A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set

Abstract

Background: The C-Score, which is 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 that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death.

Objective: Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices.

Methods: We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test.

Results: Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs 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 changes and lifestyle modifications.

Keywords: C-Score; mobile phone; mortality; predictive models; validation.

Figure 1

Flowchart for sample sizes for National Health and Nutrition Examination Survey (NHANES) study samples.

Figure 2

Receiver operating characteristic curve for original C-Score versus expanded models for all-cause mortality. Model 1: C-Score; model 2: C-Score+sociodemographic variables; model 3: C-Score+sociodemographic variables+medical variables; model 4: C-Score+sociodemographic variables+medical history+interactions.

Figure 3

Calibration plots of predicted versus observed probabilities for original C-Score versus expanded models for all-cause mortality. Model 1: C-Score; model 2: C-Score+sociodemographic variables; model 3: C-Score+sociodemographic variables+medical variables; model 4: C-Score+sociodemographic variables+medical history+interactions.

Figure 4

Internal validation using k-fold procedure (folds=10). cvAUC: cross-validation area under the curve; ROC: receiver operating characteristic.

Figure 5

Calibration plot of predicted versus observed probabilities of all-cause mortality for model 3 for all-cause mortality. Model 3: C-Score+sociodemographic variables+medical variables. NHANES: National Health and Nutrition Examination Survey.