Papers

Peer-reviewed publications, grouped by theme.

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Peer-reviewed publications 53

First-in-class clinical development program: negative symptoms of schizophrenia 5
  1. Pratap A, Lakhan SE, Snipes C, et al. A Digital Therapeutic Intervention for Negative Symptoms of Schizophrenia: A Randomized Clinical Trial. JAMA Netw Open. 2026;9(9):e2635753. doi:10.1001/jamanetworkopen.2026.35753 link
  2. Lakhan SE, Dorner-Ciossek C, …, Tang W, Willis B, Fernandez JW, von der Goltz C, Pratap A. Effectiveness, Engagement, and Safety of a Digital Therapeutic (CT-155/BI 3972080) for Treating Negative Symptoms in Schizophrenia: Protocol for the Phase 3 CONVOKE RCT. JMIR Res Protoc 2025;14:e81293. link
  3. Gandhi A, Firmin RL, …, Strauss GP, Dorner-Ciossek C, Pratap A. Patient Perspectives on a Digital Therapeutic for Schizophrenia: A Qualitative Evaluation of an App for Negative Symptoms. Schizophrenia Bulletin Open, 6(1), 2025. link
  4. Goenjian H, Pratap A, Snipes C, Hare BD, Kantrowitz J, Dennis T, …, Vaidyanathan U, Lakhan S, Dorner-Ciossek C. Feasibility of a Digital Therapeutic for Experiential Negative Symptoms of Schizophrenia: Results from an Exploratory Study. npj Schizophrenia, 2025. link
  5. Snipes C, Dorner-Ciossek C, …, Lakhan SE, Pratap A. Establishment and Maintenance of a Digital Therapeutic Alliance in People Living With Negative Symptoms of Schizophrenia: Two Exploratory Single-Arm Studies. JMIR Ment Health 2025;12:e64959. PMID: 39869902. link
Using digital measures to assess disease severity in CNS disorders 14
  1. Zhang Y, Folarin A, …, Pratap A, Dobson R. Characterizing Physical Activity Trajectories Preceding Incident Major Depressive Disorder Diagnosis With Consumer Wearable Devices in the All of Us Research Program: Retrospective Nested Case-Control Study. J Med Internet Res 2026;28:e93164. DOI: 10.2196/93164. link
  2. Zhang Y, Folarin AA, Ranjan Y, Cummins N, Rashid Z, Conde P, Stewart C, Sun S, Vairavan S, Matcham F, Oetzmann C, Siddi S, Lamers F, Simblett S, Wykes T, Mohr DC, Haro JM, Penninx BWJH, Narayan VA, Hotopf M, Dobson RJB, Pratap A & RADAR-CNS consortium. Assessing seasonal and weather effects on depression and physical activity using mobile health data. npj Mental Health Res 4, 11 (2025). link
  3. Halabi R, Gonzalez-Torres C, MacLean S, Husain MI, Pratap A, Alda M, Mulsant BH, Ortiz A. A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder. IEEE J Biomed Health Inform, 28(8):4903-4911, Aug 2024. link
  4. Goodday SM, Karlin E, Alfarano A, Brooks A, Chapman C, Desille R, Karlin DR, Emami H, Woods N, Foschini L, Kipping E, Wildman M, Cormack F, Taptiklis N, Pratap A, Ghassemi M, Goldenberg A, Nagaraj S, Walsh E, Stress and Recovery Participants, Friend S. An alternative to the ‘light touch’ digital health remote study: The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study. JMIR Form Res 2021;5(12):e32165. link
  5. Omberg L, Neto EC, Perumal TM, Pratap A, Tediarjo A, Adams J, Bloem BR, Bot BM, Elson M, Goldman SM, Kellen MR, Kieburtz K, Klein A, Little MA, Schneider R, Suver C, Tarolli C, Tanner CM, Trister AD, Wilbanks J, Dorsey E, Mangravite LM. Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nature Biotechnology (2021). link
  6. Renn BN, Pratap A, Atkins DC, Mooney SD, Areán PA. Smartphone-Based Passive Assessment of Mobility in Depression: Challenges and Opportunities. Ment Health Phys Act. 2018;14:136–139. link
  7. Pratap A, Renn BN, Volponi J, Mooney SD, Gazzaley A, Arean PA, et al. Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial. J Med Internet Res. 2018;20:e10130. link
  8. Larsen ME, Vo LC, Pratap A, Peters D. (2023). Integrated Digital Platforms for Clinical Care. In: Tasman A, et al. Tasman’s Psychiatry. Springer, Cham. link
  9. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Arean PA. The accuracy of passive phone sensors in predicting daily mood. Depress Anxiety, 2019. link
  10. Torous J, Firth J, Huckvale K, Larsen ME, Cosco TD, Carney R, Chan S, Pratap A, Yellowlees P, Wykes T, Keshavan M, Christensen H. The Emerging Imperative for a Consensus Approach Toward the Rating and Clinical Recommendation of Mental Health Apps. J Nerv Ment Dis, 2018. link
  11. Pratap A, Anguera JA, Renn BN, Neto EC, Volponi J, Mooney SD, et al. The feasibility of using smartphones to assess and remediate depression in Hispanic/Latino individuals nationally. UbiComp ’17, 2017. link
  12. Renn BN, Schurr M, Zaslavsky O, Pratap A. Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Front. Psychiatry 12:734909, 2021. link
  13. Areán PA, Pratap A, Hsin H, Huppert TK, Hendricks KE, Heagerty PJ, Cohen T, Bagge C, Comtois KA. Perceived Utility and Characterization of Personal Google Search Histories to Detect Data Patterns Proximal to a Suicide Attempt in Individuals Who Previously Attempted Suicide: Pilot Cohort Study. J Med Internet Res 2021;23(5):e27918. link
  14. Pratap A, Grant D, Vegesna A et al. Evaluating the Utility of Smartphone-Based Sensor Assessments in Persons with Multiple Sclerosis in the Real-World: elevateMS Study. JMIR mHealth uHealth, Oct 2020;8(10):e22108. link
Patient and provider needs and concerns in medical product development and deployment 2
  1. Kolovson S, Pratap A, Munson S, Arean P. Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote mHealth Studies. Pervasive Health, 2020. link
  2. Areán PA, Friedman EC, Pratap A, Allred R, Duffy J, Gille S, Reetz S, Keast E, Clarke G. Using Real-World Data for Decision Support: Recommendations from a Primary Care Provider Survey. The Permanente Journal, Oct 2020. link
Population representativeness, adherence and engagement in clinical research 8
  1. Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, Mohebbi M, Mooney S, Suver C, Wilbanks J, Mangravite L, Heagerty P, Arean P, Omberg L. Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants. npj Digital Medicine 3, 21 (2020). link
  2. Pratap A, Allred R, Duffy J, Rivera D, Lee H, Renn B, Areán P. Contemporary Views of Research Participant Willingness to Participate and Share Digital Data in Biomedical Research. JAMA Network Open, 2019;2(11):e1915717. link
  3. Zhang Y, Pratap A, Folarin A, …, Dobson R, RADAR-CNS consortium. Long-term Participant Retention and Engagement Patterns in an App and Wearable-based Multinational Remote Digital Depression Study. npj Digital Medicine 6, 25 (2023). link
  4. Yadav V, Neto EC, Doerr M, Pratap A, Omberg L. Long-Term Engagement of Diverse Study Cohorts in Decentralized Research: Longitudinal Analysis of “All of Us” Data. Interact J Med Res 2025;14:e56803. PMID: 40106344. link
  5. Kim E, …, Baker F, Pratap A. Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children. JAMA Netw Open. 2023;6(3):e235681. link
  6. Xueying S, Halabi R, Selvarajan R, Woerner M, Fillipo IG, Banerjee S, Mosser B, Jain F, Areán P, Pratap A. Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study. JMIR Formative Research, 2022. link
  7. Halabi R, …, Tummalacherla M, Chaibub Neto E, Pratap A. Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings. Sensors 2024, 24, 6246. link
  8. Xie SJ, …, Chen C, Hartzler AL, Pratap A. Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity. AMIA Informatics Summit, Summits Transl Sci Proc. 2023 Jun 16;2023:572-581. PMID: 37350875. link
FAIR data repositories from clinical research programs 3
  1. Pratap A, Homiar A, Waninger L, Herd C, Suver C, Volponi J, Anguera J, Areán P. Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression. Nature Sci Data 9, 522 (2022). link
  2. Bot B, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey E, Friend S, Trister A. The mPower Study: Parkinson Disease Mobile Data Collected Using ResearchKit. Nature Scientific Data. 2016 Mar 3;3:160011. link
  3. Deering S, Pratap A, Suver C, Borelli AJ Jr, Amdur A, Headapohl W, Stepnowsky CJ. Real-world longitudinal data collected from the SleepHealth mobile app study. Nature Scientific Data, Oct 2020. link
Review papers with cross-industry partners 6
  1. Izmailova ES, Wagner JA, Ammour N, Amondikar N, Vlasov AB, Berman S, Bloomfield D, Brady LS, Cai X, Calle RA, Campbell M, Cerreta F, Clay I, Foschini L, Furlong P, Goldel R, Goldsack JS, Groenen PMA, Folarin A, Heemskerk J, Honig P, Hotopf M, Kamphaus T, Karlin D, Leptak C, Liu Q, Manji H, Mather RJ, Menetski JP, Narayan VA, Papadopoulos E, Patel B, Lake B, Podichetty J, Pratap A, Ramos E, Raymond M, Servais L, Stephenson D, Tenaerts P, Tromberg BJ, Usdin S, Vasudevan S, Zipunnikov V, Hoffmann SC. Remote Digital Monitoring for Medical Product Development. Clinical and Translational Science, July 2020. link
  2. Lutz J, Pratap A, Lenze EJ, Bestha D, Lipschitz JM, Karantzoulis S, Vaidyanathan U, Robin J, Horan W, Brannan S, Mittoux A, Davis MC, Lakhan SE, Keefe R. Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness. Innov Clin Neurosci. 2023 Sep 1;20(7-9):40-46. PMID: 37817816; PMCID: PMC10561984. link
  3. Olaye IM, Belovsky MP, Bataille L, Cheng R, Ciger A, Fortuna KL, Izmailova E, McCall D, Miller CJ, Muehlhausen W, Northcott CA, Pratap A, Rodriguez-Chavez IR, Vandendriessche B, Zisman-Ilani Y, Bakker JP. Recommendations for defining and reporting adherence measured by Biometric Monitoring Technologies (BioMeTs): a systematic review. JMIR, 2022. link
  4. Kruzan KP, Fitzsimmons-Craft EE, Dobias M, Schleider JL, Pratap A. Developing, Deploying, and Evaluating Digital Mental Health Interventions in Spaces of Online Help- and Information-Seeking. ISRII 2022. ISSN 1877-0509. link
  5. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of RCTs. World Psychiatry. 2017;16:287–298. link
  6. Fulford D, Marsch LA, Pratap A. Prescription Digital Therapeutics: An Emerging Treatment Option for Negative Symptoms in Schizophrenia. Biol Psychiatry. 2024 Oct 15;96(8):659-665. PMID: 38960019; PMCID: PMC11410508. link
Method development: digital health measurement and collaborative research 9
  1. Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Bot BM, Mangravite L, et al. Disentangling personalized treatment effects from “time-of-the-day” confounding in mobile health studies. PLoS ONE 17(8): e0271766 (2022). link
  2. Cokelaer T, Bansal M, Bare C, Bilal E, Bot BM, Neto EC, Eduati F, Gönen M, Hill SM, Hoff B, Karr JR, Küffner R, Menden M, Meyer P, Norel R, Pratap A, Prill RJ, Weirauch MT, Costello JC, Stolovitzky G, Saez-Rodriguez J. DREAMTools: a Python package for scoring collaborative challenges. F1000Research 2015, 4:1030. link
  3. Pratap A, Steinhubl S, Chaibub Neto E, Wegerich SW, Peterson CT, Weiss L, Patel S, Chopra D, Mills PJ. Changes in Continuous, Long-Term Heart Rate Variability and Individualized Physiologic Responses to Wellness and Vacation Interventions Using a Wearable Sensor. Front. Cardiovasc. Med., June 2020. link
  4. Chaibub Neto E, Pratap A, Perumal T, Tummalacherla M, Snyder P, Bot B, Trister A, Friend SH, Mangravite L, Omberg L. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. npj Digital Medicine, 2019. link
  5. Chaibub Neto E, Pratap A, Perumal T, Tummalacherla M, Bot B, Mangravite L, Omberg L. A permutation approach to assess confounding in machine learning applications for digital health. KDD 2019. link
  6. Torous J, Anderson G, Bertagnoli A, Christensen H, Cuijpers P, Firth J, Haim A, Hsin H, Hollis C, Lewis S, Mohr DC, Pratap A, Roux S, Sherrill J, Arean PA. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry, 2019;18:97–98. link
  7. Chaibub Neto E, Pratap A, Perumal T, Tummalacherla M, Bot B, Mangravite L, Omberg L. Using permutations to assess confounding in machine learning applications for digital health. NeurIPS Machine Learning for Health, 2018. link
  8. Chaibub Neto E, Pratap A, Perumal TM, Tummalacherla M, Bot BM, Mangravite L, Omberg L. Detecting confounding due to subject identification in clinical machine learning diagnostic applications: a permutation test approach. 2017. arXiv:1712.03120v1. link
  9. Chaibub Neto E, Perumal TM, Pratap A, Bot BM, Mangravite L, Omberg L. On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study. 2017. arXiv:1706.09574 link
Genomics 6
  1. Angus SP, Oblinger JL, et al. (Children’s Tumor Foundation Synodos for NF2 Consortium - Pratap A). Traditional and systems-biology based drug discovery for NF2; EPH receptor signaling as a novel therapeutic target in NF2-deficient meningioma. PLoS One 2018;13:e0197350 & Neuro Oncol. 2018 Aug 2;20(9):1185-1196. PMID: 29982664. link
  2. Sieberts SK, Zhu F, García J, Stahl E, Pratap A, Pandey G, Pappas D, …, Friend S, Plenge R, Stolovitzky G, Oliva B, Guan Y, Mangravite L. Crowdsourced assessment of genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications 7, 12460 (2016). link
  3. Ngan CY, Wong CH, Choi C, Yoshinaga Y, Louie K, Jia J, Chen C, Bowen B, Cheng H, Leonelli L, Kuo R, Baran R, García-Cerdán JG, Pratap A, Wang M, Lim J, Tice H, Daum C, Xu J, Northen T, Visel A, Bristow J, Niyogi KK, Wei CL. Lineage-specific chromatin signatures reveal a regulator of lipid metabolism in microalgae. Nature Plants, 2015, 15107. link
  4. Cole JW, Stine OC, Liu X, Pratap A, Cheng Y, Tallon LJ, Sadzewicz LK, Dueker N, Wozniak MA, Stern BJ, Meschia JF, Mitchell BD, Kittner SJ, O’Connell JR. Rare Variants in Ischemic Stroke: An Exome Pilot Study. PLoS One 2012;7(4):e35591. link
  5. Dorsey SG, Lovering RM, Renn CL, Leitch CC, Liu X, Tallon LJ, Sadzewicz LD, Pratap A, Ott S, Sengamalay N, Jones KM, Barrick C, Fulgenzi G, Becker J, Voelker K, Talmadge R, Harvey BK, Wyatt RM, Vernon-Pitts E, Zhang C, Shokat K, Liggett CF, Balice-Gordon RJ, Tessarollo L, Ward CW. Genetic Deletion of Trkb.T1 Increases Neuromuscular Function. Am J Physiol Cell Physiol, 2012;302(1):C141-53. link
  6. Koide T, Reiss DJ, Bare JC, Pang WL, Facciotti MT, Schmid AK, Pan M, Marzolf B, Van PT, Lo FY, Pratap A, Deutsch EW, Peterson A, Martin D, Baliga NS. Prevalence of Transcription Promoters within Archaeal Operons and Coding Sequences. Mol Syst Biol, 2009;5:285. link