4555 jmirai JMIR AI JMIR AI JMIR Publications Inc. PMC13536982 13536982 13536982 42684674 10.2196/89981 A Clinical AI-Based System (MoodMon) for Affective Disorders: Algorithm Development and Validation Sokół-Szawłowska Marlena PhD 1 ✉ Święcicki Łukasz Professor 2 Kolasa Katarzyna Professor 3 Kaczmarek-Majer Katarzyna Professor 4 Malin Bradley 1 Psychiatric Outpatient Clinic, Institute of Psychiatry and Neurology, Sobieskiego 9, Warsaw, Mazovia, 02-957, Poland, 48 604217639 2 II Clinic of Psychiatry, Institute of Psychiatry and Neurology, Warsaw, Mazovia, Poland 3 Department of Economics, Kozminski University, Warsaw, Poland 4 Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland ✉ Marlena Sokół-Szawłowska, PhD, Psychiatric Outpatient Clinic, Institute of Psychiatry and Neurology, Sobieskiego 9, Warsaw, Mazovia, 02-957, Poland, 48 604217639; marlenasokolsz@gmail.com 2 9 2026 5 e89981 e89981 3 9 2026 Copyright © Marlena Sokół-Szawłowska, Łukasz Święcicki, Katarzyna Kolasa, Katarzyna Kaczmarek-Majer. Originally published in JMIR AI (https://ai.jmir.org) This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR AI, is properly cited. The complete bibliographic information, a link to the original publication on https://www.ai.jmir.org/ , as well as this copyright and license information must be included. Abstract Background Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. An AI-based system for mood monitoring (MoodMon) was developed along with a mobile app for smartphones to detect changes in the mental state of individuals with major depressive disorder (MDD) and bipolar disorder (BD) based on acoustic features derived from speech signals. A long-term, naturalistic study of the MoodMon system represents a breakthrough in biomarker validation. Objective The aim of the study was to determine whether acoustic features are effective biomarkers of mental status changes in individuals with affective disorders and whether they are useful in the remote clinical monitoring of patients by psychiatrists. Methods To evaluate the effectiveness of AI algorithms in detecting changes in mental state based on acoustic features, data from 75 patients with BD and 25 patients with MDD over a period of 944 days were used. This makes this the longest analysis in the world covering two of the most common mental disorder diagnoses. A wealth of clinical, behavioral, and technical data were collected and used to train the MoodMon machine learning models under the supervision of human experts—experienced psychiatrists. In the first stage, the AI was trained using objective data and clinical assessments conducted by psychiatrists, including 17-item version of the Hamilton Depression Rating Scale and the Young Mania Rating Scale, as well as the Clinical Global Impression Scale. The second stage involved further refinement of the AI models using individual and population data, generating alerts when subtle changes in mental state were detected. Results In total, 243 acoustic features were extracted from the speech signals of patients and considered as input for the AI models aimed at detecting changes in mental state. The system demonstrated high performance, achieving the following sensitivity (true positive rate [TPR]) and specificity (true negative rate [TNR]) values: for both diagnoses, a TPR of 89.5% (1151/1286) and a TNR of 98.8% (30,224/30,579); for BD only, a TPR of 89.6% (866/966) and a TNR of 98.9% (22,664/22,905); and for MDD only, a TPR of 89.1% (285/320) and a TNR of 98.5% (7560/7674). Voice alerts in the MoodMon system are a key tool supporting clinical decision-making. They increase the probability of a clinical visit and exert a significant influence on the likelihood of treatment modification. Conclusions The system confirmed the presence of parameters that may serve as biomarkers of mental state changes in individuals with BD and MDD. A key clinical implication is the increased probability of prompt treatment modification following an alert, thereby supporting the primary objective underlying the development of the AI-based MoodMon system. status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2025 Dec 19; Revised 2026 May 4; Accepted 2026 Jun 12; Collection date 2026. Introduction Recent systematic reviews indicate that the integration of digital biomarkers derived from ubiquitous consumer devices (eg, smartphones and wearables) into the diagnosis, treatment, and longitudinal monitoring of mood disorders constitutes a highly promising paradigm. When combined with the capacity of AI and machine learning algorithms to process large-scale, high-dimensional, and temporally continuous datasets, these approaches enable the extraction of clinically meaningful patterns that are otherwise inaccessible through conventional assessment methods. Consequently, such technologies offer substantial potential for the implementation of personalized and adaptive monitoring and therapeutic strategies in mood disorders, with an additional prospective benefit of improving adherence to pharmacological treatments. Nevertheless, the primary objective of deploying AI-driven solutions in mental health care extends beyond diagnostic optimization and treatment compliance. The overarching goal remains the identification of scalable, evidence-based strategies that effectively enhance the quality of life and overall psychosocial functioning of patients [ 1 ]. Despite advances in clinical psychiatry and digital health technologies, the diagnosis and management of mood disorders, including major depressive disorder (MDD), remain intrinsically complex. Key barriers include limited access to specialized mental health services and persistently low long-term treatment adherence, which are strongly influenced by sociodemographic and systemic factors [ 2 , 3 ]. Notably, even in high-income countries, only approximately 50% of individuals with MDD receive a formal diagnosis and adequate follow-up care. On a global scale, this proportion decreases to nearly 30% in low- and middle-income countries, where additional obstacles, such as stigma related to mental health, further restrict access to care [ 4 , 5 ]. The urgency of addressing these challenges is underscored by the high global burden of affective disorders, which—alongside anxiety disorders—represent the most prevalent categories of mental health conditions worldwide. Bipolar disorder (BD) is a chronic, recurrent affective illness characterized by marked fluctuations in mood, cognition, behavior, psychomotor activity, and functional capacity, frequently leading to substantial impairments in interpersonal relationships and occupational functioning. The estimated lifetime prevalence of BD ranges from 0.4% to 1.1% globally [ 6 , 7 ]. Accurate diagnosis of BD remains a major clinical challenge due to its heterogeneous presentation, variable course, and symptom overlap with other psychiatric conditions, particularly unipolar depression. These factors contribute to high rates of misdiagnosis and significant delays in the initiation of appropriate treatment [ 3 , 8 , 9 ]. Depressive disorders represent one of the leading causes of disability worldwide, affecting an estimated 300 million individuals [ 10 - 12 ]. Beyond the profound psychological burden, depression is associated with substantial impairments in social functioning, including the inability to fulfill age-appropriate social and occupational roles. Moreover, depression is frequently comorbid with somatic conditions, particularly metabolic disorders, which are often inadequately treated as a consequence of depressive symptomatology itself [ 13 - 16 ]. Methods Study Design The MoodMon system was developed by an interdisciplinary team. The study was conducted between 2021 and 2023. The analytical engine (AI) was trained on data collected from patients over a study period of 944 days. A total of 100 patients participated: 75 patients with BD (type I or II) and 25 patients with recurrent depression. Informed consent was required. A longitudinal, naturalistic study was designed. Patients were recruited while undergoing standard treatment (in accordance with Polish pharmacological and psychological recommendations) in 8 outpatient clinics in Warsaw and Poznań, Poland. Ethical Considerations The Bioethics Committee at the District Medical Chamber approved the study (approval number: KB 170/2021; extension number: 366/2021). Patients provided their initial informed consent to participate in the study, which allowed for analysis without requiring additional consent for secondary analyses of the research data. Privacy and confidentiality were protected by anonymizing the research data. Participants did not receive any compensation for their participation. Recruitment Only individuals at high risk of relapse were recruited. The aim of this procedure was to enable the potential observation of a large number of changes in the natural environment of standard outpatient psychiatric treatment. Inclusion criteria were at least 2 phase transitions in the previous 14 months for patients with BD and at least 1 episode during this period for patients with recurrent depression. After the first (observational) phase, 12 patients withdrew from the study, with 88 patients remaining: 66 with BD and 22 with recurrent depression. Eighty-four patients (BD: n=64; MDD: n=20) participated in the study for over 18 months. Tested AI System The MoodMon system includes (1) a mobile app for patients (for Android smartphones) that collects and transmits data to secure servers; (2) an AI-based analytical engine that processes the collected data and learns to send alerts based on objective device data, including acoustic features and psychiatrists’ clinical assessments; and (3) a web portal for physicians that collects demographic data, visit data, and physician reviews and visualizes observation results. In the study’s initial phase, researchers evaluated the mental states of patients during every interaction, recording the results via the web portal. These evaluations served as ground-truth labels for the corresponding voice samples used to train the model. During the second phase, the AI engine generated automated alerts based on patient voice data. These notifications were sent to the patient, their caretaker (if designated), and the clinician’s web portal, triggering a psychiatric consultation. Subsequently, the portal facilitated a formal validation of the clinical justification for each alert. The MoodMon system is schematically illustrated in Figure 1 . Figure 1. Main functions and components of the MoodMon system. Acoustic Features From Speech and Behavioral Data Collection Participants were asked to complete a daily voice survey consisting of responses to three randomly selected questions from a list of 85, which did not refer to the disease or other sensitive topics. Voice recordings were collected each day. Mental state assessments were conducted by 8 independent psychiatric researchers, none of whom had access to the voice recordings, and individuals with the condition were blinded to the speech signal processing process. Voice recordings were processed using an automated signal processing pipeline that extracted standard acoustic features. Behavioral data were continuously collected via smartphones, whereas clinical assessments occurred at substantially lower temporal resolution. Since supervised training of the AI model required temporal alignment of both data modalities, psychiatric labels were extrapolated across intervals without direct clinical assessment. Specifically, if no change in the CGI score was observed between 2 consecutive assessments conducted 2 weeks apart, all intervening days were assigned the same CGI label. For the days between 2 assessments with different CGI scores, the logic generated two probabilistic entries per day, weighted by 1/N (change) and (N-1)/N (no change), to account for the temporal uncertainty of the transition. Periods missing an initial or final score were excluded from the training dataset. The validation was performed on the test dataset, which was separate from the training one. Data Preprocessing Datasets were considered complete when all parameters were available; partial datasets were supplemented when one or more daily parameters were missing. Missing values of observed parameters were imputed using the mean of the observations over the preceding 14 days, provided that no more than 7 days of data were missing within that interval. When such an average could not be reliably computed, the corresponding dataset was excluded from further analysis. Values to supplement the missing data were calculated over 14-day periods only, as longer periods could cover different patient conditions and make the imputed values unreliable. Baseline Data Protocol According to the protocol, historical clinical data from the 2 years preceding the study, such as the number of disease episodes and the number of hospitalizations of patients, came from the original medical records of the patients and were entered into the electronic documentation of the project. Detailed Description of the AI Algorithm and Acoustic Features Selection Data collected in the first phase of the study allowed for the identification of parameters with the highest predictive power. Based on interviews with specialist physicians and the literature, 243 parameters with predictive potential were identified. In the first stage of narrowing down this list, we eliminated those whose collection was difficult or expensive for technical reasons, or inadvisable due to the need to maintain patient privacy and comply with personal data regulations. To analyze the stability of the obtained results, a bootstrap analysis was performed, and a list of 50 variables with the highest weights was selected. This analysis revealed that acoustic features were the most variable-monitored attributes. The final feature selection was performed using principal component analysis, applying the criterion of selecting the number of principal components at 90% of the cumulative variance of the explained variable (patient condition variable). As a result of feature engineering and selection processes, a subset of 19 acoustic features was selected for the decision-making module, including fundamental frequency; selected mel-frequency cepstral coefficients; energy within predefined frequency bands; pause counts and durations; and voice quality features, for example, jitter and shimmer. Our analysis confirmed previous reports, which indicated the variability of fundamental frequency and mel-frequency cepstral coefficients as the acoustic features most strongly associated with emotional states in the course of affective disorders [ 17 - 20 ]. Study Phases and Validation In this study, we included individuals with documented transitions to ensure that the model’s performance reflects its predictive capability in clinically dynamic and real-life scenarios. Given the extremely individual course of affective disorders, it was decided that comparison with a different group of patients was not adequate [ 21 , 22 ]. In affective disorders, the patient in the euthymic state serves as a reference for themselves. For this reason, the study was a single-arm study without a control group. Phase I: Data Acquisition and Model Pretraining Overview The first phase comprised a 12-month observation period for each participant. Its primary objectives were as follows: (1) to collect multimodal data for evaluating their predictive use and (2) to obtain a dataset of sufficient size and diversity for the initial training of the AI models. In the initial phase of the experimental evaluation, we considered the top-performing benchmark machine learning algorithms, including random forest, decision trees, support vector classifier, recurrent neural network, logistic regression, and light gradient boosting machine. For variable selection, the recursive feature elimination and Boruta algorithms were considered. Although full transparency of gradient boosting algorithms, such as light gradient boosting machine, cannot be achieved, we acknowledge interpretability approaches, for example, Shapley additive explanations–based methods, that enable us to gain further insights into the model’s behavior. The final decision of the system was the result of the logical product of both algorithms, that is, the system alerted about a change if both algorithms predicted the change. At baseline, an in-person visit was conducted to collect demographic and clinical information. The current mental state was assessed using the 17-item Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS). Symptom severity was additionally quantified using a version of the Clinical Global Impression Scale (CGI) adapted to the study protocol. Subsequent clinical assessments were scheduled at 3-month intervals. Due to the COVID-19 pandemic, both in-person and remote visits were permitted. At each visit, HDRS and YMRS scores constituted the primary outcome measures for symptom severity evaluation. Previous works, aimed at identifying early and subtle indicators of affective phase transitions, suggest the following diagnostic cutoffs: euthymia (HDRS<8 and YMRS<6), depression (HDRS≥8 and YMRS<6), hypomania or mania (HDRS<8 and YMRS≥6), and mixed state (HDRS ≥8 and YMRS≥6) [ 23 , 24 ]. In this study, following each clinical contact, the psychiatrist—based on examination and clinical expertise—assigned 1 of 7 affective states: mania, hypomania, euthymia (balanced mood), subthreshold depression, depression, severe depression, or mixed state. For analytical purposes, this expert assessment was encoded as a CGI value and used as a supervisory label for the behavioral and acoustic features collected on the corresponding day. In addition to scheduled visits, every 2 weeks a psychiatrist evaluated the mental state of the patient based on structured telephone interviews, focusing on potential changes in mood and psychomotor activity. If the questionnaire results suggested a clinically relevant change, the patient was invited for an intervention visit involving a full clinical assessment using standardized rating scales. Feature Selection and Engineering The categorical variable with diagnosis (BD vs MDD) was considered in the initial exploratory analyses. We performed a weight of evidence analysis to determine the initial set of categorical features for further analyses. Next, secondary bootstrap analyses and principal component analysis were performed, including both continuous and categorical variables. During the course of the experiments, and considering the limited sample size while aiming to avoid overfitting, the final subset of features considered only selected acoustic features that proved to be the most powerful. Neither diagnosis (BD vs MDD) nor gender proved to be more significant than acoustic features for inclusion in the final predictive modeling. Phase II: Model Deployment and Prospective Evaluation Overview In the second phase, the pretrained AI module analyzed incoming acoustic features on a daily basis to determine whether they indicated an imminent change in the mental state of the patient. Upon detection of such a change, the system generated an alert, which was visualized for the clinician via the MoodMon physician portal. Importantly, the system did not infer the direction of the detected change (eg, depressive vs manic shift); it solely indicated the presence of a significant deviation from the patient’s baseline pattern. The system was also capable of detecting changes potentially unrelated to the underlying affective disorder, which might act as triggers for clinical episodes occurring in the prodrome stage. The inclusion of these factors in this study is well-supported by the current literature for both diagnoses [ 25 , 26 ]. A key innovation of the MoodMon system was its real-time alerting mechanism, which signaled potential shifts in mental state. This enabled clinicians to perform comprehensive assessments at the onset of an episode, allowing for timely intervention or informed observation. Crucially, the system maintained a “human-in-the-loop” approach: the final clinical judgment and therapeutic decisions rested solely on the physician’s subjective expertise, ensuring AI recommendations were filtered through professional experience. Clinical Assessment In the second phase of the project, visits took place at 3-month intervals and in response to an alert sent to the physician by the MoodMon system. Short telephone visits followed the alert, or if clinically necessary, longer in-person visits. Data collection continued as in the first phase. The predictive model was continuously and systematically trained, and after collecting sufficient data for each patient, individualized predictions were enabled by applying an individual decision-making threshold for the alert. The validity of each alert was assessed by the psychiatrist based on the patient’s description of the change or lack thereof in their mental state, taking into account important life circumstances. Each alert received 1 of 4 possible ratings: justified, unjustified, justified by other circumstances, and impossible to assess. Each alert also provided a CGI rating of the patient’s condition on the day of the alert. Treatment modifications in the clinical trial included changing the drug dosage, adding medications, changing medications, discontinuing medications, adding psychotherapy, and issuing a referral to a hospital. To reduce confirmation bias during alert assessment, the project used procedures consisting of (1) initial training on hypothetical clinical situations for a team of psychiatrists and (2) implementation of systematic meetings of psychiatrists (every 2 wk) throughout the first and second stages of the study, with ongoing supervision over the standardization of model training and alert validation. System Validation The assessments of the validity of the alerts provided by the psychiatrists were used to calculate model quality measures, specifically the true positive rate (TPR; sensitivity) and the true negative rate (TNR; specificity). TPR is the ratio of correctly predicted mood changes. T P R = Σ T P Σ T P + Σ F N (1) TNR is the ratio of correctly predicted “no changes.” T N R = Σ T N Σ T N + Σ F P (2) True and false positives are defined in the following way: (1) true positive (TP) is denoted when an alert is issued and assessed as justified (a mood change occurred since the patient’s previous clinical assessment); the alert about a change in the patient’s mood, assessed as justified by factors other than an affective disorder, was labeled TP, as a circumstance requiring attention and exclusion of the upcoming episode; (2) true negative (TN) is denoted when no alert is issued and its absence is assessed as correct (no mood change since the previous clinical assessment of the patient); (3) false positive (FP) is denoted when an alert is sent but assessed as unjustified (no mood change since the patient’s previous clinical assessment); and (4) false negative (FN) is denoted when no alert is issued and this situation is assessed as incorrect (a mood change occurred since the patient’s previous clinical assessment). To calculate the above measures, each decision made by the system to send an alert or not was evaluated; that is, for each day of each patient participating in the second stage of the study, 1 of the 4 possible labels (TP, FP, TN, and FN) was selected. Technological details are provided in a separate publication [ 27 ]. Alert-to-Action Timelines Analysis All clinical data and alert evaluations were integrated into a single analytical database. Basic descriptive statistics were used to characterize the dataset and evaluate alert performance. Continuous variables (eg, time from alert to visit, time to treatment modification, and acoustic parameters) were summarized using mean and median. Event-based metrics, such as the number of alerts, visits, and treatment changes, were reported as sums and proportions. Alert-to-action timelines were analyzed by calculating mean and median intervals for visits occurring within 48 hours, between 48 hours and 7 days, and beyond 7 days. These descriptive measures provided an initial assessment of system responsiveness and the clinical relevance of AI-generated alerts. Results The average duration of the disease among the study participants: 8.6 years for patients with BD and 8 years for patients with MDD. Other demographic data are provided in Table 1 . Table 1. Demographics of the population of patients using the MoodMon system in the study. Category MDD a (n=25), n (%) BD b (n=75), n (%) c Age range (y) 18‐24 6 (24) 6 (8) 25‐34 6 (24) 18 (24) 35‐44 6 (24) 20 (27) 45‐54 6 (24) 16 (21) 55‐64 1 (4) 15 (20) Gender Men 7 (28) 33 (44) Women 18 (72) 42 (56) Marital status Divorced 2 (8) 11 (15) Single 13 (52) 35 (47) Married 10 (40) 27 (36) Widow 0 (0) 2 (3) Education Higher 13 (52) 56 (75) Secondary 11 (44) 18 (24) Vocational 1 (4) 1 (1) Professional activity Employed 17 (68) 52 (69) Other 3 (12) 4 (5) Pensionist 1 (4) 10 (13) Student 4 (16) 7 (9) Unemployed 0 (0) 2 (3) Type of household Alone 6 (24) 18 (24) With a partner 3 (12) 13 (17) With family 16 (64) 44 (59) Town size >200,000 15 (60) 57 (76) 5000‐200,000 5 (20) 13 (17) <5000 5 (20) 5 (7) a MDD: major depressive disorder. b BD: bipolar disorder. c Percentage may not sum to 100% due to rounding. Phase I and II Results Throughout the study period, there were 24,258 single instances of system use for patients with BD and 7636 for individuals with MDD. A total of 1394 changes in patients’ mental states were documented. A total of 97,428 behavioral datasets were collected, of which 53,934 were complete and accompanied by a corresponding clinical evaluation of the patient’s condition on the same day. Among these, 1344 affective state transitions were identified with full clinical and behavioral correspondence, forming the basis for the training of AI models. During the study, 4570 phases were captured and assigned to 7 distinct affective states. The following clinical activities were conducted: 100 baseline visits, 410 in-person follow-up visits, 1908 telephone assessments, 760 intervention visits, and 100 final evaluations. In phase II, a total of 2656 automated alerts were generated, of which 1587 were subsequently reviewed and verified by psychiatrists. Validation Results The counts of TP, TN, FP, and FN labels obtained in the second phase of the study are as follows: there were 1151 TPs, 355 FPs, 30,224 TNs, and 135 FNs. The overall system performance, calculated across both diagnostic groups, demonstrated a TPR of 89.5% (1151/1286) and a TNR of 98.8% (30,224/30,579). For patients with BD, the model achieved a TPR of 89.6% (866/966) and a TNR of 98.9% (22,664/22,905), whereas for individuals with MDD, the results were a TPR of 89.1% (285/320) and a TNR of 98.5% (7560/7674). The precision of the system was as follows: a positive predictive value of 76.4% and an F 1 -score of 0.824. Alert-to-Action Timelines Results Phase II lasted 16 months, during which system alerts were sent to clinicians for evaluation and to patients and caregivers where applicable. To avoid alert fatigue and patient anxiety, a 5-day suppression window was applied; alerts generated within 5 days of a primary notification were withheld as duplicate representations of the same clinical change. Summary statistics for generated, sent, and assessed alerts are presented in Table 2 . Table 2. Number of alerts generated, suppressed, and evaluated. Alert action type Count, n Alerts generated 2656 Alerts sent for evaluation 1705 Alerts assessed 1588 During the second phase of the study, scheduled care adhered to standard clinical treatment protocols for affective disorders, consisting of quarterly in-person consultations. Unscheduled visits were arranged upon patient request, and system-generated alerts were intended to prompt contact between the patient and psychiatrist. Table 3 summarizes the breakdown of alert-triggered visits, follow-up consultations indirectly related to alerts, and visits not related to alerts. Table 3. Breakdown of visits carried out in the second phase of the study. Visit type Count (N=1754), n (%) Visits directly related to alerts 982 (56) Follow-up visits (indirectly related to alert) 87 (5) Visits not related to alerts 685 (39) Total visits 1754 (100) The high proportion of alert-driven visits (combined alert-triggered and follow-up consultations)—accounting for 61% (1069/1754) of all encounters—indicates that the alert system functioned as a major driver of patient-clinician contact. Categorization by contact type reveals that telephone consultation was frequently chosen as postalert communication modality ( Table 4 ). Table 4. Breakdown of alert-triggered visits by contact type. Contact type Count, n In person 448 Phone contact 534 Temporal stratification of postalert consultations revealed a distinct pattern. The distribution of visits categorized by alert-to-contact interval is detailed in Table 5 . Table 5. Temporal stratification of alert-driven visits. Temporal strata Count, n Up to 48 h 720 Between 3 and 7 days 194 Between 8 to 14 d 68 Mean and median time intervals were calculated for each stratum. These metrics, presented in Table 6 , provide insight into the operational responsiveness of the AI-supported monitoring system. Table 6. Mean and median alert-to-action intervals by temporal strata. Alert-to-visit interval Medium interval (SD) Medium interval (IQR) Up to 48 h 0.85 (0.71) 1 (0-1) Between 3 and 7 days 4.31 (1.35) 4 (3-5.75) Between 8 to 14 d 10.25 (2.03) 10 (8-12) Automated alerts facilitated earlier and more frequent treatment modifications following clinical changes in patient mental state. Compared with non–alert-related encounters, alert-triggered visits exhibited a higher proportion of treatment adjustments ( Table 7 ) as well as a significantly higher modification frequency ( Table 8 ). Table 7. Number of treatment modifications in visits related to alerts and those not related. Relation of alert with treatment modification Total, N Resulting in treatment modification, n (%) Visits related to alert 982 141 (14) Visits not related to alert 772 78 (10) Table 8. Consecutive treatment modifications intervals. Patient-clinician contact interval Consecutive treatment modifications interval Mean (SD) Median (IQR) Visits up to 48 h after alert 36.69 (54.98) 19 (5-41) Visits between 3 and 7 days after alert 30.77 (34.18) 14 (5-36) Visits between 8 to 14 d after alert 23.83 (14.78) 15.5 (7.88-23.13) Visits not related to alert 72.71 (93.26) 30.5 (6.15-100.15) These findings underscore the clinical utility of AI-generated alerts in psychiatric management. By translating continuous monitoring into timely patient-clinician engagement, the system facilitates dynamic, data-driven therapeutic adjustments, enabling proactive interventions prior to significant clinical deterioration. Integrating AI-driven alerts into routine clinical workflows bridges the gap between scheduled visits and acute patient needs. This approach transforms passive monitoring into actionable clinical decisions, laying the foundation for more responsive and individualized care in affective disorders. Discussion Main Results The MoodMon system demonstrated technical capability in distinguishing affective states in individuals with BD using acoustic features [ 27 ]. Notably, monitoring dynamic changes in mental state based on a limited set of acoustic features appears particularly effective in the very early or prodromal phase of affective episodes. Through real-time alert generation, both patients and their treating psychiatrists gained rapid access to objective information regarding potential changes in mental status. When such alerts were subsequently confirmed as clinically relevant during psychiatric evaluation, therapeutic interventions could be initiated earlier than would typically be possible under standard organizational conditions. By enabling continuous, objective monitoring beyond scheduled clinical visits, MoodMon facilitates greater continuity of care for patients with affective disorders. The present findings support prior theoretical and empirical work suggesting that continuous analysis of vocal biomarkers may substantially enhance the early detection of affective state changes, particularly during prodromal stages, thereby introducing novel clinical value [ 20 , 28 - 31 ]. Although behavioral markers were also collected during the study, their correlation with clinician-confirmed changes in mental state was weaker than that observed for acoustic features. High alert performance was achieved using the analysis of a limited number of acoustic parameters, indicating that effective monitoring does not require complex or resource-intensive sensing modalities. Consequently, simpler, cost-efficient, and widely accessible solutions can be used, as patients are not required to wear dedicated monitoring devices; instead, natural speech recorded via standard smartphones is sufficient. These findings align with existing literature demonstrating the high potential of objective acoustic features in longitudinal patient monitoring [ 20 , 32 ]. The model achieved high sensitivity and specificity (>89% and>98%, respectively) by adopting a binary classification approach (detecting “change” vs “no change”). This simplified framework, supported by previous research [ 27 ] and clinical consultation, met the practical requirements of practitioners who did not require more granular data. Furthermore, this binary design helped minimize potential confirmation bias during the evaluation. The other important factor increasing the system’s effectiveness was continuous training and adjustments to the decision threshold for individual patients. The dataset used for the system assessment was dominated by stable days, as is expected in an outpatient setting and corresponds to the intended use of the device. The model demonstrated high sensitivity and robust precision. Given the inherent class imbalance, precision serves as a more reliable performance indicator than accuracy [ 31 ]. The results confirm that the model remains highly effective despite the low prevalence of TP cases and that the risk of “alert fatigue” is relatively low. Additionally, it should be mentioned that in stable outpatient monitoring, the dominance of TN cases reflects the intended use and is not a methodological artifact. The methodological framework of the MoodMon system adheres to contemporary recommendations for machine learning–based voice biomarker development, which emphasize the central role of human oversight in AI-assisted clinical systems [ 33 ]. In MoodMon, alerts generated during the second phase of the study underwent clinical validation by psychiatrists with relevant expertise, supported by standardized assessment scales. Critically, final clinical decisions remained exclusively the responsibility of the physician. The AI system was limited to detecting statistically significant deviations within the analyzed data streams, while the psychiatrist determined whether a clinical consultation was warranted and, if so, whether therapeutic modifications were necessary. This design ensures patient safety and preserves the primacy of clinical judgment, consistent with ethical and practical recommendations for the application of AI in psychiatry [ 33 , 34 ]. Just over 9% of alerts were assessed more than 7 days after their occurrence. The most common factors contributing to this were difficulties in contacting patients (during or after the COVID-19 pandemic). The psychiatrist or researcher always had the option to select “alert unassessable” if they were clinically unsure, for example, because the assessment was too far removed from the alert. Additionally, in the second phase of the study, scheduled visit slots could be used to assess alerts to avoid excessive involvement of patients at short intervals. A deliberate design choice is for the AI to detect deviations from the baseline without identifying the direction (eg, depressive vs manic) and to alert users to factors documented in clinical studies as triggers for an episode (eg, psychosocial stress and somatic illness). For psychiatrists, who have previously relied on often-delayed retrospective reports from the patient with a psychiatric condition and/or their loved ones, this provides an opportunity to identify triggers or subtle initial (prodromal) symptoms earlier. In this context, transient mood swings signaled by alerts are equally clinically important for psychiatrists, as they practically represent, for example, insufficient efficacy of medications. The primary goal in the long-term treatment of affective disorders is achieving full remission (symptomatic and functional). Clinical use of the MoodMon system demonstrated that AI-generated alerts constitute a meaningful support tool in psychiatric decision-making. Alerts not only increased the probability of follow-up visits for patients but also significantly influenced the likelihood of treatment modification. This represents a key clinical implication, confirming that the interdisciplinary objective underlying the development of MoodMon—namely, the creation of a clinically actionable, AI-supported monitoring system for patients and psychiatrists—has been achieved. The clinical safety of the studied AI system was determined by the absence of adverse events associated with the MoodMon app. While not a primary end point, self-reported data—inclusive of dropouts—revealed a shift from 12 hospitalizations in the 2 years preceding the study to zero upon its conclusion, providing a compelling signal of the system’s clinical safety and use. Limitations The system evaluated in this study was specifically designed for the remote monitoring of outpatients, and accordingly, the study population consisted exclusively of outpatient individuals. As a consequence, a disproportionately high number of TN cases was observed, corresponding to days without clinically relevant changes in the condition of the patient and without system-generated alerts. Therefore, the observed class distribution reflects real-world clinical conditions rather than methodological bias. Importantly, the results demonstrate genuinely high specificity, which is a critical prerequisite for clinical applicability; insufficient specificity would result in excessive false alerts and, consequently, limited clinical utility of the device. The limitation of our naturalistic study is the omission of the influence of possible side effects of the medications used on voice parameters, although the outpatients did not receive very strong treatments typically used for severe conditions treated in closed wards of psychiatric hospitals in our country. The researchers were aware that the standard practice of supplementing data with averages from periods preceding clinical assessments was applied, and that such a procedure could mask the rapid or ultrarapid changes in the mental state of a patient with affective disorders. An important consideration for in-depth analysis is that when the psychiatrist obtained information from the patient on this topic during the study, he placed it in the appropriate place in the system to provide a complete picture of the psychopathology. Due to budgetary constraints, the mobile app used for voice sample acquisition was implemented exclusively for smartphones running the Android operating system (version 9 or later). While this restricted the rate of participant recruitment and introduced a degree of demographic stratification attributable to the correlation between smartphone operating systems and socioeconomic status, it did not affect the validity or quality of the collected data, especially as users of iOS devices are typically characterized by a high readiness to adopt novel digital solutions. Moreover, it does not limit the feasibility of real-world deployment, as the app architecture can be readily adapted for the Apple iOS platform. Nonetheless, reliance on smartphone-based voice recording may pose usability challenges for older patients or individuals with limited digital literacy. To mitigate this issue, the app was designed with a strong emphasis on simplicity and user-friendliness. Patient selection for this study was limited to individuals with a high frequency of mental state changes in order to ensure sufficient data for training and evaluation purposes. This approach may limit the generalizability to a more stable population. Most of the limitations of future research work will gradually be eliminated as technology develops and broader populations of patients with mental disorders are included. Considering the nature of validation and the request that each alert be assessed by a doctor, we decided to use only one machine learning model. Therefore, comparative performance between candidate models cannot be fully evaluated. Due to the high variability between subjects and the complexity of the problem being considered, individualized thresholds are necessary. The study’s naturalistic design involved clinicians recruiting patients from their own practices. During the evaluation phase, researchers were aware when contact with a patient was prompted by a system alert, which may have introduced confirmation bias. To mitigate this risk, the protocol mandated the use of standardized psychiatric tools, including the HDRS and the YMRS, along with structured questionnaires. These standardized procedures were also used to minimize incorporation bias. However, this still likely had the effect of inflating the perceived usefulness of the alert. The Polish-only training sample was validated. It is expected that the acoustic feature-mood relationships will transfer across languages, but empirical verification is required for clear confirmation (at the time of this study’s publication, our team is currently conducting further research on a large group of Spanish-speaking patients). Comparison With Previous Work The present findings confirm and extend previous evidence that acoustic features are robust biomarkers of mood states in individuals with BD, a line of research that historically began as early as the 1940 s and continued with the development of new technical capabilities [ 20 , 28 , 29 , 35 ]. Early studies demonstrated that changes in speech activity and voice characteristics are sensitive and reliable indicators of prodromal symptoms preceding affective episodes. This allows for early and objective quantification of psychopathological changes in patients [ 29 , 36 , 37 ]. Although speech was confirmed as a promising marker for mental health assessments by those early studies, there were still many technical obstacles to overcome in designing and implementing effective systems and reliable algorithms [ 38 ], and many further efforts were reported in this area. An example is a clinically validated digital mental health platform that integrates patient-reported outcomes and passive sensor data to support symptom monitoring, which was validated for many groups, including young patients [ 39 ]. However, advanced statistical and AI tools like pattern analysis and risk assessments are still limited to research purposes. Another large-scale European project that uses wearable devices and mobile technology to monitor and prevent relapses in BD, depression, and epilepsy is Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) [ 40 ]. However, RADAR-CNS is mainly dedicated to remote data collection and efficient integration of various sensor data. In comparison, the MoodMon system focuses on advanced, AI-driven voice monitoring, tightly integrated with the combined processing of speech signals and psychiatric assessment. Another recent project is BEHAPP [ 41 ], which is a research platform for passive data collection through smartphones to support scientific and medical research. Depending on the study, BEHAPP may collect various types of data such as location, call and SMS text messaging history, Wi-Fi access points, app usage, motion through accelerometers, ambient light, and noise levels. Again, MoodMon goes beyond the current state of the art due to its AI toolbox tailored to psychiatrically guided voice analysis. The key innovation of the MoodMon system lies in the use of AI to predict dynamic changes in mental state, whereas previous solutions relied primarily on the collection of objective but largely static data reflecting remission or the presence of an affective episode. Therefore, direct comparison of MoodMon with previously described systems for monitoring affective disorders is inherently difficult. Compared to previous solutions, the MoodMon system represents a methodological and technological advancement. Its longitudinal implementation has generated an exceptionally large dataset for training machine learning models. Previous studies have primarily focused on differentiating affective episodes from remissions in individuals with BD based on voice, demonstrating moderate accuracy compared to conventional mood scales (approximately 0.61‐0.74). These studies have highlighted the potential of speech-derived features for classifying affective states (depressive, manic, and euthymic) as a complement to established clinical tools, thereby improving monitoring of individuals in real-world settings [ 34 , 42 ]. It is important to emphasize that a critical element of patient safety in managing AI in the MoodMon system is constant human supervision by a qualified psychiatrist. The clinician is responsible for continuously assessing whether the AI is performing its function properly—namely, generating timely alerts in response to changes in the mental state of the patient. In this study, the frequency of such human assessments exceeded that reported in comparable studies, contributing to improved AI performance and accuracy. The MoodMon system’s analytical framework is based on both individual longitudinal data and aggregated group data, resulting in significantly larger training datasets than those used in previous studies [ 20 , 30 , 43 ]. Furthermore, the study adhered to the latest recommendations for AI applications in psychiatry, including incorporating patient-reported subjective assessments of the MoodMon system [ 33 ]. Consistent with the findings of a recent Italian study examining the correlations between speech characteristics and clinical status in individuals with bipolar depression or mania, our results support the conclusion that acoustic features are an important complement to clinical practice in mobile health ecosystems [ 40 , 44 ]. The improved performance is achieved by limiting the classification to 2 classes, whereas previous works strived to classify the states of the patient into 3 or more classes [ 17 , 20 , 27 ]. Moreover, MoodMon used voice exclusively as input for detecting changes in mental state, while previous works [ 17 , 20 ] also used other behavioral markers and self-reported evaluations. The naturalistic study gathered a patient population typical of groups treated in outpatient settings; however, detailed data, broken down by the psychopathological differences of both diagnoses, can be found in separate publications from the project [ 45 , 46 ]. Furthermore, in Kaczmarek-Majer et al [ 47 ], we propose additional explanations based on linguistic summaries and fuzzy rule-based reasoning models to support alerts generated by the system. Clinical use of the MoodMon system in the monitoring and management of patients with affective disorders demonstrated that system-generated alerts constitute clinically meaningful support for decision-making. The presence of alerts was associated not only with an increased probability of patient-clinician contact but also with a significantly higher likelihood of therapeutic modification. This represents a key clinical implication, as it confirms that the primary objective underlying the interdisciplinary development of the MoodMon system—namely, to provide an effective, clinically actionable tool for both patients and psychiatrists—has been successfully achieved. Conclusions The MoodMon study confirmed that acoustic features derived from speech serve as objective biomarkers of mental state changes in individuals with BD and MDD. The high effectiveness achieved confirmed the validity of the approach based on the limitation of the classification to only 2 classes. Alerts generated by the system effectively support psychiatrists in clinical decision-making while maintaining their responsibility for final diagnostic and therapeutic decisions. The high efficiency and practicality of the presented AI system translate into a high potential for implementation in clinical practice as a modern, smartphone-based tool for continuous monitoring of patients with affective disorders. Acknowledgments The authors thank the entire team of researchers from various fields of AI, led by MS-S. The authors also thank the psychiatrists who shared their clinical experience during the first and second stages of this research project. In addition to the first and second authors, contributions were made by Agnieszka Remlinger-Molenda, Dariusz Myszka, Katarzyna Derda-Matkowska, Joanna Miłoszewska-Maj, Wiktor Buczek, and consultant Bartłomiej Antoniak. Generative AI was not used in the manuscript. Abbreviations BD bipolar disorder CGI Clinical Global Impression Scale FN false negative FP false positive HDRS Hamilton Depression Rating Scale MDD major depressive disorder RADAR-CNS Remote Assessment of Disease and Relapse-Central Nervous System TN true negative TNR true negative rate TP true positive TPR true positive rate YMRS Young Mania Rating Scale Footnotes Funding: Study approval was obtained under study UR.D.WM.DNB.39.2021. The study was funded by the National Centre for Research and Development, Poland, under the project Development of a System Supporting the Monitoring of the Course and Early Detection of Relapses of Affective Disorders Based on Artificial Intelligence Algorithms (Agreement No. POIR.01.01.01-00-0342/20). Author KK-M acknowledges funding from the project “ExplainMe: Explainable ArtificialIntelligence for Monitoring Acoustic Features Extracted from Speech” (FENG.02.02-IP.05-0302/23), which is carried out within the First Team program of the Foundation for Polish Scienceco—financed by the European Union under the European Funds for Smart Economy 2021-2027(FENG). Authors’ Contributions: Conceptualization: MS-S (lead), LS (supporting) Formal analysis: MS-S (lead), LS (supporting), KK (supporting), KK-M (supporting) Investigation: MS-S (lead), LS (equal), KK (supporting), KK-M (supporting) Methodology: MS-S (lead), LS (equal), KK (supporting), KK-M (supporting) Project administration: MS-S (lead), LS (supporting) Validation: MS-S (lead), LS (equal), KK (supporting), KK-M (supporting) Visualization: MS-S (lead), KK-M (supporting) Writing – original draft: MS-S (lead), LS (supporting), KK (supporting), KK-M (supporting) Writing – review & editing: MS-S (lead), LS (supporting), KK (supporting), KK-M (supporting) Data Availability: The datasets generated or analyzed during this study are available from the corresponding author upon reasonable request. To support research transparency and reproducibility, MoodMon welcomes collaborations with external research groups. Upon reasonable request, the company is prepared to provide system access for replication studies or related clinical investigations. The official owner of the system can also provide a list of 19 acoustic features upon a well-reasoned request. Conflicts of Interest: None declared. References 1. Garzón-Partida AP, Padilla-Gómez CB, Martínez-Fernández DE, García-Estrada J, Luquin S, Fernández-Quezada D. The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review. Front Digit Health. 2025;7:1595243. doi: 10.3389/fdgth.2025.1595243. doi. Medline. 2. World Health Organization; 2022. [27-06-2026]. World mental health report: transforming mental health for all. https://iris.who.int/server/api/core/bitstreams/40e5a13a-fe50-4efa-b56d-6e8cf00d5bfa/content URL. Accessed. 3. Song YW, Lee HS, Kim S, Kim K, Kim BN, Kim JS. How to solve clinical challenges in mood disorders; machine learning approaches using electrophysiological markers. Clin Psychopharmacol Neurosci. 2024 Aug 31;22(3):416–430. doi: 10.9758/cpn.24.1165. doi. Medline. 4. Latifian M, Abdi K, Raheb G, Islam SMS, Alikhani R. Stigma in people living with bipolar disorder and their families: a systematic review. Int J Bipolar Disord. 2023 Feb 20;11(1):9. doi: 10.1186/s40345-023-00290-y. doi. Medline. 5. Trefflich F, Kalckreuth S, Mergl R, Rummel-Kluge C. Psychiatric patients’ internet use corresponds to the internet use of the general public. Psychiatry Res. 2015 Mar 30;226(1):136–141. doi: 10.1016/j.psychres.2014.12.037. doi. Medline. 6. Bipolar disorder. National Institute of Mental Health (NIMH) [17-11-2025]. https://www.nimh.nih.gov/health/topics/bipolar-disorder URL. Accessed. 7. GBD 2019 Mental Disorders Collaborators Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022 Feb;9(2):137–150. doi: 10.1016/S2215-0366(21)00395-3. doi. Medline. 8. Sadr SS, Samimi-Ardestani SM, Semnani Y, Adel N, Tajari F, Samani N. Neurological soft signs in type I bipolar disorder and bipolar spectrum patients and their unaffected first-degree relatives: a cross-sectional study. Health Sci Rep. 2025 Jan;8(1):e70341. doi: 10.1002/hsr2.70341. doi. Medline. 9. Oliva V, Fico G, De Prisco M, Gonda X, Rosa AR, Vieta E. Bipolar disorders: an update on critical aspects. Lancet Reg Health Eur. 2025 Jan;48:101135. doi: 10.1016/j.lanepe.2024.101135. doi. Medline. 10. Grande I, Berk M, Birmaher B, Vieta E. Bipolar disorder. Lancet. 2016 Apr 9;387(10027):1561–1572. doi: 10.1016/S0140-6736(15)00241-X. doi. Medline. 11. Depressive disorder (depression) World Health Organization. [17-11-2025]. https://www.who.int/news-room/fact-sheets/detail/depression URL. Accessed. 12. Xu Y, Li R, Hu C, He Y, Zhang X, Jin L. Global, regional, and national incidence trends of depressive disorder, 1990-2019: an age-period-cohort analysis based on the Global Burden of Disease 2019 study. Gen Hosp Psychiatry. 2024;88:51–60. doi: 10.1016/j.genhosppsych.2024.03.003. doi. Medline. 13. Ribeiro WS, Bauer A, Andrade MCR, et al. Income inequality and mental illness-related morbidity and resilience: a systematic review and meta-analysis. Lancet Psychiatry. 2017 Jul;4(7):554–562. doi: 10.1016/S2215-0366(17)30159-1. doi. Medline. 14. Fu XL, Qian Y, Jin XH, et al. Suicide rates among people with serious mental illness: a systematic review and meta-analysis. Psychol Med. 2023 Jan;53(2):351–361. doi: 10.1017/S0033291721001549. doi. Medline. 15. Semkovska M, Quinlivan L, O’Grady T, et al. Cognitive function following a major depressive episode: a systematic review and meta-analysis. Lancet Psychiatry. 2019 Oct;6(10):851–861. doi: 10.1016/S2215-0366(19)30291-3. doi. Medline. 16. McLachlan C, Shelton R, Li L. Obesity, inflammation, and depression in adolescents. Front Psychiatry. 2023;14:1221709. doi: 10.3389/fpsyt.2023.1221709. doi. Medline. 17. Faurholt-Jepsen M, Rohani DA, Busk J, et al. Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls. Acta Psychiatr Scand. 2022 Mar;145(3):255–267. doi: 10.1111/acps.13391. doi. Medline. 18. de Boer JN, Voppel AE, Begemann MJH, Schnack HG, Wijnen F, Sommer IEC. Clinical use of semantic space models in psychiatry and neurology: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2018 Oct;93:85–92. doi: 10.1016/j.neubiorev.2018.06.008. doi. Medline. 19. Matton K, McInnis MG, Provost EM. Into the wild: transitioning from recognizing mood in clinical interactions to personal conversations for individuals with bipolar disorder. INTERSPEECH 2019; Sep 15-19, 2019; Graz, Austria. pp. 1438–1442. Presented at. doi. 20. Faurholt-Jepsen M, Busk J, Frost M, et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry. 2016 Jul 19;6(7):e856. doi: 10.1038/tp.2016.123. doi. Medline. 21. Kjærstad HL. The heterogeneity of bipolar disorder: from clinical observations to precision psychiatry. [27-06-2026];Neurotorium. 2024 Feb 5; https://neurotorium.org/wp-content/uploads/the-heterogeneity-of-bipolar-disorder-from-clinical-observations-to-precision-psychiatry.pdf URL. Accessed. 22. Thomas NS, Gillespie NA, Neale MC, Rosmalen JGM, van Loo HM, Kendler KS. Clinical heterogeneity in major depressive disorder underlies comorbidity with functional disorders. J Psychiatr Res. 2025 Mar;183:16–24. doi: 10.1016/j.jpsychires.2025.01.056. doi. Medline. 23. Cummins N, Baird A, Schuller BW. Speech analysis for health: current state-of-the-art and the increasing impact of deep learning. Methods. 2018 Dec 1;151:41–54. doi: 10.1016/j.ymeth.2018.07.007. doi. Medline. 24. Festersen PL, Corradini A. Re: mind: a mobile application for bipolar disorder patients. 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH); Nov 3-5, 2014; Athens, Greece. Presented at. doi. 25. Rodrigues Cordeiro C, Côrte-Real BR, Saraiva R, Frey BN, Kapczinski F, de Azevedo Cardoso T. Triggers for acute mood episodes in bipolar disorder: a systematic review. J Psychiatr Res. 2023 May;161:237–260. doi: 10.1016/j.jpsychires.2023.03.008. doi. Medline. 26. Meisenzahl E, Schultze-Lutter F, Stegmüller V, et al. Stressors during the prodromal phase of major depressive episode (CHR-D) Front Psychiatry. 2024;15:1389370. doi: 10.3389/fpsyt.2024.1389370. doi. Medline. 27. Kamińska D, Kamińska O, Sochacka M, Sokół-Szawłowska M. The role of selected speech signal characteristics in discriminating unipolar and bipolar disorders. Sensors (Basel) 2024 Jul 20;24(14):4721. doi: 10.3390/s24144721. doi. Medline. 28. Newman S, Mather VG. Analysis of spoken language of patients with affective disorders. Am J Psychiatry. 1938 Jan;94(4):913–942. doi: 10.1176/ajp.94.4.913. doi. 29. Guidi A, Salvi S, Ottaviano M, et al. Smartphone application for the analysis of prosodic features in running speech with a focus on bipolar disorders: system performance evaluation and case study. Sensors (Basel) 2015 Nov 6;15(11):28070–28087. doi: 10.3390/s151128070. doi. Medline. 30. Flanagan O, Chan A, Roop P, Sundram F. Using acoustic speech patterns from smartphones to investigate mood disorders: scoping review. JMIR mHealth uHealth. 2021 Sep 17;9(9):e24352. doi: 10.2196/24352. doi. Medline. 31. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015;10(3):e0118432. doi: 10.1371/journal.pone.0118432. doi. Medline. 32. Jędrzejewska B, Borycka A, Kotulska M, et al. Efficiency of mobile apps for monitoring and management of mental health - review article. J Educ Health Sport. 2023;35(1):62–80. doi: 10.12775/JEHS.2023.35.01.006. doi. 33. Singh V, Sarkar S, Gaur V, Grover S, Singh OP. Clinical practice guidelines on using artificial intelligence and gadgets for mental health and well-being. Indian J Psychiatry. 2024 Jan;66(Suppl 2):S414–S419. doi: 10.4103/indianjpsychiatry.indianjpsychiatry_926_23. doi. Medline. 34. Berardi M, Brosch K, Pfarr JK, et al. Relative importance of speech and voice features in the classification of schizophrenia and depression. Transl Psychiatry. 2023 Sep 19;13(1):298. doi: 10.1038/s41398-023-02594-0. doi. Medline. 35. Or F, Torous J, Onnela JP. High potential but limited evidence: using voice data from smartphones to monitor and diagnose mood disorders. Psychiatr Rehabil J. 2017 Sep;40(3):320–324. doi: 10.1037/prj0000279. doi. Medline. 36. Greden JF, Carroll BJ. Decrease in speech pause times with treatment of endogenous depression. Biol Psychiatry. 1980 Aug;15(4):575–587. Medline. 37. Alpert M, Pouget ER, Silva RR. Reflections of depression in acoustic measures of the patient’s speech. J Affect Disord. 2001 Sep;66(1):59–69. doi: 10.1016/s0165-0327(00)00335-9. doi. Medline. 38. Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: a systematic review. Laryngoscope Investig Otolaryngol. 2020 Feb;5(1):96–116. doi: 10.1002/lio2.354. doi. Medline. 39. Melbye SA, Stanislaus S, Vinberg M, et al. Mood, activity, and sleep measured via daily smartphone-based self-monitoring in young patients with newly diagnosed bipolar disorder, their unaffected relatives and healthy control individuals. Eur Child Adolesc Psychiatry. 2021 Aug;30(8):1209–1221. doi: 10.1007/s00787-020-01611-7. doi. Medline. 40. Ranjan Y, Rashid Z, Stewart C, et al. RADAR-Base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR mHealth uHealth. 2019 Aug 1;7(8):e11734. doi: 10.2196/11734. doi. Medline. 41. Sverdlov O, Curcic J, Hannesdottir K, et al. A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression. Front Psychiatry. 2021;12:640741. doi: 10.3389/fpsyt.2021.640741. doi. Medline. 42. Karam ZN, Provost EM, Singh S, et al. Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); May 4-9, 2014; Florence, Italy. pp. 4858–4862. Presented at. doi. 43. Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-based monitoring of objective and subjective data in affective disorders: where are we and where are we going? Systematic review. J Med Internet Res. 2017 Jul 24;19(7):e262. doi: 10.2196/jmir.7006. doi. Medline. 44. Crocamo C, Cioni RM, Canestro A, et al. Acoustic and natural language markers for bipolar disorder: a pilot, mHealth cross-sectional study. JMIR Form Res. 2025 Apr 16;9:e65555. doi: 10.2196/65555. doi. Medline. 45. Sokół-Szawłowska M, Sochacka M. High effectiveness of the Polish artificial intelligence-based MoodMon system in monitoring depression using physical voice parameters — results with extended clinical description. Psychiatria. 2025;22:62–70. doi: 10.5603/psych.106883. doi. 46. Sokół-Szawłowska M, Sochacka M. High effectiveness of the AI-based MoodMon system in monitoring mental state in bipolar disorder using voice biomarkers. Psychiatria. 2025;22:71–79. doi: 10.5603/psych.106884. doi. 47. Kaczmarek-Majer K, Daňková M, Ostrowski M, et al. How to evaluate fuzzy linguistic summaries and fuzzy association rules? A pilot user study in monitoring bipolar and depressive disorders. Inf Syst Front. 2026 doi: 10.1007/s10796-026-10712-8. doi.