence of undernutrition and overnutrition has become increasingly prevalent in low- and middle-income countries [7], including Ethiopia [8]. In Ethiopia, one in five schoolchildren are stunted or wasted [9], while a quarter of children from food-insecure households are overweight/obese [10]. However, the reliability of these findings may be affected by the quality of the anthropometric measurements. Evidence suggests heterogeneity in the quality of anthropometric data from demographic and health surveys of different countries worldwide, which can compromise the accurate estimates of malnutrition at the population level [11].

A study conducted in Ghana reported that length measurements could be underestimated due to a lack of confidence among measurers and overburden or increased number of measured individuals [12]. Another study also reported that a significant number of anthropometrists in limited-resource settings attain only a satisfactory level of precision in anthropometric measurement after training and standardization [13] which may have implications for the estimate of malnutrition [11].

In a setting where malnutrition is highly prevalent, researchers must ensure the accuracy and reliability of measurements during fieldwork. It is also the researchers’ responsibility to report all necessary quality control approaches and methodologies used in assessing anthropometric measurements to enhance the validity of the results. However, only a few studies examined the methodological quality of anthropometric measurements. Therefore, this review assessed the methodological quality and reporting characteristics of anthropometric measurements in primary studies conducted in Ethiopia, focusing on the nutritional status of schoolchildren.

Methods

Search strategy

The review was based on the primary studies conducted in Ethiopia from 1 January 2020–13 June 2024 and carried out based on the Preferred Reporting Items for Systematic and Meta-Analysis-Extension for Scoping Reviews guideline (S1_File) [14]. The search was performed in Medline, AJOL, Embase and CINAHL using the keywords with Boolean operators as nutritional status OR protein energy malnutrition OR severe acute malnutrition OR nutritional assessment OR anthropometric measurement OR malnutrition OR undernutrition OR overnutrition OR underweight OR overweight OR obesity OR wasting OR thinness OR stunting AND school children OR 5–10 years OR older children AND Ethiopia in all fields and MeSH terms. However, the search was customized for each of the databases. Furthermore, it was supplemented by Google Scholar, ProQuest, Addis Ababa and Jimma University’s repository searches, considering them as the sources of unpublished studies (S2 _File).

Eligibility criteria

Studies whose primary objective was to evaluate the nutritional status of children measured by anthropometric data, conducted in Ethiopia and published between 1 January 2020 and 13 June 2024 were included in the review. To maintain the alignment of the method with the objective of the review, studies that included nutritional status (undernutrition or overnutrition of macronutrients) as an explanatory variable, secondary data sources, focused on the treatment outcome of malnutrition, and studies for which their full text is unavailable (up to 13 June 2024) were excluded from the review.

Study selection, data extraction and synthesis

The database search results of articles from all databases were recorded, exported to EndNote and stored in a respective database labelled group. Two independent reviewers (MTY and AZO) screened records using Covidence Systematic Review management software based on eligibility criteria by title/abstract and full-text revision after duplicates were automatically removed. Any discrepancies were resolved by a third reviewer (MB) and through discussion.

Data were extracted from included studies in Excel using a pre-structured sheet by MTY, focusing on the characteristics of the studies (study design, study year, region, sample size, objective, population, health status of children, inclusion and exclusion criteria, type of malnutrition (dependent variable) and conclusion. Furthermore, anthropometric measurement-related data were extracted based on the recommended good epidemiological practice that determines the quality of anthropometric measurements, such as calibration method, standardization procedure, measurement qualification, setting, equipment used and number of measurements [15,16]. The full article and supplementary files were reviewed to extract the methodological detail. We extracted the mean age from studies that did not report the age range in the inclusion or results sections.

The data extracted in Excel were imported into Stata version 17 for analysis. After cleaning for incomplete data by cross-checking the article and data structure, a descriptive analysis of frequencies and percentages was conducted using the number of studies as a denominator.

Results

Search results and study selection

A total of 678 articles were found, including 664 from the databases and 14 studies were found through other methods: nine through citation searches and five from university repositories. Of these, only 30 articles fulfilled the inclusion criteria and were included in the review from 65 studies reviewed in full texts, while the remaining 35 studies were excluded for various reasons, as presented in Fig 1.

Study characteristics

All 30 studies (n = 8,059) included in this review used a cross-sectional study design. Of which, 23 (76.7%) studies were conducted on healthy school children and seven (23.3%) on children living with a medical condition such as epilepsy [17], HIV [18,19], congenital heart disease [20,21], cardiac disease [22] and admitted to an intensive care unit [23]. The majority 13 (43.3%) of included studies were conducted in the Amhara National Regional State [17,18,23–33].

Exclusion criteria’s varied across the studies where 14 (46.7%) studies excluded children with physical deformities [10,19,24,25,31,32,34–39], 11 (36.7%) excluded critically ill children [10,19,21,23,32,34,36,37,39], seven (23.3%) excluded children who received anti-parasitic treatment within a specified time [25–27,34,38] and four (13.3%) excluded children who received nutritional supplements [26,27,34]. As the reasons for and sociodemographic characteristics of refusals could differ from the participants, excluding them could affect the representativeness and magnitude of the problem [40]. However, some studies excluded population samples that refused to participate in the study [26,27,34,37,41]. Nonetheless, measuring anthropometry requires a certain position and there is a possibility of finding a sick child during the study. Studies on diseased children and patients admitted to the intensive care unit did not report how they measure nutritional status and anthropometric measurements of quality control [17–23] (Table 1).

Table 1. Characteristics of included studies, a scoping review in Ethiopia (n = 30).

Study	Study year	Region	Design	Sample size	Age (yrs)	Studies exclusion criteria	Health status
Adisu Tafari 2023	2022	Oromia	Community based cross-sectional	551	6-14	Critically ill, children with deformity and disability children with deformity and disability, closed home for two visits and non-respondent	Healthy children
Ahmed 2022	2021	Addis Ababa	School based cross-sectional	607	7-14	Deformities and <7 years of age	Healthy children
Argaw 2022	2021	SNNPR	School based cross-sectional	527	6-14	Critically ill, deformed and unknown age	Healthy children
Asrade 2021	2019	Addis Ababa	Hospital based cross-sectional	269	<18	Premature infants, children with a known genetic disorder, and children with other, non-CD chronic illnesses	Children with cardiac disease
Ayele 2023	2022	Amhara	School based cross-sectional	600	7-12	Deformity and edematous condition	Healthy children
Bazie 2021	2019	Amhara	School based cross-sectional	341	6-17	Deformities and child who had taken deworming one month prior to the survey	Healthy children
Berhanu 2022	2019	Oromia	Community based cross-sectional	606	5-14	Children who had difficulty standing steady or straight, children in a wheelchair, seriously ill during data collection, and those who refused	Healthy children
Berhanu 2023	2021	SNNPR	School based cross-sectional	494	5-19	Known history of health problems	Healthy children
Biadgilign 2021		Addis Ababa	School based cross-sectional	632	5-18	Children who are permanently ill, have deformities and whose mother is in a morbid state.	Healthy children
Bisetegn 2023	2021	Amhara	School based cross-sectional	450	6-17	Children who took antiparasitic drugs in the past 4 weeks and nutritional supplements, children who did not volunteer and with other chronic diseases.	Healthy children
Debash 2023	2021	Amhara	Community based cross-sectional	402	6-14	Children who are taking antiparasitic medication or nutritional supplements and children whose guardian refused to provide consent.	Healthy children
Ayalew 2020	2017	Amhara	School based cross-sectional	505	mean age 9	No specific exclusion criteria were specified.	Healthy children
Geletaw 2021	2019	Somalia	School based cross-sectional	671	6-17	Treated for intestinal parasite (2 weeks prior) and deformity	Healthy children
Genet 2022	2021	Amhara	Hospital based cross-sectional	239	<=18	Children who had major surgery other than epilepsy surgery	Children with Epilepsy
Hussein 2023	2021	Afar	School based cross-sectional	936	10.96	children with known disease and refusers.	Healthy children
Molla 2022	2021	SNNPR	School based cross-sectional	500	6-14	Unknown age, seriously ill and deformities	Healthy children
Teshager 2022	2018-2019	Amhara	Hospital based cross-sectional	376	<18	Patient without a care giver or caregiver with limited information, terminally ill, and on mechanical ventilation	Children admitted to intensive care unit
Tewabe 2020	2018	Amhara	Community based cross-sectional	392	6-12	No specific exclusion criteria were specified.	Healthy children
Tewabe 2023	2018	Amhara	Community based cross-sectional	392	6-12	No specific exclusion criteria was specified.	Healthy children
Tiruneh 2022	2021	Amhara	Hospital based cross-sectional	379	<15	Incomplete medical information, children without caregiver and caregiver with mental illness or hearing impairment or medical disorder	HIV-positive children
Tiruneh 2021	2021	SNNPR	Hospital based cross-sectional	383	<15	Incomplete medical information, children without caregiver and caregiver with mental illness, children with physical deformities and seriously ill children.	HIV-positive children
Tsega 2022	2020	Addis Ababa	Hospital based cross-sectional	228	<18	All patients with risk factors other than congenital heart disease that contribute to malnutrition.	Children with congenital heart disease
Woldesenbet 2021	2021	Addis Ababa	Hospital based cross-sectional	373	<15	Other congenital anomalies and critically ill children	Children with congenital heart disease
Yisak 2021	2019	Amhara	School based cross-sectional	300	6-12	Visible physical deformities	Healthy children
Bantie 2021	2019	Amhara	School based cross-sectional	370	6-11(+)	Dropouts or absent during the data collection period	Healthy children
Sisay 2022	2020	Amhara	School based cross-sectional	821	6-14	Seriously ill children and those unable to stand upright by themselves	Healthy children
Eyuel Bekele 2021	2021	Addis Ababa	School based cross-sectional	350	6-18	New transferred student, who are in the Extension programme are treated with anti-helminths drugs.	Healthy children
Zelalem Destaw 2021	2019-2021	Addis Ababa	Longitudinal	4500	5-19	Not specified	Healthy children
Abrachew Datiko 2020	2020	SNNPR	School based cross-sectional	514	7-14	Sick and absent children during data collection time, chest, lower and upper deformities	Healthy children
Etalemahu Mulugeta 2022	2021	Oromia	Community based cross-sectional	351	7-12	Unknown age, children who are taking antiparasitic drugs, nutrition supplements, and children who are seriously ill or had chronic illness and deformity	Healthy children

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SNNPR: refers to the South Nation Nationality and Peoples Region.

Anthropometric measurement-related findings

One study assessed nutritional status using the Communicable Disease Control growth chart for children and analyzed the data using WHO Anthro Plus software [24]. The study employed a mixed approach without clear explanation, highlighting a methodological weakness that affects the interpretability of the findings. Regarding the recommended epidemiological practice, 12 (40%) studies did not specify the setting where the measurement was taken [25–28,31–33,35,37,39,41], eight (26.7%) studies did not specify the equipment’s used [17–19,22,23,29,35,42] and three (10%) studies did not report the qualification of the measurer [26,32,39]. Moreover, only three (10%) of studies [25,34,38] reported training on anthropometric measurements, while most studies reported provision of training on the objective of the study, content of the tool and approach of data collection without specifically addressing anthropometric measurements.

Weight, height and mid-upper arm circumference were the common anthropometry measurements for primary school children aged 5–10 years. Fourteen (46.7%) studies took two measurements and used the average values to minimize measurement error, whereas the same number of studies did not report the number of measurements they took.

Calibration is not a one-time process; however, 17 (56.7%) studies did not mention the calibration of instruments. Some studies mentioned utilizing calibrated instruments without enough detail [24,31–34,39,41]. Similarly, the majority 22 (73.3%) of studies did not mention standardization of procedure, and only three (10%) of them reported standardization without detail on the number of samples used, how they took the measurements, how they analyzed the data, reported technical error of measurement and their decision and action following the result [25,34,38]. Almost all studies described the standard position for measuring height (Table 2). A summary of the identified methodological gaps is presented in Fig 2.

Table 2. Study reported characteristics of anthropometry measurements and reporting of limitations regarding anthropometry of included studies, A scoping review in Ethiopia (n = 30).

Study	Anthropometry measurement related report	Studys’ reported limitation
Adisu Tafari 2023	■ Dependent variable: Stunting and thinness ■ Material: Weight: digital weight scale; height: no information ■ Calibration: Weight: prior and before every measurement ■ Standardization: Done to estimate the Technical Error of the Measurements (TEM) but no result of agreement and further action following the report was discussed. ■ Number of measurements: two	No reported anthropometry measurement related limitation
Ahmed 2022	■ Dependent variable: Stunting ■ Material: Not specified ■ Calibration: They mentioned as calibrated but did not describe how they calibrate the instrument throughout the field work ■ Standardization: They mentioned as standardized but did not describe how they standardize the procedure and data collectors. ■ Number of measurements: two	No limitation reported
Argaw 2022	■ Dependent variable: Stunting ■ Material: Tanita HR-200 stadiometer ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: three	Exclusion of children with deformities may affect the findings.
Asrade 2021	■ Dependent variable: Undernutrition ■ Material: not specified ■ Calibration: No specific information ■ Standardization: The author mentioned standardize procedure but did not discuss the procedure. ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Ayele 2023	■ Dependent variable: Obesity ■ Material: Weight: digital balance scale; Height: Stadiometer ■ Calibration: The weight scale was calibrated at zero with no object on it and placed on the level surface before the measurement was carried out. ■ Standardization: No specific information except training ■ Number of measurements: not specified	Skin fold thickness measurement was not done, which might eliminate the limitation of BMI measurement.
Bazie 2021	■ Dependent variable: Stunting ■ Material: Digital Seca Germany height scale: a measuring board ■ Calibration: No specific information ■ Standardization: Height measurement was standardized using a reference anthropometric measurer before deploying the data collectors to the field. ■ Number of measurements: two	No reported anthropometry measurement related limitation
Berhanu 2022	■ Dependent variable: Stunting ■ Material: Stadiometer with a movable headpiece ■ Calibration: No specific information ■ Standardization: No specific information except training. ■ Number of measurements: two	No reported anthropometry measurement related limitation
Berhanu 2023	■ Dependent variable: Undernutrition ■ Material: not specified ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: two	No reported anthropometry measurement related limitation
Biadgilign 2021	■ Dependent variable: Overweight/obesity ■ Material: Weight: SECA digital scale; height: measuring board ■ Calibration: Measurement was taken after calibration; however, no specific method of calibration was mentioned. ■ Standardization: No specific information ■ Number of measurements: two	No reported anthropometry measurement related limitation
Bisetegn 2023	■ Dependent variable: Undernutrition ■ Material: Weight: electronic digital scale; height: meter ■ Calibration: No specific information ■ Standardization: no specific information ■ Number of measurements: two	No reported anthropometry measurement related limitation
Debash 2023	■ Dependent variable: Undernutrition ■ Material: Weight: digital weighting scale; height: vertical meter ■ Calibration: No specific information ■ Standardization: no specific information ■ Number of measurements: two	No reported anthropometry measurement related limitation
Ayalew 2020	■ Dependent variable: Undernutrition ■ Material: Weight: electronic scale; Height: wooden stadiometer ■ Calibration: No specific information ■ Standardization: They mentioned as standardize procedure (without discussing the specific method, result and action). ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Geletaw 2021	■ Dependent variables: Stunting, and thinness ■ Material: Weight: digital scale; Height: Seca Rod stadiometer ■ Calibration: Calibrated tool, but there was not enough detail. ■ Standardization: The standardization procedure was clearly described with the result. However, they did not mention the method of analysis and software. ■ Number of measurements: two	Recall bias in the age of children may affect anthropometry.
Genet 2022	■ Dependent variable: Stunting and wasting. ■ Material: No information ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Hussein 2023	■ Dependent variable: Stunting, thinness and overweight/obese ■ Material: Weight: digital scale; Height: Seca 217 stadiometer ■ Calibration: The weighing scale was checked each day prior to the actual data collection. However, there is not enough detail about the calibration of instruments. ■ Standardization: No specific information ■ Number of measurements: two	No reported anthropometry measurement related limitation
Molla 2022	■ Dependent variable: Wasting ■ Material: Weight: digital electronic scale; Height: Tanita HR-200 stadiometer. ■ Calibration: Calibration of the weight scale was done using the standard calibration weight of 2 kg iron bars on a daily basis. ■ Standardization: No specific information ■ Number of measurements: three	No limitation reported
Teshager 2022	■ Dependent variable: Wasting ■ Material: not specified ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No anthropometry related limitation except the use of anthropometric measurement alone to determine nutritional status.
Tewabe 2020	■ Dependent variable: Undernutrition ■ Material: not specified ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No limitation reported
Tewabe 2023	■ Dependent variable: Undernutrition ■ Material: Weight: digital weight scale (7506 digital scale); Height: a fixed non-bending wooden meter ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Tiruneh 2022	■ Dependent variable: Underweight ■ Material: not specified ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Tiruneh 2021	■ Dependent variable: Wasting and stunting. ■ Material: not specified ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Tsega 2022	■ Dependent variable: Undernutrition ■ Material: Weight: weight scale; Height: tape meter ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Woldesenbet 2021	■ Dependent variable: Undernutrition ■ Material: Weight: weight scale ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Yisak 2021	■ Dependent variable: Undernutrition ■ Material: Weight: digital portable weighting scale ■ Height: A height measuring length board ■ Calibration: A weighing scale calibrated to zero before taking every measurement ■ Standardization: No specific information ■ Number of measurements: two	No limitation reported
Bantie 2021	■ Dependent variable: Stunting ■ Material: Weight: digital scale; Height: wooden measuring board ■ Calibration: Calibration of the digital weight scale (to zero) and cross-checking it using a pre-known weight material before weighing each pupil. ■ Standardization: No specific information ■ Number of measurements: not specified	Measurement bias might also occur, as not having a standardized height measurement.
Sisay 2022	■ Dependent variable: Stunting and thinness ■ Material: Weight: weight scale; Height: stadiometer ■ Calibration: The weight scale was calibrated by 2 kg iron bar every day. ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Eyuel Bekele 2021	■ Dependent variable: Stunting and underweight/thinness ■ Material: Weight: portable digital balance ■ Calibration: Weighing scales were checked and validated with standard weights every day before the actual weighing of the children. ■ Standardization: No specific information ■ Number of measurements: two	No limitation reported
Zelalem Destaw 2021	■ Dependent variable: Malnutrition ■ Material: Weight: UNISCALE; Height Stadiometer ■ Calibration: No specific information ■ Standardization: No specific information ■ Number of measurements: not specified	No reported anthropometry measurement related limitation
Abrachew Datiko 2020	■ Dependent variable: Undernutrition ■ Material: Weight: Seca digital scale; Height: stadiometer ■ Calibration: Calibration was done after each child’s measurements. ■ Standardization: Done using 10 student and done TEM by ENA and it was acceptable. ■ Number of measurements: two	No reported anthropometry measurement related limitation
Etalemahu Mulugeta 2022	■ Dependent variable: Stunting ■ Material: Height: a vertical wooden height board ■ Calibration: The scale is validated by known height and weight and adjusted to zero level after each measurement. ■ Standardization: states only standard procedure with WHO citation ■ Number of measurements: two	No reported anthropometry measurement related limitation

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Discussion

In this scoping review, we found inconsistencies in the reporting of anthropometric measurement methods and quality control mechanisms, including the number of measurements, qualification and training of data collectors, setting where measurements were taken, materials used, calibration of anthropometric tools, and standardization of measurers in child nutrition studies from Ethiopia. Furthermore, there were variations in the exclusion criteria, such as children with deformities, nutritional supplements, and a recent history of anti-parasitic medication or treatment of intestinal parasites. This review identified a significant gap in reporting anthropometric measurement techniques used for those who are unable to stand independently. The lack of detailed information about the assessment methods raises concerns about the reliability and validity of the findings. Although there was insufficient information about how it was measured, almost all studies did not address the limitations regarding the effect of the above-mentioned anthropometric measurement quality control on the findings. Furthermore, the settings in which the measurements were taken were not reported in many studies. The findings of this scoping review are also in line with another school-based scoping review [43].

Anthropometric measurements are the most common, least expensive and non-invasive methods for assessing nutritional status [1,15]. However, it is highly sensitive to measurement errors due to the contextual setting [44]. Such errors could be imprecision, unreliability, undependability, inaccuracy and bias, which can be estimated by technical error measurement, coefficient of reliability and intraclass correlation [45]. Unless it is reported how it is done and the result of those assessment measurement error findings from standardization analysis, it is difficult for readers to judge the reliability of the measurements and the findings of the study because imprecise measurement could lead to misclassification in the nutritional status of children [46].

The inherent measurement error of anthropometric measurements can be minimized by calibrating and standardizing of the instrument, procedure and measurer. Calibration is a continuous process that should be done before, during and after taking measurements regularly to examine the functionality of devices and enhance the accuracy of measurements [15]. However, most studies in this review did not report the method of calibration and standardization. In addition to preparing all required instruments and team composition, the demographic health survey also suggests having a standard weight-to-caliber weight scale and conducting a standardization exercise on at least ten samples to enhance the quality of anthropometric data [47].

This review also found that the training of data collectors for standard anthropometric measurements was underreported. Having an anthropometrist is recommended for measuring anthropometry; however, in resource-limited settings like Ethiopia, where anthropometrists are few, it is required to train a measurer to minimize measurement error [15]. Even having anthropometrists with little experience can affect measurement precision [44]. Therefore, training data collectors and field workers on the standard guidelines of anthropometric measurement, continuous calibration of the instrument and securing participants’ safety is a critical aspect for improving the quality of anthropometric measurement [47].

The other important factor that determines the quality of anthropometric measurements is the type of instrument used. A simulation study revealed that inaccurate instruments were overestimated the magnitude of obesity [48]. Taking multiple measurements and using average values instead of a single measure also minimize measurement error [45]. Though most included studies did not report the number of measurements taken, a couple of studies even took three measurements to enhance the quality of the data [32,36].

Anthropometry is important for acutely sick children, including those in ICU, to plan care, fluid administration and medication. It can be estimated using various methods, including parental estimate, previous values, measurements at admission, and at regular intervals [49]. Inaccurate measurements in such situations compromise the effect of treatments and hinder individualized care [50]. However, studies conducted in children admitted to the intensive care unit and with other chronic diseases like cardiac disease and epilepsy did not report information about the calibration of instruments and standardization of procedures. Furthermore, most included studies did not report the inclusion or exclusion of children with deformities, acute illness, and casting. However, measuring anthropometry in such conditions may affect the accuracy of measurements [1].

The accuracy of any of the anthropometric measurements significantly affects the child growth indicators; weight-for-height, weight-for-age, height-for-age, and BMI-for-age, as they require at least one anthropometric measurement. Errors in such measurements could result in a systematic error that affects prevalence estimates at population level, trend analysis, determinants and consequences of malnutrition, and targeted program impact [4,51,52]. For example, a child who appears well nourished can be misclassified as malnourished (false positive), resulting in unnecessary strain on the healthcare system. On the other hand, a child who is malnourished may go undetected (false negative), which could prevent them from receiving essential care. Therefore, examining the quality of anthropometric measurement before, during and after data collection is critical for obtaining a precise estimate of nutritional status.

The STROBE extension checklist (STROBE-nut) is available for observational studies evaluating nutritional status, but the checklist lacks sufficient emphasis on ensuring the quality of anthropometric measurements [53]. Although it under point 8 states it as “details of assessment methods”, it may be interpreted differently how specific one should be. Therefore, this review highlights the gap in underreporting of the essential aspect of anthropometric measurements, which is required to ensure the reliability and validity of the estimate of nutritional status. For a country like Ethiopia, where resources and infrastructure are limited and the burden of malnutrition is high, this review will provide insight to enhance the quality of the data and to report a significant aspect of anthropometric measurement. However, its main limitation was the inclusion of studies since 2020; however, this may not affect the findings as the review aims to examine the current methodological aspect of anthropometric measurement. Excluding studies that measured anthropometry as an independent variable may affect the findings of this study in different ways. Yet this review provides insights into the need for improving the quality of anthropometric measurements. Exclusion of studies without full text availability upholds the methodological integrity of the review while acknowledging its effect on comprehensiveness. Moreover, inclusion of studies only from Ethiopia also affects the generalizability of the findings. Publication bias could affect the results in either way however, we have minimized its effect through systematic database searches, comprehensive grey literature and citation searches to include all potential published and unpublished studies. Last, but not least, stating what was not found during critical reading may not imply that the methods were not adhered to or reported in adjacent reporting to the published manuscripts. However, if that was the case, a more specific guideline and adherence would have eased reading and interpretation of the work done.

Conclusion

In this review, we found a considerable gap and inconsistencies in reporting a key methodological detail of anthropometric measurements, which highlights the gap in the reporting checklist for studies that measured anthropometry to assess the nutritional status of children or interpretation issues with respect to depth of reporting. Therefore, we recommend strengthening the STROBE-nut checklist by giving emphasis to standardization, calibration of instruments, materials or equipment used, setting where the measurements were taken, number of measurements taken, and qualifications of the measurers. Furthermore, expanding the review at a regional or global level may provide detailed evidence to the existing body of knowledge and improve the reliability of nutritional status estimates.

Supporting information

S1 File. PRISMA-ScR Checklist.

(DOCX)

pone.0341038.s001.docx^{(85.5KB, docx)}

S2 File. Search strategies for different databases.

(XLSX)

pone.0341038.s002.xlsx^{(16.4KB, xlsx)}

S3 File. Extracted data generated in this study.

(XLS)

pone.0341038.s003.xls^{(54KB, xls)}

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0341038.r001

Decision Letter 0

Ayodeji Babatunde Oginni

28 Oct 2025

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Reviewer #1: This manuscript presents a scoping review of primary studies that assessed the nutritional status of children in Ethiopia using anthropometric measurements. The review specifically examines how methodological quality and reporting practices related to anthropometric measurements are described in these studies.

The authors conclude that reporting is inconsistent across the included studies. In particular, many studies failed to specify key methodological details such as the setting in which measurements were taken, whether instruments were calibrated, and whether measurement procedures were standardized.

Based on these findings, the review highlights the need to strengthen reporting guidelines, specifically by recommending that the STROBE-nut checklist be expanded to incorporate quality assurance aspects of anthropometric measurements.

All in all the paper is clear and well written and the conclusion is justified by the findings.

Reviewer #2: See these comments, and recommended article if they would be useful:

Clarify Inclusion/Exclusion Criteria and Rationale

Please clearly justify and elaborate the inclusion and exclusion criteria, particularly exclusion of studies that used anthropometric data as an independent variable and those without full text availability. This is essential to understand potential bias in the representative studies included.

Detail Search Strategy and Reproducibility

Expand on the search strategy details, including exact keywords, Boolean operators, database-specific search strings, and date limits in the Methods section. Providing the full search strings as supplementary material would improve reproducibility and transparency.

Explain Data Extraction and Quality Assessment Procedures

More detail is necessary on how data extraction was conducted, e.g., were forms pilot-tested, how discrepancies between reviewers were resolved, and whether a formal methodological quality assessment or risk of bias was performed on included studies.

Provide Quantitative Summary of Reporting Gaps

While narrative descriptions are given, a more structured presentation (e.g., tables or figures) quantifying missing methodological details (calibration, standardization, measurer qualification) across studies would enhance clarity and impact of results.

Discuss Impact of Reporting Gaps on Nutritional Status Results

The discussion should more deeply analyze how inconsistent reporting and measurement quality potentially biased key nutritional status indicators like stunting and wasting prevalence, with examples if possible.

Include More Detail on Handling Studies with Special Populations

Studies involving sick children or those unable to stand were included but lack details on measurement adaptations. The manuscript should address this critical issue and its implications on data validity.

Address Limitations in More Depth

Limitations related to the focus on Ethiopia only, restriction to recent years, exclusion of micronutrient status studies, and potential publication bias need fuller discussion on how they may have influenced findings.

Strengthen Recommendations with Practical Guidance

The conclusion advocates for checklist improvements but lacks specific actionable recommendations or example items for the proposed quality assurance checklist to guide future researchers.

Language and Formatting Consistency

There are some typographical and formatting inconsistencies (e.g., spacing issues, punctuation, reference citation format) that should be carefully revised to improve readability and professionalism.

Supplementary Materials and Data Transparency

If available, include supplementary files such as the PRISMA flowchart, data extraction templates, and detailed study characteristics in tabular form to enable reader verification and facilitate future research replication.

Author response to Decision Letter 1

25 Nov 2025

Point-by-point response to editor and reviewers

Response to editor

Editor’s feedback

Based on my evaluation of the manuscript, I would like you to improve its grammatical structure to ensure that your study findings are clearly understood by the audience. For example, the statements found in lines 130-138, 148-164, 183-184, and 255-256 need to be rephrased for better clarity.

Response

• Thank you for your valuable feedback. We have carefully addressed the grammatical errors and clarity issues pointed out in lines 130-138, 148-164, 183-184, and 255-256, along with all reviewers’ comments. We have included the captions for supporting information files at the end of the manuscript. We have also reuploaded the extracted data as S3_File in Excel format. The ethics statement also revised as it is specified in the submission system which is N/A for study that does not require ethics statement. The manuscript has been proofread, and we believe necessary corrections have been made to improve the clarity and flow of the text.

Response to reviewers

Reviewer #1

Comment: This manuscript presents a scoping review of primary studies that assessed the nutritional status of children in Ethiopia using anthropometric measurements. The review specifically examines how methodological quality and reporting practices related to anthropometric measurements are described in these studies.

All in all the paper is clear and well written and the conclusion is justified by the findings.

Response

• Thank you for your positive feedback. We appreciate your conclusion that the manuscript is clear and well-written and that you found the conclusions to be justified by the findings. This alignment between our results and their interpretation was a primary goal of our work, and we are happy to learn that the proposed implications are agreeable.

Reviewer #2

Dear reviewer,

• Thank you for your insightful comments.

Comment 1: See these comments, and recommended article if they would be useful:

Clarify Inclusion/Exclusion Criteria and Rationale

Response

• Thank you for raising this point; we have now revised the inclusion and exclusion section. In short, we excluded studies that used anthropometric data as an independent variable mainly to maintain conceptual and methodological alignment with the specific objective of the review. Otherwise, the review could have been very broad, as many studies have some adjustment for nutritional status. Furthermore, if the full text is unavailable or not open access, we could not review the study and extract relevant data, which is a foundation for review (as we don't have funding for paid articles). Exclusion of those studies introduces publication bias and affects the representativeness of the evidence; thus, our conclusion is primarily relevant for online access publications. We also acknowledged the potential limitation in the last paragraph of discussion.

Comment 2: Detail Search Strategy and Reproducibility

Response

• Dear reviewer, thank you for your suggestion, and we agree to the value of sharing the search strategies. However, it could have been very informative if you had specified the gap in search strategy statements because we wrote the search strategies by using exact keywords, Boolean operators and MeSH/Emtree terms as well as the period of relevant findings from January 1, 2020, to June 13, 2024. We have provided the search strategies of three databases in supplementary file 2.

Comment 3: Explain Data Extraction and Quality Assessment Procedures

Response

• We appreciate your concern. The form was not pretested because it was pre-specified based on the characteristic of the study and the recommended good epidemiological practice of anthropometric measurement. Regarding discrepancies during the screening process, that was resolved by the third reviewer, and this is also specified in the method (lines 106-107). However, if you mean during data extraction, there was no discrepancy.

• For this scoping review objective, a formal methodological quality assessment (or risk of bias appraisal) is not relevant and typically not a standard or required component of a scoping review. That is why we did not perform a risk assessment. Furthermore, not having a risk of bias assessment is why scoping review is fundamentally different from systematic reviews.

Comment 4: Provide Quantitative Summary of Reporting Gaps

While narrative descriptions are given, a more structured presentation (e.g., tables or figures) quantifying missing methodological details (calibration, standardisation, measurer qualification) across studies would enhance clarity and impact of results.

Response

• Agreed. In addition to Table 1 and Table 2, we have added Fig 2 to summarise the major identified methodological gaps of included studies such as calibration, standardisation, measurer qualifications, number of measurements taken, training for anthropometry measurement, material and setting.

Comment 5: Discuss Impact of Reporting Gaps on Nutritional Status Results

The discussion should more deeply analyse how inconsistent reporting and measurement quality potentially biased key nutritional status indicators like stunting and wasting prevalence, with examples if possible.

Response

• We really appreciate the concern, and we have added a paragraph in the discussion that addresses how inaccurate anthropometric measurements impact the estimate of nutritional indicators like wasting, stunting, underweight and so on with examples.

Comment 6: Include More Detail on Handling Studies with Special Populations

Response

• Thank you for your suggestion. We believe the point you raised is a relevant research gap. However, providing details on measurement adaptation for inclusion of special populations is out of the scope of this study because this review is about methodological gaps and reporting characteristics in conducted studies. According to our study, studies conducted on children living with medical conditions fail to report all methodological details about how they measured the intended anthropometry, even though it was relevant to ensure the quality of the data.

• If we agreed to suggest a measurement method, it could not be logical because there is no single standard for all special situations. Therefore, suggesting measurement adaptation for special populations requires evaluation of existing methods for each category, like for critically sick children, children with deformities and so on.

Comment 7: Address Limitations in More Depth

Response

• We appreciate your significant concern. It is now revised. However, limitations related to exclusion of micronutrient status studies were deleted from the manuscript because anthropometric measurements are not a direct measure of micronutrient status. Therefore, we believed it was irrelevant and removed it from the text.

Comment 8: Strengthen Recommendations with Practical Guidance

The conclusion advocates for checklist improvements but lacks specific actionable recommendations or example items for the proposed quality assurance checklist to guide future researchers.

Response

• Thank you for reviewing our study in depth. We have recommended strengthening the STROBE-nut checklist by giving emphasis to the identified methodological gaps for studies that measured anthropometry. Therefore, instead of recommending to strengthen the checklist, we have specified the area of improvement for future application.

Comment 9: Language and Formatting Consistency

There are some typographical and formatting inconsistencies (e.g., spacing issues, punctuation, reference citation format) that should be carefully revised to improve readability and professionalism.

Response

• We thank you for your feedback. We have thoroughly revised the entire manuscript to correct all typographical, spacing, and punctuation errors. We have also meticulously checked the reference list to ensure it is complete and consistently formatted according to the Plos style requirements.

Comment 10: Supplementary Materials and Data Transparency

Response

• We thank the reviewer for highlighting this. All supplementary files were uploaded with our original submission. For example, the flow diagram is presented in Figure 1, study characteristics are described in Table 1, the PRISMA checklist is uploaded as S1_File, the search strategy is uploaded as S2_File, and the dataset used to generate these findings was uploaded as S3_File. However, to ensure they are perfectly clear and easily accessible, we have re-uploaded all supplementary files to avoid any potential technical issues. Regarding the data extraction checklist, as we have extracted the data using a pre-specified Excel sheet, it is the same as the S3_File. So, we didn’t upload it to avoid redundancy.

Comment 11: Recommended article:

What is the carbon footprint of reverse osmosis in water treatment plants? A systematic review protocol

Response

• Dear reviewer, thank you for recommending an article (Abolli, S. et al., 2023). However, we have decided not to incorporate this reference, as it falls outside the scope of our manuscript. Our paper reports on a completed study of methodological quality and reporting characteristics of anthropometric measurement, whereas the suggested study protocol is not a complete study and its focus is on the carbon footprint of reverse osmosis in water treatment plants. Therefore, the aims, methods, and subject matter of the two works are fundamentally different; its topic is not directly relevant to the framework of our research question.

Reviewer #3:

Thank you for providing a critical assessment of our manuscript. We appreciate the opportunity to address the concerns you have raised and to improve the clarity and rigour of our work.

Comment 1: The original Abstract percentages appear to be based on an earlier count of n=26 studies, while the Results section correctly uses n=30, making the Abstract factually incorrect.

Response

• Thank you for identifying this mistake. The percentages in the abstract and result section are now revised and consistent.

Comment 2: Location in manuscript Original Text Suggested Correction

Keywords/Abstract/ Main body scooping review scoping review (This appears twice)

Response

• Thank you for identifying an important aspect. We have removed scoping review from the keywords.

Comment 3:

Methods, Search Strategy in all field and MeSH terms. in all **fields** and MeSH terms.

Methods, Study Selection after duplicates had automatically removed. after duplicates **were** automatically removed.

Results, Study Characteristics children having different disease conditionslike school children living children having different disease **conditions like** school children living

Response

• We agreed to all of the above three suggested typological errors in the methods and results sections and revised them accordingly. We have revised

• field� fields,

• had� were and

• from “...seven on children having different disease conditions like school children living with...” to ”... seven (23.3%) on children living with a medical condition such as ….”

Comment 4

Results, Anthropometry Except one study [24] all studies defined... Except one study [24]**,** all studies defined... (Missing comma)

Response

• We agreed that the previous statement was confusing, so to resolve the lack of clarity in this statement, which was noted by reviewers 2 and 3, we have implemented a comprehensive revision and it now states, “One study assessed nutritional status using the Communicable Disease Control growth chart and analysed the data with WHO Anthro Plus software (optimal growth) [24]. However, the study employed a mixed approach without clear explanation, highlighting a methodological weakness that affects the interpretability of the findings”. The CDC chart is based on children in the U.S., whereas the WHO is for optimal growth in different contexts. We believed the statement is now improved, clear and easy to understand.

Comment 5: Results, Anthropometry training does not specifically address anthropometric measurement; Ensure correct spacing (the line break

Response: We have checked the specific line referenced (page 12, lines 155-157) and confirmed that the spacing in the phrase 'Except in three (10%) of studies [25, 34, 39], the training does not specifically address anthropometric measurement; instead, most studies provide training on the objective of the study, content of the tool and approach of data collection.' is correct, with single spaces between words and after punctuation. Therefore, no spacing changes were made at this specific location except for typos (provide to provided).

Comment 5: General Check and correct the truncated citation at the end of the sentence on page 9: measurements [1, 31.. This is likely a formatting error.

Response

• Thank you. On page 9, there is only Table 1. However, we have reviewed and updated all citations according to the Plos style.

Comment 6: Clarity and Phrasing

Abstract/Conclusion: The recommendation to strengthen the STROBE-nut checklist is a strong conclusion.

A STROBE extension checklist, specifically designed for observational studies focusing on nutritional status (referred to as STROBE-nut), is now accessible. Nonetheless, it is noted that the checklist does not place sufficient emphasis on the quality of anthropometric measurements, indicating a potential area for improvement in the assessment methodologies used within these studies.

Response

• Thank you for your opinion. We agree with you. We have revised the conclusion and specified the key methodological gaps. For example, in the revised draft, the conclusion in the abstract states, “Inconsistencies in reporting key methodological details of anthropometric measurements were identified, highlighting the gap in the STROBE-nut reporting checklist for studies that measure anthropometry to assess the nutritional status of children. Therefore, we recommend strengthening the STROBE-nut by giving emphasis to the quality assurance aspect of anthropometric measurements including standardisation, calibration, material, setting, number of measurements taken and measurer qualification.”

Comment 7: Ensure the wording is consistent and clear throughout the manuscrip

Attachment

Submitted filename: Reponse to Reviewers.docx

pone.0341038.s005.docx^{(41.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0341038.r003

Decision Letter 1

Ayodeji Babatunde Oginni

2 Jan 2026

Methodological quality and reporting characteristics of anthropometric measurements in studies assessing the nutritional status of children in Ethiopia: a scoping review

PONE-D-24-54149R1

Dear Yilma,

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Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0341038.r004

Acceptance letter

Ayodeji Babatunde Oginni

PONE-D-24-54149R1

PLOS One

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. PRISMA-ScR Checklist.

(DOCX)

pone.0341038.s001.docx^{(85.5KB, docx)}

S2 File. Search strategies for different databases.

(XLSX)

pone.0341038.s002.xlsx^{(16.4KB, xlsx)}

S3 File. Extracted data generated in this study.

(XLS)

pone.0341038.s003.xls^{(54KB, xls)}

Attachment

Submitted filename: Reponse to Reviewers.docx

pone.0341038.s005.docx^{(41.8KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Methodological quality and reporting characteristics of anthropometric measurements in studies assessing the nutritional status of children in Ethiopia: A scoping review

Mekdes Tigistu Yilma

Alemselam Zebdewos Orsango

Mehretu Belayneh

Ingunn Marie Stadskleiv Engebretsen

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Search strategy

Eligibility criteria

Study selection, data extraction and synthesis

Results

Search results and study selection

Fig 1. Flow diagram showing the number of screened, evaluated and included studies from different sources.

Study characteristics

Table 1. Characteristics of included studies, a scoping review in Ethiopia (n = 30).

Anthropometric measurement-related findings

Table 2. Study reported characteristics of anthropometry measurements and reporting of limitations regarding anthropometry of included studies, A scoping review in Ethiopia (n = 30).

Fig 2. Summary of major identified methodological gaps of included studies.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Ayodeji Babatunde Oginni

Roles

Author response to Decision Letter 1

Decision Letter 1

Ayodeji Babatunde Oginni

Roles

Acceptance letter

Ayodeji Babatunde Oginni

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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