Abstract. Mobility research is mainly concerned with understanding mobility on a higher level, including environmental factors, e.g., measuring the time out of home or tracking revisited places. This requires preprocessing the raw data obtained from GPS sensors, like clustering significant locations and distinguishing these from periods on the go. We introduce a new stop and trip detection algorithm to transform a list of position records into intervals of dwelling and transit. The system is based on geometrical analyses of the signal noise: Imperfect GPS data tends to scatter around an actual dwell position in a star-like pattern, and this imperfection is what we leverage for our classification. The system contains four independent classification methods, comparing different aspects of the geometrical properties of a given trajectory. If available, accelerometer readings can be used to improve the system’s accuracy further. To evaluate the classifier’s performance, we recorded a large dataset containing gold-standard labels and compared the classification results of our system with the results of Scikit Mobility and Moving Pandas. Our Stop Go Classifier outperforms the traditional distance/time-threshold-based systems. The described system is available as free software.

Abstract. Identifying stops and trips from raw GPS traces is a fundamental preprocessing step for most mobility research applications. Thus, ensuring the excellent accuracy of such systems is of high interest to researchers designing such analysis pipelines. While there are plenty of GPS datasets available, these usually do not provide annotations and thus cannot be used for benchmarking stop/trip classifiers easily. This manuscript introduces a GPS & accelerometer dataset, including accurate stop/trip annotations. It contains 122,808 GPS samples as one continuous trajectory, spanning over 126 days. The recorded time frame includes working days, vacations, travelling, everyday life and all regular modes of transportation. During recording, a detailed mobility diary was conducted to capture each dwelling period’s exact beginning and end. The position and diary data combined contain 78,900 labelled stops and 43,908 labelled trips. This serves as ground truth for stop/trip classification algorithms to test existing tools or develop new analysis methods. The introduced dataset is freely available under a CC-By Attribution 4.0 International license, the annotation tool under the BSD 3-Clause license.

Background Maintaining mobility in old age is crucial for healthy ageing including delaying the onset and progress of frailty. However, the extent of an individuals´ mobility relies largely on their personal, social, and environmental resources as outlined in the Life-Space Constriction Model. Recent studies mainly focus on facilitating habitual out-of-home mobility by fostering one type of resources only. The MOBILE trial aims at testing whether tablet-assisted motivational counselling enhances the mobility of community-dwelling older adults by addressing personal, social, and environmental resources. Methods In the MOBILE randomized controlled trial, we plan to enrol 254 community-dwelling older adults aged 75 and older from Havelland, a rural area in Germany. The intervention group will receive a tablet-assisted motivational counselling at the participant´s home and two follow-up telephone sessions. Main focus of the counselling sessions lays on setting and adapting individual mobility goals and applying action planning and habit formation strategies by incorporating the personal social network and regional opportunities for engaging in mobility related activities. The control group will receive postal general health information. The primary mobility outcome is time out-of-home assessed by GPS (GPS.Rec2.0-App) at three points in time (baseline, after one month, and after three months for seven consecutive days each). Secondary outcomes are the size of the GPS-derived life-space convex hull, self-reported life-space mobility (LSA-D), physical activity (IPAQ), depressive symptoms (GDS), frailty phenotype, and health status (SF-12). Discussion The MOBILE trial will test the effect of a motivational counselling intervention on out-of-home mobility in community-dwelling older adults. Novel aspects of the MOBILE trial include the preventive multi-level intervention approach in combination with easy-to-use technology. The ecological approach ensures low-threshold implementation, which increases the benefit for the people in the region. Trial registration The MOBILE trial is prospectively registered at DRKS (Deutsches Register Klinischer Studien, German Registry of Clinical Trials) DRKS00025230. Registered 5 May 2021.