Automatic detection of social rhythms in bipolar disorder
To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. Seven patients with BD used smartphones for 4 weeks passively co...
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| Veröffentlicht in: | Journal of the American Medical Informatics Association : JAMIA 2016-05, Vol.23 (3), p.538-543 |
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| container_title | Journal of the American Medical Informatics Association : JAMIA |
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| creator | Abdullah, Saeed Matthews, Mark Frank, Ellen Doherty, Gavin Gay, Geri Choudhury, Tanzeem |
| description | To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.
Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score |
| doi_str_mv | 10.1093/jamia/ocv200 |
| format | Article |
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Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86).
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Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86).
Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.</description><subject>Adult</subject><subject>Bipolar Disorder - physiopathology</subject><subject>Bipolar Disorder - psychology</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Monitoring, Physiologic - methods</subject><subject>Periodicity</subject><subject>Smartphone</subject><subject>Social Behavior</subject><issn>1067-5027</issn><issn>1527-974X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo9kDtPwzAYRS0EoqWwMSOPDIT6EcfxWFW8pEosILFZzmdHdZXUxXaQ-u8JtDDdOxyd4SB0Tck9JYrPN6b3Zh7gixFygqZUMFkoWX6cjp9UshCEyQm6SGlDCK0YF-dowiolJSVsitRiyKE32QO2LjvIPmxxaHEK4E2H43qf133Cfosbvwudidj6FKJ18RKdtaZL7uq4M_T--PC2fC5Wr08vy8WqAK5ELrhpWNVQykuhLOcggbNGGGgBaNmW0tlKNZI6xRlYWytwppKtkoyY2oGo-QzdHry7GD4Hl7LufQLXdWbrwpA0lUrUNRGVGNG7AwoxpBRdq3fR9ybuNSX6J5b-jaUPsUb85mgemt7Zf_ivDv8GfYtnNQ</recordid><startdate>20160501</startdate><enddate>20160501</enddate><creator>Abdullah, Saeed</creator><creator>Matthews, Mark</creator><creator>Frank, Ellen</creator><creator>Doherty, Gavin</creator><creator>Gay, Geri</creator><creator>Choudhury, Tanzeem</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20160501</creationdate><title>Automatic detection of social rhythms in bipolar disorder</title><author>Abdullah, Saeed ; Matthews, Mark ; Frank, Ellen ; Doherty, Gavin ; Gay, Geri ; Choudhury, Tanzeem</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-3ab26b113459d33c7c32b5acfcc14f47ed69b71e932cdd89cea67f9720a8ec583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adult</topic><topic>Bipolar Disorder - physiopathology</topic><topic>Bipolar Disorder - psychology</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Monitoring, Physiologic - methods</topic><topic>Periodicity</topic><topic>Smartphone</topic><topic>Social Behavior</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abdullah, Saeed</creatorcontrib><creatorcontrib>Matthews, Mark</creatorcontrib><creatorcontrib>Frank, Ellen</creatorcontrib><creatorcontrib>Doherty, Gavin</creatorcontrib><creatorcontrib>Gay, Geri</creatorcontrib><creatorcontrib>Choudhury, Tanzeem</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdullah, Saeed</au><au>Matthews, Mark</au><au>Frank, Ellen</au><au>Doherty, Gavin</au><au>Gay, Geri</au><au>Choudhury, Tanzeem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic detection of social rhythms in bipolar disorder</atitle><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle><addtitle>J Am Med Inform Assoc</addtitle><date>2016-05-01</date><risdate>2016</risdate><volume>23</volume><issue>3</issue><spage>538</spage><epage>543</epage><pages>538-543</pages><issn>1067-5027</issn><eissn>1527-974X</eissn><abstract>To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.
Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86).
Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.</abstract><cop>England</cop><pmid>26977102</pmid><doi>10.1093/jamia/ocv200</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of the American Medical Informatics Association : JAMIA, 2016-05, Vol.23 (3), p.538-543 |
| issn | 1067-5027 1527-974X |
| language | eng |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Adult Bipolar Disorder - physiopathology Bipolar Disorder - psychology Feasibility Studies Female Humans Male Middle Aged Monitoring, Physiologic - methods Periodicity Smartphone Social Behavior |
| title | Automatic detection of social rhythms in bipolar disorder |
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