An Observational Pilot Study using a Digital Phenotyping Approach in Patients with Major Depressive Disorder Treated with Trazodone

. 2023 ; 14 () : 1127511. [epub] 20230324

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid37032913

This 8-week study was designed to explore any correlation between a passive data collection approach using a wearable device (i.e., digital phenotyping), active data collection (patient's questionnaires), and a traditional clinical evaluation [Montgomery-Åsberg Depression Rating Scale (MADRS)] in patients with major depressive disorder (MDD) treated with trazodone once a day (OAD). Overall, 11 out of 30 planned patients were enrolled. Passive parameters measured by the wearable device included number of steps, distance walked, calories burned, and sleep quality. A relationship between the sleep score (derived from passively measured data) and MADRS score was observed, as was a relationship between data collected actively (assessing depression, sleep, anxiety, and warning signs) and MADRS score. Despite the limited sample size, the efficacy and safety results were consistent with those previously reported for trazodone. The small population in this study limits the conclusions that can be drawn about the correlation between the digital phenotyping approach and traditional clinical evaluation; however, the positive trends observed suggest the need to increase synergies among clinicians, patients, and researchers to overcome the cultural barriers toward implementation of digital tools in the clinical setting. This study is a step toward the use of digital data in monitoring symptoms of depression, and the preliminary data obtained encourage further investigations of a larger population of patients monitored over a longer period of time.

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Torous J, Keshavan M. The future of psychoses as seen from the history of its evolution. Curr Behav Neurosci Rep. (2014) 1:94–9. doi: 10.1007/s40473-014-0011-4 DOI

Melbye S, Kessing LV, Bardram JE, Faurholt-Jepsen M. Smartphone-based self-monitoring, treatment, and automatically generated data in children, adolescents, and young adults with psychiatric disorders: systematic review. JMIR Ment Health. (2020) 7:e17453. doi: 10.2196/17453, PMID: PubMed DOI PMC

Haller CS, Padmanabhan JL, Lizano P, Torous J, Keshavan M. Recent advances in understanding schizophrenia. F1000Prime Rep. (2014) 6:57. doi: 10.12703/P6-57 PubMed DOI PMC

Onnela J-P, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. (2016) 41:1691–6. doi: 10.1038/npp.2016.7, PMID: PubMed DOI PMC

Bankmycell (2022). How many smartphones are in the world? Available at: https://www.bankmycell.com/blog/how-many-phones-are-in-the-world (Accessed August 31, 2022).

Torous J, Chan SR, Yee-Marie Tan S, Behrens J, Mathew I, Conrad EJ, et al. . Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Ment Health. (2014) 1:e5. doi: 10.2196/mental.4004, PMID: PubMed DOI PMC

Stawarz K, Preist C, Coyle D. Use of smartphone apps, social media, and web-based resources to support mental health and well-being: online survey (preprint). JMIR Ment Health. (2019). 6:e12546. doi: 10.2196/12546 PubMed DOI PMC

Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. . Utilizing a personal smartphone custom app to assess the patient health questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. JMIR Ment Health. (2015) 2:e8. doi: 10.2196/mental.3889, PMID: PubMed DOI PMC

Mohr DC, Shilton K, Hotopf M. Digital phenotyping, behavioral sensing, or personal sensing: names and transparency in the digital age. NPJ Digit Med. (2020) 3:45. doi: 10.1038/s41746-020-0251-5, PMID: PubMed DOI PMC

Torous J, Kiang MV, Lorme J, Onnela J-P. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health. (2016) 3:e16. doi: 10.2196/mental.5165, PMID: PubMed DOI PMC

Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. (2017) 7:e1053. doi: 10.1038/tp.2017.25, PMID: PubMed DOI PMC

Insel TR. Digital phenotyping: a global tool for psychiatry. World Psychiatry. (2018) 17:276–7. doi: 10.1002/wps.20550, PMID: PubMed DOI PMC

Hollis C, Sampson S, Simons L, Davies EB, Churchill R, Betton V, et al. . Identifying research priorities for digital technology in mental health care: results of the James Lind Alliance priority setting partnership. Lancet Psychiatry. (2018) 5:845–54. doi: 10.1016/S2215-0366(18)30296-7, PMID: PubMed DOI

Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the internet. Curr Psychiatry Rep. (2014) 16:523. doi: 10.1007/s11920-014-0523-3, PMID: PubMed DOI

Miller G. The smartphone psychology manifesto. Perspect Psychol Sci. (2012) 7:221–37. doi: 10.1177/1745691612441215, PMID: PubMed DOI

World Health Organization (WHO) . Depression and other common mental disorders: Global Health estimates. World health Organization (2017).

American Psychiatric Association (APA) . Diagnostic and Statistic Manual of Mental Disorders. 5th ed (2013).

Gutiérrez-Rojas L, Porras-Segovia A, Dunne H, Andrade-González N, Cervilla JA. Prevalence and correlates of major depressive disorder: a systematic review. Braz J Psychiatry. (2020) 42:657–72. doi: 10.1590/1516-4446-2020-0650 PubMed DOI PMC

Proudman D, Greenberg P, Nellesen D. The growing burden of major depressive disorders (MDD): implications for researchers and policy makers. PharmacoEconomics. (2021) 39:619–25. doi: 10.1007/s40273-021-01040-7, PMID: PubMed DOI PMC

Prigerson HGMTH, Reynolds CF, Begley A, Houck PR, Bierhals AJ, Kupfer DJ. Lifestyle regularity and activity level as protective factors against bereavement-related depression in late-life. Depression. (1995) 3:297–302. doi: 10.1002/depr.3050030607 DOI

Vallée J, Cadot E, Roustit C, Parizot I, Chauvin P. The role of daily mobility in mental health inequalities: the interactive influence of activity space and neighbourhood of residence on depression. Soc Sci Med. (2011) 73:1133–44. doi: 10.1016/j.socscimed.2011.08.009, PMID: PubMed DOI

Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. . Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. (2015) 17:e175. doi: 10.2196/jmir.4273, PMID: PubMed DOI PMC

Stahl SM. Essential Psychopharmacology of Depression and Bipolar Disorder. New York: Cambridge University Press; (2000). 175 p.

Saltiel PF, Silvershein DI. Major depressive disorder: mechanism-based prescribing for personalized medicine. Neuropsychiatr Dis Treat. (2015) 11:875–88. doi: 10.2147/NDT.S73261, PMID: PubMed DOI PMC

Stahl SM. Mechanism of action of trazodone: a multifunctional drug. CNS Spectr. (2009) 14:536–46. doi: 10.1017/s1092852900024020, PMID: PubMed DOI

Fagiolini A, Comandini A, Catena Dell'Osso M, Kasper S. Rediscovering trazodone for the treatment of major depressive disorder. CNS Drugs. (2012) 26:1033–49. doi: 10.1007/s40263-012-0010-5, PMID: PubMed DOI PMC

Sheehan DV, Croft HA, Gossen ER, Levitt RJ, Brulle C, Bouchard S, et al. . Extended-release trazodone in major depressive disorder: a randomized, double-blind, placebo-controlled study. Psychiatry (Edgmont). (2009) 6:20–33. PubMed PMC

Ceskova E, Sedova M, Kellnerova R, Starobova O. Once-a-day trazodone in the treatment of depression in routine clinical practice. Pharmacology. (2018) 102:206–12. doi: 10.1159/000492079, PMID: PubMed DOI

Goldberg HM, Finnerty RJ. A double-blind study of trazodone. Psychopharmacol Bull. (1980) 16:47–9. PubMed

Patten SB. The comparative efficacy of trazodone and imipramine in the treatment of depression. CMAJ. (1992) 146:1177–82. PubMed PMC

Falk WE, Rosenbaum JF, Otto MW, Zusky PM, Weilburg JB, Nixon RA. Fluoxetine versus trazodone in depressed geriatric patients. J Geriatr Psychiatry Neurol. (1989) 2:208–14. doi: 10.1177/089198878900200407, PMID: PubMed DOI

Beasley CM, Jr, Dornseif BE, Pultz JA, Bosomworth JC, Sayler ME. Fluoxetine versus trazodone: efficacy and activating-sedating effects. J Clin Psychiatry. (1991) 52:294–9. PubMed

Munizza C, Olivieri L, Di Loreto G, Dionisio P. A comparative, randomized, double-blind study of trazodone prolonged-release and sertraline in the treatment of major depressive disorder. Curr Med Res Opin. (2006) 22:1703–13. doi: 10.1185/030079906X121039, PMID: PubMed DOI

Cunningham LA, Borison RL, Carman JS, Chouinard G, Crowder JE, Diamond BI, et al. . A comparison of venlafaxine, trazodone, and placebo in major depression. J Clin Psychopharmacol. (1994) 14:99–106. PubMed

Fagiolini A, Albert U, Ferrando L, Herman E, Muntean C, Pálová E, et al. . A randomized, double-blind study comparing the efficacy and safety of trazodone once-a-day and venlafaxine extended-release for the treatment of patients with major depressive disorder. Int Clin Psychopharmacol. (2020) 35:137–46. doi: 10.1097/YIC.0000000000000304, PMID: PubMed DOI PMC

Montgomery SA, Åsberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. (1979) 134:382–9. doi: 10.1192/bjp.134.4.382 PubMed DOI

Quilty LC, Robinson JJ, Rolland JP, Fruyt FD, Rouillon F, Bagby RM. The structure of the Montgomery-Åsberg depression rating scale over the course of treatment for depression. Int J Methods Psychiatr Res. (2013) 22:175–84. doi: 10.1002/mpr.1388, PMID: PubMed DOI PMC

Greiwe J. Telemedicine lessons learned during the COVID-19 pandemic. Curr Allergy Asthma Rep. (2022) 22:1–5. doi: 10.1007/s11882-022-01026-1, PMID: PubMed DOI PMC

Seivert S, Badowski ME. The rise of telemedicine: lessons from a global pandemic. EMJ Innov. (2020) 5:64–9. doi: 10.33590/emjinnov/20-00239 DOI

Rogers A, De Paoli G, Subbarayan S, Copland R, Harwood K, Coyle J, et al. . A systematic review of methods used to conduct decentralised clinical trials. Br J Clin Pharmacol. (2022) 88:2843–62. doi: 10.1111/bcp.15205, PMID: PubMed DOI PMC

Rosenbaum L. The untold toll—the pandemic’s effects on patients without Covid-19. N Engl J Med. (2020) 382:2368–71. doi: 10.1056/NEJMms2009984, PMID: PubMed DOI

Pew Research Center . Mobile fact sheet (2021). Available at: https://www.pewresearch.org/internet/fact-sheet/mobile/ (Accessed August 31, 2022)

Bessenyei K, Suruliraj B, Bagnell A, McGrath P, Wozney L, Huguet A, et al. . Comfortability with the passive collection of smartphone data for monitoring of mental health: an online survey. Comput Hum Behav. (2021) 4:100134. doi: 10.1016/j.chbr.2021.100134 DOI

Sheikh M, Qassem M, Kyriacou PA. Wearable, environmental, and smartphone-based passive sensing for mental health monitoring. Front Digit Health. (2021) 3:662811. doi: 10.3389/fdgth.2021.662811, PMID: PubMed DOI PMC

Roberts LW, Chan S, Torous J. New tests, new tools: mobile and connected technologies in advancing psychiatric diagnosis. NPJ Digit Med. (2018) 1:20176. doi: 10.1038/s41746-017-0006-0, PMID: PubMed DOI PMC

Coyle J, Rogers A, Copland R, De Paoli G, MacDonald TM, Mackenzie IS, et al. . Learning from remote decentralised clinical trial experiences: a qualitative analysis of interviews with trial personnel, patient representatives and other stakeholders. Br J Clin Pharmacol. (2022) 88:1031–42. doi: 10.1111/bcp.15003, PMID: PubMed DOI PMC

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