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Mobile health (mHealth) technologies enable frequent sampling of physiological and psychological signals over time. In our recent work we used a convolutional neural network (CNN) model to predict self-reported phenotypes of chronic conditions from step and sleep data recorded from passive trackers in free living conditions. We investigated the impact of the time-granularity of the collected data and showed that training the models on higher-resolution (minute-level) data improved classification performance on conditions related to mental health and nervous system disorders, as compared to using only day-level totals.

A model trained on 30 days of mHealth data attains the same performance as using the full 147-day window of data

In the present work we shift the focus from the time resolution of the observation window to its duration. We study how the performance of the best-performing model on the highest-resolution data changes as the length of the data collection window is varied from 3 to 147 days for each user.

We found that for mental health and nervous system disorders, a model trained on 30 days of mHealth data attains the same performance as using the full 147-day window of data, in terms of AUC increase over a baseline model that uses only demographics, height, and weight. Additionally, for the same cluster of conditions, only 7 days of data are sufficient to realize 62% of the maximum increase in AUC over baseline attainable using the full window. The results suggest that for some conditions health-related digital phenotyping in free-living conditions can potentially be performed in a relatively short amount of time, imposing minimal disruptions on user habits.

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