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Assessment of the measurement properties of the Peabody Developmental Motor Scales-2 by applying the COSMIN methodology.

Yuanye ZhuJiahui HuWei-Bing YeMallikarjuna KoriviYongdong Qian
Published in: Italian journal of pediatrics (2024)
The Peabody Developmental Motor Scales-2 (PDMS-2) has been used to assess the gross and fine motor skills of children (0-6 years); however, the measurement properties of the PDMS-2 are inconclusive. Here, we aimed to systematically review the measurement properties of PDMS-2, and synthesize the quality of evidence using the Consensus-based Standards for the Selection of Health Measurements Instruments (COSMIN) methodology. Electronic databases, including PubMed, EMBASE, Web of Science, CINAHL and MEDLINE, were searched for relevant studies through January 2023; these studies used PDMS-2. The methodological quality of each study was assessed by the COSMIN risk-of-bias checklist, and the measurement properties of PDMS-2 were evaluated by the COSMIN quality criteria. Modified GRADE was used to evaluate the quality of the evidence. We included a total of 22 articles in the assessment. Among the assessed measurement properties, the content validity of PDMS-2 was found to be sufficient with moderate-quality evidence. The structural validity, internal consistency, test-retest reliability and interrater reliability of the PDMS-2 were sufficient for high-quality evidence, while the intrarater reliability was sufficient for moderate-quality evidence. Sufficient high-quality evidence was also found for the measurement error of PDMS-2. The overall construct validity of the PDMS-2 was sufficient but showed inconsistent quality of evidence. The responsiveness of PDMS-2 appears to be sufficient with low-quality evidence. Our findings demonstrate that the PDMS-2 has sufficient content validity, structural validity, internal consistency, reliability and measurement error with moderate to high-quality evidence. Therefore, PDMS-2 is graded as 'A' and can be used in motor development research and clinical settings.
Keyphrases
  • public health
  • healthcare
  • risk assessment
  • mental health
  • young adults
  • big data
  • human health
  • deep learning