Step counts and other data from these patients’ records were used to develop and test an elastic net-regularized logistic regression model, a type of machine-learning model that can analyze a large amount of complex information. The goal of their model was to predict the likelihood that a patient would be hospitalized in the next week, based on their previous two weeks of data.

Researchers first created the model by examining how well different variables predicted hospitalization, using data from 70% of the trial participants (151 people). Potential predictors in the model included patient characteristics (e.g., age, ECOG performance status), as well as activity data measured before and during treatment. In addition to daily step totals, the researchers computed other metrics, such as relative changes to a person’s week-by-week averages or the difference in the minimum and maximum number of steps each week.


The research team then validated the model using the remaining 30% of patients (63 people). The model that integrated step counts was strongly predictive of hospitalization the following week (AUC = 0.80, 95% confidence interval [CI] 0.60-0.90), and it significantly outperformed the model without step counts (AUC = 0.46, 95% CI 0.24-0.66, p<0.001).

“The step counts immediately preceding the prediction window ended up being generally more predictive than clinical variables. The dynamic nature of the step counts, the fact that they're changing every day, seems to make them a particularly good indicator of a patient's health status,” said Dr. Hong.

The top predictive variables in the model included step counts from each of the past two days, as well as the relative changes in maximum step count and step count range over the past two weeks.

The use of dynamic data differentiates this model from those based on clinical data like performance status and tumor histology. “One of the unique parts of this model is that it’s designed to be a running prediction,” explained Ms. Friesner. “You can run the algorithm on any given day and have an idea of a patient’s risk level one week out, giving you time to provide that additional support they need.”

This additional support is key to reducing hospitalizations, explained Dr. Hong, whether it’s scheduling more frequent follow-ups, changing something about the patient’s treatment plan or another personalized approach. “The core of what works is that this is an extra touchpoint for a doctor to see a patient. It gives the patient reassurance to know that we are watching out for them.”

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