### Measurable Features of MDD  
While there is no single or definitive cause for MDD, disruptions of several biological processes have been related to the disease. One prominent example is the circadian clock. Using external time cues, this system modulates certain processes in the body (most notably the sleep-wake cycle) to follow a roughly 24 h rhythm [17]. It has been widely reported that the circadian clock is disrupted in patients with depression, as reflected, among other things, by the prevalence of insomnia and disrupted sleep reported by this population [18; 19]. Another major regulatory mechanism found to be disrupted in MDD is the hypothalamic pituitary adrenal (HPA) axis [20; 21]. This system is crucial for stress adaptation and is normally activated in response to emotional or physical stressors [22]. The HPA axis is involved in the regulation of blood pressure, metabolism, and the fight-flight response, which in turn may increase heart rate, body temperature, and perspiration when activated [23; 24; 25].  
Both the circadian clock and the HPA axis involve physiological processes that can be measured using sensors available in most wearables. Accelerometers can further measure physical activity, which can also be used to quantify dimensions of sleep. Existing research has shown that depressed patients generally spend more time sedentary compared to healthy controls [26], and people with lower activity levels are more likely to show signs of depression [27]. The level of physical activity has been found to increase as treatment progresses, making it a promising feature for monitoring depressive symptoms [28; 29]. Furthermore, the activity of patients was found to be shifted towards later in the day, with a dampened diurnal amplitude [30]. Photoplethysmogram (PPG) sensors can measure heart rate. Measured heart rate, and specifically lowered heart rate variability (HRV), has been found to be associated with poor sleep and increased risk of depressive symptoms, and HRV was found to increase following successful treatment [31; 32]. Additionally, there appears to be a relation between HRV and disease severity, as the reduction in HRV was found to be more prominent when depressive symptoms were worse [33; 34]. Electrodermal activity (EDA) sensors can measure perspiration. Multiple studies have found reduced EDA in depressed patients [35; 36; 37], and one even claimed to be able to differentiate between depressed and non-depressed individuals within their population based on whether skin conductance was above or below a certain threshold [38]. Finally, many smartwatches include temperature sensors. Body temperature is commonly elevated in patients with depression [39; 40; 41; 42]. This was related to the circadian rhythm, as it was specifically the nocturnal core temperature [39; 40] and evening peripheral temperature [42] that were higher in depressed patients compared to non-depressed participants. As such, these two well-understood mechanisms are often invoked to explain why it might be possible to predict mood from wearables in theory. It should be noted, however, that these studies identified associations between isolated physiological features and depression. Individually, these associations are too small to meaningfully predict a complex psychological outcome such as depression in a broader population. Instead, we found that studies aiming to predict depression typically combine a number of these features in an attempt to model the intricacies of how they interact.