Beschreibung:
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<jats:bold>Introduction:</jats:bold>
The American Heart Association (AHA) Life’s Simple 7 (LS7) is a metric that is known to predict cardiovascular disease. What is unknown are the factors that predict cardiovascular health (CVH) scores over time, and what patterns of CVH scores are most common. Machine learning algorithms can identify these patterns using a data-driven approach. Our objective was to identify most common risk patterns over time and assess smartphone usage statistics to identify their predictors.
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<jats:bold>Methods:</jats:bold>
We recruited 303 Northwestern University undergraduates and cluster randomized them to one of two health intervention groups. The intervention group focused on cardiovascular health behaviors (n=130) were provided with an app that delivered an intervention targeting the LS7 metrics smoking, physical activity, fruit and vegetable consumption, and weight. All participants reported levels of four behaviors weekly. Health scores were calculated as the sum of metrics meeting ideal criteria; CVH behaviors scored per the AHA LS7. Behavioral patterns over time used in the K-means unsupervised clustering algorithm include the mean, slope and variance of trends. K-Silhouette was used to determine optimal number of clusters, and predictors from the CVH intervention app use data were examined to determine which contributed to distinguishing between clusters.
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<jats:bold>Results:</jats:bold>
We identified four clusters using K-silhouette criteria (Figure 1). Of the smartphone meta-data collected, 2 were significantly different between clusters. We observed greater number of interactions/week (p < .0001) and greater number of app-use days/week (p < .0001) for both “healthy-steady” and “improvers” as compared with “decliners” and “unhealthy-steady” clusters.
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<jats:bold>Conclusions:</jats:bold>
Our findings suggest that frequency and consistency in health behavior monitoring predict healthier trends over time. These usage behaviors may be targets for behavioral interventions. Further work will investigate if these factors meaningfully predict CVH at the end of a 2 year intervention. In the future, predictors could be used as signals that an individual needs behavioral intervention.
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