Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability

npj Schizophrenia

Volumn 6, Issue 1, 2020, Pages

  He Yueya, Joy ^a   Buck, Benjamin ^b   Campbell, Andrew ^c   Choudhury, Tanzeem ^d   Kane, John M ^e   Ben Zeev, Dror ^b   Althoff, Tim ^a  

^a University of Washington   (United States)

^b UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^c Dartmouth College   (United States)

^d The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell ^*  (United States)

^e DONALD AND BARBARA ZUCKER SCHOOL OF MEDICINE AT HOFSTRA NORTHWELL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; BEHAVIOR; BEHAVIOR ASSESSMENT; CLINICAL RESEARCH; CONTROLLED STUDY; ECOLOGICAL MOMENTARY ASSESSMENT; FOLLOW UP; HUMAN; MAJOR CLINICAL STUDY; MEMORY; MENTAL DISEASE; PHYSICAL ACTIVITY; PRIORITY JOURNAL; REPORTING BIAS; RETROSPECTIVE STUDY; SCHIZOPHRENIA; SCHIZOPHRENIA SPECTRUM DISORDER; SECONDARY ANALYSIS; SELF REPORT; SOCIAL BEHAVIOR; STABILITY INDEX;

EID: 85096438907     PISSN: None     EISSN: 2334265X     Source Type: Journal    
DOI: 10.1038/s41537-020-00123-2     Document Type: Article

References (39)

1. Bond, M. J. & Feather, N. Some correlates of structure and purpose in the use of time. J. Personal. Soc. Psychol. 55, 321 (1988). DOI: 10.1037/0022-3514.55.2.321
2. Charzynska, K., Kucharska, K. & Mortimer, A. Does employment promote the process of recovery from schizophrenia? A review of the existing evidence. Int. J. Occup. Med. Environ. Health 28, 407 (2015). DOI: 10.13075/ijomeh.1896.00341
3. Ringeisen, H. et al. Supported education for individuals with psychiatric disabilities: state of the practice and policy implications. Psychiatr. Rehabil. J. 40, 197 (2017). DOI: 10.1037/prj0000233
4. Reeve, S., Sheaves, B. & Freeman, D. The role of sleep dysfunction in the occurrence of delusions and hallucinations: a systematic review. Clin. Psychol. Rev. 42, 96–115 (2015). DOI: 10.1016/j.cpr.2015.09.001
5. Tew, J. et al. Social factors and recovery from mental health difficulties: a review of the evidence. Br. J. Soc. Work 42, 443–460 (2012). DOI: 10.1093/bjsw/bcr076
6. Holley, J., Crone, D., Tyson, P. & Lovell, G. The effects of physical activity on psychological well-being for those with schizophrenia: a systematic review. Br. J. Clin. Psychol. 50, 84–105 (2011). DOI: 10.1348/014466510X496220
7. Cuijpers, P., Van Straten, A. & Warmerdam, L. Behavioral activation treatments of depression: a meta-analysis. Clin. Psychol. Rev. 27, 318–326 (2007). DOI: 10.1016/j.cpr.2006.11.001
8. Cook, J. A. et al. Results of a randomized controlled trial of mental illness self-management using wellness recovery action planning. Schizophr. Bull. 38, 881–891 (2011). DOI: 10.1093/schbul/sbr012
9. McGuire, A. B. et al. Illness management and recovery: a review of the literature. Psychiatr. Serv. 65, 171–179 (2014). DOI: 10.1176/appi.ps.201200274
10. Blum, L. H. et al. Depressed mood in individuals with schizophrenia: a comparison of retrospective and real-time measures. Psychiatry Res. 227, 318–323 (2015). DOI: 10.1016/j.psychres.2015.03.008
11. Khan, A. et al. Assessing the sources of unreliability (rater, subject, time-point) in a failed clinical trial using items of the positive and negative syndrome scale (panss). J. Clin. Psychopharmacol. 33, 109–117 (2013). DOI: 10.1097/JCP.0b013e3182776ebe
12. Ben-Zeev, D., McHugo, G. J., Xie, H., Dobbins, K. & Young, M. A. Comparing retrospective reports to real-time/real-place mobile assessments in individuals with schizophrenia and a nonclinical comparison group. Schizophr. Bull. 38, 396–404 (2012). DOI: 10.1093/schbul/sbr171
13. Shiffman, S., Stone, A. A. & Hufford, M. R. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008). DOI: 10.1146/annurev.clinpsy.3.022806.091415
14. Eisner, E. et al. Development and long-term acceptability of express, a mobile phone app to monitor basic symptoms and early signs of psychosis relapse. JMIR mHealth uHealth 7, e11568 (2019). DOI: 10.2196/11568
15. Granholm, E., Loh, C. & Swendsen, J. Feasibility and validity of computerized ecological momentary assessment in schizophrenia. Schizophr. Bull. 34, 507–514 (2007). DOI: 10.1093/schbul/sbm113
16. Palmier-Claus, J. E. et al. Integrating mobile-phone based assessment for psychosis into people’s everyday lives and clinical care: a qualitative study. BMC Psychiatry 13, 34 (2013). DOI: 10.1186/1471-244X-13-34
17. Kumar, D. et al. A mobile health platform for clinical monitoring in early psychosis: implementation in community-based outpatient early psychosis care. JMIR Mental Health 5, e15 (2018). DOI: 10.2196/mental.8551
18. Niendam, T. A. et al. Enhancing early psychosis treatment using smartphone technology: a longitudinal feasibility and validity study. J. Psychiatr. Res. 96, 239–246 (2018). DOI: 10.1016/j.jpsychires.2017.10.017
19. Saeb, S. et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17, e175 (2015). DOI: 10.2196/jmir.4273
20. Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P. & Mohr, D. C. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4, e2537 (2016). DOI: 10.7717/peerj.2537
21. Renn, B. N., Pratap, A., Atkins, D. C., Mooney, S. D. & Areán, P. A. Smartphone-based passive assessment of mobility in depression: challenges and opportunities. Mental Health Phys. Act. 14, 136–139 (2018). DOI: 10.1016/j.mhpa.2018.04.003
22. Faurholt-Jepsen, M., Bauer, M. & Kessing, L. V. Smartphone-based objective monitoring in bipolar disorder: status and considerations. Int. J. Bipolar Disord. 6, 1–7 (2018). DOI: 10.1186/s40345-017-0110-8
23. Faurholt-Jepsen, M. et al. Objective smartphone data as a potential diagnostic marker of bipolar disorder. Aust. N. Z. J. Psychiatry 53, 119–128 (2019). DOI: 10.1177/0004867418808900
24. Buck, B. et al. Relationships between smartphone social behavior and relapse in schizophrenia: a preliminary report. Schizophr. Res. 208, 167–172 (2019). DOI: 10.1016/j.schres.2019.03.014
25. Wang, R. et al. Predicting symptom trajectories of schizophrenia using mobile sensing. Proc. ACM Interact. Mobile Wearable Ubiquitous Technol. 1, 110 (2017).
26. Kim, H. et al. Depression prediction by using ecological momentary assessment, actiwatch data, and machine learning: observational study on older adults living alone. JMIR mHealth uHealth 7, e14149 (2019). DOI: 10.2196/14149
27. Henson, P., Barnett, I., Keshavan, M. & Torous, J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophr. 6, 1–7 (2020). DOI: 10.1038/s41537-020-0100-1
28. Canzian, L. & Musolesi, M. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proc. 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’15, 1293–1304 (Association for Computing Machinery, New York, NY, USA, 2015).
29. Wisniewski, H., Henson, P. & Torous, J. Using a smartphone app to identify clinically relevant behavior trends via symptom report, cognition scores, and exercise levels: a case series. Front. Psychiatry 10, 652 (2019). DOI: 10.3389/fpsyt.2019.00652
30. Ben-Zeev, D. et al. Crosscheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr. Rehabil. J. 40, 266 (2017). DOI: 10.1037/prj0000243
31. Wang, W. et al. Sensing behavioral change over time: using within-person variability features from mobile sensing to predict personality traits. Proc. ACM Interact. Mobile Wearable Ubiquitous Technol. 2, 1–21 (2018).
32. Wang, R. et al. Crosscheck: toward passive sensing and detection of mental health changes in people with schizophrenia. In Proc. 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’16, 886–897 (Association for Computing Machinery, New York, NY, USA, 2016).
33. Wilkinson, G. & Robertson, G. Wide Range Achievement Test 4th edn (WRAT–4) Professional Manual (Psychological Assessment Resources, Lutz, FL, 2004).
34. Buck, B. et al. Capturing behavioral indicators of persecutory ideation using mobile technology. J. Psychiatr. Res. 116, 112–117 (2019). DOI: 10.1016/j.jpsychires.2019.06.002
35. Cabin, R. J. & Mitchell, R. J. To bonferroni or not to bonferroni: when and how are the questions. Bull. Ecol. Soc. Am. 81, 246–248 (2000).
36. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001). DOI: 10.1214/aos/1013203451
37. Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
38. Doryab, A. et al. Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: statistical analysis, data mining and machine learning of smartphone and fitbit data. JMIR mHealth uHealth 7, e13209 (2019). DOI: 10.2196/13209
39. Sefidgar, Y. S. et al. Passively-sensed behavioral correlates of discrimination events in college students. Proc. ACM Hum.–Comput. Interact. 3, 1–29 (2019). DOI: 10.1145/3359216