Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study
Objective: To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms. Patients and Methods:...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Mayo Clinic Proceedings: Digital Health |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949761225000471 |
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| author | Stephanie Zawada, PhD, MS Jestrii Acosta, MS Caden Collins, BA Oana Dumitrascu, MD, MS Ehab Harahsheh, MBBS Clinton Hagen, MS Ali Ganjizadeh, MD Elham Mahmoudi, MD Bradley Erickson, MD, PhD Bart Demaerschalk, MD, MSc |
| author_facet | Stephanie Zawada, PhD, MS Jestrii Acosta, MS Caden Collins, BA Oana Dumitrascu, MD, MS Ehab Harahsheh, MBBS Clinton Hagen, MS Ali Ganjizadeh, MD Elham Mahmoudi, MD Bradley Erickson, MD, PhD Bart Demaerschalk, MD, MSc |
| author_sort | Stephanie Zawada, PhD, MS |
| collection | DOAJ |
| description | Objective: To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms. Patients and Methods: Participants with IS/TIA symptoms were monitored in real-world settings using the Beiwe application for 8 or more weeks during March 1, 2024 to November 15, 2024. Depression symptoms were tracked via weekly Patient Health Questionnaire (PHQ)-8 surveys, monthly personnel-administered Montgomery–Åsberg Depression Rating Scale (MADRS) assessments, and weekly averages of smartphone sensor measures. Repeated measures correlation established associations between PHQ-8 scores and objective behavior measures. To investigate how closely smartphone data predicted MADRS scores, linear mixed models were used. Results: Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (r=0.173), time spent at home (r=−0.147) and PHQ-8 administration duration (r=0.151). Using demographic data and the most recent PHQ-8 scores, average root-mean-squared error for depression severity prediction across models was 1.64 with only PHQ-8 scores, 1.49 also including accelerometer and GPS data, and 1.36 also including PHQ-8 administration duration. Conclusion: Smartphone sensors captured objective behavior measures in patients with IS/TIA. In predictive models, the accuracy of depression severity scores improved as measures from additional smartphone sensors were included. Future research should validate this decentralized, exploratory approach in a larger cohort. Our work is a step toward showing that real-world monitoring with active and passive data may triage patients with IS/TIA for efficient depression screening and provide digital mobility and response time endpoints. |
| format | Article |
| id | doaj-art-5cbf13ec61ab49759f96707b85e4ebed |
| institution | Kabale University |
| issn | 2949-7612 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Mayo Clinic Proceedings: Digital Health |
| spelling | doaj-art-5cbf13ec61ab49759f96707b85e4ebed2025-08-20T03:50:25ZengElsevierMayo Clinic Proceedings: Digital Health2949-76122025-09-013310024010.1016/j.mcpdig.2025.100240Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept StudyStephanie Zawada, PhD, MS0Jestrii Acosta, MS1Caden Collins, BA2Oana Dumitrascu, MD, MS3Ehab Harahsheh, MBBS4Clinton Hagen, MS5Ali Ganjizadeh, MD6Elham Mahmoudi, MD7Bradley Erickson, MD, PhD8Bart Demaerschalk, MD, MSc9College of Medicine and Science, Mayo Clinic, Scottsdale, AZ; Correspondence: Address to Stephanie Zawada, PhD, MS, Mayo Clinic College of Medicine and Science, 5777 E Mayo Boulevard, Phoenix, AZ 85054.Division of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZDivision of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZDivision of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZDivision of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZDivision of Statistics, Mayo Clinic, Rochester, MNArtificial Intelligence Laboratory, Mayo Clinic, Rochester, MNArtificial Intelligence Laboratory, Mayo Clinic, Rochester, MNArtificial Intelligence Laboratory, Mayo Clinic, Rochester, MNDivision of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZObjective: To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms. Patients and Methods: Participants with IS/TIA symptoms were monitored in real-world settings using the Beiwe application for 8 or more weeks during March 1, 2024 to November 15, 2024. Depression symptoms were tracked via weekly Patient Health Questionnaire (PHQ)-8 surveys, monthly personnel-administered Montgomery–Åsberg Depression Rating Scale (MADRS) assessments, and weekly averages of smartphone sensor measures. Repeated measures correlation established associations between PHQ-8 scores and objective behavior measures. To investigate how closely smartphone data predicted MADRS scores, linear mixed models were used. Results: Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (r=0.173), time spent at home (r=−0.147) and PHQ-8 administration duration (r=0.151). Using demographic data and the most recent PHQ-8 scores, average root-mean-squared error for depression severity prediction across models was 1.64 with only PHQ-8 scores, 1.49 also including accelerometer and GPS data, and 1.36 also including PHQ-8 administration duration. Conclusion: Smartphone sensors captured objective behavior measures in patients with IS/TIA. In predictive models, the accuracy of depression severity scores improved as measures from additional smartphone sensors were included. Future research should validate this decentralized, exploratory approach in a larger cohort. Our work is a step toward showing that real-world monitoring with active and passive data may triage patients with IS/TIA for efficient depression screening and provide digital mobility and response time endpoints.http://www.sciencedirect.com/science/article/pii/S2949761225000471 |
| spellingShingle | Stephanie Zawada, PhD, MS Jestrii Acosta, MS Caden Collins, BA Oana Dumitrascu, MD, MS Ehab Harahsheh, MBBS Clinton Hagen, MS Ali Ganjizadeh, MD Elham Mahmoudi, MD Bradley Erickson, MD, PhD Bart Demaerschalk, MD, MSc Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study Mayo Clinic Proceedings: Digital Health |
| title | Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study |
| title_full | Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study |
| title_fullStr | Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study |
| title_full_unstemmed | Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study |
| title_short | Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study |
| title_sort | real world smartphone data predicts mood after ischemic stroke and transient ischemic attack symptoms and may constitute digital endpoints a proof of concept study |
| url | http://www.sciencedirect.com/science/article/pii/S2949761225000471 |
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