## 4 Discussion
Data augmentation techniques, such as simulating variations in sleep stage transitions or durations, could also improve the robustness of the model by increasing the diversity of training data. In addition, multitask learningapproaches could improve the model's ability to detect overlapping conditions such as OSA, depression, and insomnia by leveraging shared representations across these disorders. These methods can significantly improve the generalizability of the model in real-world clinical applications. Due to the subjectivity of the annotation task and the resulting inter-rater disagreements between experts, ambiguous or low-certainty epoch-by-epoch labeling may contain errors. The sleep staging methods in portable sleep trackers are optimized for epoch-by-epoch performance, which does not guarantee that clinically relevant characteristics will be captured throughout the night [36, 37]. As such, fragmented sleep identification with respect to the reference PSG is poor, and there is no standardized approach for the validation of the wearable computation of the overnight statistics [38]. Thereby, the goal of our work was to combine multiple sources of data that may be readily available on wearables and study if deep learning approaches can prove useful in screening for multi-morbidity in limited data settings. We used the gold standard PSG data to perform our study, and this establishes a baseline for extensions to applications such as narcolepsy [39], emotional climate [40], and other sleep disorders. In terms of limitations, a lack of diversity in the collected data (single dataset) and the artificial structure of the experiments (PSG in controlled settings) may pose challenges while adapting to noisy real-world datasets. However, due to the scarcity of such data, we believe that this work affirms that single-instance, overnight, multimodal sleep stage information for a patient is insufficient to reveal signs of depression or insomnia but has the potential to detect the onset of OSA. This is in agreement with recent studies [41, 42] in which sleep and circadian measures alone could not strongly discriminate depression outcomes, but several characteristics that overlap with our study (insomnia, excessive daytime sleepiness, time spent in N1) and others (snoring, inactivity at night, and lower morning activity) were shown to be common features between sleep and mental health problems. This emphasizes that sociodemographic, lifestyle, and genetic characteristics should be considered in addition to the metrics of existing wearable devices [36, 38]. Recent studies consider sensing modalities such as EEG, electrocardiograms (ECG), or respiratory signals to classify OSA or depression [43]. We conducted this study to be modality-agnostic, such that only the epoch-wise sleep stage scores and routine demographic information are available, as proprietary algorithms used by wearables are difficult to access, and sought to validate the effectiveness of the hypnograms/hypnodensity graphs as a proxy nocturnal feature set. Our approach was found to underperform compared to methods using ECG [44], EEG [45], or a combination [46]. This is expected as we do not use information directly corresponding to individual physiological states, which better reflect the characteristics attributed to different conditions. In WSC [10], it appears that the cohort included primarily healthy individuals in the community, which led the models to learn a poorer separation between positive and negative classes. Notably, there was significant data imbalance in the case of depressed and non-depressed patients, and it appears on average that none of the feature sets used showed a clear distinction between the two populations. This is likely due to having a sufficient sample size representative of the conditions and the lower levels of disease severity between populations. The dataset is also skewed toward the Caucasian demographic as well as limited by geography, limiting its generalization to other ethnicities in other locations.