Tina R. Goldstein

Continuous ambulatory HF-HRV (high-frequency heart rate variability) monitoring using a chest-worn ECG (electrocardiogram) device met a priori feasibility benchmarks (≥75% compliance and data quality) in adolescents at high risk for suicide over approximately two to three weeks, including during school days and sleep.

BACKGROUND Given widespread smartphone adoption, passive sensing has emerged as a promising, scalable method for monitoring various health behaviors, including sleep. Yet, how these methods are being used to measure sleep and their validity remains unclear. OBJECTIVE This scoping review aimed to summarize the literature on smartphone-based passive sensing methods for sleep monitoring with respect to: 1) the methodological approaches used to estimate sleep duration and timing using smartphone sensors and 2) the convergence of these mobile sensing estimates with established sleep assessment measures. METHODS Following Arksey and O’Malley’s framework for conducting scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we searched MEDLINE, Embase, PsycINFO, Web of Science, ACM, and IEEE Xplore databases through February 28, 2026 for articles using smartphone passive sensing to monitor sleep. Eligible studies were peer-reviewed journal articles or conference papers published in English between January 2010 and February 2026 that reported original human subject research which 1) utilized smartphone passive sensing methods to measure sleep duration or timing, and 2) assessed convergence with one or more alternative sleep measurement methods, including self-reports, wearables or actigraphy, and polysomnography. Exclusions were sensing methods wholly or partially reliant on wearables, external environmental sensors, or active user input, sample sizes fewer than five, pediatric populations younger than 12, nonempirical research (e.g., protocols, reviews), incomplete reports, and non-English studies. RESULTS Of 8422 identified records, 38 met inclusion criteria. We narratively synthesized 1) methodological approaches to estimating sleep duration and timing using smartphone sensors, including information about the sensors, features, and algorithms employed across studies, and 2) validity of these estimates compared to sleep diaries (n=21 studies), actigraphy and wearables (n=11), polysomnography (n=5), and other self-report measures (n=10). Comparator findings are further organized to reflect three convergent validity indicators: estimation accuracy, systematic bias (e.g., over/underestimation), and strength of associations or consistency. Overall, findings suggest that smartphones can estimate sleep patterns with moderate resolution, though convergence varied across studies, sleep outcomes, and comparator methods CONCLUSIONS Smartphone passive sensing shows promise as a scalable and unobtrusive method for sleep monitoring, although mixed validity findings and substantial methodological heterogeneity preclude drawing definitive conclusions. Key areas of consideration for future research are highlighted. CLINICALTRIAL None

UNSTRUCTURED Continuous ambulatory HF-HRV monitoring using a chest-worn ECG device is feasible for approximately two to three weeks in adolescents at high risk for suicide, including during school days and sleep.

Background: Sleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Although actigraphy provides objective sleep measurement, it is limited by cost and monitoring duration. Passive smartphone-based mobile sensing offers a scalable alternative, but its validity in youths with BD is unclear. Methods: Analyses included adolescents and young adults (ages 14–25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics (total sleep time [TST], sleep onset, sleep offset, midsleep, wake after sleep onset [WASO]) were compared with smartphone-derived proxies (total offline time [TOT], onset, offset, midsleep, phone use after sleep onset [PASO]). Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between WASO and PASO. Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence vs. absence of mood symptoms and anxiety, and weekend effects. Results: Mobile sensing showed strong convergence with actigraphy for sleep timing and duration (standardized β = 0.54-0.75, all p < .0001). RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing. PASO underestimated WASO (RMSE = 48.8 minutes), but greater WASO significantly increased the odds of detecting any PASO (OR per 15 minutes = 1.35, p < .0001). Findings were robust across sensitivity analyses. Conclusions: Passive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.

Objective: Given the episodic nature of bipolar disorder (BD) and the variability in mood episode recurrence across individuals, accurate recurrence prediction is critical. The original COBY recurrence risk calculator (RC) was developed in a longitudinal youth cohort to estimate threshold recurrence risk. However, its accuracy for predicting subthreshold recurrences had not been fully evaluated. The objective of this study was to extend the previously developed COBY mood recurrence RC to predict both threshold and subthreshold mood recurrences and evaluate its performance in an independent sample. Method: Adolescents and young adults with BD-I/II (N= 51; BD-I: 38, BD-II: 13; 14-24 years old) were interviewed with standard instruments at intake and during the follow-up on average every 6 months for a median of 54 weeks. We assessed the degree to which the COBY RC predicted mood recurrence (threshold or subthreshold) in this independent sample. Discrimination was measured using the area under the receiver operating characteristic curves (AUC); calibration and variable importance were also assessed. Results: The model demonstrated good prediction of any recurrence within the next six months (any threshold recurrence AUC = 0.72, any subthreshold or worse recurrence AUC = 0.77). Calibration analysis demonstrated the model tended to overestimate risk in the external sample, plausibly attributable to differences in recurrence ascertainment strategy (prospective vs retrospective) or the significant difference in prior remission length, a key predictor. Recalibration greatly improved calibration without loss of discrimination. Conclusion: The mood recurrence RC demonstrated good discrimination for both threshold and subthreshold mood recurrences in an independent young adult cohort, consistent with prior youth and adult validations. Validation now spans across developmental stages and different degrees of severity of mood symptoms opening the opportunity for clinical implementation to provide personalized monitoring and early intervention for people with BD.

Introduction: Risk for suicide is disproportionately elevated among youth with bipolar spectrum disorder (BD), which is characterized by recurrent mood episodes and significant shifts in sleep and circadian rhythm. Disruptions in circadian rhythm, such as 24‐h rest‐activity patterns, are a relevant risk factor for SI, yet understudied in BD youth. The present cross‐sectional study examined the associations between actigraphy‐derived rest‐activity rhythms (RARs) and self‐reported suicidal ideation (SI) severity in a sample of 54 BD youth (aged 12–18 years). Method: Participants completed daily wrist actigraphy (M _days = 18.89, range: 6 to 29 days) and non‐parametric RAR measures were derived for analyses. At the same time, participants provided self‐report data on depressive symptoms and SI severity (number of days between actigraphy start and questionnaire completion: M _days = −7.74, SD = 13.33, range: −46 to 28 days). Primary analyses examined associations between RAR variables and SI severity, controlling for several key covariates including age, BD subtype, psychotropic medication use, and depressive symptoms. Results: Results revealed that greater interdaily stability and higher average activity levels during wake were associated with lower SI severity. These results remained significant even when controlling for the aforementioned covariates. Conclusion: Results indicated that greater and more consistent activity levels within and across days are associated with lower suicide risk. Findings suggest that RARs are relevant for suicide risk detection and intervention for BD youth.

High-frequency heart rate variability (HF-HRV) is a promising transdiagnostic biomarker for real-time changes in suicide risk states. Chest-worn electrocardiogram (ECG) devices have demonstrated higher reliability and validity for ambulatory HF-HRV than wrist- or finger-worn devices. However, chest-worn devices present some practical challenges (e.g., discomfort, user error), and the predictive validity of ambulatory HF-HRV for predicting fluctuations in suicidal ideation is unknown. Thus, we sought to examine the feasibility and predictive validity of continuous long-term ambulatory HF-HRV measurement using a chest-worn ECG sensor in depressed and acutely suicidal youth enrolled in intensive outpatient treatment. Youth were asked to wear a research-grade ECG sensor on their chest continuously for up to three weeks while concurrently completing twice-daily electronic diary surveys and wearing a wrist-worn actigraphy device. Among 100 adolescents, we observed strong compliance with the protocol, as evidenced by greater than 14 days of ECG sensor wear (on average) and wearing the sensor 76% of the time. We also obtained valid HF-HRV data 81% of the time that the sensor was worn. Both compliance and data quality declined over time and were higher during sleep as compared to wake periods, but did not differ based on school attendance. Furthermore, within-person decreases in HF-HRV were proximally related to increased likelihood of suicidal ideation within the same 4-hour period, suggesting the potential clinical utility of monitoring real-time changes in HF-HRV in daily life. Our results support the feasibility and validity of continuous ambulatory HF-HRV measurement in depressed/suicidal adolescents for up to two weeks.