Validity of Measured vs. Self-Reported Weight and Height and Practical Considerations for Enhancing Reliability in Clinical and Epidemiological Studies: A Systematic Review.
Khadijah FayyazMo'ath F BatainehHabiba I AliAli M Al-NawaisehRami H Al-Rifai'Hafiz M ShahbazPublished in: Nutrients (2024)
Self-reported measures of height and weight are often used in large epidemiological studies. However, concerns remain regarding the validity and reliability of these self-reported measures. The aim of this systematic review was to summarise and evaluate the comparative validity of measured and self-reported weight and height data and to recommend strategies to improve the reliability of self-reported-data collection across studies. This systematic review adopted the PRISMA guidelines. Four online sources, including PubMed, Medline, Google Scholar, and CINAHL, were utilised. A total of 17,800 articles were screened, and 10 studies were eligible to be included in the SLR based on the defined inclusion and exclusion criteria. The findings from the studies revealed good agreement between measured and self-reported weight and height based on intra-class correlation coefficient and Bland-Altman plots. Overall, measured weight and height had higher validity and reliability (ICC > 0.9; LOA < 1 SD). However, due to biases such as social pressure and self-esteem issues, women underreported their weight, while men overreported their height. In essence, self-reported measures remain valuable indicators to supplement the restricted direct anthropometric data, particularly in large-scale surveys. However, it is essential to address potential sources of bias.
Keyphrases
- body mass index
- systematic review
- weight gain
- physical activity
- weight loss
- case control
- meta analyses
- electronic health record
- healthcare
- body weight
- magnetic resonance imaging
- mental health
- type diabetes
- single cell
- computed tomography
- randomized controlled trial
- big data
- body composition
- metabolic syndrome
- adipose tissue
- middle aged
- insulin resistance
- skeletal muscle
- data analysis
- human health