Abstract A growing number of studies are using accelerometers to examine activity level patterns in aquatic animals. However, given the amount of data generated from accelerometers, most of these studies use loggers that archive acceleration data, thus requiring physical recovery of the loggers or acoustic transmission from within a receiver array to obtain the data. These limitations have restricted the duration of tracking (ranging from hours to days) and/or type of species studied (e.g., relatively sessile species or those returning to predictable areas). To address these logistical challenges, we present and test a satellite-transmitted metric for the remote monitoring of changes in activity, measured via a pop-off satellite archival tag (PSAT) with an integrated accelerometer. Along with depth, temperature, and irradiance for geolocation, the PSAT transmits activity data as a time-series (ATS) with a user-programmable resolution. ATS is a count of high-activity events, relative to overall activity/mobility during a summary period. An algorithm is used to identify the high-activity events from accelerometer data and reports the data as a count per time-series interval. Summary statistics describing the data used to identify high-activity events accompany the activity time-series. In this study, we first tested the ATS activity metric through simulating PSAT output from accelerometer data logger archives, comparing ATS to vectorial dynamic body acceleration. Next, we deployed PSATs with ATS under captive conditions with cobia (Rachycentron canadum). Lastly, we deployed seven pop-off satellite archival tags (PSATs) able to collect and transmit ATS in the wild on adult sandbar sharks (Carcharhinus plumbeus). In the captive trials, we identified both resting and non-resting behavior for species and used logistic regression to compare ATS values with observed activity levels. In captive cobia, ATS was a significant predictor of observed activity levels. For 30-day wild deployments on sandbar sharks, satellites received 57.4–73.2% of the transmitted activity data. Of these ATS datapoints, between 21.9 and 41.2% of records had a concurrent set of temperature, depth, and light measurements. These results suggest that ATS is a practical metric for remotely monitoring and transmitting relative high-activity data in large-bodied aquatic species with variable activity levels, under changing environmental conditions, and across broad spatiotemporal scales.