Digital clinical system for the identification, risk stratification, and longitudinal monitoring of women at risk of postpartum depression
Lagutin V.V., Frankevich N.A., Kalina S.A., Kirilina N.V., Ignateva A.A., Gardanova Zh.R., Borovikov P.I., Frankevich V.E.
Background. Postpartum depression (PPD) affects 10–20% of postpartum women and is associated with adverse maternal and neonatal outcomes. Conventional screening tools are administered only intermittently, limiting both early detection and longitudinal follow-up. There is a growing need for digital solutions that enable continuous monitoring during the postpartum period.
Objective. To develop and conduct a pilot clinical evaluation of a digital system for early screening and longitudinal monitoring of postpartum depression risk among postpartum women.
Materials and methods. In the pilot phase of a prospective observational study, all postpartum women who met the predefined inclusion criteria were enrolled, irrespective of baseline depressive symptom severity. A digital platform was developed, comprising a physician web interface, a patient web portal, a messenger-based chatbot, a database, and an integrated scheduling and notification system. Of the 280 women invited to participate, 242 (86.4%) provided informed consent, and 237 (97.9%) completed the baseline assessment on postpartum days 2–5, constituting the analytical cohort. Participants completed the Edinburgh Postnatal Depression Scale (EPDS), the Perinatal Anxiety Screening Scale–Revised (PASS-R), and investigator-developed questionnaires. Participant adherence was assessed using Fisher’s exact test. The diagnostic performance of the notification algorithm was evaluated against the reference clinical diagnosis established by a psychotherapist according to the International Classification of Diseases, 11th revision (ICD-11).
Results. Adherence to longitudinal monitoring was 140/169 (82.8%) among chatbot users and 13/19 (68.4%) among web portal users. The estimate for the web portal subgroup should be interpreted cautiously because of its small sample size (n=19). The system generated 15 alerts indicating persistently elevated EPDS scores (>12 on two consecutive assessments). A clinical diagnosis of PPD was confirmed in 12 (80 %) of these 15 women. A retrospective analysis of the entire cohort (n=237) demonstrated a sensitivity of 80%, specificity of 98.6%, and positive predictive value (PPV) of 80%. Given the pilot study design and the absence of complete clinical verification for all participants, these performance estimates require confirmation in future validation studies.
Conclusion. A clinically integrated digital platform enabling continuous screening, risk stratification, and longitudinal monitoring of postpartum depression risk was developed successfully. The platform is technically ready for prospective clinical validation in patients. Routine implementation in perinatal care settings should be considered only after its effectiveness, reliability, and safety are confirmed in subsequent prospective studies.
Authors' contributions. Lagutin V.V. – conception and design of the study, data analysis, software development, systematic analysis, manuscript writing; Frankevich N.A. – conception and design of the study, clinical data analysis, systematic analysis, drafting of the manuscript; Kalina S.A., Kirilina N.V. – clinical data analysis, questionnaire scale development, questionnaire data processing and analysis, identification of PPD risk groups; Ignateva A.A. – clinical data analysis, collection and processing of clinical and laboratory data for subsequent information analysis, study design; Gardanova Zh.R. – systematic analysis, clinical data analysis, questionnaire scale development, questionnaire data processing and analysis, identification of PPD risk groups; Borovikov P.I. – analysis of the obtained data, editing of the manuscript; Frankevich V.E. – systematic analysis, editing of the manuscript.
Conflicts of interest. The authors have no conflicts of interest to declare.
Funding. The study was supported by the state task “Development of objective biomarkers for the diagnosis of postpartum depression (PPD) using mass spectrometry and artificial intelligence” (registration number 126020516657-5).
Ethical Approval. The study was reviewed and approved by the Research Ethics Committee of the V.I. Kulakov NMRC for OG&P (protocol No. 2 dated 02/19/2026).
Generative Artificial Intelligence. No artificial intelligence tools were used in the preparation of this manuscript.
Patient Consent for Publication. All patients provided informed consent for the publication of their data.
Authors' Data Sharing Statement. The data supporting the findings of this study are available upon request from the corresponding author after approval from the principal investigator.
For citation: Lagutin V.V., Frankevich N.A., Kalina S.A., Kirilina N.V., Ignateva A.A., Gardanova Zh.R.,
Borovikov P.I., Frankevich V.E. Digital clinical system for the identification, risk stratification,
and longitudinal monitoring of women at risk of postpartum depression.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2026; (8): 110-118 (in Russian)
https://dx.doi.org/10.18565/aig.2026.167
Keywords
postpartum depression
digital health
chatbot
early detection
longitudinal monitoring
risk stratification
perinatal psychiatry
References
- Министерство здравоохранения Российской Федерации. Клинические рекомендации. Депрессивный эпизод, рекуррентное депрессивное расстройство. 2019. Доступно по: https://psychiatr.ru/download/4235?view=1&name=КР+депрессивный+эпизод.pdf [Ministry of Health of the Russian Federation. Clinical guidelines. Depressive episode, recurrent depressive disorder. 2019 (in Russian). Available at: https://psychiatr.ru/download/4235?view=1&name=КР+депрессивный+эпизод.pdf(in Russian)].
- Oliveira T.A., Luzetti G.M., Rosalém M.A., Mariani Neto C. Screening of perinatal depression using the Edinburgh Postpartum Depression Scale. Rev. Bras. Ginecol. Obstet. 2022; 44(3): 452-7. https://dx.doi.org/10.1055/s-0042-1743095
- Макарова М.А., Тихонова Ю.Г., Авдеева Т.И., Игнатко И.В., Кинкулькина М.А. Послеродовая депрессия – факторы риска развития, клинические и терапевтические аспекты. Неврология, нейропсихиатрия, психосоматика. 2021; 13(4): 75-80. https://dx.doi.org/10.14412/2074-2711-2021-4-75-80 [Makarova M.A., Tikhonova Yu.G., Avdeeva T.I., Ignatko I.V., Kinkulkina M.A. Postpartum depression: risk factors, clinical and therapeutic aspects. Neurology, Neuropsychiatry, Psychosomatics. 2021; 13(4): 75-80 (in Russian). https://dx.doi.org/10.14412/2074-2711-2021-4-75-80].
- Faulks F., Edvardsson K., Mogren I., Gray R., Copnell B., Shafiei T. Common mental disorders and perinatal outcomes in Victoria, Australia: a population-based retrospective cohort study. Women Birth. 2024; 37(2): 428-35. https://dx.doi.org/10.1016/j.wombi.2024.01.001
- Fisher S.D., Walsh T., Wongwai C. The importance of perinatal non-birthing parents' mental health and involvement for family health. Semin. Perinatol. 2024; 48(6): 151950. https://dx.doi.org/10.1016/j.semperi.2024.151950
- Stefana A., Mirabella F., Gigantesco A., Camoni L. The screening accuracy of the Edinburgh postnatal depression scale (EPDS) to detect perinatal depression with and without the self-harm item in pregnant and postpartum women. J. Psychosom. Obstet. Gynaecol. 2024; 45(1): 2404967. https://dx.doi.org/10.1080/0167482X.2024.2404967
- Mu T.Y., Li Y.H., Xu R.X., Chen J., Wang Y.Y., Shen C.Z. Internet-based interventions for postpartum depression: a systematic review and meta-analysis. Nurs. Open. 2021; 8(3): 1125-34. https://dx.doi.org/10.1002/nop2.724
- Singla D.R., Silver R.K., Vigod S.N., Schoueri-Mychasiw N., Kim J.J., La Porte L.M. et al. Task-sharing and telemedicine delivery of psychotherapy to treat perinatal depression: a pragmatic, noninferiority randomized trial. Nat. Med. 2025; 31(4): 1214-24. https://dx.doi.org/10.1038/s41591-024-03482-w
- Grussu P., Severo M., Jorizzo G.J., Quatraro R.M. Use of Whooley questions and GAD-2 tools in screening for perinatal mental health: current expert considerations. Healthcare (Basel). 2024; 12(24): 2549. https://dx.doi.org/10.3390/healthcare12242549
- Wosik J., Fudim M., Cameron B., Gellad Z.F., Cho A., Phinney D. et al. Telehealth transformation: COVID-19 and the rise of virtual care. J. Am. Med. Inform. Assoc. 2020; 27(6): 957-62. https://dx.doi.org/:10.1093/jamia/ocaa067
- Liu Q., Yuan J., Shi S., Du M., Jiang Y. The impact of online mindfulness interventions on postpartum depression: a systematic review and meta-analysis. Int. J. Ment. Health Nurs. 2025; 34(4): e70085. https://dx.doi.org/10.1111/inm.70085
- Lewkowitz A.K., Whelan A.R., Ayala N.K., Hardi A., Stoll C., Battle C.L. et al. The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials. Am. J. Obstet. Gynecol. 2024; 230(1): 12-43. https://dx.doi.org/10.1016/j.ajog.2023.06.028
- Chang E., Lewkowitz A.K., Unger J.A., Garfield C.F., Miller E.S. Smartphone applications to support perinatal mental health. Obstet. Gynecol. 2026; 147(2): 229-38. https://dx.doi.org/10.1097/AOG.0000000000006139
- Sun Y., Li Y., Wang J., Chen Q., Bazzano A.N., Cao F. Effectiveness of smartphone-based mindfulness training on maternal perinatal depression: randomized controlled trial. J. Med. Internet Res. 2021; 23(1): e23410. https://dx.doi.org/10.2196/23410
- Suharwardy S., Ramachandran M., Leonard S.A., Gunaseelan A., Lyell D.J., Darcy A. et al. Feasibility and impact of a mental health chatbot on postpartum mental health: a randomized controlled trial. AJOG Glob. Rep. 2023; 3(3): 100165. https://dx.doi.org/10.1016/j.xagr.2023.100165
- Rognmo K., Haga S., Garthus-Niegel S., Wang C.A., Eberhard-Gran M. Validity and accuracy of the Whooley questions to identify symptoms of depression in Norwegian postpartum women. Acta Obstet. Gynecol. Scand. 2026; 105(3): 436-43. https://dx.doi.org/10.1111/aogs.70152
- Чаусов А.А., Гависова А.А., Долгушина Н.В., Назаренко Т.А., Гарданова Ж.Р. Разработка и валидация опросника оценки андрогенного дефицита у женщин (FAD – Female Androgen Deficiency) репродуктивного возраста. Акушерство и гинекология. 2022; 11: 75-89. https://dx.doi.org/10.18565/aig.2022.11.75-89 [Chausov A.A., Gavisova A.A., Dolgushina N.V., Nazarenko T.A., Gardanova Zh.R. Development and validation of a questionnaire for assessing androgen deficiency in women (FAD – Female Androgen Deficiency) of reproductive age. Obstetrics and Gynecology. 2022; (11): 75-89 (in Russian). https://dx.doi.org/10.18565/aig.2022.11.75-89].
- Наэль-Прупес М.В., Харькова О.А., Соловьев А.Г., Нефедова С.С. Динамика депрессивного состояния у женщин в антенатальный и постнатальный период. Клиническая и специальная психология. 2024; 13(3): 205-15. https://dx.doi.org/10.17759/cpse.2024130310 [Nael-Prupes M.V., Kharkova O.A., Soloviev A.G., Nefedova S.S. The dynamics of depression in women in the antenatal and postnatal period. Clinical Psychology and Special Education. 2024; 13(3): 205-15 (in Russian). https://dx.doi.org/10.17759/cpse.2024130310].
Received 20.05.2026
Accepted 16.07.2026
About the Authors
Vadim V. Lagutin, Software Engineer at the Bioinformatics Laboratory, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4, laggi@mail.ru, https://orcid.org/0000-0001-7536-0631Natalia A. Frankevich, Dr. Med. Sci., Senior Researcher at the Department of Obstetrics, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Moscow, Russia, Ac. Oparina str., 4, natasha-lomova@yandex.ru, https://orcid.org/0000-0002-6090-586X
Svetlana A. Kalina, Medical Psychologist at the Therapeutic Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Moscow, Russia, Ac. Oparina str., 4, s_kalina@oparina4.ru, https://orcid.org/0000-0003-4385-3276
Nadezhda V. Kirilina, Medical Psychologist at the Therapeutic Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Moscow, Russia, Ac. Oparina str., 4, n_kirilina@oparina4.ru, https://orcid.org/0000-0002-4534-1373
Alla A. Ignateva, PhD, Head of the 2nd Physiological Obstetrics Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4, a_ignateva@oparina4.ru, https://orcid.org/0009-0009-4194-3199
Zhanna R. Gardanova, Dr. Med. Sci., Professor, Senior Researcher at the Therapeutic Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Moscow, Russia, Ac. Oparina str., 4, z_gardanova@oparina4.ru,
https://orcid.org/0000-0002-9796-0846
Pavel I. Borovikov, Head of the Bioinformatics Laboratory, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology,
Ministry of Health of Russia, 117997, Moscow, Russia, Ac. Oparina str., 4, p_borovikov@oparina4.ru, https://orcid.org/0000-0003-4880-7500
Vladimir E. Frankevich, Dr. Sci. (Phys.-Math.), Director for Science – Head of the Department of Systems Biology in Reproduction, Institute of Translational Medicine, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia,
117997, Moscow, Russia, Ac. Oparina str., 4, v_frankevich@oparina4.ru, https://orcid.org/0000-0002-9780-4579
Corresponding author: Natalia A. Frankevich, natasha-lomova@yandex.ru
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