Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function.
The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics.
SKDH is open source, free to use, and extend under a permissive MIT license and available from GitHub (PfizerRD/scikit-digital-health), as well as the Python Package Index and Anaconda (conda-forge channel). Our package simplifies construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy or mildly impaired adult populations. Methods: Results: SKDH combines data ingestion, pre-processing, and data analysis methods geared towards modern data science workflows and streamlines the generation of digital endpoints in ``good practice" (GxP) environments by combining all the necessary data processing steps in a single pipeline.
Objective: In this work, we introduce SciKit Digital Health (SKDH), a Python software package containing various algorithms for deriving clinical features of gait, sit-to-stand, physical activity, and sleep, wrapped in an easily extensible framework.
Furthermore, few studies include code for replication or off-the-shelf software packages. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings, however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Background: Wearable inertial sensors are providing enhanced insight into patient mobility and health.