Wearable activity monitors are increasingly prevalent in health research, but there is as yet no data-driven study of artefact removal in datasets collected from typically developing children across childhood. Here, stride count data were collected via a commercially available activity monitor (StepWatch), which employs an internal filter for sub-threshold accelerations, but does not post-process supra-threshold activity data. We observed 428 typically-developing children, ages 2-15, wearing the StepWatch for 5 consecutive days. We developed a minimum per-minute stride-count below which the data outputted from the StepWatch could be considered 'idle' and not 'productive'. We found that a threshold stride count of 10 steps per minute captured 90% of samples in a weighted average among isolated non-zero stride-count samples offset by inactivity. This threshold did not vary by age, gender, or by an age-gender interaction. Filtering the activity data according to this threshold reduced overall stride count by 8-10% by age group, from 8177 ± 2659 to 7432 ± 2641 strides per day. The impact on number of bouts per day decreased from an overall average of 79.3 ± 17.2 to 72.7 ± 12.1; this effect varied by age group. This study delivers the first data-driven estimate of a minimum activity threshold in step- or stride units that may extend to other studies. We conclude that the impact of production-idle filtering on activity data is substantial and suggests a possible impetus for re-contextualizing extant studies and guidelines reported without such filtering.