How does weight prediction work?
Weight Prediction offers a way to check self-reported weight through a BodyScan. As weight is a major component in calculating total body fat determining health risks, an incorrect body weight can lead to miscalculation.
Bias exists in a lot of individuals to some degree. Most people do not weigh themselves every day and will not recall the last time they had weighed themselves. Some overweight and obese individuals might under-report their weight, leading to a lower risk. Similarly, there are cases where individuals are underweight and over-report their weight.
BMI remains an important indicator for underwriting prices in Life & Health Insurance. Misreported weight will lead to higher premiums as individuals will be miscategorized and lead to incorrect premium pricing. Intervention will not be possible to help those individuals, leading to late claims and increased risk of mortality.
Weight Prediction allows partners to compare self-reported weight with a prediction and flag the result as a mis-reported value.
Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods
Body composition and anthropometry assessment from two-dimensional smartphone images is possible through advancement of computational hardware and artificial intelligence (AI) techniques. This study established agreement of a novel smartphone assessment, compared with traditional bioelectrical impedance analysis (BIA), and criterion measures.
https://pubmed.ncbi.nlm.nih.gov/35094958/
Other Articles of Interest
The reliability and validity of self-reported weight and height
Biases in self-reported height and weight measurements and their effects on modeling health outcomes
Self-reported anthropometrics are often used as proxies for measured anthropometrics, but research has shown that heights and weights are often misreported. Using the Study on global AGEing and adult health, I analyze misreporting patterns of height, weight, and BMI in China, India, Russia, and South Africa. Adjustments of self-reported heights and weights using demographic, social, and anthropometric characteristics are evaluated and found to be useful in studying the distribution of anthropometrics within a population. Measured, self-reported, and adjusted BMI are then compared in logistic regression models on the reporting of health outcomes, as well as the resulting accuracy of individual prediction. When BMI is used as a continuous variable in models of health outcomes, measured, self-reported, and adjusted BMI produce similar coefficient estimates, and so self-reported data would be a natural choice because of its accessibility and convenience. In other applications, such as models using categorical BMI and individual prediction using either continuous or categorical BMI, self-reported data in lieu of measured data might not be accurate enough, but adjustments could serve as a potential compromise
Comparisons of Self-Reported and Measured Height and Weight, BMI, and Obesity Prevalence from National Surveys: 1999–2016
The aim of this study was to compare national estimates of self-reported and measured height and weight, BMI, and obesity prevalence among adults from US surveys.
PNS117 Correlation between Self-Reported and Clinical Measures of Weight, Height and Body MASS INDEX in Adults Members of a Private Health Insurance Company in Colombia, 2019
Objective is to determine the correlation between weight, height and body mass index (BMI) self-reported and clinically measured in members of a private health insurance company in Colombia.
PNS117 Correlation between Self-Reported and Clinical Measures of Weight, Height and Body MASS INDEX in Adults Members of a Private Health Insurance Company in Colombia, 2019
Objective is to determine the correlation between weight, height and body mass index (BMI) self-reported and clinically measured in members of a private health insurance company in Colombia.
