Risk of psychotic relapse in schizophrenic patients is commonly measured by social functioning (SF), which focuses on patients' daily activities. Monitoring of SF usually relies on infrequent clinic visits, limiting the capacity to detect sudden changes. GPS data that is passively collected with smartphones introduce new opportunities to monitor SF. We conducted a five-day pilot study with five schizophrenic patients to assess the feasibility of this approach. Participants used a smartphone to continuously record their GPS location, and completed a paper-based SF diary to register out-of-home activities. We implemented a time-based method and a density-based method to identify the geolocations visited and then we clustered geolocations visited in places visited. Finally, we used semantic enrichment to classify places types and associated activities. We evaluated the performance of the two approaches by comparing the activities detected from the GPS data with those recorded in the SF diary. Recall was better for the density-based method, ranging from 0.686 (Standard Deviation [SD] 0.168) to 0.771 (SD 0.264) while precision was better for the time-based method (0.722 (SD 0.197) to 0.954 (SD 0.093)). To conclude, using routinely collected GPS data and relatively simple analytical methods we detected patients' out-of-home activities with moderate recall, more sophisticated analytical methods may obtain better performance.
Out-of-home activity recognition from GPS data in schizophrenic patients
Bellazzi R.;
2016-01-01
Abstract
Risk of psychotic relapse in schizophrenic patients is commonly measured by social functioning (SF), which focuses on patients' daily activities. Monitoring of SF usually relies on infrequent clinic visits, limiting the capacity to detect sudden changes. GPS data that is passively collected with smartphones introduce new opportunities to monitor SF. We conducted a five-day pilot study with five schizophrenic patients to assess the feasibility of this approach. Participants used a smartphone to continuously record their GPS location, and completed a paper-based SF diary to register out-of-home activities. We implemented a time-based method and a density-based method to identify the geolocations visited and then we clustered geolocations visited in places visited. Finally, we used semantic enrichment to classify places types and associated activities. We evaluated the performance of the two approaches by comparing the activities detected from the GPS data with those recorded in the SF diary. Recall was better for the density-based method, ranging from 0.686 (Standard Deviation [SD] 0.168) to 0.771 (SD 0.264) while precision was better for the time-based method (0.722 (SD 0.197) to 0.954 (SD 0.093)). To conclude, using routinely collected GPS data and relatively simple analytical methods we detected patients' out-of-home activities with moderate recall, more sophisticated analytical methods may obtain better performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
