Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 25, 2022
Open Peer Review Period: Jan 25, 2022 - Mar 22, 2022
Date Accepted: Apr 28, 2022
Date Submitted to PubMed: Apr 28, 2022
(closed for review but you can still tweet)
A novel score for mobile health applications to predict and prevent mortality: Further validation and adaptation to US population using the US NHANES dataset
ABSTRACT
Background:
The “C-Score”, an Individual Health Score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized ‘point-in-time’ health assessment tool, which could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications which reduce the risk of premature death.
Objective:
Our study 1) validates the C-score in the US population and 2) expand it in order to improve its predictive capabilities in the US population.
Methods:
We conducted a literature review to identify relevant variables that were missing in the original C-score. Subsequently, we used data from the US National Health and Nutrition Examination Survey (NHANES) 2005-2014 (N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III 1988-1994 (N=1,440) to conduct an external validation of the test. Only participants with complete data were included. Discrimination and calibration tests to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test were conducted.
Results:
Higher C-scores were related to reduced odds of all-cause mortality (OR=0.96, p<0.0001). We found a good fit of the C-score for all-cause mortality with area under the curve (AUC) of 0.72. Among participants between 40-69 years old, C-score models had good fit for all-cause mortality and AUC above 0.72. A sensitivity analysis using NHANES III (1988-1994) was performed yielding similar results. The inclusion of sociodemographic and clinical variables to the basic C-Score increased the areas under the curve (AUC) from 0.72 (95% CI 0.71 – 0.73) to 0.87 (95% CI 0.85 – 0.88).
Conclusions:
Our study shows that this digital biomarker, the C-score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk, and as a counseling tool to motivate behavior change and lifestyle modifications. Clinical Trial: N/A
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