Ilya Baryshnikov

Background: Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography remains the gold standard for measuring sleep, its reliance on laboratory settings limits its feasibility for long-term, naturalistic monitoring, particularly for patients with mental disorders. Objective: This study assesses sleep-tracking reliability and alignment in healthy individuals and patients with mood disorders using wearables, nearables, and ecological momentary assessment, while examining measurement biases and the impact of seasonal and demographic factors on discrepancies across methods. Methods: We conducted a 14-day study in Finland and enrolled a total of 201 participants, comprising patients with a major depressive episode and healthy controls. Of these, 169 participants with sufficient observations were retained for further analyses. Participants’ sleep patterns (onset, offset, and total sleep time [TST]) were gathered daily from an actigraph (Actiwatch 2), a bed sensor (Murata SCA11H), mobile screen events, and a daily survey. The alignment between sleep measurement methods was evaluated using Bland-Altman plots and Pearson correlation. Linear mixed models were used to assess the effects of demographics, season, and disorder type on the sleep measures alignment. Results: Patients exhibited greater variability in sleep measures than healthy controls. For sleep onset, mean biases between devices were small and not statistically significant in either group, with moderate to strong correlations. In contrast, sleep offset showed significantly larger biases in patients: actigraph versus bed (+34.9 minutes; P=.01), smartphone versus bed (–45.3 minutes; P=.004), and actigraph versus smartphone (+78.7 minutes; P<.001), while controls exhibited minimal and nonsignificant differences. For TST, smartphone underestimates sleep compared to both bed sensors (–0.71 minutes; P<.001) and actigraphs (–1.35 minutes; P<.001). Across devices, TST correlations remained low, spanning r=0.12 (P=.58) to r=0.55 (P=.10) in controls and r=0.17 (P=.19) to r=0.43 (P=.002) in patients. Mixed models showed that older age was linked to better sleep offset alignment between actigraphy and bed sensors (β=–0.02, 95% CI –0.04 to 0.00; P=.048), as well as smartphone and bed sensor (β=–0.03, 95% CI –0.06 to 0.00; P=.03). Patients with bipolar/borderline personality disorder showed lower TST alignment, and alignment between smartphone and bed sensor was worse in females (β=−1.03, 95% CI −1.74 to −0.33, P=.004). Longer daylight duration was also associated with improved alignment in sleep offset and TST. Conclusions: This study demonstrates the feasibility of using actigraphy, smartphone data, and bed sensors for sleep tracking in naturalistic settings with patients. It highlights measurement biases across devices, the impact of seasonal variations on sleep research in unique geographical regions like Finland, and key demographic factors influencing sleep measurement discrepancies.

BACKGROUND Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography remains the gold standard for measuring sleep, its reliance on laboratory settings limits its feasibility for long-term, naturalistic monitoring, particularly for patients with mental disorders. OBJECTIVE This study assesses sleep-tracking reliability and alignment in healthy individuals and patients with mood disorders using wearables, nearables, and ecological momentary assessment, while examining measurement biases and the impact of seasonal and demographic factors on discrepancies across methods. METHODS We conducted a 14-day study in Finland and enrolled a total of 201 participants, comprising patients with a major depressive episode and healthy controls. Of these, 169 participants with sufficient observations were retained for further analyses. Participants’ sleep patterns (onset, offset, and total sleep time [TST]) were gathered daily from an actigraph (Actiwatch 2), a bed sensor (Murata SCA11H), mobile screen events, and a daily survey. The alignment between sleep measurement methods was evaluated using Bland-Altman plots and Pearson correlation. Linear mixed models were used to assess the effects of demographics, season, and disorder type on the sleep measures alignment. RESULTS Patients exhibited greater variability in sleep measures than healthy controls. For sleep onset, mean biases between devices were small and not statistically significant in either group, with moderate to strong correlations. In contrast, sleep offset showed significantly larger biases in patients: actigraph versus bed (+34.9 minutes; P=.01), smartphone versus bed (–45.3 minutes; P=.004), and actigraph versus smartphone (+78.7 minutes; P<.001), while controls exhibited minimal and nonsignificant differences. For TST, smartphone underestimates sleep compared to both bed sensors (–0.71 minutes; P<.001) and actigraphs (–1.35 minutes; P<.001). Across devices, TST correlations remained low, spanning r=0.12 (P=.58) to r=0.55 (P=.10) in controls and r=0.17 (P=.19) to r=0.43 (P=.002) in patients. Mixed models showed that older age was linked to better sleep offset alignment between actigraphy and bed sensors (β=–0.02, 95% CI –0.04 to 0.00; P=.048), as well as smartphone and bed sensor (β=–0.03, 95% CI –0.06 to 0.00; P=.03). Patients with bipolar/borderline personality disorder showed lower TST alignment, and alignment between smartphone and bed sensor was worse in females (β=−1.03, 95% CI −1.74 to −0.33, P=.004). Longer daylight duration was also associated with improved alignment in sleep offset and TST. CONCLUSIONS This study demonstrates the feasibility of using actigraphy, smartphone data, and bed sensors for sleep tracking in naturalistic settings with patients. It highlights measurement biases across devices, the impact of seasonal variations on sleep research in unique geographical regions like Finland, and key demographic factors influencing sleep measurement discrepancies. CLINICALTRIAL

Digital phenotyping studies using smartphone-sensed data have identified several behavioral markers associated with depression. However, the generalizability of these markers is constrained by multiple factors, including variability in depressive symptoms and associated behaviors, both between and within individuals over time. This study examines heterogeneity in depression and aims to identify behavioral markers indicative of depression in smartphone-sensed data collected from participants diagnosed with depression. We analyzed smartphone-sensed behavioral data from 62 patients with major depressive episodes across three subgroups: major depressive disorder (MDD, n=41), borderline personality disorder (BPD, n=12), and bipolar disorder (BD, n=9). Depression symptoms were assessed with the 9-item Patient Health Questionnaire (PHQ-9). Symptoms varied between subgroups and across severity levels. Association analysis revealed variability in correlations between depression severity and behavioral markers, both between participants and over time. Multilevel modeling revealed demographic predictors: employment status (β = −4.79, 95% CI = [−7.65, −1.80], P = .004) and age (β = −0.12, 95% CI = [−0.25, −0.00], P = .050) and lower nighttime movement (β = −0.79, 95% CI = [−1.29, −0.29], P = .024), as behavioral markers of depression.

Background: Mood disorders are among the most common mental health conditions worldwide. Wearables and consumer-grade personal digital devices create digital traces that can be collected, processed, and analyzed, offering a unique opportunity to quantify and monitor individuals with mental disorders in their natural living environments. Objective: This study comprised (1) 3 subcohorts of patients with a major depressive episode, either with major depressive disorder, bipolar disorder, or concurrent borderline personality disorder, and (2) a healthy control group. We investigated whether differences in behavioral patterns could be observed at the group level, that is, patients versus healthy controls. We studied the volume and temporal patterns of smartphone screen and app use, communication, sleep, mobility, and physical activity. We investigated whether patients or controls exhibited more homogenous temporal patterns of activity when compared with other individuals in the same group. We examined which variables were associated with the severity of depression. Methods: In total, 188 participants were recruited to complete a 2-phase study. In the first 2 weeks, data from bed sensors, actigraphy, smartphones, and 5 sets of daily questions were collected. In the second phase, which lasted up to 1 year, only passive smartphone data and biweekly 9-item Patient Health Questionnaire data were collected. Survival analysis, statistical tests, and linear mixed models were performed. Results: Survival analysis showed no statistically significant difference in adherence. Most participants did not stay in the study for 1 year. Weekday location variance showed lower values for patients (control: mean –10.04, SD 2.73; patient: mean –11.91, SD 2.50; Mann-Whitney U [MWU] test P=.004). Normalized entropy of location was lower among patients (control: mean 2.10, SD 1.38; patient: mean 1.57, SD 1.10; MWU test P=.05). The temporal communication patterns of controls were more diverse compared to those of patients (MWU test P<.001). In contrast, patients exhibited more varied temporal patterns of smartphone use compared to the controls. We found that the duration of incoming calls (β=–0.08, 95% CI –0.12 to –0.04; P<.001) and the SD of activity magnitude (β=–2.05, 95% CI –4.18 to –0.20; P=.02) over the 14 days before the 9-item Patient Health Questionnaire records were negatively associated with depression severity. Conversely, the duration of outgoing calls showed a positive association with depression severity (β=0.05, 95% CI 0.00-0.09; P=.02). Conclusions: Our work shows the important features for future analyses of behavioral markers of mood disorders. However, among outpatients with mild to moderate depressive disorders, the group-level differences from healthy controls in any single modality remain relatively modest. Therefore, future studies need to combine data from multiple modalities to detect more subtle differences and identify individualized signatures. The high dropout rates for longer study periods remain a challenge and limit the generalizability.

Background: Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. Objective: This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. Methods: In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Results: Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns. Conclusions: The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.

BACKGROUND Mood disorders are among the most common mental health conditions worldwide. Wearables and consumer-grade personal digital devices create digital traces that can be collected, processed, and analyzed, offering a unique opportunity to quantify and monitor individuals with mental disorders in their natural living environments. OBJECTIVE This study comprised (1) 3 subcohorts of patients with a major depressive episode, either with major depressive disorder, bipolar disorder, or concurrent borderline personality disorder, and (2) a healthy control group. We investigated whether differences in behavioral patterns could be observed at the group level, that is, patients versus healthy controls. We studied the volume and temporal patterns of smartphone screen and app use, communication, sleep, mobility, and physical activity. We investigated whether patients or controls exhibited more homogenous temporal patterns of activity when compared with other individuals in the same group. We examined which variables were associated with the severity of depression. METHODS In total, 188 participants were recruited to complete a 2-phase study. In the first 2 weeks, data from bed sensors, actigraphy, smartphones, and 5 sets of daily questions were collected. In the second phase, which lasted up to 1 year, only passive smartphone data and biweekly 9-item Patient Health Questionnaire data were collected. Survival analysis, statistical tests, and linear mixed models were performed. RESULTS Survival analysis showed no statistically significant difference in adherence. Most participants did not stay in the study for 1 year. Weekday location variance showed lower values for patients (control: mean –10.04, SD 2.73; patient: mean –11.91, SD 2.50; Mann-Whitney U [MWU] test P=.004). Normalized entropy of location was lower among patients (control: mean 2.10, SD 1.38; patient: mean 1.57, SD 1.10; MWU test P=.05). The temporal communication patterns of controls were more diverse compared to those of patients (MWU test P<.001). In contrast, patients exhibited more varied temporal patterns of smartphone use compared to the controls. We found that the duration of incoming calls (β=–0.08, 95% CI –0.12 to –0.04; P<.001) and the SD of activity magnitude (β=–2.05, 95% CI –4.18 to –0.20; P=.02) over the 14 days before the 9-item Patient Health Questionnaire records were negatively associated with depression severity. Conversely, the duration of outgoing calls showed a positive association with depression severity (β=0.05, 95% CI 0.00-0.09; P=.02). CONCLUSIONS Our work shows the important features for future analyses of behavioral markers of mood disorders. However, among outpatients with mild to moderate depressive disorders, the group-level differences from healthy controls in any single modality remain relatively modest. Therefore, future studies need to combine data from multiple modalities to detect more subtle differences and identify individualized signatures. The high dropout rates for longer study periods remain a challenge and limit the generalizability.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. Objective: This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. Methods: In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Results: Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, app usage, and battery charge level patterns. Similarly, for predicting changes in depression state, the most important features were related to battery level, communication, screen, and location data patterns. Conclusions: The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.