using geolocation data in serious mental illness phenotyping · using geolocation data in serious...
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Using geolocation data in serious mental
illness phenotypingPaolo Fraccaro, Stuart Lavery-Blackie, Anna Beukenhorst, Sabine Van
der veer, John Ainsworth, Charlotte Stockton-Powdrell, Matthew Sperrin,
Shon Lewis, Niels Peek
The Wearable Clinic launch event
05/07/2017
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Digital phenotyping*
Physical activity
Calendars
Social graph
Interests Purchases
Geolocation
* Onnela J-P et al. Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health.Vol. 41, Neuropsychopharmacology.; 2016. p. 1691–6.
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• Increasing Smartphone ownership
in people living with SMI
• Passive monitoring of important
disease indicators (i.e. mobility,
rhythmicity and routines)
• Real-time monitoring, as opposed
to infrequent visits and
questionnaires
Why geolocation data to study serious mental
illness (SMI)?
To what extent have these opportunities been
explored?
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Systematic review of the literature
Records screened(n=641)
Records excluded (n=586)
Duplicates removed (n=366)
Identification
Screening
Eligibility
Included
Medline search(n=267)
Full-text articles assessed for eligibility
(n=55)Full-text articles excluded (n=33):• Not on SMI (2)• Not measuring
geolocation (18)• Review (11)• Other (2)
Studies included in synthesis
(n=22)
PsycInfo search(n=218)
Search on 16/5/2017:
(<smartphone> OR
<geolocation>)
AND
(<schizophrenia> OR
<bipolar disorder>)
Scopus search(n=522)
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• 15 individual studies, with 6 being feasibility studies
• Mostly density-based methods to process the geolocation
data
• Main metrics reported:
– number of locations visited and distance travelled
– time spent outdoor or specific location
– cell tower movements
• Only two studies looking at more complex metrics (i.e. out-of-
home behaviours and entropy of life)
• Seven studies reported clinical significance of the
geolocation-derived metrics
Preliminary findings
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Monitoring SMI: Social functioning
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Feasibility study
Research question: To what extent is it feasible to collect the data necessary to validate algorithms inferring daily out-of-home activities?
Methods• 21 healthy volunteers (7 male:14 female)
• Mix of personal and rental devices
• Measured 10 days over 4 weeks
• Completed feedback questionnaire
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GPS Data
• Logging application
• Start on waking, end on
sleeping
• Upload to secure server
• Valid if 10+ hours long
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Activity diary
• Total time out of home
• Flowchart of activities
• Valid if time out of home
clearly stated
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Activity Diaries
Valid Invalid Total
GPS
Data
Valid 114 48 162
Invalid 24 24 48
Total 138 72 210
Total number of analysible days
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0
1
2
3
4
5
6
0 1 2 3 4 5 6 7 8 9 10
Number of analysable days
Number of analysible days per participant
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Diary
Smartphone
Intrusive
Diary
Smartphone
Time-consuming
Diary
Smartphone
Complicated
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
Results from feedback questionnaires
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Conclusion
• More GPS files returned than activity diaries
• Smartphones found to be less bothersome
• Results suggest potential
• More studies needed, including participants with mental
health issues
• Next step processing the data to infer out-of-home activities
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Thanks for listening!