## 4 Discussion
### Application of Wearables in Mental Health Care  
The widespread application of wearables in mental healthcare is the obvious end goal for this type of research. However, there are a number of real-world complexities yet to be solved. To start with, wearables collect massive amounts of personal data, which usually means they are reliant on cloud storage and capacity. This not only requires a lot of resources but can also have a significant environmental impact [67]. Moreover, the collection, transmission, and storage of large amounts of personal data need to meet a high standard of data security [68; 69]. Similarly, there is the non-trivial issue of data privacy, which is an important ethical consideration but also a deciding factor for whether patients would trust these types of algorithms enough to use them [70; 71]. There would be much to gain by independent systematic evaluation of the many different data collectors and health apps, preferably through international collaboration [72]. Finally, from a socioeconomic point of view, it is important to consider that access to wearables is not universal, and the accessibility of predictive algorithms is based in large part on the user's economic status and insurance. There is also considerable sampling and selection bias, as health-conscious people are more likely to participate in both studies and applications [73]. It should be noted that the US Food and Drug Administration (FDA) has outlined plans to regulate artificial intelligence and machine learning algorithms for health applications [74]. Their primary focus is to ensure that algorithms implemented in healthcare are explainable, transparent, secure to cyberattacks, and not biased towards certain sociodemographic groups. It remains to be seen how future academic research on mental healthcare responds to institutional regulation on wearables and machine learning.  
Given the importance of the doctor-patient relationship, it is unlikely that wearables will ever fully replace the diagnosis by a clinician [75]. Instead, wearables may prove more useful as a complementary tool. For example, they could be used to monitor recovery progress in between visits and for early detection of relapse for patients with a previous diagnosis. By contrast, algorithms that predict the future onset of depression several weeks in advance seem able to detect physiological or behavioral traits indicating prodromal symptoms of depression, symptoms that go under the radar of traditional diagnoses. Since both reviewed studies with a longer prediction horizon looked at highly specific populations they considered to be higher risk [49; 53], it would be interesting to initially apply these models to specific groups of at-risk individuals. The reviewed studies both looked at first-year medical students or trainees, but other groups could include people who are grieving or who have recently become unemployed [76; 77]. Predicting this far in advance is admittedly a much less common approach in research, so there is still a lot of progress to be made in this direction, but the results so far are promising.  
Alternatively, a more research-focused approach can also be explored. Most studies simply observe which features are used by the algorithm, and some do report which features have the biggest predictive value for the model. A question that is often left open is why these features are so informative. This is a very interesting and unexplored area of research that may provide novel insights into the underlying mechanisms of depression. Furthermore, these important features could potentially inspire new or more personalized interventions. For example, if a patient is classified as depressed based most importantly on their excessive late-night activity, they might benefit from a treatment that focuses more on their sleeping habits. However, the efficacy of this approach is merely speculative, as no studies were found looking at such personalized treatment adjustments.