The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Relapse prediction in schizophrenia through digital phenotyping: a pilot study
Open accessAbstract
Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months. By testing for changes in mobility patterns and social behavior over time as measured through smartphone use, we were able to identify statistically significant anomalies in patient behavior in the days prior to relapse. We found that the rate of behavioral anomalies detected in the 2 weeks prior to relapse was 71% higher than the rate of anomalies during other time periods. Our findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic. Real-time detection of behavioral anomalies could signal the need for an intervention before an escalation of symptoms and relapse occur, therefore reducing patient suffering and reducing the cost of care.
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Cites
Socioeconomic Disparities In Health: Pathways And Policies
Adler, N. E., & Newman, K. (2002) · Health Affairs · doi:10.1377/hlthaff.21.2.60 · Cited by 2,104
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782
Torous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016) · JMIR Mental Health · doi:10.2196/mental.5165 · Cited by 758 · cited 2×
“While still nascent, smartphone interventions for schizophrenia offer an immediate target for relapse prediction systems [19].”
BackgroundMethodSelf-citationHarnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
Onnela, J.-P., & Rauch, S. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.7 · Cited by 726
Self-citationCsernansky, J. G., Mahmoud, R., & Brenner, R. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa002028 · Cited by 605
Feasibility, Acceptability, and Preliminary Efficacy of a Smartphone Intervention for Schizophrenia
Ben-Zeev, D., Brenner, C. J., Begale, M., Duffecy, J., Mohr, D. C., & Mueser, K. T. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu033 · Cited by 493
The nature of relapse in schizophrenia
Emsley, R., Chiliza, B., Asmal, L., & Harvey, B. H. (2013) · BMC Psychiatry · doi:10.1186/1471-244x-13-50 · Cited by 478
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477
Socioeconomic Status and Health: Mediating and Moderating Factors
Chen, E., & Miller, G. E. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185634 · Cited by 402
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384
Cited by
Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021) · World Psychiatry · doi:10.1002/wps.20883 · Cited by 1,199
Self-citationNonsocial and social cognition in schizophrenia: current evidence and future directions
Green, M. F., Horan, W. P., & Lee, J. (2019) · World Psychiatry · doi:10.1002/wps.20624 · Cited by 612
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety
Huckvale, K., Venkatesh, S., & Christensen, H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0166-1 · Cited by 433
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408
Virtual reality exposure therapy for social anxiety disorder: A randomized controlled trial.
Anderson, P. L., Price, M., Edwards, S. M., Obasaju, M. A., Schmertz, S. K., Zimand, E., & Calamaras, M. R. (2013) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0033559 · Cited by 371
Metadata
- Authors
- Barnett, I., Torous, J., Staples, P., Sandoval, L., Keshavan, M., & Onnela, J.-P.
- Venue
- Neuropsychopharmacology
- Published
- 2018-02-22
- Volume
- 43
- Issue
- 8
- Pages
- 1660-1666
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- OpenAlex
- W2792224659
- Cited by
- 422
- References
- 34
- FWCI
- 27.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.springer.com/tdm (tdm)
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