Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study
Background: Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explor...
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Elsevier
2024-12-01
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| Series: | Biomarkers in Neuropsychiatry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666144624000236 |
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| author | Carsten Langholm Scott Breitinger Lucy Gray Fernando Goes Alex Walker Ashley Xiong Cindy Stopel Peter P. Zandi Mark A. Frye John Torous |
| author_facet | Carsten Langholm Scott Breitinger Lucy Gray Fernando Goes Alex Walker Ashley Xiong Cindy Stopel Peter P. Zandi Mark A. Frye John Torous |
| author_sort | Carsten Langholm |
| collection | DOAJ |
| description | Background: Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explores their derivation, comparison to naive models, and replicability across two different research sites/teams. Methods: 84 participants (bipolar disorder, depression, controls) used the mindLAMP app for 12 weeks to capture digital phenotypes on their personal smartphones. mindLAMP was used to deliver surveys about mood symptoms while collecting device acceleration, geolocation, and screen on/off state. Participant chronotype was derived from this sensor data. Within-participant and between-participant models were created to assess how time-varying features collected through digital phenotyping could predict weekly anhedonia survey responses. Results: Within-person models outperformed between-person models in predicting anhedonia. Chronotype was the strongest predictor of weekly anhedonia scores as indicated by Shapley scores. Shapley scores also revealed that many of the time-varying predictor variables are significant but differ in their direction of action. Discussion: This analysis reveals the meaningful but potentially misleading nature of digital phenotyping signals. Results suggest that each participant has a unique set of relationships between time-varying digital phenotype variables; therefore, it is challenging to predict trends between participants. Bayesian models, with appropriate population priors, may offer the next step for improving the potential of personalized digital phenotyping insights. |
| format | Article |
| id | doaj-art-c8f485e33fe143e0b92aa5f6aa5d2aba |
| institution | OA Journals |
| issn | 2666-1446 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Biomarkers in Neuropsychiatry |
| spelling | doaj-art-c8f485e33fe143e0b92aa5f6aa5d2aba2025-08-20T02:21:04ZengElsevierBiomarkers in Neuropsychiatry2666-14462024-12-011110010510.1016/j.bionps.2024.100105Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker studyCarsten Langholm0Scott Breitinger1Lucy Gray2Fernando Goes3Alex Walker4Ashley Xiong5Cindy Stopel6Peter P. Zandi7Mark A. Frye8John Torous9Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA 02115, USADepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55902, USADivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA 02115, USADepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21218, USADepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21218, USADepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55902, USADepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55902, USADepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21218, USADepartment of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55902, USADivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA; Corresponding author.Background: Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explores their derivation, comparison to naive models, and replicability across two different research sites/teams. Methods: 84 participants (bipolar disorder, depression, controls) used the mindLAMP app for 12 weeks to capture digital phenotypes on their personal smartphones. mindLAMP was used to deliver surveys about mood symptoms while collecting device acceleration, geolocation, and screen on/off state. Participant chronotype was derived from this sensor data. Within-participant and between-participant models were created to assess how time-varying features collected through digital phenotyping could predict weekly anhedonia survey responses. Results: Within-person models outperformed between-person models in predicting anhedonia. Chronotype was the strongest predictor of weekly anhedonia scores as indicated by Shapley scores. Shapley scores also revealed that many of the time-varying predictor variables are significant but differ in their direction of action. Discussion: This analysis reveals the meaningful but potentially misleading nature of digital phenotyping signals. Results suggest that each participant has a unique set of relationships between time-varying digital phenotype variables; therefore, it is challenging to predict trends between participants. Bayesian models, with appropriate population priors, may offer the next step for improving the potential of personalized digital phenotyping insights.http://www.sciencedirect.com/science/article/pii/S2666144624000236SmartphoneDigital phenotypingMood disordersAnhedonia |
| spellingShingle | Carsten Langholm Scott Breitinger Lucy Gray Fernando Goes Alex Walker Ashley Xiong Cindy Stopel Peter P. Zandi Mark A. Frye John Torous Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study Biomarkers in Neuropsychiatry Smartphone Digital phenotyping Mood disorders Anhedonia |
| title | Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study |
| title_full | Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study |
| title_fullStr | Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study |
| title_full_unstemmed | Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study |
| title_short | Using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness: Results of a digital phenotyping biomarker study |
| title_sort | using data processing to understand inconsistency in smartphone behavior among patients with serious mental illness results of a digital phenotyping biomarker study |
| topic | Smartphone Digital phenotyping Mood disorders Anhedonia |
| url | http://www.sciencedirect.com/science/article/pii/S2666144624000236 |
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