Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

By Luca Canzian, Mirco Musolesi. Ubiquitous Computing, 2015

509 citations recorded by the source corpus.

How researchers see this paper

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Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing (2018, methodology): “For example, prior work propose location features [14, 61, 62] based on the assumption that depressed persons would travel less and have more irregular mobility patterns.”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, unknown): “Results were promising, yet their evaluation strategy involved training and testing of data from all users, a scenario often encountered in studies involving mood score predictions using mobile phone data [72–74].”
Systematic review of smartphone-based passive sensing for health and wellbeing (2017, methodology): “Seven studies demonstrated individualized or similar-user models as better for predicting participant status compared to generalized models [39,43 – 45, Six studies conducted interviews or usability testing with their participants [36,38,40,52,55,60].”
A Survey on Deep Learning for Human Mobility (2020, background): “Predicting individuals’ future locations is relevant in multiple applications such as monitoring public health [13, 29], well-being [141, 197], and traffic congestions [167], and to improve travel recommendation, geomarketing, and link prediction in social network platforms [25, 208, 233, 234, 241].”