Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a standalone Python package for research use. We believe that this material will provide a useful resource for the research community, accelerate digital health science and facilitate clinical applications of wearable accelerometry.