Purpose: Sleep apnea is a prevalent sleep disorder with serious health implications. This study introduces ApneaWhisper, a Transformer-based audio segmentation model designed for noninvasive detection of sleep apnea subtypes using PSG-Audio data. Patients and Methods: We utilized a PSG-Audio dataset from 284 patients. ApneaWhisper leverages a pretrained Whisper encoder to extract 10 ms-resolution frame-level features from 20-second audio clips. A lightweight Transformer decoder with token-based segmentation and a classification head aggregates these features for both frame-level and clip-level predictions. The model was fine-tuned using class-balanced cross-entropy loss to address data imbalance across apnea subtypes. Results: ApneaWhisper achieved strong performance for sleep apnea detection, with a clip-level F1-score of 0.82 and a frame-level F1-score of 0.70, outperforming conventional baselines including MFCC+DNN, VGGish+bi-LSTM, and VAD-based models. It also showed promising ability in distinguishing between OSA, MSA, CSA, and hypopnea, though with varying success. Conclusion: The model’s fine-grained temporal resolution enables precise apnea event localization, duration estimation, and subtype classification. While ApneaWhisper performs robustly for OSA, challenges remain in distinguishing central (CSA) and mixed (MSA) sleep apnea, due to subtle or ambiguous acoustic patterns. The frame-level segmentation also facilitates accurate apnea–hypopnea index (AHI) estimation, which could reduce dependence on full PSG studies in certain clinical and home-monitoring scenarios. Future improvements may involve multimodal integration (eg, oxygen saturation) and noise-robust training techniques.

The latest mobile alarm apps provide wake-up tasks (e.g., solving math problems) to dismiss the alarm, and many users willingly accept such an inconvenience in return for successfully waking up on time. However, there have been no studies that investigate how the wake-up tasks are used and their effects from a human–computer interaction perspective. This study aims to deepen our understanding of how users engage and utilize the task-based alarm app by (1) examining the characteristics of different wake-up tasks and (2) extracting usage factors of hard tasks which involve physical or cognitive task loads over a certain level. We developed and deployed Alarmy, which is a task-based mobile alarm app with four wake-up task features: touching a button, taking a picture, shaking the device, and solving math problems. We collected 42.9 million in situ usage data from 211,273 US users for five months. Their alarm app usage behaviors were measured in two folds: eight alarm-set variables and five alarm-dismiss variables. Our statistical test results reveal the significant differences in alarm usage behaviors depending on the wake-up task, and the multiple regression analysis results show key usage patterns that affect the frequent uses of hard tasks, which are late alarm hours, many snoozes, and relatively more use on weekends. Our study results provide theoretical implications on behavior change as well as practical implications for designing task-based mobile alarm.

Background: Early morning behaviors between waking up and beginning daily work can develop into productive habits. However, sleep inertia limits the level of human ability immediately after waking, lowering a person’s motivation and available time for productive morning behavior.Objective: This study explores a design for morning behavior change using a wake-up task, a simple assignment the user needs to finish before alarm dismissal. Specifically, we set two research objectives: (1) exploring key factors that relate to morning behavior performance, including the use of wake-up tasks in an alarm app and (2) understanding the general practice of affecting morning behavior change by implementing wake-up tasks.Methods: We designed and implemented an apparatus that provides wake-up task alarms and facilities for squat exercises. We recruited 36 participants to perform squat exercises in the early morning using the wake-up tasks for 2 weeks. First, we conducted a generalized estimating equation (GEE) analysis for the first research objective. Next, we conducted a thematic analysis of the postsurvey answers to identify key themes about morning behavior change with the wake-up tasks for the second objective.Results: The use of wake-up tasks was significantly associated with both the completion of the target behavior (math task: P=.005; picture task: P<.001) and the elapsed time (picture task: P=.08); the time to alarm dismissal was significantly related to the elapsed time to completion (P<.001). Moreover, the theory of planned behavior (TPB) variables, common factors for behavior change, were significant, but their magnitudes and directions differed slightly from the other domains. Furthermore, the survey results reveal how the participants used the wake-up tasks and why they were effective for morning behavior performance.Conclusions: The results reveal the effectiveness of wake-up tasks in accomplishing the target morning behavior and address key factors for morning behavior change, such as (1) waking up on time, (2) escaping from sleep inertia, and (3) quickly starting the desired target behavior.

Accurate assessment of sleep architecture is critical for diagnosing and managing sleep disorders, which significantly impact global health and well-being. While polysomnography (PSG) remains the clinical gold standard, its inherent intrusiveness, high cost, and logistical complexity limit its utility for routine or home-based monitoring. Recent advances highlight that subtle variations in respiratory dynamics, such as respiratory rate and cycle regularity, exhibit meaningful correlations with distinct sleep stages and could serve as valuable non-invasive biomarkers. In this work, we propose a framework for estimating sleep stage distribution—specifically Wake, Light (N1+N2), Deep (N3), and REM—based on respiratory audio captured over a single sleep episode. The framework comprises three principal components: (1) a segmentation module that identifies distinct respiratory cycles in respiratory sounds using a fine-tuned Transformer-based architecture; (2) a feature extraction module that derives a suite of statistical, spectral, and distributional descriptors from these segmented respiratory patterns; and (3) stage-specific regression models that predict the proportion of time spent in each sleep stage. Experiments on the public PSG-Audio dataset (287 subjects; mean 5.3 h per subject), using subject-wise cross-validation, demonstrate the efficacy of the proposed approach. The segmentation model achieved lower RMSE and MAE in predicting respiratory rate and cycle duration, outperforming classical signal-processing baselines. For sleep stage proportion prediction, the proposed method yielded favorable RMSE and MAE across all stages, with the TabPFN model consistently delivering the best results. By quantifying interpretable respiratory features and intentionally avoiding black-box end-to-end modeling, our system may support transparent, contact-free sleep monitoring using passive audio.

Background: Morning emotions can significantly affect daily wellness. While many studies have analyzed daily survey responses to identify factors influencing morning emotions, these methods require additional time and effort from individuals for emotional monitoring. Objective: This study aims to identify daily alarm usage patterns related to morning emotions. Methods: We recruited 373 users of the Alarmy app (DelightRoom) in the United States and South Korea and surveyed their demographics and usual behaviors related to morning emotions. Participants described their morning emotions over a 2-week period, during which we collected daily alarm app logs. We used a generalized estimating equation (GEE) method to identify factors affecting morning emotions. Results: The findings indicate that varied alarm usage is related to morning emotions. Alarm set time was positively associated with feelings of peacefulness and refreshment in the morning, while task-based alarms were related to nervousness. The time taken to deactivate the alarm after it rang was negatively correlated with happiness. In addition, usual behaviors and demographic factors were found to be related to morning emotions, consistent with previous studies. Conclusions: The study reveals that daily alarm usage is related to morning emotions, suggesting that daily alarm logs can supplement survey methods to facilitate daily emotion monitoring.

[This corrects the article DOI: 10.2196/39497.].