Getting a Grip on Strength Measurement in Children (6-13 Y): Impact of Typical Error of Measurement

Rob Buck1, Michael Ian Lambert1

  • 1University of Cape Town.

Insights

The smallest worthwhile change in children's handgrip strength (HGS) is 1.3 kg. Changes exceeding this measurement error are considered practically significant for research.

Area of Science:

  • Pediatric physical assessment
  • Biomechanical analysis
  • Measurement error in clinical trials

Background:

  • Handgrip strength (HGS) is a key indicator of overall health and physical function in children.
  • Establishing the minimal detectable change in HGS is crucial for accurately interpreting results in pediatric research.
  • Previous studies have not clearly defined the smallest change in HGS with practical significance for pediatric populations.

Purpose of the Study:

  • To determine the smallest change in handgrip strength (HGS) in children that signifies practical importance.
  • To establish a benchmark for interpreting meaningful HGS changes in pediatric studies.
  • To differentiate between measurement variability and true physiological changes in children's HGS.

Main Methods:

  • A cohort of 290 children aged 6-13 years underwent a standardized HGS testing protocol three times within a week.
  • Calculated the typical error of measurement (TE), coefficient of variation, and smallest worthwhile change (SWC) for HGS.
  • Analyzed TE, SWC, and other metrics stratified by sex and grade level (1-7).

Main Results:

  • The typical error of measurement (TE) for HGS in children aged 6-13 was 1.3 kg.
  • A 1.3 kg change in HGS was identified as the smallest worthwhile change (SWC), aligning with the TE.
  • Larger HGS changes (medium: 3.3 kg, large: 5.3 kg) consistently exceeded the TE, indicating greater practical significance.

Conclusions:

  • Changes in HGS exceeding both the typical error of measurement (TE) and smallest worthwhile change (SWC) represent genuine, practically significant alterations.
  • This study provides researchers with critical thresholds for assessing the clinical relevance of HGS changes in children.
  • The findings enhance the interpretability of HGS data in pediatric research, aiding in the evaluation of interventions and developmental changes.
Abstract

Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
94.8K
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
13.1K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
23.2K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Binet's Contribution to Measures of Intelligence01:23

Binet's Contribution to Measures of Intelligence

Alfred Binet, along with his student Théophile Simon, was tasked by the French Ministry of Education in 1904 to create a method for identifying students who struggled to learn through conventional classroom instruction. This initiative aimed to address overcrowding by placing such students in specialized schools. Binet and Simon developed an intelligence test comprising 30 tasks, ranging from simple commands, like touching one's nose or ear, to more complex tasks, such as drawing...
1.4K
Distance Corrections01:15

Distance Corrections

To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
102