## 3 Results
### Common Limitations  
Finally, while wearables offer a lot of potential and many of the discussed models can predict depressive symptoms with impressive accuracy, it is also important to go over a number of common limitations and pitfalls that should be avoided when further researching this topic. Some of these have been mentioned throughout the previous sections. First, when best practices are not followed, there is a high risk of overfitting a model during feature selection, thus limiting generalizability [54; 55; 61]. Second, multiple studies used very small groups of participants, which is also likely to limit generalizability [50; 51; 52; 54; 59; 60]. Third, some models relied on active user input through mood-scoring apps or surveys [53; 57; 53]. This directly conflicts with one of the main strengths of wearables: the ability to collect data without active user contribution. Fourth, none of the studies reviewed tested their model on independently collected validation datasets, thus increasing the risk of bias. This is an issue also highlighted in a recent review of models using smartphone data to predict depression [63]. These limitations combined make it highly likely that the impressive results observed in individual studies might fall short if the algorithms were applied to the real world. The following section will discuss some common limitations found in the reviewed studies, as well as general limitations that need to be considered when researching wearable predictive models. One of these recurring limitations was that study populations had limited generalizability due to the overrepresentation of certain demographics. For example, many studies used populations that were predominantly female [51; 56; 58; 59; 60; 61]. Additionally, people over the age of 65 were only included in a single study [54]. This is unfortunate, as this type of research may be particularly useful for the elderly population, which is at an increased risk of experiencing depression [64]. Lastly, most studies looked at participants who were predominantly White [56; 58; 59; 60] or Asian [48; 54; 61; 62], whereas Black people were in the minority if they were included at all. The latter is especially relevant because one of the common wearable sensors, the photoplethysmography (PPG) sensor, relies on how green light is absorbed by the skin and blood flowing underneath to measure heart rate. The accuracy of this sensor was found to be dependent on skin color, with less accuracy for darker skin tones [65]. Moreover, the fact that Black people are often underrepresented in validation studies is a systemic issue that should be taken into account when considering the use of wearables for health tracking in these populations. Some other methodological challenges include the lack of standard protocols due to the novelty of this type of research. This is illustrated by the fact that many studies use different methods to establish the ground truth. Some relied on mood-scoring methods newly designed for their study [50], others used in-person visits with a psychiatrist [62], and the majority used one or more in-person or self-reported questionnaires such as the Hamilton Depression Rating Scale, Patient Health Questionnaire-9, or Beck Depression Inventory-II [49; 54; 58]. This large variety of methods complicates direct comparisons between studies. The reporting of methodological details is not always clear and complete. For example, the model of the wearable is not always specified [52; 58], and the operating systems of smartphones used are not always disclosed [48; 50; 55]. Similarly, the way the predictive quality of the algorithms is reported also varies. In part, this is because binary classifications are simply assessed in a different way than when the outcome is a numeric score. However, even within the same types of predictions, the reported quality outcomes vary. This makes direct comparisons especially complicated when common measures (e.g., specificity, F1-score, mean absolute error) are not reported [48; 51; 56].