Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

Gruenerbl, Agnes; Osmani, Venet; Bahle, Gernot; Carrasco, Jose C.; Oehler, S.; Mayora, Oscar; Häring, Christian; Lukowicz, Paul

doi:10.1145/2582051.2582089

Cited by 116 publications

(128 citation statements)

References 17 publications

(25 reference statements)

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“…Among the pioneer projects, the MONARCA project used a variety of phone sensors such as GPS and accelerometer to detect the mental states as well as changes in mental states of bipolar patients [9]. In their study that involved 12 patients over a period of 12 weeks, they have shown that mobile behavioral sensing system could identify both depression and mania states with an accuracy of more than 70% and detect state-change with very high precision and recall [10] . The StudentLife project pioneered the use of mobile behavioral sensing technology for monitoring the daily behavior of college students to assess the impact of academic workload on the students' mental states [11].…”

Section: Related Workmentioning

confidence: 99%

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Meng¹,

Hussain²,

Mohr³

et al. 2018

Preprint

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Background: Depression is the leading health issue on college campuses in the U.S. The increasing depression prevalence has resulted in record numbers of students seeking mental health services in colleges and universities across the country. Unfortunately, as the primary mental health service provider, university counseling centers are limited in their capacity and efficiency due to time constraints and reliance on student patients' selfreports. We propose to develop a mobile behavioral sensing platform, named iSee, to enhance the efficiency, accessibility, and scalability of university counseling services.

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Section: Related Workmentioning

confidence: 99%

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Meng¹,

Hussain²,

Mohr³

et al. 2018

Preprint

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“…For example, accelerometry and GPS systems that can capture sedentary behaviour or variability in gait have been shown to predict a range of cognitive issues as well as risk of falling [21,22]. Bed restlessness (as measured by a load sensor or a smart watch) can predict a range of health problems in older adults [4,34] and is also seen as one of the most reliable forms of embedded health assessment [47]; and a range of further indicators including speech, mobility and sleep disturbance have been used to predict depression and other forms of mood disorder [11,13,20].…”

Section: Activity Monitoring and The Deficit Modelmentioning

confidence: 99%

Privacy Considerations when Designing Social Network Systems to Support Successful Ageing

McNeill

Coventry

Pywell

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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A number of interventions exist to support older adults in ageing well and these typically involve support for an active and sociable ageing process. We set out to examine the privacy implications of an intervention that would monitor mobility and share lifestyle and health data with a community of trusted others. We took a privacy-by-design approach to the system in the early stages of its development, working with older adults to firstly understand their networks of trust and secondly understand their privacy concerns should information be exchanged across that network. We used a Johari Windows framework in the thematic analysis of our data, concluding that the social sharing of information in later life carried significant risk. Our participants worried about the social signaling associated with data sharing and were cautious about a system that had the potential to disrupt established networks.

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“…Additionally, location-based data acquired from mobile devices can be used to assess physical activity [3], exposures to hazardous substances [4], and symptoms of mental health conditions such as depression [5][6][7][8]. Accordingly, there is growing interest in the use of location-tracking devices as a data collection tool for health research.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Data Quality of GPS Devices and Smartphones for Assessing Relationships between Place, Mobility, and Health: A Field Study (Preprint)

Goodspeed¹,

Yan²,

Hardÿ³

et al. 2018

Preprint

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Background: Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or smartphones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. Objectives: In this study, we compared the data quality of location information collected from two mobile devices which determine location in different ways -a GPS watch and a smartphone with Google's Location History feature enabled. Methods: Twenty-one chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A smartphone-based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at four random times throughout each day. Participants also took also part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent phone and watch trace data to compare the devices' trace data to their memory of their activities on those days. Trace data from the devices were compared on the basis of: (1) missing data days; (2) reasons for

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Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

Cited by 116 publications

References 17 publications

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

Privacy Considerations when Designing Social Network Systems to Support Successful Ageing

Comparing the Data Quality of GPS Devices and Smartphones for Assessing Relationships between Place, Mobility, and Health: A Field Study (Preprint)

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