JMIR Publications

ABSTRACT

This article introduces population digital health (PDH) – the use of digital health information sourced from Health IoT and wearable devices for population health modeling – as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing/networking infrastructure to increase the scale, coverage, equity, and cost effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (e.g., electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (i) data inadequacy; (ii) inaccuracy of the Health IoT devices’ sensor measurements; and (iii) the spatiotemporal sparsity in the available digital health information.