Personalized relapse prediction in patients with major depressive disorder using digital biomarkers
Open accessAbstract
Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a ‘predict and preempt’ paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2–3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Relapse prediction performance in the OBSERVEMDD cohort (balanced accuracy of 73.5%) was replicated in the independent CBN-WELL cohort (71%).
“The relapse prediction performance in the OBSERVEMDD cohort (balanced accuracy of 73.5%) was replicated in the independent CBN-WELL cohort (71%). 47”
The predictive ability of actigraphy combined with symptom self-ratings was examined in a personalized N-of-1 relapse prediction model framework in OBSERVEMDD (n = 286).
“the predictive ability of actigraphy combined with symptom self-ratings was examined in a personalized N-of-1 relapse prediction model framework in OBSERVEMDD (n = 286). 47”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
A new depression scale designed to be sensitive to change
Montgomery, S. A., & Åsberg, M. (1979) · British Journal of Psychiatry · doi:10.1192/bjp.134.4.382 · Cited by 13,962 · see citing works
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., Markowitz, J. C., Ninan, P. T., Kornstein, S., Manber, R., et al. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(02)01866-8 · Cited by 3,829 · see citing works
Self-citationMultifractal detrended fluctuation analysis of nonstationary time series
Kantelhardt, J. W., Zschiegner, S. A., Koscielny–Bunde, E., Havlin, S., Armin Bunde, A., & Stanley, E. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)01383-3 · Cited by 3,654 · see citing works
Fractal dynamics in physiology: Alterations with disease and aging
Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C., & Stanley, E. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.012579499 · Cited by 2,220 · see citing works
The clinical global impressions scale: applying a research tool in clinical practice.
Busner, J., & Targum, S. D. (2007) · PubMed Central · Cited by 1,952 · see citing works
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219845 · Cited by 1,771 · see citing works
Multifractality in human heartbeat dynamics
Ivanov, P. C., Amaral, L. A. N., Goldberger, A. L., Havlin, S., Rosenblum, M., Struzik, Z. R., & Stanley, H. E. (1999) · Nature · doi:10.1038/20924 · Cited by 1,666 · see citing works
Multifractality in Human Heartbeat Dynamics.
Ivanov, P. C., Amaral, L. A. N., Goldberger, A. L., Havlin, S., Rosenblum, M. G., Struzik, Z. R., & Stanley, E. (1999) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · Cited by 1,327 · see citing works
Tsuno, N., Besset, A., & Ritchie, K. (2005) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v66n1008 · Cited by 1,084 · see citing works
Introduction to Multifractal Detrended Fluctuation Analysis in Matlab
Ihlen, E. A. F. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00141 · Cited by 915 · see citing works
Hausdorff, J. M., Mitchell, S. L., Firtion, R., Peng, C., Cudkowicz, M., Wei, J. Y., & Goldberger, A. L. (1997) · Journal of Applied Physiology · doi:10.1152/jappl.1997.82.1.262 · Cited by 848 · see citing works
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016) · arXiv · doi:10.48550/arxiv.1607.00148 · Cited by 819 · see citing works
Behavioral-Independent Features of Complex Heartbeat Dynamics
Amaral, L. A. N., Ivanov, P. C., Aoyagi, N., Hidaka, I., Tomono, S., Goldberger, A. L., Stanley, E., & Yamamoto, Y. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.6026 · Cited by 259 · see citing works
Depner, C. M., Cheng, P., Devine, J. K., Khosla, S., de Zambotti, M., Robillard, R., Vakulin, A., & Drummond, S. P. A. (2019) · SLEEP · doi:10.1093/sleep/zsz254 · Cited by 245 · see citing works
The Montgomery Äsberg and the Hamilton ratings of depression: A comparison of measures
Carmody, T. J., Rush, A. J., Bernstein, I. H., Warden, D., Brannan, S. K., Burnham, D. B., Woo, A., & Trivedi, M. H. (2006) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2006.04.008 · Cited by 243 · see citing works
Self-citationRepeatable Battery for the Assessment of Neuropsychological Status (RBANS)
Gontkovsky, S. T. (2023) · SAGE Publications Ltd eBooks · doi:10.4135/9781529789539.n34 · Cited by 200 · see citing works
Hu, K., Someren, E. J. W. V., Shea, S. A., & Scheer, F. A. J. L. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806087106 · Cited by 171 · see citing works
Non-random fluctuations and multi-scale dynamics regulation of human activity
Hu, K., Ivanov, P. C., Chen, Z., Hilton, M. F., Stanley, E., & Shea, S. A. (2004) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.01.042 · Cited by 165 · see citing works
Personalized machine learning of depressed mood using wearables
Shah, R. V., Grennan, G., Zafar-Khan, M., Alim, F., Dey, S., Ramanathan, D., & Mishra, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01445-0 · Cited by 123 · see citing works
Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis
Tazawa, Y., Wada, M., Mitsukura, Y., Takamiya, A., Kitazawa, M., Yoshimura, M., Mimura, M., & Kishimoto, T. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.087 · Cited by 115 · see citing works
Zhang, Y., Folarin, A., Sun, S., Cummins, N., Bendayan, R., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/24604 · Cited by 106 · see citing works
Self-citationAdler, D. A., Ben-Zeev, D., Tseng, V. W. S., Kane, J., Brian, R., Campbell, A. T., Hauser, M., Scherer, E. A., & Choudhury, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/19962 · Cited by 104 · see citing works
Revisiting sample entropy analysis
Govindan, R. B., Wilson, J. D., Eswaran, H., Lowery, C. L., & Preissl, H. T. (2006) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.10.077 · Cited by 102 · see citing works
Hauge, E. R., Berle, J. Ø., Oedegaard, K. J., Holsten, F., & Fasmer, O. B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016291 · Cited by 98 · see citing works
Lim, A. S. P., Yu, L., Costa, M. D., Buchman, A. S., Bennett, D. A., Leurgans, S. E., & Saper, C. B. (2011) · SLEEP · doi:10.5665/sleep.1400 · Cited by 76 · see citing works
Noninvasive fractal biomarker of clock neurotransmitter disturbance in humans with dementia
Hu, K., Harper, D. G., Shea, S. A., Stopa, E. G., & Scheer, F. A. J. L. (2013) · Scientific Reports · doi:10.1038/srep02229 · Cited by 60 · see citing works
More random motor activity fluctuations predict incident frailty, disability, and mortality
Li, P., Lim, A. S. P., Gao, L., Hu, C., Yu, L., Bennett, D. A., Buchman, A. S., & Hu, K. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aax1977 · Cited by 45 · see citing works
Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis
Matić, V., Cherian, P. J., Koolen, N., Ansari, A. H., Naulaers, G., Govaert, P. P., Huffel, S. V., Vos, M. D., & Vanhatalo, S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00189 · Cited by 39 · see citing works
Judd, L. L., Schettler, P. J., & Rush, A. J. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15111462 · Cited by 29 · see citing works
Torre, K., Vergotte, G., Viel, E. J., Perrey, S., & Dupeyron, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-52091-y · Cited by 29 · see citing works
Interaction between the progression of Alzheimer's disease and fractal degradation
Li, P., Yu, L., Yang, J., Lo, M., Hu, C., Buchman, A. S., Bennett, D. A., & Hu, K. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.08.023 · Cited by 26 · see citing works
Difrancesco, S., Penninx, B. W. J. H., Merikangas, K. R., van Hemert, A. M., Riese, H., & Lamers, F. (2022) · Depression and Anxiety · doi:10.1002/da.23298 · Cited by 19 · see citing works
Toward a very brief self‐report to assess the core symptoms of depression (VQIDS‐SR5)
de la Garza, N., Rush, A. J., Grannemann, B. D., & Trivedi, M. H. (2017) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12720 · Cited by 18 · see citing works
Self-citationA systems biology approach to the digital behaviorome
Rashidisabet, H., Thomas, P. J., Ajilore, O., Zulueta, J., Moore, R. C., & Leow, A. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.003 · Cited by 16 · see citing works
Self-citationSequenced Treatment Alternatives to Relieve Depression
Lin, E. C., & Ho, P. (2018) · Oxford University Press eBooks · doi:10.1093/med/9780190625085.003.0027 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [23]Malhotra, P., Vig, L., Shroff, G., & Agarwal P. Long Short Term Memory Networks for Anomaly Detection in Time Series. In Proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) Proceedings vol. 89, 89–94 (2015).
Cited by
Sorted by citations
Zhang, Y., Stewart, C., Ranjan, Y., Conde, P., Sankesara, H., Rashid, Z., Sun, S., Dobson, R., & Folarin, A. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.01.124 · Cited by 31 · see citing works
Zhao, X., Lou, Z., Shah, P. T., Wu, C., Liu, R., Xie, W. B., & Zhang, S. (2025) · Sensors · doi:10.3390/s25154858 · Cited by 21 · see citing works
Yan, A. Y., Speed, T. J., & Taylor, C. O. (2025) · Scientific Reports · doi:10.1038/s41598-025-03856-1 · Cited by 10 · see citing works
One-year actigraphy study of sleep and rest-activity rhythms as markers of relapse in depression
Tonon, A. C., Nexha, A., Cunningham, J. E. A., d’Eon, J., Chakrabarty, T., Farzan, F., Foster, J. A., Harkness, K. L., Hassel, S., Ho, K., et al. (2026) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2025.4453 · Cited by 9 · see citing works
Self-citationGarzón‐Partida, A. P., Padilla-Gómez, C. B., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1595243 · Cited by 8 · see citing works
Lam, R. W., Rnic, K., Nunez, J.-J., Ho, K., LeMoult, J., Nunes, A., Chakrabarty, T., Foster, J. A., Frey, B. N., Harkness, K. L., et al. (2025) · The Canadian Journal of Psychiatry · doi:10.1177/07067437251337603 · Cited by 6 · see citing works
Self-citationWhat patients and caregivers want to know when consenting to the use of digital behavioral markers
Sonig, A., Deeney, C., Hurley, M. E., Storch, E. A., Herrington, J. D., Lázaro‐Muñoz, G., Zampella, C., Tunç, B., Parish‐Morris, J., Blumenthal-Barby, J. S. S., et al. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00022-9 · Cited by 6 · see citing works
Nunes, A., Pavlova, B., Cunningham, J. E. A., Nuñez, J.-J., Quilty, L. C., Foster, J. A., Harkness, K. L., Ho, K., Lam, R. W., Li, Q. S., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.06.023 · Cited by 5 · see citing works
Self-citationGarzón‐Partida, A. P., Magaña-Plascencia, K., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · JMIR Research Protocols · doi:10.2196/71374 · Cited by 5 · see citing works
Biomarker Methodologies: A NIMH Perspective
Grabb, M. C., & Brady, L. S. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_1 · Cited by 4 · see citing works
Dikaios, K., Rempel, S., Dumpala, S. H., d’Eon, J., Alda, M., Kiefte, M., Matwin, S., Oore, S., & Uher, R. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116895 · Cited by 2 · see citing works
Self-citationNeurochemical biomarkers in major depressive disorder
Khatami, S. H., Chamardani, T. M., Motamedzadeh, A., Nateghi, M., & Dadgostar, E. (2026) · Clinica Chimica Acta · doi:10.1016/j.cca.2026.120936 · Cited by 2 · see citing works
Mobile technology for just-in-time prediction of depression: a scoping review
Zwalmen, Y. V., Maerevoet, M., Coenen, T., Hoorelbeke, K., Chen, S., Brouwer, M. D., Vanderhasselt, M., Hoecke, S. V., Bombeke, K., Raedt, R. D., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00624-6 · Cited by 2 · see citing works
Xue, G., Chen, B., Zhang, Y., & Hou, L. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105026 · Cited by 1 · see citing works
Dine, C., Rekkas, Z., Frey, B. N., Krishnan, S., Duarte, D. G. G., Gomes, F. A., Mak, M. S. B., & Espinola, C. W. (2026) · JMIR Research Protocols · doi:10.2196/89466
Self-citationChen, S., Fang, Y.-F., Du, Q., Li, G., & Zhu, Z. (2026) · World Journal of Psychiatry · doi:10.5498/wjp.125255
- Authors
- Vairavan, S., Rashidisabet, H., Li, Q. S., Ness, S., Morrison, R. L., Soares, C. N., Uher, R., Frey, B. N., Lam, R. W., Kennedy, S. H., Trivedi, M., Drevets, W. C., & Narayan, V. A.
- Affiliations
- Janssen Research & Development (United States) (US)
- University of Illinois Chicago (US)
- Dalhousie University (CA)
- St. Joseph’s Healthcare Hamilton (CA)
- University of British Columbia (CA)
- University Health Network (CA)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Vairavan, Srinivasan
- ORCID
- Venue
- Scientific Reports
- Published
- 2023-10-30
- Volume
- 13
- Issue
- 1
- Pages
- 18596-18596
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10616277
- OpenAlex
- W4388023568
- Cited by
- 14
- References
- 36
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 3
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Janssen Research & Development, LLC based on the license agreement
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- major depressive disorder, recurrence prediction, digital biomarkers, actigraphy, anomaly detection, encoder-decoder architecture, wearable sensors, self-reported symptoms, anxiety and depressive symptoms, early warning
- MeSH terms
- Chronic Disease, Major Depressive Disorder, Humans, Recurrence, Biomarkers, Self Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
