using geolocation data in serious mental illness phenotyping · using geolocation data in serious...

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Using geolocation data in serious mental illness phenotyping Paolo 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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Page 1: Using geolocation data in serious mental illness phenotyping · Using geolocation data in serious mental illness phenotyping ... Smartphone-Based Digital ... Activity diary •Total

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

Page 2: Using geolocation data in serious mental illness phenotyping · Using geolocation data in serious mental illness phenotyping ... Smartphone-Based Digital ... Activity diary •Total
Page 3: Using geolocation data in serious mental illness phenotyping · Using geolocation data in serious mental illness phenotyping ... Smartphone-Based Digital ... Activity diary •Total

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!