## 3 Results
### Prediction Outcomes and Horizons
A similar study was conducted by the same group with a larger sample size ([PERSON] et al., 2018). They predicted mood episodes over the same prediction horizon, removing the reliance on AMS distribution for the ground truth. The reported accuracy for predicting depressive episodes in MDD patients was very high. However, for lack of reporting otherwise, presumably, all recorded episodes were used for retrospective feature selection, resulting in overfitting. The prediction of impending mood episodes a few days before they occur would give patients forewarning, allowing them to contact their clinician and be better prepared to deal with the episode. Further expanding the prediction horizon, [PERSON] et al. (2018) aimed to predict symptoms of depression several weeks in advance. Specifically, over the course of a 16-week study period, two binary outcomes were predicted each week using the data available at that time: whether participants would have depression at the final check-up and whether their symptoms then would be worse compared to baseline. Depression and worsening symptoms were predicted with acceptable accuracy (> 80%) up to 9 and 14 weeks in advance, respectively. A caveat is that no mood data were collected outside of the Beck Depression Inventory-II (BDI-II) at the start and end of the 16-week study period, so it is unknown when exactly symptoms started developing. As such, it is unclear exactly how far in advance the model is able to predict the onset of depression. However, because it is unlikely that all 36 new cases would have been classified as newly depressed after the first two weeks, it is safe to assume that the number of cases was indeed predicted correctly several weeks in advance. A major benefit of predicting this far in advance is that these passive sensing models may be able to pick up on early signs of depression that participants themselves may not be aware of yet and that would, therefore, be missed through regular self-reported measures. This allows earlier detection and, most importantly, the possibility of earlier intervention. Similarly, [PERSON] et al. (2018) also aimed to predict whether depression occurred several weeks in advance. Specifically, the two outcomes they predicted were the presence of depression and suicidal ideation at the end of a quarter (13 weeks). Each day, a new iteration of the models was made using the data available up to that time. To predict depression with an acceptable accuracy (AUC > 0.7), a minimum of two weeks' worth of data was needed, whereas prediction of suicidal ideation required at least 7 weeks' worth of data. Predicting participants' mood status was thus possible 6-11 weeks in advance. Model variations were created that used only the mood variables based on the self-reported daily mood scores or using all variables, including mood and wearable data. For predicting depression, accuracy was comparable across variations, while suicidal ideation was predicted slightly better using only mood variables. This implies wearable data are not necessary for accurate predictions as long as participants submit mood data. However, a major benefit of wearables is that they do not require user input and are, therefore, less obtrusive than daily mood surveys.