The strong integration of smartphones in everyday life offers many new investigative opportunities. In particular, digital traces from smartphones can now increasingly be used to infer information about actions performed by its user in the physical world. In this study, we further explore this area by searching for digital traces indicative of phone movement. Using a new differential analysis technique, a number of popular Apps and system files on three types of iPhones were examined for files containing timestamped information related to movement of the phone. This yielded, besides well-known traces from the iPhone Health App, additional traces of phone movement in WhatsApp logfiles and in the iPhone system file cache_encryptedC.db.In order to understand the reliability and accuracy of these new traces for movement detection, additional experiments were carried out, consisting of walking, driving and short-term loading in drop tests. From these experiments, it follows that traces in WhatsApp logfiles can be used to detect periods of walking and short-term loading, although reliability strongly depends on whether or not WhatsApp is in the foreground and whether or not the phone is locked. In walking, averaged accuracy of periods of movement from traces in WhatsApp logfiles is estimated to be 30–40s.The file cache_encryptedC.db contains a plethora of information on phone movement, which allows, for instance, distinction of periods in which walking, running and driving took place. For some of the traces, reliability also depends on whether or not the phone is locked. Estimated averaged accuracy of periods of movements from traces in cache_encryptedC.db is estimated to be 30s for walking and somewhat larger for driving.Although certainly promising, many of the traces are of an intricate nature and some of them are presently not well understood. Therefore, some care should be taken not to draw unwarrently strong conclusions from them. Possible use of these traces includes estimating the instant of a traffic incident, estimating the time the phone user was still alive and making probability statements about hypotheses in the form of a likelihood ratio in an Baysian evaluative methodology.