Chatbot remote monitoring system in patients with schizophrenia: a pilot study of user experience

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INTRODUCTION

Digital technologies in healthcare are rapidly evolving, and an increasing number of healthcare institutions worldwide, including those in low- and middle-income countries, are incorporating them into routine healthcare practice [1]. The adoption and implementation of remote digital technologies accelerated during the COVID-19 pandemic, and online consultations became an alternative to in-person specialist visits in a wide range of medical disciplines [2, 3]. Digitalization has also extended to mental healthcare services.

Digital technologies, such as chatbots and mobile applications, represent a promising clinical solution by combining regular monitoring, objective data collection, and an individualized approach. Their advantages include automated reminders, user-friendly interfaces, and gamification elements that support treatment adherence. They also allow for more frequent monitoring with rapid symptom recording, as well as the minimization of biases inherent in retrospective reporting. Collectively, these features facilitate the detection of early relapse and timely intervention before full-blown symptom exacerbation occurs [4].

Digital solutions for psychiatric patients are currently being developed and implemented worldwide. The internationally developed m-RESIST program supports patients with treatment-resistant schizophrenia [5]. The use of PsyMate, an integrated platform for remote monitoring of psychotic disorders, has shown that self-monitoring of both positive and negative psychotic symptoms provides a more detailed picture of the illness and reveals patterns of behavior relevant to treatment. With PsyMate, patients can become active partners in the therapeutic process, resulting in improved adherence to therapy, increased awareness of their illness, and improved daily functioning [6]. A study of the ClinTouch application for self-monitoring in schizophrenia spectrum disorders (SSDs) patients in remission confirmed the reliability and stability of the assessment tools (i.e., questionnaires) used, as well as the positive correlation between diary entries and clinical examination results [7]. The authors concluded that these findings support the feasibility of digital self-monitoring tools as a complement to traditional patient monitoring approaches.

Systematic reviews highlight growing interest in digital technologies for monitoring mental disorders, but also important gaps in their use [8, 9]. In particular, an analysis of applications for monitoring patients with SSDs showed that most such tools use self-reports and, in some cases, GPS and accelerometer data, screen-time metrics, and log files to track physical and social activity [9]. Only one-quarter of the publications included an assessment of the clinical effectiveness of digital solutions, only a few studies assessed the reproducibility of the results [9], and no studies examined the long-term effectiveness of such applications. Authors of systematic reviews emphasize the need to develop standardized research methodologies for the validation of digital tools [8, 9]. Additional recommendations for future applications include ensuring cross-platform compatibility, data protection, feedback mechanisms, as well as assessment of usability and long-term acceptability of the tools [9].

All of the above studies were conducted in psychiatric healthcare systems that differ substantially from those in Russia, particularly with regard to outpatient psychiatric care and the legal regulation of telemedicine technologies. Sociocultural factors (i.e., persistent stigma, data confidentiality concerns, limited illness awareness, and low trust in digital technologies among older adults) may also affect perceptions of digital monitoring [10]. At the same time, in Russia, there are currently no validated digital products for monitoring remission status in patients with SSDs. According to a review of mobile applications for mental health support, the solutions available on the Russian market are either not designed for patients with psychotic disorders or lack evidence of efficacy and safety [11]. In this context, both the development of a digital tool for remote monitoring of remission status in patients with SSDs and a preliminary assessment of its usability appear necessary. These factors may influence long-term adherence to self-monitoring and the clinical benefits of the tool.

The aim of the study was to evaluate the user experience associated with chatbot-based remote monitoring of remission in patients with SSDs.

METHODS

Study design

We conducted a pilot clinical study to develop a self-monitoring tool for patients with SSDs and to conduct a preliminary assessment of its clinical feasibility.

Setting

The study included patients hospitalized at the S.S. Korsakov Clinic of Psychiatry, University Clinical Hospital No. 3, I.M. Sechenov First Moscow State Medical University (Sechenov University). The study was conducted from December 2024 to March 2025.

Participants

The following criteria were used to select the study sample.

Inclusion criteria:

• age 18–65 years;
• established diagnosis of an acute polymorphic psychotic disorder (International Classification of Diseases, 10th Revision (ICD-10) codes F23.1–F23.3), schizophrenia with episodic pattern of course (ICD-10 codes F20x1–F20x3), or schizoaffective disorder (ICD-10 code F25);
• clinical remission.

Thus, the study included patients with recurrent psychotic episodes or a single resolved psychotic episode and a psychopathological profile consistent with SSDs. Psychiatric diagnoses were established by psychiatrists according to ICD-10 criteria, and remission status was confirmed according to the Remission in Schizophrenia Working Group (RSWG) criteria [12].

Non-inclusion criteria:

• comorbid organic mental disorders or substance-related mental disorders;
• uncontrolled chronic somatic disease.

Exclusion criteria:

• patient’s refusal to continue participation in the study;
• change in diagnosis during the observation period;
• decompensation of a chronic somatic disease during the study;
• loss to follow-up.

Remote monitoring of remission status

Chatbot model

The chatbot was developed on the Telegram platform¹ — a cloud-based mobile and desktop instant messaging application for Android, iOS, Microsoft Windows, macOS, Linux, and various web browsers, with cross-platform compatibility. The vast majority of participants were already using Telegram, so this messenger was chosen to eliminate the need to install or learn to use a new application and reduce technological barriers to participation.

The chatbot architecture and code requirements were developed in accordance with the aim of the study and included the following:

• functionality (personalized interaction based on user ID, a system of reminders and motivational messages);
• data security (minimization of personal data and differentiated access rights);
• usability (welcome messages, communication format, integration of a graphical user interface, i.e., inline buttons and a bot settings menu);
• research relevance (aggregation of user-generated data into a database for subsequent multifactorial analysis).

The chatbot architecture was designed in the VSCode development environment using the Python programming language, version 3.10². The chatbot was developed using the Aiogram framework, version 3.7³. Several functional commands were developed and tested (see Appendix 1 in the Supplementary):

• sending an informational message describing chatbot functions and patient tasks;
• sending motivational messages to improve patient adherence;
• sending diary completion reminders via APScheduler 3⁴.

The following functions were evaluated:

• the timely delivery and display of all message types (informational, motivational, and reminder messages) in the Telegram interface;
• APScheduler stability: error-free reminder delivery at scheduled times;
• system fault tolerance: handling of incorrect user actions and stability under high load;
• data security: restriction of access to administrative functions and protection of user data.

Confidentiality of the submitted data was ensured by restricting access to the database containing de-identified survey results exclusively to authorized researchers involved in data analysis. Study participants were informed that their personal data would not be shared with third parties and that the survey results would be used exclusively for research purposes. This information was included in the informed consent form and included in the welcome message sent immediately after chatbot activation.

Self-assessment diary

The questionnaires were selected to assess key aspects of remission, including changes in symptoms over time, treatment adherence, and functioning associated with relapse prevention.

To assess positive, negative, and depressive symptoms, the Positive and Negative Syndrome Scale (PANSS) [13] and the Calgary Depression Scale for Schizophrenia (CDSS) [14] were used. Twelve PANSS items were included: “P1. Delusions”, “P2. Conceptual Disorganization”, “P3. Hallucinatory Behavior”, “P4. Excitement”, “P5. Grandiosity”, “P6. Suspiciousness/Persecution”, “P7. Hostility”, “N2. Emotional Withdrawal”, “G1. Somatic Concern”, “G2. Anxiety”, “G3. Guilt Feelings”, and “G6. Depression”, as well as two CDSS items: “Hopelessness” (Item 2) and “Suicide” (Item 8)⁵. These specific items were selected because this set had previously been validated by Palmier-Claus et al. [7] specifically for outpatient self-assessment by patients with SSDs. Responses to these 14 items showed moderate-to-strong correlations (ρ=0.6–0.8) with the corresponding items of clinical PANSS and CDSS interviews, supporting the validity of their use in a remote format without direct involvement of a clinician [7]. These 14 items of the questionnaire adapted for self-report by Palmier-Claus et al. [7] were translated by the research team without formal validation.

Sleep disturbances, including reduced sleep duration and impaired sleep quality, are among the earliest signs of impending relapse in patients with SSDs [15]. Sleep quality was assessed using the Spiegel Sleep Questionnaire (SSQ) [16], adapted for use in Russian [17]. The six SSQ items assess sleep onset latency, sleep quality, sleep duration, nocturnal awakenings, and well-being upon awakening. The questionnaire was selected because of its brevity, ability to assess key sleep parameters, established validity, and successful application in patients with mental disorders [18].

For the assessment of quality of life and functioning, we used the short-form Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF) [19] with 14 items from the general activities scale of the full Q-LES-Q version and two items addressing prescribed medications and overall life satisfaction. The psychometric properties of the Q-LES-Q-SF have been validated in the Russian population [20].

Adherence to psychopharmacotherapy and adverse events were assessed using the 4-item Morisky–Green–Levine Medication Adherence Questionnaire (MGL-4) [21] and the patient version of the UKU Side Effect Rating Scale (UKU-SERS-Pat) [22]. We used a widely accepted Russian-language version of the Morisky–Green scale [23]. This version has not undergone formal validation because of its widespread use, simplicity, and brevity [24]. The UKU-SERS-Pat included 48 items (symptoms) divided into four subgroups: psychic, neurological, autonomic, and other side effects⁶. The patient version was translated by the research team. Items from the “Other Side Effects” subgroup were adapted according to the patient’s sex. Specifically, depending on the participant’s sex, the chatbot excluded clearly inapplicable items. Ultimately, two versions of the evening diary were created: 45 items for women and 43 items for men.

Since the primary aim of this pilot study was to evaluate user experience rather than clinical dynamics, patient scores on the PANSS, CDSS, SSQ, MGL-4, Q-LES-Q-SF, and UKU-SERS-Pat were not analyzed.

Notification schedule

During the study, participants received chatbot reminders to complete the diary twice daily (morning and evening) every other day for nine consecutive days, totaling five pairs of reminders. This interval was selected to test the usability of the technology, assess patient readiness for frequent interaction, and evaluate the risk of excessive questionnaire-related burden. To reduce participant fatigue and improve completion rates, the questionnaires were divided into morning and evening blocks (notifications sent at 10:00 and 19:00, UTC+3:00) (Figure 1). The morning session included 25 questions derived from the PANSS, CDSS, SSQ, and MGL-4 scales, as well as a general yes/no question regarding changes in condition associated with chatbot use. The evening session included 45 items for women and 43 items for men from the UKU-SERS-Pat, in addition to 14 items from the Q-LES-Q-SF questionnaire. Participants were asked to select the response option that best reflected their condition (“not at all”, “slightly more than usual”, “more than usual”, or “much more than usual”).

 

Figure 1. Sequence of reminders to complete the self-assessment diary.

Source: Efimochkina et al., 2026.

 

User acceptance testing

User acceptability was assessed using a combination of subjective and objective indicators reflecting patients’ willingness to use the chatbot for monitoring their mental state. The subjective assessment included analysis of responses to the final questionnaire addressing usability, clarity of item wording, willingness to continue using the chatbot, and recommendations for improving its functionality (see Appendix 2 in the Supplementary). The objective assessment was based on an analysis of chatbot metadata: the proportion of fully and partially completed diaries, the proportion of participants who completed the user experience survey, and the frequency of missing questionnaire responses.

User acceptability was assessed on study day 10 (see Figure 1). For this purpose, all participants received a notification inviting them to complete a user experience survey in the following areas: comprehensiveness of condition assessment (Questions 1 and 2); willingness to use the application long term (Questions 3 and 4); response candor and methods for improving it (Questions 5 and 6); the impact of diary completion on emotional state and self-understanding (Questions 7 and 8); usability and potential improvements (Questions 9–17); preferences regarding the use of the chatbot (Question 18); attitudes toward sharing health-related information with their treating psychiatrist (Question 19); and adherence to diary completion and ways to improve it (Questions 20 and 21) (see Appendix 2 in the Supplementary). In addition, chatbot metadata were analyzed (an objective assessment of user acceptance). The following parameters were evaluated: adherence to diary completion (total number of diaries completed by each participant), quality of diary completion (number of completed questionnaire items), and adherence to completion of the user experience survey.

Statistical analysis

Data analysis was performed using the StatTech statistical software package, version 4.3.2 (StatTech, Russia). Quantitative variables were described using the median and range, binary variables were described using proportions (percentages).

Ethical considerations

The clinical study was approved by the Local Ethics Committee of I.M. Sechenov First Moscow State Medical University (Sechenov University), protocol No 29–24 dated 5 December 2024. All patients provided written informed consent to participate in the study.

RESULTS

Participants

Of 19 screened patients, 15 were included in the study; two declined participation and two did not meet remission criteria at hospital discharge (Figure 2). Most participants were female, half were between 22 and 29 years old, three-quarters were either students or employed at the time of enrollment, and one quarter were married. The medical history for the primary disorder spanned four years, and the median duration of remission exceeded one month (Table 1).

 

Figure 2. Study flow chart.

Source: Efimochkina et al., 2026.

 

Table 1. Study sample characteristics (n=15)

Characteristic	Value
Age (years), Me (Q1; Q3)	25 (22; 29)
Sex (female), n (%)	10 (68)
Employment status, n (%) Unemployed Student Employed	4 (27) 5 (33) 6 (40)
Marital status, n (%) Single Married	11 (73) 4 (26)
Diagnosis, n (%) F20x1–F20x3 F25 F23.1–F23.3	6 (40) 6 (40) 3 (20)
Remission duration (days)	36 (28; 45)
Age at illness onset (years)	21 (19; 24)

 

Adherence to diary completion

Of the 15 participants, six (40%) completed all 10 diaries in full (five morning and five evening sessions). Another 6 (40%) participants completed more than half of the diaries, and the remaining participants (n=3) completed fewer than half of the diaries (three or four sessions). Only two submitted diaries were incomplete because responses to sexual health items in the side effect questionnaire were missing. All 15 participants completed the user experience questionnaire (see Appendix 2 in the Supplementary). Only one participant did not answer the question regarding improvements facilitating response candor.

User experience survey

Comprehensiveness of user’s current condition assessment (Questions 1 and 2)

All participants except one considered the diary questions sufficient to adequately reflect their condition. At the same time, nine (60%) participants reported that certain aspects of their lives were insufficiently represented in the diaries. The following domains were identified as underrepresented: education and work, self-realization, and treatment side effects, each reported by three (20%) participants; cognitive functioning and level of physical activity, each reported by two (13%) participants; and interpersonal relationships, reported by one (7%) participant. One participant indicated that the libido-related question (an item from the UKU-SERS-Pat questionnaire) was not applicable because of the absence of sexual activity.

Willingness for long-term use (Questions 3 and 4)

For seven (47%) participants, completing the digital diary was perceived as a boring and burdensome process with an excessive number of items. Among the proposed modifications to the chatbot beta version, 10 of 15 participants (67%) preferred fewer questions, nine (60%) suggested incentives for diary completion, six (40%) favored alternating questions, and five (33%) supported reminders about the importance of self-monitoring. One participant each (7%) suggested reducing the number of quality-of-life questions or using more detailed wording.

Response candor and ways to improve it (Questions 5 and 6)

Eight (53%) participants reported that they responded mostly honestly (the reasons for less-than-completely honest responses were not analyzed in the study). Of the 14 participants who answered Question 6 (one participant did not respond), six (43%) indicated that feedback from a psychiatrist could increase response honesty, 12 (86%) identified visualization of changes in their condition as a facilitating factor, and three (21%) suggested tailoring the notification schedule to individual needs.

Impact of diary completion on emotional state and its understanding (Questions 7 and 8)

Nearly all participants, except one, considered the chatbot helpful in improving their understanding of their condition. More than half of the participants (8) reported that diary completion improved their condition, whereas 33% (5) noted no significant effect on their emotional state. None of the participants reported any negative emotional impact associated with the use of the chatbot.

Usability and potential improvements (Questions 9–17)

Analysis of participant preferences regarding the frequency and format of chatbot notifications, as well as their willingness to complete the diaries, showed that eight (53%) respondents preferred weekly diary completion, while three (20%) participants each preferred either twice-weekly completion or determining the frequency independently. One participant (7%) reported a preference for receiving notifications more frequently. Seven participants (47%) selected 18:00–22:00 as the most convenient time interval for diary completion, whereas six participants (40%) preferred to determine the time of completion independently.

Twelve participants (80%) preferred to continue receiving reminders as messenger notifications. Ten participants (67%) supported repeated reminders when diary entries were missed.

Twelve of 15 participants found the wording of the questions and response options understandable. At the same time, 12 participants considered it useful to add instructions for chatbot use, including interpretation of the questionnaires.

Recommendations and preferences regarding chatbot use (Question 18)

Thirteen participants (87%) would like the chatbot to include reminders for medication intake, while eight (53%) each favored reminders about psychiatrist appointments and psychoeducational messages. Seven (47%) respondents considered feedback from the treating psychiatrist based on interim diary completion results to be useful, and 9 (60%) participants would like to monitor changes in their condition visually through graphs and tables.

Sharing health-related information with the treating psychiatrist (Question 19)

Regarding sharing diary data with their psychiatrist, seven participants (47%) agreed, seven (47%) were uncertain, and one (7%) declined sharing results.

Adherence to diary completion and ways to improve it (Questions 20 and 21)

Among participants who did not complete all diaries, five attributed this to forgetfulness, three to technical problems with the chatbot, and one participant reported that diary completion had become burdensome.

DISCUSSION

The findings of our pilot study demonstrate that patients with SSDs are willing to use a chatbot for remote monitoring of their remission. The chatbot was perceived as user-friendly, which contributed to high adherence to regular self-monitoring procedures. The observed 80% diary completion rate is consistent with findings from numerous studies reporting high (70–100%) and sustained engagement in digital monitoring tools [7, 25]. However, this result should be interpreted with caution. In the study by Kidd et al. with a longer observation period (mean duration of 25 days), only 52.5% of participants remained active users of the application [26]. The high level of engagement observed in our pilot study may be explained by the short duration of observation (9 days), the novelty effect associated with use of the technology, and characteristics of the sample (all participants were in stable remission and provided informed consent, suggesting an initially higher level of motivation to cooperate).

Consistent with previous studies [25, 27], our findings indicated high levels of satisfaction with the chatbot and its usability. At the same time, most participants supported adding medication reminders and symptom change visualization to expand the chatbot’s functionality. Findings from other studies indicate that users particularly value reminders, scheduling features, and communication with healthcare professionals [28, 29]. The identified user preferences should be taken into account during further chatbot refinement given that design-related issues, inconvenient interfaces, and overall system complexity may constitute substantial barriers to the use of digital solutions by patients with SSDs [26].

The positive effect of diary use on participants’ emotional state supports its potential to increase awareness of their condition. As previous studies have demonstrated [30], regular self-monitoring contributes to improved illness awareness, development of symptom management skills, and improved control over one’s mental state. An important therapeutic mechanism of digital interventions is the promotion of self-reflection, which may help correct cognitive distortions associated with psychotic disorders [29]. Additionally, digital interventions may help normalize experiences associated with psychosis and reduce stigma. The availability of feedback may also foster optimistic expectations and reduce the sense of social isolation and fear of judgment [30].

The absence of a significant emotional effect in approximately one-third of participants highlights the need for a personalized approach to dialogue design within digital environments. Some users may perceive regular monitoring as a routine obligation rather than a therapeutic tool [29]. Interventions focused exclusively on symptom documentation without considering individual perceptions and needs may therefore provide limited therapeutic benefit. Visualizing symptom changes over time and using adaptive algorithms tailored to different clinical profiles and user preferences may improve motivation for long-term use of digital tools.

This is the first study to demonstrate the feasibility of chatbot-based remote remission monitoring in a Russian sample of patients with SSDs. At the same time, our findings also highlight the need to balance the clinical value of collected data with user burden. Participant dissatisfaction with diary length suggests the need to shorten the questionnaires and optimize completion frequency without compromising the timely detection of psychotic relapse. User feedback also indicated the potential value of expanding data collection to include objective measures of physical activity and sleep, as well as cognitive functioning, which is considered one of the most reliable indicators of long-term remission [31]. Future development of the platform should therefore focus on a balanced, personalized, multicomponent system integrating both clinical objectives and user preferences.

This pilot study has several design and methodological limitations. First, the small sample size and open-label design without a control group limit the generalizability of the findings and preclude analysis of the impact of the technology on clinically meaningful outcomes. This design reflected the early developmental stage of the technology and the aims of the pilot study, which focused on the preliminary assessment of the feasibility of remote remission monitoring in patients with SSDs and identifying potential issues before clinical validation.

Second, the study relied exclusively on patient self-reports as the source of information. The subjective nature of the collected data may have affected their accuracy. For example, participant responses may have been influenced by limited insight into their condition or a desire to meet researchers’ expectations. In addition, user experience was not compared with objective measures, such as the treating psychiatrist’s assessment of changes in the patient’s condition during chatbot use.

Third, the monitoring diary included PANSS and CDSS items originally developed for English-speaking populations and translated into Russian by the study team. The accuracy of the adapted wording and participants’ interpretation of the translated items were not formally validated.

Fourth, the analysis of the user experience survey revealed several methodological limitations. The survey did not explore the reasons for less-than-completely honest responses. The survey also did not include questions comparing interaction with the chatbot with communication with a psychiatrist. Specifically, the study did not evaluate whether patients felt they would be more open or better supported during in-person conversations with a psychiatrist than when completing a digital diary. This limits the interpretation of the findings related to response candor and should be considered in future versions of the questionnaire. Including such questions in future studies may help clarify whether remote monitoring should serve as a supplement to, rather than a replacement for, regular psychiatric consultations.

Fifth, 80% of study participants indicated the need to add instructions for chatbot use. The fact that this need was not identified during study planning or included in the initial questionnaire suggests that ease of technology adoption was insufficiently assessed. This limitation will be addressed in the next version of the product.

Sixth, the chatbot prototype did not fully comply with the telemedicine regulations applicable at the time of the study. According to Ministry of Health Order No. 965n dated November 30, 2017, “On approval of the procedure for organizing and providing medical care using telemedicine technologies”, which was in effect during the study period, remote patient monitoring was required to be performed through certified information systems. Our chatbot prototype, developed for the pilot assessment of user experience, did not meet these requirements, as it used a simplified architecture based on the Telegram platform. In April 2025, after the completion of the study, Ministry of Health Order No. 193n dated April 11, 2025, “On approval of the procedure for organizing and providing medical care using telemedicine technologies”, entered into force. This regulation additionally introduced a requirement for documentation of telemedicine consultations using an enhanced qualified electronic signature (article 48). In this study, the chatbot was used exclusively as a tool for collecting self-monitoring data rather than for providing telemedicine consultations in the strict legal sense of the term. All clinical decisions were made by the treating psychiatrist during in-person visits. The obtained data regarding patients’ willingness to use this technology provide a basis for the next stage of development, namely, the development of software fully compliant with regulatory requirements, integrated into a secure medical information system, and fully consistent with the provisions of Order No. 193n, including user identification and document management procedures. A comparative controlled study is planned to evaluate the clinical effectiveness of the technology.

The development and preliminary clinical testing of the chatbot were completed before restrictions on the Telegram platform were introduced. Therefore, further development of the digital tool for monitoring remission in patients with SSDs will be carried out using platforms compliant with national regulatory requirements.

CONCLUSION

This pilot study showed that patients with SSDs generally viewed chatbot-based remote monitoring of remission positively. High adherence to regular self-monitoring procedures was observed, and several key user preferences relevant to optimizing the digital tool were identified, including reducing user burden, visualizing symptom changes over time, integrating medication reminders, and expanding functionality to include objective indicators such as physical activity and sleep. Despite the study’s limitations, including its small sample size and non-comparative design, the findings support the feasibility of developing a chatbot-based digital tool for monitoring remission in patients with SSDs in Russian psychiatric practice. Future studies will focus on developing a version of the platform that complies with regulatory and technical requirements. This will be followed by an evaluation of its clinical effectiveness under controlled conditions.

 

Authors’ contribution: All the authors made a significant contribution to the article, checked and approved its final version prior to publication.

Funding: The research was carried out without additional funding.

Conflict of interest: The authors declare no conflicts of interest.

Generative AI use statement: Nothing to disclose.

Supplementary data

Supplementary material to this article can be found in the online version by doi:

Appendix 1: 10.17816/CP15735-145702

Appendix 2: 10.17816/CP15735-145703

 

¹ Telegram Bot APIs documentation. URL: https://core.telegram.org/bots/api. At the time of the study (December 2024 to March 2025), Telegram was not yet banned in the Russian Federation. Under Federal Law No. 303-FZ of 8 August 2024, requirements for the registration of public channels with more than 10,000 subscribers came into effect on 1 January 2025.

² Python official documentation. URL: https://docs.python.org/release/3.10.0/

³ Aiogram official documentation. URL: https://aiogram.dev/

⁴ APScheduler official documentation. URL: https://github.com/agronholm/apscheduler/tree/3.x

⁵ URL: https://cr.minzdrav.gov.ru/view-cr/451_3#doc_g

⁶ URL: https://www.psychiatry.ru/stat/80

About the authors

Sofia M. Efimochkina

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0000-0002-6196-4095
SPIN-code: 5299-3290
Scopus Author ID: 57963183200

Research trainee, Department of Psychiatry and Narcology

Russian Federation, Moscow

Valentina A. Sedelkova

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0000-0003-0569-6898
SPIN-code: 5335-1580

Assistant, Department of Psychiatry and Narcology

Russian Federation, Moscow

Yulia S. Gotko

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0009-0003-9729-485X
SPIN-code: 5933-3433

Student, N.V. Sklifosovsky Institute of Clinical Medicine

Russian Federation, Moscow

Maria D. Khloponina

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0009-0002-1976-6287
SPIN-code: 5539-7215

Student, N.V. Sklifosovsky Institute of Clinical Medicine

Russian Federation, Moscow

Elizaveta S. Kobets

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0009-0002-9826-1091
SPIN-code: 9868-9282

Student, N.V. Sklifosovsky Institute of Clinical Medicine

Russian Federation, Moscow

Anastasia S. Yakuba

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0009-0008-7705-3166

Student, N.V. Sklifosovsky Institute of Clinical Medicine

Russian Federation, Moscow

Yulia G. Tikhonova

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Author for correspondence.
Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0000-0001-6071-2796
SPIN-code: 7978-4247
Scopus Author ID: ID: 56558516000
ResearcherId: MCY-7146-2025

Dr. Sci (Med.), Professor, Department of Psychiatry and Narcology

Russian Federation, Moscow

Marina A. Kinkulkina

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: tikhonova_yu_g@staff.sechenov.ru
ORCID iD: 0000-0001-8386-758X
SPIN-code: 9040-4108
Scopus Author ID: 23977940700
ResearcherId: U-8234-2017

Dr. Sci (Med.), Corresponding Member of the Russian Academy of Sciences, Professor, Head of the Department of Psychiatry and Narcology

Russian Federation, Moscow

References

Supplementary files